Classifying injury narratives of large administrative databases for surveillance-A practical approach combining machine learning ensembles and human review.

PubMed

Marucci-Wellman, Helen R; Corns, Helen L; Lehto, Mark R

2017-01-01

Injury narratives are now available real time and include useful information for injury surveillance and prevention. However, manual classification of the cause or events leading to injury found in large batches of narratives, such as workers compensation claims databases, can be prohibitive. In this study we compare the utility of four machine learning algorithms (Naïve Bayes, Single word and Bi-gram models, Support Vector Machine and Logistic Regression) for classifying narratives into Bureau of Labor Statistics Occupational Injury and Illness event leading to injury classifications for a large workers compensation database. These algorithms are known to do well classifying narrative text and are fairly easy to implement with off-the-shelf software packages such as Python. We propose human-machine learning ensemble approaches which maximize the power and accuracy of the algorithms for machine-assigned codes and allow for strategic filtering of rare, emerging or ambiguous narratives for manual review. We compare human-machine approaches based on filtering on the prediction strength of the classifier vs. agreement between algorithms. Regularized Logistic Regression (LR) was the best performing algorithm alone. Using this algorithm and filtering out the bottom 30% of predictions for manual review resulted in high accuracy (overall sensitivity/positive predictive value of 0.89) of the final machine-human coded dataset. The best pairings of algorithms included Naïve Bayes with Support Vector Machine whereby the triple ensemble NB SW =NB BI-GRAM =SVM had very high performance (0.93 overall sensitivity/positive predictive value and high accuracy (i.e. high sensitivity and positive predictive values)) across both large and small categories leaving 41% of the narratives for manual review. Integrating LR into this ensemble mix improved performance only slightly. For large administrative datasets we propose incorporation of methods based on human-machine pairings such as we have done here, utilizing readily-available off-the-shelf machine learning techniques and resulting in only a fraction of narratives that require manual review. Human-machine ensemble methods are likely to improve performance over total manual coding. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Optimization of large matrix calculations for execution on the Cray X-MP vector supercomputer

NASA Technical Reports Server (NTRS)

Hornfeck, William A.

1988-01-01

A considerable volume of large computational computer codes were developed for NASA over the past twenty-five years. This code represents algorithms developed for machines of earlier generation. With the emergence of the vector supercomputer as a viable, commercially available machine, an opportunity exists to evaluate optimization strategies to improve the efficiency of existing software. This result is primarily due to architectural differences in the latest generation of large-scale machines and the earlier, mostly uniprocessor, machines. A sofware package being used by NASA to perform computations on large matrices is described, and a strategy for conversion to the Cray X-MP vector supercomputer is also described.

Robotic inspection of fiber reinforced composites using phased array UT

NASA Astrophysics Data System (ADS)

Stetson, Jeffrey T.; De Odorico, Walter

2014-02-01

Ultrasound is the current NDE method of choice to inspect large fiber reinforced airframe structures. Over the last 15 years Cartesian based scanning machines using conventional ultrasound techniques have been employed by all airframe OEMs and their top tier suppliers to perform these inspections. Technical advances in both computing power and commercially available, multi-axis robots now facilitate a new generation of scanning machines. These machines use multiple end effector tools taking full advantage of phased array ultrasound technologies yielding substantial improvements in inspection quality and productivity. This paper outlines the general architecture for these new robotic scanning systems as well as details the variety of ultrasonic techniques available for use with them including advances such as wide area phased array scanning and sound field adaptation for non-flat, non-parallel surfaces.

myChEMBL: a virtual machine implementation of open data and cheminformatics tools.

PubMed

Ochoa, Rodrigo; Davies, Mark; Papadatos, George; Atkinson, Francis; Overington, John P

2014-01-15

myChEMBL is a completely open platform, which combines public domain bioactivity data with open source database and cheminformatics technologies. myChEMBL consists of a Linux (Ubuntu) Virtual Machine featuring a PostgreSQL schema with the latest version of the ChEMBL database, as well as the latest RDKit cheminformatics libraries. In addition, a self-contained web interface is available, which can be modified and improved according to user specifications. The VM is available at: ftp://ftp.ebi.ac.uk/pub/databases/chembl/VM/myChEMBL/current. The web interface and web services code is available at: https://github.com/rochoa85/myChEMBL.

Man+Machine+Master Plan.

ERIC Educational Resources Information Center

Atwood, E. Barrett, Jr.

1982-01-01

Computer hardware and software alone do not improve a financial management system. They are only the tools that carry out commands. College business offices and related administrative functions must commit effort to improving the overall system. Available from Peat, Marwick, Mitchell & Co., 345 Park Avenue, New York, NY 10154. (MSE)

Application of design sensitivity analysis for greater improvement on machine structural dynamics

NASA Technical Reports Server (NTRS)

Yoshimura, Masataka

1987-01-01

Methodologies are presented for greatly improving machine structural dynamics by using design sensitivity analyses and evaluative parameters. First, design sensitivity coefficients and evaluative parameters of structural dynamics are described. Next, the relations between the design sensitivity coefficients and the evaluative parameters are clarified. Then, design improvement procedures of structural dynamics are proposed for the following three cases: (1) addition of elastic structural members, (2) addition of mass elements, and (3) substantial charges of joint design variables. Cases (1) and (2) correspond to the changes of the initial framework or configuration, and (3) corresponds to the alteration of poor initial design variables. Finally, numerical examples are given for demonstrating the availability of the methods proposed.

Machine learning: Trends, perspectives, and prospects.

PubMed

Jordan, M I; Mitchell, T M

2015-07-17

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

Replication and Reconfiguration in a Distributed Mail Repository.

DTIC Science & Technology

1987-04-01

a single machine and sees no improvement in availability over the old repository. Further, the static allocation of users to particular machines means...Reconfiguration Good old Wateon! You are the one fixed point in a changing universel -Sir Arthur Conan Doyle How can I be sure In a world that’s constantly...automatic storage allocation , and the Argus debugger. Then I discuss the drawbacks involved in using Argus: deadlocks, the awkwardness of retrying actions

Satisloh centering technology developments past to present

NASA Astrophysics Data System (ADS)

Leitz, Ernst Michael; Moos, Steffen

2015-10-01

The centering of an optical lens is the grinding of its edge profile or contour in relationship to its optical axis. This is required to ensure that the lens vertex and radial centers are accurately positioned within an optical system. Centering influences the imaging performance and contrast of an optical system. Historically, lens centering has been a purely manual process. Along its 62 years of assembling centering machines, Satisloh introduced several technological milestones to improve the accuracy and quality of this process. During this time more than 2.500 centering machines were assembled. The development went from bell clamping and diamond grinding to Laser alignment, exchange chuckor -spindle systems, to multi axis CNC machines with integrated metrology and automatic loading systems. With the new centering machine C300, several improvements for the clamping and grinding process were introduced. These improvements include a user friendly software to support the operator, a coolant manifold and "force grinding" technology to ensure excellent grinding quality and process stability. They also include an air bearing directly driven centering spindle to provide a large working range of lenses made of all optical materials and diameters from below 10 mm to 300 mm. The clamping force can be programmed between 7 N and 1200 N to safely center lenses made of delicate materials. The smaller C50 centering machine for lenses below 50 mm diameter is available with an optional CNC loading system for automated production.

Translingual Fine-Grained Morphosyntactic Analysis and Its Application to Machine Translation

ERIC Educational Resources Information Center

Drabek, Elliott Franco

2009-01-01

English and a small set of other languages have a wealth of available linguistic knowledge resources and annotated language data, but the great majority of the world's languages have little or none. This dissertation describes work which leverages the detailed and accurate morphosyntactic analyses available for English to improve analytical…

Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval

PubMed Central

Karisani, Payam; Qin, Zhaohui S; Agichtein, Eugene

2018-01-01

Abstract The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, machine learning-based approach to biomedical dataset retrieval as part of this challenge. We report on a series of experiments carried out to evaluate the performance of both probabilistic and machine learning-driven techniques from information retrieval, as applied to this challenge. Our experiments with probabilistic information retrieval methods, such as query term weight optimization, automatic query expansion and simulated user relevance feedback, demonstrate that automatically boosting the weights of important keywords in a verbose query is more effective than other methods. We also show that although there is a rich space of potential representations and features available in this domain, machine learning-based re-ranking models are not able to improve on probabilistic information retrieval techniques with the currently available training data. The models and algorithms presented in this paper can serve as a viable implementation of a search engine to provide access to biomedical datasets. The retrieval performance is expected to be further improved by using additional training data that is created by expert annotation, or gathered through usage logs, clicks and other processes during natural operation of the system. Database URL: https://github.com/emory-irlab/biocaddie PMID:29688379

Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy.

PubMed

Asadi, Hamed; Dowling, Richard; Yan, Bernard; Mitchell, Peter

2014-01-01

Stroke is a major cause of death and disability. Accurately predicting stroke outcome from a set of predictive variables may identify high-risk patients and guide treatment approaches, leading to decreased morbidity. Logistic regression models allow for the identification and validation of predictive variables. However, advanced machine learning algorithms offer an alternative, in particular, for large-scale multi-institutional data, with the advantage of easily incorporating newly available data to improve prediction performance. Our aim was to design and compare different machine learning methods, capable of predicting the outcome of endovascular intervention in acute anterior circulation ischaemic stroke. We conducted a retrospective study of a prospectively collected database of acute ischaemic stroke treated by endovascular intervention. Using SPSS®, MATLAB®, and Rapidminer®, classical statistics as well as artificial neural network and support vector algorithms were applied to design a supervised machine capable of classifying these predictors into potential good and poor outcomes. These algorithms were trained, validated and tested using randomly divided data. We included 107 consecutive acute anterior circulation ischaemic stroke patients treated by endovascular technique. Sixty-six were male and the mean age of 65.3. All the available demographic, procedural and clinical factors were included into the models. The final confusion matrix of the neural network, demonstrated an overall congruency of ∼ 80% between the target and output classes, with favourable receiving operative characteristics. However, after optimisation, the support vector machine had a relatively better performance, with a root mean squared error of 2.064 (SD: ± 0.408). We showed promising accuracy of outcome prediction, using supervised machine learning algorithms, with potential for incorporation of larger multicenter datasets, likely further improving prediction. Finally, we propose that a robust machine learning system can potentially optimise the selection process for endovascular versus medical treatment in the management of acute stroke.

Automatic Earthquake Detection by Active Learning

NASA Astrophysics Data System (ADS)

Bergen, K.; Beroza, G. C.

2017-12-01

In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

Experimental Characterization and Modeling of Thermal Contact Resistance of Electric Machine Stator-to-Cooling Jacket Interface Under Interference Fit Loading

DOE PAGES

Cousineau, Justine Emily; Bennion, Kevin S.; Chieduko, Victor; ...

2018-05-08

Cooling of electric machines is a key to increasing power density and improving reliability. This paper focuses on the design of a machine using a cooling jacket wrapped around the stator. The thermal contact resistance (TCR) between the electric machine stator and cooling jacket is a significant factor in overall performance and is not well characterized. This interface is typically an interference fit subject to compressive pressure exceeding 5 MPa. An experimental investigation of this interface was carried out using a thermal transmittance setup using pressures between 5 and 10 MPa. Furthermore, the results were compared to currently available modelsmore » for contact resistance, and one model was adapted for prediction of TCR in future motor designs.« less

Severe acute respiratory distress syndrome caused by unintentional sewing machine lubricant ingestion: A case report.

PubMed

Kishore, Sunil; Chandelia, Sudha; Patharia, Neha; Swarnim

2016-11-01

Sewing machine oil ingestion is rare but is possible due to its availability at home. Chemically, it belongs to hydrocarbon family which is toxic if aspirated, owing to their physical properties such as high volatility and low viscosity. On the contrary, sewing machine lubricant has high viscosity and low volatility which makes it aspiration less likely. The main danger of hydrocarbon ingestion is chemical pneumonitis which may be as severe as acute respiratory distress syndrome (ARDS). We report a case of a 5-year-old girl with accidental ingestion of sewing machine lubricant oil, who subsequently developed ARDS refractory to mechanical ventilation. There was much improvement with airway pressure release ventilation mode of ventilation, but the child succumbed to death due to pulmonary hemorrhage.

Severe acute respiratory distress syndrome caused by unintentional sewing machine lubricant ingestion: A case report

PubMed Central

Kishore, Sunil; Chandelia, Sudha; Patharia, Neha; Swarnim

2016-01-01

Sewing machine oil ingestion is rare but is possible due to its availability at home. Chemically, it belongs to hydrocarbon family which is toxic if aspirated, owing to their physical properties such as high volatility and low viscosity. On the contrary, sewing machine lubricant has high viscosity and low volatility which makes it aspiration less likely. The main danger of hydrocarbon ingestion is chemical pneumonitis which may be as severe as acute respiratory distress syndrome (ARDS). We report a case of a 5-year-old girl with accidental ingestion of sewing machine lubricant oil, who subsequently developed ARDS refractory to mechanical ventilation. There was much improvement with airway pressure release ventilation mode of ventilation, but the child succumbed to death due to pulmonary hemorrhage. PMID:27994384

Experimental Characterization and Modeling of Thermal Contact Resistance of Electric Machine Stator-to-Cooling Jacket Interface Under Interference Fit Loading

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cousineau, Justine Emily; Bennion, Kevin S.; Chieduko, Victor

Cooling of electric machines is a key to increasing power density and improving reliability. This paper focuses on the design of a machine using a cooling jacket wrapped around the stator. The thermal contact resistance (TCR) between the electric machine stator and cooling jacket is a significant factor in overall performance and is not well characterized. This interface is typically an interference fit subject to compressive pressure exceeding 5 MPa. An experimental investigation of this interface was carried out using a thermal transmittance setup using pressures between 5 and 10 MPa. Furthermore, the results were compared to currently available modelsmore » for contact resistance, and one model was adapted for prediction of TCR in future motor designs.« less

Nutritional value of foods sold in vending machines in a UK University: Formative, cross-sectional research to inform an environmental intervention.

PubMed

Park, Hanla; Papadaki, Angeliki

2016-01-01

Vending machine use has been associated with low dietary quality among children but there is limited evidence on its role in food habits of University students. We aimed to examine the nutritional value of foods sold in vending machines in a UK University and conduct formative research to investigate differences in food intake and body weight by vending machine use among 137 University students. The nutrient content of snacks and beverages available at nine campus vending machines was assessed by direct observation in May 2014. Participants (mean age 22.5 years; 54% males) subsequently completed a self-administered questionnaire to assess vending machine behaviours and food intake. Self-reported weight and height were collected. Vending machine snacks were generally high in sugar, fat and saturated fat, whereas most beverages were high in sugar. Seventy three participants (53.3%) used vending machines more than once per week and 82.2% (n 60) of vending machine users used them to snack between meals. Vending machine accessibility was positively correlated with vending machine use (r = 0.209, P = 0.015). Vending machine users, compared to non-users, reported a significantly higher weekly consumption of savoury snacks (5.2 vs. 2.8, P = 0.014), fruit juice (6.5 vs. 4.3, P = 0.035), soft drinks (5.1 vs. 1.9, P = 0.006), meat products (8.3 vs. 5.6, P = 0.029) and microwave meals (2.0 vs. 1.3, P = 0.020). No between-group differences were found in body weight. Most foods available from vending machines in this UK University were of low nutritional quality. In this sample of University students, vending machine users displayed several unfavourable dietary behaviours, compared to non-users. Findings can be used to inform the development of an environmental intervention that will focus on vending machines to improve dietary behaviours in University students in the UK. Copyright © 2015 Elsevier Ltd. All rights reserved.

Reliability analysis of component of affination centrifugal 1 machine by using reliability engineering

NASA Astrophysics Data System (ADS)

Sembiring, N.; Ginting, E.; Darnello, T.

2017-12-01

Problems that appear in a company that produces refined sugar, the production floor has not reached the level of critical machine availability because it often suffered damage (breakdown). This results in a sudden loss of production time and production opportunities. This problem can be solved by Reliability Engineering method where the statistical approach to historical damage data is performed to see the pattern of the distribution. The method can provide a value of reliability, rate of damage, and availability level, of an machine during the maintenance time interval schedule. The result of distribution test to time inter-damage data (MTTF) flexible hose component is lognormal distribution while component of teflon cone lifthing is weibull distribution. While from distribution test to mean time of improvement (MTTR) flexible hose component is exponential distribution while component of teflon cone lifthing is weibull distribution. The actual results of the flexible hose component on the replacement schedule per 720 hours obtained reliability of 0.2451 and availability 0.9960. While on the critical components of teflon cone lifthing actual on the replacement schedule per 1944 hours obtained reliability of 0.4083 and availability 0.9927.

Parental attitudes towards soft drink vending machines in high schools.

PubMed

Hendel-Paterson, Maia; French, Simone A; Story, Mary

2004-10-01

Soft drink vending machines are available in 98% of US high schools. However, few data are available about parents' opinions regarding the availability of soft drink vending machines in schools. Six focus groups with 33 parents at three suburban high schools were conducted to describe the perspectives of parents regarding soft drink vending machines in their children's high school. Parents viewed the issue of soft drink vending machines as a matter of their children's personal choice more than as an issue of a healthful school environment. However, parents were unaware of many important details about the soft drink vending machines in their children's school, such as the number and location of machines, hours of operation, types of beverages available, or whether the school had contracts with soft drink companies. Parents need more information about the number of soft drink vending machines at their children's school, the beverages available, the revenue generated by soft drink vending machine sales, and the terms of any contracts between the school and soft drink companies.

Productivity improvement through cycle time analysis

NASA Astrophysics Data System (ADS)

Bonal, Javier; Rios, Luis; Ortega, Carlos; Aparicio, Santiago; Fernandez, Manuel; Rosendo, Maria; Sanchez, Alejandro; Malvar, Sergio

1996-09-01

A cycle time (CT) reduction methodology has been developed in the Lucent Technology facility (former AT&T) in Madrid, Spain. It is based on a comparison of the contribution of each process step in each technology with a target generated by a cycle time model. These targeted cycle times are obtained using capacity data of the machines processing those steps, queuing theory and theory of constrains (TOC) principles (buffers to protect bottleneck and low cycle time/inventory everywhere else). Overall efficiency equipment (OEE) like analysis is done in the machine groups with major differences between their target cycle time and real values. Comparisons between the current value of the parameters that command their capacity (process times, availability, idles, reworks, etc.) and the engineering standards are done to detect the cause of exceeding their contribution to the cycle time. Several friendly and graphical tools have been developed to track and analyze those capacity parameters. Specially important have showed to be two tools: ASAP (analysis of scheduling, arrivals and performance) and performer which analyzes interrelation problems among machines procedures and direct labor. The performer is designed for a detailed and daily analysis of an isolate machine. The extensive use of this tool by the whole labor force has demonstrated impressive results in the elimination of multiple small inefficiencies with a direct positive implications on OEE. As for ASAP, it shows the lot in process/queue for different machines at the same time. ASAP is a powerful tool to analyze the product flow management and the assigned capacity for interdependent operations like the cleaning and the oxidation/diffusion. Additional tools have been developed to track, analyze and improve the process times and the availability.

ProQ3D: improved model quality assessments using deep learning.

PubMed

Uziela, Karolis; Menéndez Hurtado, David; Shu, Nanjiang; Wallner, Björn; Elofsson, Arne

2017-05-15

Protein quality assessment is a long-standing problem in bioinformatics. For more than a decade we have developed state-of-art predictors by carefully selecting and optimising inputs to a machine learning method. The correlation has increased from 0.60 in ProQ to 0.81 in ProQ2 and 0.85 in ProQ3 mainly by adding a large set of carefully tuned descriptions of a protein. Here, we show that a substantial improvement can be obtained using exactly the same inputs as in ProQ2 or ProQ3 but replacing the support vector machine by a deep neural network. This improves the Pearson correlation to 0.90 (0.85 using ProQ2 input features). ProQ3D is freely available both as a webserver and a stand-alone program at http://proq3.bioinfo.se/. arne@bioinfo.se. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Ex-vivo machine perfusion for kidney preservation.

PubMed

Hamar, Matyas; Selzner, Markus

2018-06-01

Machine perfusion is a novel strategy to decrease preservation injury, improve graft assessment, and increase organ acceptance for transplantation. This review summarizes the current advances in ex-vivo machine-based kidney preservation technologies over the last year. Ex-vivo perfusion technologies, such as hypothermic and normothermic machine perfusion and controlled oxygenated rewarming, have gained high interest in the field of organ preservation. Keeping kidney grafts functionally and metabolically active during the preservation period offers a unique chance for viability assessment, reconditioning, and organ repair. Normothermic ex-vivo kidney perfusion has been recently translated into clinical practice. Preclinical results suggest that prolonged warm perfusion appears superior than a brief end-ischemic reconditioning in terms of renal function and injury. An established standardized protocol for continuous warm perfusion is still not available for human grafts. Ex-vivo machine perfusion represents a superior organ preservation method over static cold storage. There is still an urgent need for the optimization of the perfusion fluid and machine technology and to identify the optimal indication in kidney transplantation. Recent research is focusing on graft assessment and therapeutic strategies.

Application of Elements of TPM Strategy for Operation Analysis of Mining Machine

NASA Astrophysics Data System (ADS)

Brodny, Jaroslaw; Tutak, Magdalena

2017-12-01

Total Productive Maintenance (TPM) strategy includes group of activities and actions in order to maintenance machines in failure-free state and without breakdowns thanks to tending limitation of failures, non-planned shutdowns, lacks and non-planned service of machines. These actions are ordered to increase effectiveness of utilization of possessed devices and machines in company. Very significant element of this strategy is connection of technical actions with changes in their perception by employees. Whereas fundamental aim of introduction this strategy is improvement of economic efficiency of enterprise. Increasing competition and necessity of reduction of production costs causes that also mining enterprises are forced to introduce this strategy. In the paper examples of use of OEE model for quantitative evaluation of selected mining devices were presented. OEE model is quantitative tool of TPM strategy and can be the base for further works connected with its introduction. OEE indicator is the product of three components which include availability and performance of the studied machine and the quality of the obtained product. The paper presents the results of the effectiveness analysis of the use of a set of mining machines included in the longwall system, which is the first and most important link in the technological line of coal production. The set of analyzed machines included the longwall shearer, armored face conveyor and cruscher. From a reliability point of view, the analyzed set of machines is a system that is characterized by the serial structure. The analysis was based on data recorded by the industrial automation system used in the mines. This method of data acquisition ensured their high credibility and a full time synchronization. Conclusions from the research and analyses should be used to reduce breakdowns, failures and unplanned downtime, increase performance and improve production quality.

Classifying BCI signals from novice users with extreme learning machine

NASA Astrophysics Data System (ADS)

Rodríguez-Bermúdez, Germán; Bueno-Crespo, Andrés; José Martinez-Albaladejo, F.

2017-07-01

Brain computer interface (BCI) allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM) has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.

Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.

PubMed

de Ávila, Maurício Boff; Xavier, Mariana Morrone; Pintro, Val Oliveira; de Azevedo, Walter Filgueira

2017-12-09

Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) for which half-maximal inhibitory concentration (IC 50 ) data is available. Polynomial scoring functions were built using as explanatory variables the energy terms present in the MolDock and PLANTS scoring functions. Prediction performance was tested and the supervised machine learning models showed improvement in the prediction power, when compared with PLANTS and MolDock scoring functions. In addition, the machine-learning model was applied to predict binding affinity of CDK2, which showed a better performance when compared with AutoDock4, AutoDock Vina, MolDock, and PLANTS scores. Copyright © 2017 Elsevier Inc. All rights reserved.

Machine Learning Toolkit for Extreme Scale

DOE Office of Scientific and Technical Information (OSTI.GOV)

2014-03-31

Support Vector Machines (SVM) is a popular machine learning technique, which has been applied to a wide range of domains such as science, finance, and social networks for supervised learning. MaTEx undertakes the challenge of designing a scalable parallel SVM training algorithm for large scale systems, which includes commodity multi-core machines, tightly connected supercomputers and cloud computing systems. Several techniques are proposed for improved speed and memory space usage including adaptive and aggressive elimination of samples for faster convergence , and sparse format representation of data samples. Several heuristics for earliest possible to lazy elimination of non-contributing samples are consideredmore » in MaTEx. In many cases, where an early sample elimination might result in a false positive, low overhead mechanisms for reconstruction of key data structures are proposed. The proposed algorithm and heuristics are implemented and evaluated on various publicly available datasets« less

Integrating Machine Learning into a Crowdsourced Model for Earthquake-Induced Damage Assessment

NASA Technical Reports Server (NTRS)

Rebbapragada, Umaa; Oommen, Thomas

2011-01-01

On January 12th, 2010, a catastrophic 7.0M earthquake devastated the country of Haiti. In the aftermath of an earthquake, it is important to rapidly assess damaged areas in order to mobilize the appropriate resources. The Haiti damage assessment effort introduced a promising model that uses crowdsourcing to map damaged areas in freely available remotely-sensed data. This paper proposes the application of machine learning methods to improve this model. Specifically, we apply work on learning from multiple, imperfect experts to the assessment of volunteer reliability, and propose the use of image segmentation to automate the detection of damaged areas. We wrap both tasks in an active learning framework in order to shift volunteer effort from mapping a full catalog of images to the generation of high-quality training data. We hypothesize that the integration of machine learning into this model improves its reliability, maintains the speed of damage assessment, and allows the model to scale to higher data volumes.

Improving Overall Equipment Effectiveness Using CPM and MOST: A Case Study of an Indonesian Pharmaceutical Company

NASA Astrophysics Data System (ADS)

Omega, Dousmaris; Andika, Aditya

2017-12-01

This paper discusses the results of a research conducted on the production process of an Indonesian pharmaceutical company. The company is experiencing low performance in the Overall Equipment Effectiveness (OEE) metric. The OEE of the company machines are below world class standard. The machine that has the lowest OEE is the filler machine. Through observation and analysis, it is found that the cleaning process of the filler machine consumes significant amount of time. The long duration of the cleaning process happens because there is no structured division of jobs between cleaning operators, differences in operators’ ability, and operators’ inability in utilizing available cleaning equipment. The company needs to improve the cleaning process. Therefore, Critical Path Method (CPM) analysis is conducted to find out what activities are critical in order to shorten and simplify the cleaning process in the division of tasks. Afterwards, The Maynard Operation and Sequence Technique (MOST) method is used to reduce ineffective movement and specify the cleaning process standard time. From CPM and MOST, it is obtained the shortest time of the cleaning process is 1 hour 28 minutes and the standard time is 1 hour 38.826 minutes.

41 CFR 101-26.509-2 - Requisitioning tabulating machine cards not available from Federal Supply Schedule contracts.

Code of Federal Regulations, 2011 CFR

2011-07-01

... machine cards not available from Federal Supply Schedule contracts. 101-26.509-2 Section 101-26.509-2... Programs § 101-26.509-2 Requisitioning tabulating machine cards not available from Federal Supply Schedule contracts. (a) Requisitions for tabulating machine cards covered by Federal Supply Schedule contracts which...

Using container orchestration to improve service management at the RAL Tier-1

NASA Astrophysics Data System (ADS)

Lahiff, Andrew; Collier, Ian

2017-10-01

In recent years container orchestration has been emerging as a means of gaining many potential benefits compared to a traditional static infrastructure, such as increased utilisation through multi-tenancy, improved availability due to self-healing, and the ability to handle changing loads due to elasticity and auto-scaling. To this end we have been investigating migrating services at the RAL Tier-1 to an Apache Mesos cluster. In this model the concept of individual machines is abstracted away and services are run in containers on a cluster of machines, managed by schedulers, enabling a high degree of automation. Here we describe Mesos, the infrastructure deployed at RAL, and describe in detail the explicit example of running a batch farm on Mesos.

Performance evaluation of NEEM oil and HONGE Oil as cutting fluid in drilling operation of mild steel

NASA Astrophysics Data System (ADS)

Jyothi, P. N.; Susmitha, M.; Sharan, P.

2017-04-01

Cutting fluids are used in machining industries for improving tool life, reducing work piece and thermal deformation, improving surface finish and flushing away chips from the cutting zone. Although the application of cutting fluids increases the tool life and Machining efficiency, but it has many major problems related to environmental impacts and health hazards along with recycling & disposal. These problems gave provision for the introduction of mineral, vegetable and animal oils. These oils play an important role in improving various machining properties, including corrosion protection, lubricity, antibacterial protection, even emulsibility and chemical stability. Compared to mineral oils, vegetable oils in general possess high viscosity index, high flash point, high lubricity and low evaporative losses. Vegetable oils can be edible or non-edible oils and Various researchers have proved that edible vegetable oils viz., palm oil, coconut oil, canola oil, soya bean oil can be effectively used as eco-friendly cutting fluid in machining operations. But in present situations harnessing edible oils for lubricants formation restricts the use due to increased demands of growing population worldwide and availability. In the present work, Non-edible vegetable oil like Neem and Honge are been used as cutting fluid for drilling of Mild steel and its effect on cutting temperature, hardness and surface roughness are been investigated. Results obtained are compared with SAE 20W40 (petroleum based cutting fluid)and dry cutting condition.

Machine Learning Techniques in Clinical Vision Sciences.

PubMed

Caixinha, Miguel; Nunes, Sandrina

2017-01-01

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration, and diabetic retinopathy, these ocular pathologies being the major causes of irreversible visual impairment.

Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints.

PubMed

Yuan, Yaxia; Zheng, Fang; Zhan, Chang-Guo

2018-03-21

Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.

Study of injection molded microcellular polyamide-6 nanocomposites

Treesearch

Mingjun Yuan; Lih-Sheng Turng; Shaoqin Gong; Daniel Caulfield; Chris Hunt; Rick Spindler

2004-01-01

This study aims to explore the processing benefits and property improvements of combining nanocomposites with microcellular injection molding. The microcellular nanocomposite processing was performed on an injection-molding machine equipped with a commercially available supercritical fluid (SCF) system. The molded samples produced based on the Design of Experiments (...

Machine learning-based methods for prediction of linear B-cell epitopes.

PubMed

Wang, Hsin-Wei; Pai, Tun-Wen

2014-01-01

B-cell epitope prediction facilitates immunologists in designing peptide-based vaccine, diagnostic test, disease prevention, treatment, and antibody production. In comparison with T-cell epitope prediction, the performance of variable length B-cell epitope prediction is still yet to be satisfied. Fortunately, due to increasingly available verified epitope databases, bioinformaticians could adopt machine learning-based algorithms on all curated data to design an improved prediction tool for biomedical researchers. Here, we have reviewed related epitope prediction papers, especially those for linear B-cell epitope prediction. It should be noticed that a combination of selected propensity scales and statistics of epitope residues with machine learning-based tools formulated a general way for constructing linear B-cell epitope prediction systems. It is also observed from most of the comparison results that the kernel method of support vector machine (SVM) classifier outperformed other machine learning-based approaches. Hence, in this chapter, except reviewing recently published papers, we have introduced the fundamentals of B-cell epitope and SVM techniques. In addition, an example of linear B-cell prediction system based on physicochemical features and amino acid combinations is illustrated in details.

Retrofit concept for small safety related stationary machines

NASA Astrophysics Data System (ADS)

Epple, S.; Jalba, C. K.; Muminovic, A.; Jung, R.

2017-05-01

More and more old machines have the problem that their control electronics’ lifecycle comes to its intended end of life, whilst the mechanics itself and process capability is still in very good condition. This article shows an example of a reactive ion etcher originally built in 1988, which was refitted with a new control concept. The original control unit was repaired several times based on manufacturer’s obsolescence management. At start of the retrofit project the integrated circuits were no longer available for further repair of the original control unit. Safety, repeatability and stability of the process were greatly improved.

Predicting a small molecule-kinase interaction map: A machine learning approach

PubMed Central

2011-01-01

Background We present a machine learning approach to the problem of protein ligand interaction prediction. We focus on a set of binding data obtained from 113 different protein kinases and 20 inhibitors. It was attained through ATP site-dependent binding competition assays and constitutes the first available dataset of this kind. We extract information about the investigated molecules from various data sources to obtain an informative set of features. Results A Support Vector Machine (SVM) as well as a decision tree algorithm (C5/See5) is used to learn models based on the available features which in turn can be used for the classification of new kinase-inhibitor pair test instances. We evaluate our approach using different feature sets and parameter settings for the employed classifiers. Moreover, the paper introduces a new way of evaluating predictions in such a setting, where different amounts of information about the binding partners can be assumed to be available for training. Results on an external test set are also provided. Conclusions In most of the cases, the presented approach clearly outperforms the baseline methods used for comparison. Experimental results indicate that the applied machine learning methods are able to detect a signal in the data and predict binding affinity to some extent. For SVMs, the binding prediction can be improved significantly by using features that describe the active site of a kinase. For C5, besides diversity in the feature set, alignment scores of conserved regions turned out to be very useful. PMID:21708012

The Smart Aerial Release Machine, a Universal System for Applying the Sterile Insect Technique

PubMed Central

Mubarqui, Ruben Leal; Perez, Rene Cano; Kladt, Roberto Angulo; Lopez, Jose Luis Zavala; Parker, Andrew; Seck, Momar Talla; Sall, Baba; Bouyer, Jérémy

2014-01-01

Background Beyond insecticides, alternative methods to control insect pests for agriculture and vectors of diseases are needed. Management strategies involving the mass-release of living control agents have been developed, including genetic control with sterile insects and biological control with parasitoids, for which aerial release of insects is often required. Aerial release in genetic control programmes often involves the use of chilled sterile insects, which can improve dispersal, survival and competitiveness of sterile males. Currently available means of aerially releasing chilled fruit flies are however insufficiently precise to ensure homogeneous distribution at low release rates and no device is available for tsetse. Methodology/Principal Findings Here we present the smart aerial release machine, a new design by the Mubarqui Company, based on the use of vibrating conveyors. The machine is controlled through Bluetooth by a tablet with Android Operating System including a completely automatic guidance and navigation system (MaxNav software). The tablet is also connected to an online relational database facilitating the preparation of flight schedules and automatic storage of flight reports. The new machine was compared with a conveyor release machine in Mexico using two fruit flies species (Anastrepha ludens and Ceratitis capitata) and we obtained better dispersal homogeneity (% of positive traps, p<0.001) for both species and better recapture rates for Anastrepha ludens (p<0.001), especially at low release densities (<1500 per ha). We also demonstrated that the machine can replace paper boxes for aerial release of tsetse in Senegal. Conclusions/Significance This technology limits damages to insects and allows a large range of release rates from 10 flies/km2 for tsetse flies up to 600 000 flies/km2 for fruit flies. The potential of this machine to release other species like mosquitoes is discussed. Plans and operating of the machine are provided to allow its use worldwide. PMID:25036274

The smart aerial release machine, a universal system for applying the sterile insect technique.

PubMed

Leal Mubarqui, Ruben; Perez, Rene Cano; Kladt, Roberto Angulo; Lopez, Jose Luis Zavala; Parker, Andrew; Seck, Momar Talla; Sall, Baba; Bouyer, Jérémy

2014-01-01

Beyond insecticides, alternative methods to control insect pests for agriculture and vectors of diseases are needed. Management strategies involving the mass-release of living control agents have been developed, including genetic control with sterile insects and biological control with parasitoids, for which aerial release of insects is often required. Aerial release in genetic control programmes often involves the use of chilled sterile insects, which can improve dispersal, survival and competitiveness of sterile males. Currently available means of aerially releasing chilled fruit flies are however insufficiently precise to ensure homogeneous distribution at low release rates and no device is available for tsetse. Here we present the smart aerial release machine, a new design by the Mubarqui Company, based on the use of vibrating conveyors. The machine is controlled through Bluetooth by a tablet with Android Operating System including a completely automatic guidance and navigation system (MaxNav software). The tablet is also connected to an online relational database facilitating the preparation of flight schedules and automatic storage of flight reports. The new machine was compared with a conveyor release machine in Mexico using two fruit flies species (Anastrepha ludens and Ceratitis capitata) and we obtained better dispersal homogeneity (% of positive traps, p<0.001) for both species and better recapture rates for Anastrepha ludens (p<0.001), especially at low release densities (<1500 per ha). We also demonstrated that the machine can replace paper boxes for aerial release of tsetse in Senegal. This technology limits damages to insects and allows a large range of release rates from 10 flies/km2 for tsetse flies up to 600,000 flies/km2 for fruit flies. The potential of this machine to release other species like mosquitoes is discussed. Plans and operating of the machine are provided to allow its use worldwide.

Virtual Manufacturing Techniques Designed and Applied to Manufacturing Activities in the Manufacturing Integration and Technology Branch

NASA Technical Reports Server (NTRS)

Shearrow, Charles A.

1999-01-01

One of the identified goals of EM3 is to implement virtual manufacturing by the time the year 2000 has ended. To realize this goal of a true virtual manufacturing enterprise the initial development of a machinability database and the infrastructure must be completed. This will consist of the containment of the existing EM-NET problems and developing machine, tooling, and common materials databases. To integrate the virtual manufacturing enterprise with normal day to day operations the development of a parallel virtual manufacturing machinability database, virtual manufacturing database, virtual manufacturing paradigm, implementation/integration procedure, and testable verification models must be constructed. Common and virtual machinability databases will include the four distinct areas of machine tools, available tooling, common machine tool loads, and a materials database. The machine tools database will include the machine envelope, special machine attachments, tooling capacity, location within NASA-JSC or with a contractor, and availability/scheduling. The tooling database will include available standard tooling, custom in-house tooling, tool properties, and availability. The common materials database will include materials thickness ranges, strengths, types, and their availability. The virtual manufacturing databases will consist of virtual machines and virtual tooling directly related to the common and machinability databases. The items to be completed are the design and construction of the machinability databases, virtual manufacturing paradigm for NASA-JSC, implementation timeline, VNC model of one bridge mill and troubleshoot existing software and hardware problems with EN4NET. The final step of this virtual manufacturing project will be to integrate other production sites into the databases bringing JSC's EM3 into a position of becoming a clearing house for NASA's digital manufacturing needs creating a true virtual manufacturing enterprise.

Derailing healthy choices: an audit of vending machines at train stations in NSW.

PubMed

Kelly, Bridget; Flood, Victoria M; Bicego, Cecilia; Yeatman, Heather

2012-04-01

Train stations provide opportunities for food purchases and many consumers are exposed to these venues daily, on their commute to and from work. This study aimed to describe the food environment that commuters are exposed to at train stations in NSW. One hundred train stations were randomly sampled from the Greater Sydney Metropolitan region, representing a range of demographic areas. A purpose-designed instrument was developed to collect information on the availability, promotion and cost of food and beverages in vending machines. Items were classified as high/low in energy according to NSW school canteen criteria. Of the 206 vending machines identified, 84% of slots were stocked with high-energy food and beverages. The most frequently available items were chips and extruded snacks (33%), sugar-sweetened soft drinks (18%), chocolate (12%) and confectionery (10%). High energy foods were consistently cheaper than lower-energy alternatives. Transport sites may cumulatively contribute to excess energy consumption as the items offered are energy dense. Interventions are required to improve train commuters' access to healthy food and beverages.

Method of calculation overall equipment effectiveness in fertilizer factory

NASA Astrophysics Data System (ADS)

Siregar, I.; Muchtar, M. A.; Rahmat, R. F.; Andayani, U.; Nasution, T. H.; Sari, R. M.

2018-02-01

This research was conducted at a fertilizer company in Sumatra, where companies that produce fertilizers in large quantities to meet the needs of consumers. This company cannot be separated from issues related to the performance/effectiveness of the machinery and equipment. It can be seen from the engine that runs every day without a break resulted in not all of the quality of products in accordance with the quality standards set by the company. Therefore, to measure and improve the performance of the machine in the unit Plant Urea-1 as a whole then used method of Overall Equipment Effectiveness (OEE), which is one important element in the Total Productive Maintenance (TPM) to measure the effectiveness of the machine so that it can take measures to maintain that level. In July, August and September OEE values above the standard set at 85%. Meanwhile, in October, November and December have not reached the standard OEE values. The low value of OEE due to lack of time availability of machines for the production shut down due to the occurrence of the engine long enough so that the availability of reduced production time.

Key improvements in machining of Ti6al4v alloy: A review

NASA Astrophysics Data System (ADS)

Katta, Sivakoteswararao; Chaitanya, G.

2017-07-01

Now a days the use of ti-6al-4v alloy is high in demand in many industries like aero space, bio medical automobile, space, military etc. the production rates in the industries are not sufficient because the machiniability of ti-6al-4v is the main problem, there are several cutting tools available for metal cutting operations still there is a gap in finding the proper cutting tool material for machining of ti-6al-4v. because the properties of titanium like high heat resistant, low thermal conductivity, low weight ratio, less corrosiveness, and more many properties attracting the industrialists to use titanium as their material for their products, many researchers done the research on machininbility of ti-6al-4v by using different tool materials. but as for my literature survey there is still lot of scope is available, to find better cutting tool with techniques for machining ti-6al-4v. in this paper iam discussing the work done by various researchers on ti-6al-4v alloy with different techniques.

Multi-level machine learning prediction of protein-protein interactions in Saccharomyces cerevisiae.

PubMed

Zubek, Julian; Tatjewski, Marcin; Boniecki, Adam; Mnich, Maciej; Basu, Subhadip; Plewczynski, Dariusz

2015-01-01

Accurate identification of protein-protein interactions (PPI) is the key step in understanding proteins' biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein-protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB) database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein-protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC). Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent).

Dual-rotor, radial-flux, toroidally-wound, permanent-magnet machine

DOEpatents

Qu, Ronghai; Lipo, Thomas A.

2005-08-02

The present invention provides a novel dual-rotor, radial-flux, toroidally-wound, permanent-magnet machine. The present invention improves electrical machine torque density and efficiency. At least one concentric surface-mounted permanent magnet dual-rotor is located inside and outside of a torus-shaped stator with back-to-back windings, respectively. The machine substantially improves machine efficiency by reducing the end windings and boosts the torque density by at least doubling the air gap and optimizing the machine aspect ratio.

The impact of the availability of school vending machines on eating behavior during lunch: the Youth Physical Activity and Nutrition Survey.

PubMed

Park, Sohyun; Sappenfield, William M; Huang, Youjie; Sherry, Bettylou; Bensyl, Diana M

2010-10-01

Childhood obesity is a major public health concern and is associated with substantial morbidities. Access to less-healthy foods might facilitate dietary behaviors that contribute to obesity. However, less-healthy foods are usually available in school vending machines. This cross-sectional study examined the prevalence of students buying snacks or beverages from school vending machines instead of buying school lunch and predictors of this behavior. Analyses were based on the 2003 Florida Youth Physical Activity and Nutrition Survey using a representative sample of 4,322 students in grades six through eight in 73 Florida public middle schools. Analyses included χ2 tests and logistic regression. The outcome measure was buying a snack or beverage from vending machines 2 or more days during the previous 5 days instead of buying lunch. The survey response rate was 72%. Eighteen percent of respondents reported purchasing a snack or beverage from a vending machine 2 or more days during the previous 5 school days instead of buying school lunch. Although healthier options were available, the most commonly purchased vending machine items were chips, pretzels/crackers, candy bars, soda, and sport drinks. More students chose snacks or beverages instead of lunch in schools where beverage vending machines were also available than did students in schools where beverage vending machines were unavailable: 19% and 7%, respectively (P≤0.05). The strongest risk factor for buying snacks or beverages from vending machines instead of buying school lunch was availability of beverage vending machines in schools (adjusted odds ratio=3.5; 95% confidence interval, 2.2 to 5.7). Other statistically significant risk factors were smoking, non-Hispanic black race/ethnicity, Hispanic ethnicity, and older age. Although healthier choices were available, the most common choices were the less-healthy foods. Schools should consider developing policies to reduce the availability of less-healthy choices in vending machines and to reduce access to beverage vending machines. Copyright © 2010 American Dietetic Association. Published by Elsevier Inc. All rights reserved.

Are food and drink retailers within NHS venues adhering to NICE Quality standard 94 guidance on childhood obesity? A cross-sectional study of two large secondary care NHS hospitals in England

PubMed Central

James, Alice; Birch, Laura; Fletcher, Peter; Pearson, Sally; Boyce, Catherine; Ness, Andy R; Hamilton-Shield, Julian P; Lithander, Fiona E

2017-01-01

Objective To assess whether the food and drink retail outlets in two major National Health Service (NHS) district general hospitals in England adhere to quality statements 1–3 of the UK National Institute for Health and Care Excellence (NICE) quality standard 94. Design Cross-sectional, descriptive study to assess the food and drink options available in vending machines, restaurants, cafes and shops in two secondary care hospitals. Main outcome measures Adherence to quality statement 1 whereby the food and drink items available in the vending machines were classified as either healthy or less healthy using the Nutrient Profiling Model (NPM). Compliance with quality statements 2 and 3 was assessed through the measurement of how clearly the shops, cafes and restaurants displayed nutrition information on menus, and the availability and prominent display of healthy food and drink options in retail outlets, respectively. Results Adherence to quality statement 1 was poor. Of the 18 vending machines assessed, only 7 (39%) served both a healthy food and a healthy drink option. Neither hospital was compliant with quality statement 2 wherein nutritional information was not available on menus of food providers in either hospital. There was inconsistent compliance with quality standard 3 whereby healthy food and drink options were prominently displayed in the two main hospital restaurants, but all shops and cafes prioritised the display of unhealthy items. Conclusions Neither hospital was consistently compliant with quality statements 1–3 of the NICE quality standard 94. Improving the availability of healthy foods and drinks while reducing the display and accessibility to less healthy options in NHS venues may improve family awareness of healthy alternatives. Making it easier for parents to direct their children to healthier choices is an ostensibly central component of our healthcare system. PMID:29150472

Bias correction for selecting the minimal-error classifier from many machine learning models.

PubMed

Ding, Ying; Tang, Shaowu; Liao, Serena G; Jia, Jia; Oesterreich, Steffi; Lin, Yan; Tseng, George C

2014-11-15

Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts. In this article, we illustrated the probabilistic framework of the problem and explored the statistical and asymptotic properties. We proposed a new bias correction method based on learning curve fitting by inverse power law (IPL) and compared it with three existing methods: nested cross-validation, weighted mean correction and Tibshirani-Tibshirani procedure. All methods were compared in simulation datasets, five moderate size real datasets and two large breast cancer datasets. The result showed that IPL outperforms the other methods in bias correction with smaller variance, and it has an additional advantage to extrapolate error estimates for larger sample sizes, a practical feature to recommend whether more samples should be recruited to improve the classifier and accuracy. An R package 'MLbias' and all source files are publicly available. tsenglab.biostat.pitt.edu/software.htm. ctseng@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Perfusion machines and hepatocellular carcinoma: a good match between a marginal organ and an advanced disease?

PubMed Central

Rreka, Erion; Pezzati, Daniele; Filipponi, Franco; De Simone, Paolo

2017-01-01

Hepatocellular carcinoma (HCC) accounts for 90% of primary liver cancers, is the second leading cause of cancer-related deaths and the leading cause of death in patients with cirrhosis. Liver transplantation (LT) represents the ideal treatment for selected patients as it removes both the tumor and the underlying cirrhotic liver with 5-year survival rates higher than 70%. Unfortunately, due to tumor characteristics, patient co-morbidities or shortage of organs available for transplant, only 20% of patients can undergo curative treatment. Ex situ machine perfusion (MP) is a technology recently introduced that might potentially improve organ preservation, allow graft assessment and increase the pool of available organs. The purpose of this review is to provide an update on the current role of ex situ liver MP in liver transplantation for HCC patients. PMID:29264425

Classifying smoking urges via machine learning

PubMed Central

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-01-01

Background and objective Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. Methods To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. Results The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. Conclusions In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms’ performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. PMID:28110725

Classifying smoking urges via machine learning.

PubMed

Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin

2016-12-01

Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Portable home hemodialysis for kidney failure.

PubMed

Scott, A

2007-11-01

(1) Home hemodialysis has been in limited use in Canada for some time. Newer, portable hemodialysis machines that are easier for patients to operate may encourage the uptake of this technology. (2) One portable system is already available in the US. The NxStage System One hemodialysis machine operates on standard electric current, does not require plumbing or specialized disinfection, and is small enough for patients to travel with. (3) It is not yet clear whether the use of the NxStage system improves long-term survival and quality of life. (4) Home hemodialysis is less costly than conventional in-centre programs, but it is unknown whether these savings extend to portable devices.

Toward Harnessing User Feedback For Machine Learning

DTIC Science & Technology

2006-10-02

machine learning systems. If this resource-the users themselves-could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users? understanding and trust of the system could improve as well. We conducted a think-aloud study to see how willing users were to provide feedback and to understand what kinds of feedback users could give. Users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. We found that users

Pricing and Availability Intervention in Vending Machines at Four Bus Garages

PubMed Central

Hannan, Peter J; Harnack, Lisa J; Mitchell, Nathan R; Toomey, Traci L; Gerlach, Anne

2009-01-01

Objective To evaluate the effects of lowering prices and increasing availability on sales of healthy foods and beverages from 33 vending machines in four bus garages as part of a multi-component worksite obesity prevention intervention. Methods Availability of healthy items was increased to 50% and prices were lowered at least 10% in the vending machines in two metropolitan bus garages for an 18-month period. Two control garages offered vending choices at usual availability and prices. Sales data were collected monthly from each of the vending machines at the four garages. Results Increases in availability to 50% and price reductions of an average of 31% resulted in 10-42% higher sales of the healthy items. Employees were most price-responsive for snack purchases. Conclusions Greater availability and lower prices on targeted food and beverage items from vending machines was associated with greater purchases of these items over an eighteen-month period. Efforts to promote healthful food purchases in worksite settings should incorporate these two strategies. PMID:20061884

Pricing and availability intervention in vending machines at four bus garages.

PubMed

French, Simone A; Hannan, Peter J; Harnack, Lisa J; Mitchell, Nathan R; Toomey, Traci L; Gerlach, Anne

2010-01-01

To evaluate the effects of lowering prices and increasing availability on sales of healthy foods and beverages from 33 vending machines in 4 bus garages as part of a multicomponent worksite obesity prevention intervention. Availability of healthy items was increased to 50% and prices were lowered at least 10% in the vending machines in two metropolitan bus garages for an 18-month period. Two control garages offered vending choices at usual availability and prices. Sales data were collected monthly from each of the vending machines at the four garages. Increases in availability to 50% and price reductions of an average of 31% resulted in 10% to 42% higher sales of the healthy items. Employees were mostly price responsive for snack purchases. Greater availability and lower prices on targeted food and beverage items from vending machines was associated with greater purchases of these items over an 18-month period. Efforts to promote healthful food purchases in worksite settings should incorporate these two strategies.

MoleculeNet: a benchmark for molecular machine learning† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02664a

PubMed Central

Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N.; Gomes, Joseph; Geniesse, Caleb; Pappu, Aneesh S.; Leswing, Karl

2017-01-01

Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm. PMID:29629118

Evaluating the electrical discharge machining (EDM) parameters with using carbon nanotubes

NASA Astrophysics Data System (ADS)

Sari, M. M.; Noordin, M. Y.; Brusa, E.

2012-09-01

Electrical discharge machining (EDM) is one of the most accurate non traditional manufacturing processes available for creating tiny apertures, complex or simple shapes and geometries within parts and assemblies. Performance of the EDM process is usually evaluated in terms of surface roughness, existence of cracks, voids and recast layer on the surface of product, after machining. Unfortunately, the high heat generated on the electrically discharged material during the EDM process decreases the quality of products. Carbon nanotubes display unexpected strength and unique electrical and thermal properties. Multi-wall carbon nanotubes are therefore on purpose added to the dielectric used in the EDM process to improve its performance when machining the AISI H13 tool steel, by means of copper electrodes. Some EDM parameters such as material removal rate, electrode wear rate, surface roughness and recast layer are here first evaluated, then compared to the outcome of EDM performed without using nanotubes mixed to the dielectric. Independent variables investigated are pulse on time, peak current and interval time. Experimental evidences show that EDM process operated by mixing multi-wall carbon nanotubes within the dielectric looks more efficient, particularly if machining parameters are set at low pulse of energy.

Proposed algorithm to improve job shop production scheduling using ant colony optimization method

NASA Astrophysics Data System (ADS)

Pakpahan, Eka KA; Kristina, Sonna; Setiawan, Ari

2017-12-01

This paper deals with the determination of job shop production schedule on an automatic environment. On this particular environment, machines and material handling system are integrated and controlled by a computer center where schedule were created and then used to dictate the movement of parts and the operations at each machine. This setting is usually designed to have an unmanned production process for a specified interval time. We consider here parts with various operations requirement. Each operation requires specific cutting tools. These parts are to be scheduled on machines each having identical capability, meaning that each machine is equipped with a similar set of cutting tools therefore is capable of processing any operation. The availability of a particular machine to process a particular operation is determined by the remaining life time of its cutting tools. We proposed an algorithm based on the ant colony optimization method and embedded them on matlab software to generate production schedule which minimize the total processing time of the parts (makespan). We test the algorithm on data provided by real industry and the process shows a very short computation time. This contributes a lot to the flexibility and timelines targeted on an automatic environment.

Modeling of Geometric Error in Linear Guide Way to Improved the vertical three-axis CNC Milling machine’s accuracy

NASA Astrophysics Data System (ADS)

Kwintarini, Widiyanti; Wibowo, Agung; Arthaya, Bagus M.; Yuwana Martawirya, Yatna

2018-03-01

The purpose of this study was to improve the accuracy of three-axis CNC Milling Vertical engines with a general approach by using mathematical modeling methods of machine tool geometric errors. The inaccuracy of CNC machines can be caused by geometric errors that are an important factor during the manufacturing process and during the assembly phase, and are factors for being able to build machines with high-accuracy. To improve the accuracy of the three-axis vertical milling machine, by knowing geometric errors and identifying the error position parameters in the machine tool by arranging the mathematical modeling. The geometric error in the machine tool consists of twenty-one error parameters consisting of nine linear error parameters, nine angle error parameters and three perpendicular error parameters. The mathematical modeling approach of geometric error with the calculated alignment error and angle error in the supporting components of the machine motion is linear guide way and linear motion. The purpose of using this mathematical modeling approach is the identification of geometric errors that can be helpful as reference during the design, assembly and maintenance stages to improve the accuracy of CNC machines. Mathematically modeling geometric errors in CNC machine tools can illustrate the relationship between alignment error, position and angle on a linear guide way of three-axis vertical milling machines.

FAIR principles and the IEDB: short-term improvements and a long-term vision of OBO-foundry mediated machine-actionable interoperability

PubMed Central

Vita, Randi; Overton, James A; Mungall, Christopher J; Sette, Alessandro

2018-01-01

Abstract The Immune Epitope Database (IEDB), at www.iedb.org, has the mission to make published experimental data relating to the recognition of immune epitopes easily available to the scientific public. By presenting curated data in a searchable database, we have liberated it from the tables and figures of journal articles, making it more accessible and usable by immunologists. Recently, the principles of Findability, Accessibility, Interoperability and Reusability have been formulated as goals that data repositories should meet to enhance the usefulness of their data holdings. We here examine how the IEDB complies with these principles and identify broad areas of success, but also areas for improvement. We describe short-term improvements to the IEDB that are being implemented now, as well as a long-term vision of true ‘machine-actionable interoperability’, which we believe will require community agreement on standardization of knowledge representation that can be built on top of the shared use of ontologies. PMID:29688354

Methods, systems and apparatus for controlling operation of two alternating current (AC) machines

DOEpatents

Gallegos-Lopez, Gabriel [Torrance, CA; Nagashima, James M [Cerritos, CA; Perisic, Milun [Torrance, CA; Hiti, Silva [Redondo Beach, CA

2012-06-05

A system is provided for controlling two alternating current (AC) machines via a five-phase PWM inverter module. The system comprises a first control loop, a second control loop, and a current command adjustment module. The current command adjustment module operates in conjunction with the first control loop and the second control loop to continuously adjust current command signals that control the first AC machine and the second AC machine such that they share the input voltage available to them without compromising the target mechanical output power of either machine. This way, even when the phase voltage available to either one of the machines decreases, that machine outputs its target mechanical output power.

Brainstorming: weighted voting prediction of inhibitors for protein targets.

PubMed

Plewczynski, Dariusz

2011-09-01

The "Brainstorming" approach presented in this paper is a weighted voting method that can improve the quality of predictions generated by several machine learning (ML) methods. First, an ensemble of heterogeneous ML algorithms is trained on available experimental data, then all solutions are gathered and a consensus is built between them. The final prediction is performed using a voting procedure, whereby the vote of each method is weighted according to a quality coefficient calculated using multivariable linear regression (MLR). The MLR optimization procedure is very fast, therefore no additional computational cost is introduced by using this jury approach. Here, brainstorming is applied to selecting actives from large collections of compounds relating to five diverse biological targets of medicinal interest, namely HIV-reverse transcriptase, cyclooxygenase-2, dihydrofolate reductase, estrogen receptor, and thrombin. The MDL Drug Data Report (MDDR) database was used for selecting known inhibitors for these protein targets, and experimental data was then used to train a set of machine learning methods. The benchmark dataset (available at http://bio.icm.edu.pl/∼darman/chemoinfo/benchmark.tar.gz ) can be used for further testing of various clustering and machine learning methods when predicting the biological activity of compounds. Depending on the protein target, the overall recall value is raised by at least 20% in comparison to any single machine learning method (including ensemble methods like random forest) and unweighted simple majority voting procedures.

Measured impacts of high efficiency domestic clothes washers in a community

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tomlinson, J.; Rizy, T.

1998-07-01

The US market for domestic clothes washers is currently dominated by conventional vertical-axis washers that typically require approximately 40 gallons of water for each wash load. Although the current market for high efficiency clothes washers that use much less water and energy is quite small, it is growing slowly as manufacturers make machines based on tumble action, horizontal-axis designs available and as information about the performance and benefits of such machines is developed and made available to consumers. To help build awareness of these benefits and to accelerate markets for high efficiency washers, the Department of Energy (DOE), under itsmore » ENERGY STAR{reg_sign} Program and in cooperation with a major manufacturers of high efficiency washers, conducted a field evaluation of high efficiency washers using Bern, Kansas as a test bed. Baseline washing machine performance data as well as consumer washing behavior were obtained from data collected on the existing machines of more than 100 participants in this instrumented study. Following a 2-month initial study period, all conventional machines were replaced by high efficiency, tumble-action washers, and the study continued for 3 months. Based on measured data from over 20,000 loads of laundry, the impact of the washer replacement on (1) individual customers` energy and water consumption, (2) customers` laundry habits and perceptions, and (3) the community`s water supply and waste water systems were determined. The study, its findings, and how information from the experiment was used to improve national awareness of high efficiency clothes washer benefits are described in this paper.« less

Development of a Workbench to Address the Educational Data Mining Bottleneck

ERIC Educational Resources Information Center

Rodrigo, Ma. Mercedes T.; Baker, Ryan S. J. d.; McLaren, Bruce M.; Jayme, Alejandra; Dy, Thomas T.

2012-01-01

In recent years, machine-learning software packages have made it easier for educational data mining researchers to create real-time detectors of cognitive skill as well as of metacognitive and motivational behavior that can be used to improve student learning. However, there remain challenges to overcome for these methods to become available to…

A Double-Edged Sword: The Merits and the Policy Implications of Google Translate in Higher Education

ERIC Educational Resources Information Center

Mundt, Klaus; Groves, Michael

2016-01-01

Machine translation, specifically Google Translate, is freely available, and is improving in its ability to provide grammatically accurate translations. This development has the potential to provoke a major transformation in the internationalization process at universities, since students may be, in the future, able to use technology to circumvent…

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

NASA Astrophysics Data System (ADS)

Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

2018-02-01

For a drug, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

PubMed Central

Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

2018-01-01

During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future. PMID:29515993

Phenotyping: Using Machine Learning for Improved Pairwise Genotype Classification Based on Root Traits

PubMed Central

Zhao, Jiangsan; Bodner, Gernot; Rewald, Boris

2016-01-01

Phenotyping local crop cultivars is becoming more and more important, as they are an important genetic source for breeding – especially in regard to inherent root system architectures. Machine learning algorithms are promising tools to assist in the analysis of complex data sets; novel approaches are need to apply them on root phenotyping data of mature plants. A greenhouse experiment was conducted in large, sand-filled columns to differentiate 16 European Pisum sativum cultivars based on 36 manually derived root traits. Through combining random forest and support vector machine models, machine learning algorithms were successfully used for unbiased identification of most distinguishing root traits and subsequent pairwise cultivar differentiation. Up to 86% of pea cultivar pairs could be distinguished based on top five important root traits (Timp5) – Timp5 differed widely between cultivar pairs. Selecting top important root traits (Timp) provided a significant improved classification compared to using all available traits or randomly selected trait sets. The most frequent Timp of mature pea cultivars was total surface area of lateral roots originating from tap root segments at 0–5 cm depth. The high classification rate implies that culturing did not lead to a major loss of variability in root system architecture in the studied pea cultivars. Our results illustrate the potential of machine learning approaches for unbiased (root) trait selection and cultivar classification based on rather small, complex phenotypic data sets derived from pot experiments. Powerful statistical approaches are essential to make use of the increasing amount of (root) phenotyping information, integrating the complex trait sets describing crop cultivars. PMID:27999587

Experimental Investigation – Magnetic Assisted Electro Discharge Machining

NASA Astrophysics Data System (ADS)

Kesava Reddy, Chirra; Manzoor Hussain, M.; Satyanarayana, S.; Krishna, M. V. S. Murali

2018-04-01

Emerging technology needs advanced machined parts with high strength and temperature resistance, high fatigue life at low production cost with good surface quality to fit into various industrial applications. Electro discharge machine is one of the extensively used machines to manufacture advanced machined parts which cannot be machined by other traditional machine with high precision and accuracy. Machining of DIN 17350-1.2080 (High Carbon High Chromium steel), using electro discharge machining has been discussed in this paper. In the present investigation an effort is made to use permanent magnet at various positions near the spark zone to improve surface quality of the machined surface. Taguchi methodology is used to obtain optimal choice for each machining parameter such as peak current, pulse duration, gap voltage and Servo reference voltage etc. Process parameters have significant influence on machining characteristics and surface finish. Improvement in surface finish is observed when process parameters are set at optimum condition under the influence of magnetic field at various positions.

Exploring Local Public Health Workflow in the Context of Automated Translation Technologies

PubMed Central

Mandel, Hannah; Turner, Anne M.

2013-01-01

Despite the growing limited English proficiency (LEP) population in the US, and federal regulations requiring multilingual health information be available for LEP individuals, there is a lack of available high quality multilingual health promotion materials. The costs and personnel time associated with creating high quality translations serve as barriers to their creation, especially in resource limited public health settings. To explore the potential adoption of novel machine translation and document dissemination technologies for improving the creation and sharing of translated public health materials, we interviewed key health department personnel in Washington State. We analyzed translation workflow, elucidated key themes regarding public health translation work, and assessed attitudes towards electronic document exchange and machine translation. Public health personnel expressed the need for human quality assurance and oversight, but appreciated the potential of novel information technologies to assist in the production and dissemination of translated materials for public health practice. PMID:24551385

Abstracts of AF Materials Laboratory Reports

DTIC Science & Technology

1975-09-01

NO: TITLE: AUTHOR(S): CONTRACT NO; CONTRACTOR: AFML-TR-73-307 200,397 IMPROVED AUTOMATED TAPE LAYING MACHINE M. Poullos, W. J. Murray, D.L...AUTOMATED IMPROVED AUTOMATED TAPE LAYING MACHINE AUTOMATION AUTOMATION OF COATING PROCESSES FOR GAS TURBINE DLADcS AND VANES 203222/111 203072...IMP90VE0 TAPE LAYING MACHINE IMPP)VED AUTOMATED TAPE LAYING MACHINE A STUDY O^ THE STRESS-STRAIN TEHAVIOR OF GRAPHITE

48 CFR 6104.402 - Filing claims [Rule 402].

Code of Federal Regulations, 2012 CFR

2012-10-01

... number, facsimile machine number, and e-mail address, if available, of the claimant; (ii) The name, address, telephone number, facsimile machine number, and e-mail address, if available, of the agency...'s telephone number is: (202) 606-8800. The Clerk's facsimile machine number is: (202) 606-0019. The...

48 CFR 6104.402 - Filing claims [Rule 402].

Code of Federal Regulations, 2011 CFR

2011-10-01

... number, facsimile machine number, and e-mail address, if available, of the claimant; (ii) The name, address, telephone number, facsimile machine number, and e-mail address, if available, of the agency...'s telephone number is: (202) 606-8800. The Clerk's facsimile machine number is: (202) 606-0019. The...

48 CFR 6104.402 - Filing claims [Rule 402].

Code of Federal Regulations, 2014 CFR

2014-10-01

... number, facsimile machine number, and e-mail address, if available, of the claimant; (ii) The name, address, telephone number, facsimile machine number, and e-mail address, if available, of the agency...'s telephone number is: (202) 606-8800. The Clerk's facsimile machine number is: (202) 606-0019. The...

Performance evaluation of various classifiers for color prediction of rice paddy plant leaf

NASA Astrophysics Data System (ADS)

Singh, Amandeep; Singh, Maninder Lal

2016-11-01

The food industry is one of the industries that uses machine vision for a nondestructive quality evaluation of the produce. These quality measuring systems and softwares are precalculated on the basis of various image-processing algorithms which generally use a particular type of classifier. These classifiers play a vital role in making the algorithms so intelligent that it can contribute its best while performing the said quality evaluations by translating the human perception into machine vision and hence machine learning. The crop of interest is rice, and the color of this crop indicates the health status of the plant. An enormous number of classifiers are available to solve the purpose of color prediction, but choosing the best among them is the focus of this paper. Performance of a total of 60 classifiers has been analyzed from the application point of view, and the results have been discussed. The motivation comes from the idea of providing a set of classifiers with excellent performance and implementing them on a single algorithm for the improvement of machine vision learning and, hence, associated applications.

Quantum-Enhanced Machine Learning

NASA Astrophysics Data System (ADS)

Dunjko, Vedran; Taylor, Jacob M.; Briegel, Hans J.

2016-09-01

The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.

Influence of Surface Features for Increased Heat Dissipation on Tool Wear

PubMed Central

Beno, Tomas; Hoier, Philipp; Wretland, Anders

2018-01-01

The critical problems faced during the machining process of heat resistant superalloys, (HRSA), is the concentration of heat in the cutting zone and the difficulty in dissipating it. The concentrated heat in the cutting zone has a negative influence on the tool life and surface quality of the machined surface, which in turn, contributes to higher manufacturing costs. This paper investigates improved heat dissipation from the cutting zone on the tool wear through surface features on the cutting tools. Firstly, the objective was to increase the available surface area in high temperature regions of the cutting tool. Secondly, multiple surface features were fabricated for the purpose of acting as channels in the rake face to create better access for the coolant to the proximity of the cutting edge. The purpose was thereby to improve the cooling of the cutting edge itself, which exhibits the highest temperature during machining. These modified inserts were experimentally investigated in face turning of Alloy 718 with high-pressure coolant. Overall results exhibited that surface featured inserts decreased flank wear, abrasion of the flank face, cutting edge deterioration and crater wear probably due to better heat dissipation from the cutting zone. PMID:29693579

An improved wrapper-based feature selection method for machinery fault diagnosis

PubMed Central

2017-01-01

A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks. PMID:29261689

Development and testing of an active boring bar for increased chatter immunity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Redmond, J.; Barney, P.

Recent advances in smart materials have renewed interest in the development of improved manufacturing processes featuring sensing, processing, and active control. In particular, vibration suppression in metal cutting has received much attention because of its potential for enhancing part quality while reducing the time and cost of production. Although active tool clamps have been recently demonstrated, they are often accompanied by interfacing issues that limit their applicability to specific machines. Under the auspices of the Laboratory Directed Research and Development program, the project titled {open_quotes}Smart Cutting Tools for Precision Manufacturing{close_quotes} developed an alternative approach to active vibration control in machining.more » Using the boring process as a vehicle for exploration, a commercially available tool was modified to incorporate PZT stack actuators for active suppression of its bending modes. Since the modified tool requires no specialized mounting hardware, it can be readily mounted on many machines. Cutting tests conducted on a horizontal lathe fitted with a hardened steel workpiece verify that the actively damped boring bar yields significant vibration reduction and improved surface finishes as compared to an unmodified tool.« less

Improvement of human operator vibroprotection system in the utility machine

NASA Astrophysics Data System (ADS)

Korchagin, P. A.; Teterina, I. A.; Rahuba, L. F.

2018-01-01

The article is devoted to an urgent problem of improving efficiency of road-building utility machines in terms of improving human operator vibroprotection system by determining acceptable values of the rigidity coefficients and resistance coefficients of operator’s cab suspension system elements and those of operator’s seat. Negative effects of vibration result in labour productivity decrease and occupational diseases. Besides, structure vibrations have a damaging impact on the machine units and mechanisms, which leads to reducing an overall service life of the machine. Results of experimental and theoretical research of operator vibroprotection system in the road-building utility machine are presented. An algorithm for the program to calculate dynamic impacts on the operator in terms of different structural and performance parameters of the machine and considering combination of external pertrubation influences was proposed.

Variable cross-section windings for efficiency improvement of electric machines

NASA Astrophysics Data System (ADS)

Grachev, P. Yu; Bazarov, A. A.; Tabachinskiy, A. S.

2018-02-01

Implementation of energy-saving technologies in industry is impossible without efficiency improvement of electric machines. The article considers the ways of efficiency improvement and mass and dimensions reduction of electric machines with electronic control. Features of compact winding design for stators and armatures are described. Influence of compact winding on thermal and electrical process is given. Finite element method was used in computer simulation.

SU-F-T-163: Improve Proton Therapy Efficiency: Report of a Workshop

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zheng, Y; Flanz, J; Mah, D

Purpose: The technology of proton therapy, especially the pencil beam scanning technique, is evolving very quickly. However, the efficiency of proton therapy seems to lag behind conventional photon therapy. The purpose of the abstract is to report on the findings of a workshop on improvement of QA, planning and treatment efficiency in proton therapy. Methods: A panel of physicists, clinicians, and vendor representatives from over 18 institutions in the United States and internationally were convened in Knoxville, Tennessee in November, 2015. The panel discussed several topics on how to improve proton therapy efficiency, including 1) lean principle and failure modemore » and effects analysis, 2) commissioning and machine QA, 3) treatment planning, optimization and evaluation, 4) patient positioning and IGRT, 5) vendor liaison and machine availability, and 6) staffing, education and training. Results: The relative time needed for machine QA, treatment planning & check in proton therapy was found to range from 1 to 2.5 times of that in photon therapy. Current status in proton QA, planning and treatment was assessed. Key areas for efficiency improvement, such as elimination of unnecessary QA items or steps and development of efficient software or hardware tools, were identified. A white paper to summarize our findings is being written. Conclusion: It is critical to improve efficiency by developing reliable proton beam lines, efficient software tools on treatment planning, optimization and evaluation, and dedicated proton QA device. Conscious efforts and collaborations from both industry leaders and proton therapy centers are needed to achieve this goal and further advance the technology of proton therapy.« less

Safety Features in Anaesthesia Machine

PubMed Central

Subrahmanyam, M; Mohan, S

2013-01-01

Anaesthesia is one of the few sub-specialties of medicine, which has quickly adapted technology to improve patient safety. This application of technology can be seen in patient monitoring, advances in anaesthesia machines, intubating devices, ultrasound for visualisation of nerves and vessels, etc., Anaesthesia machines have come a long way in the last 100 years, the improvements being driven both by patient safety as well as functionality and economy of use. Incorporation of safety features in anaesthesia machines and ensuring that a proper check of the machine is done before use on a patient ensures patient safety. This review will trace all the present safety features in the machine and their evolution. PMID:24249880

sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces.

PubMed

Jrad, N; Congedo, M; Phlypo, R; Rousseau, S; Flamary, R; Yger, F; Rakotomamonjy, A

2011-10-01

In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.

A comparison of machine learning and Bayesian modelling for molecular serotyping.

PubMed

Newton, Richard; Wernisch, Lorenz

2017-08-11

Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease. Genomic microarrays provide an effective method for molecular serotyping. Previously we developed an empirical Bayesian model for the classification of serotypes from a molecular serotyping array. With only few samples available, a model driven approach was the only option. In the meanwhile, several thousand samples have been made available to us, providing an opportunity to investigate serotype classification by machine learning methods, which could complement the Bayesian model. We compare the performance of the original Bayesian model with two machine learning algorithms: Gradient Boosting Machines and Random Forests. We present our results as an example of a generic strategy whereby a preliminary probabilistic model is complemented or replaced by a machine learning classifier once enough data are available. Despite the availability of thousands of serotyping arrays, a problem encountered when applying machine learning methods is the lack of training data containing mixtures of serotypes; due to the large number of possible combinations. Most of the available training data comprises samples with only a single serotype. To overcome the lack of training data we implemented an iterative analysis, creating artificial training data of serotype mixtures by combining raw data from single serotype arrays. With the enhanced training set the machine learning algorithms out perform the original Bayesian model. However, for serotypes currently lacking sufficient training data the best performing implementation was a combination of the results of the Bayesian Model and the Gradient Boosting Machine. As well as being an effective method for classifying biological data, machine learning can also be used as an efficient method for revealing subtle biological insights, which we illustrate with an example.

Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology.

PubMed

Syeda-Mahmood, Tanveer

2018-03-01

The field of diagnostic decision support in radiology is undergoing rapid transformation with the availability of large amounts of patient data and the development of new artificial intelligence methods of machine learning such as deep learning. They hold the promise of providing imaging specialists with tools for improving the accuracy and efficiency of diagnosis and treatment. In this article, we will describe the growth of this field for radiology and outline general trends highlighting progress in the field of diagnostic decision support from the early days of rule-based expert systems to cognitive assistants of the modern era. Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.

48 CFR 6104.402 - Filing claims [Rule 402].

Code of Federal Regulations, 2010 CFR

2010-10-01

... number, and facsimile machine number, if available, of the claimant; (ii) The name, address, telephone number, and facsimile machine number, if available, of the agency employee who denied the claim; (iii) A... Clerk's facsimile machine number is: (202) 606-0019. The Board's working hours are 8:00 a.m. to 4:30 p.m...

Enhancements to the IBM version of COSMIC/NASTRAN

NASA Technical Reports Server (NTRS)

Brown, W. Keith

1989-01-01

Major improvements were made to the IBM version of COSMIC/NASTRAN by RPK Corporation under contract to IBM Corporation. These improvements will become part of COSMIC's IBM version and will be available in the second quarter of 1989. The first improvement is the inclusion of code to take advantage of IBM's new Vector Facility (VF) on its 3090 machines. The remaining improvements are modifications that will benefit all users as a result of the extended addressing capability provided by the MVS/XA operating system. These improvements include the availability of an in-memory data base that potentially eliminates the need for I/O to the PRIxx disk files. Another improvement is the elimination of multiple load modules that have to be loaded for every link switch within NASTRAN. The last improvement allows for NASTRAN to execute above the 16 mega-byte line. This improvement allows for NASTRAN to have access to 2 giga-bytes of memory for open core and the in-memory data base.

Leveraging human oversight and intervention in large-scale parallel processing of open-source data

NASA Astrophysics Data System (ADS)

Casini, Enrico; Suri, Niranjan; Bradshaw, Jeffrey M.

2015-05-01

The popularity of cloud computing along with the increased availability of cheap storage have led to the necessity of elaboration and transformation of large volumes of open-source data, all in parallel. One way to handle such extensive volumes of information properly is to take advantage of distributed computing frameworks like Map-Reduce. Unfortunately, an entirely automated approach that excludes human intervention is often unpredictable and error prone. Highly accurate data processing and decision-making can be achieved by supporting an automatic process through human collaboration, in a variety of environments such as warfare, cyber security and threat monitoring. Although this mutual participation seems easily exploitable, human-machine collaboration in the field of data analysis presents several challenges. First, due to the asynchronous nature of human intervention, it is necessary to verify that once a correction is made, all the necessary reprocessing is done in chain. Second, it is often needed to minimize the amount of reprocessing in order to optimize the usage of resources due to limited availability. In order to improve on these strict requirements, this paper introduces improvements to an innovative approach for human-machine collaboration in the processing of large amounts of open-source data in parallel.

Modeling and Analysis of High Torque Density Transverse Flux Machines for Direct-Drive Applications

NASA Astrophysics Data System (ADS)

Hasan, Iftekhar

Commercially available permanent magnet synchronous machines (PMSM) typically use rare-earth-based permanent magnets (PM). However, volatility and uncertainty associated with the supply and cost of rare-earth magnets have caused a push for increased research into the development of non-rare-earth based PM machines and reluctance machines. Compared to other PMSM topologies, the Transverse Flux Machine (TFM) is a promising candidate to get higher torque densities at low speed for direct-drive applications, using non-rare-earth based PMs. The TFMs can be designed with a very small pole pitch which allows them to attain higher force density than conventional radial flux machines (RFM) and axial flux machines (AFM). This dissertation presents the modeling, electromagnetic design, vibration analysis, and prototype development of a novel non-rare-earth based PM-TFM for a direct-drive wind turbine application. The proposed TFM addresses the issues of low power factor, cogging torque, and torque ripple during the electromagnetic design phase. An improved Magnetic Equivalent Circuit (MEC) based analytical model was developed as an alternative to the time-consuming 3D Finite Element Analysis (FEA) for faster electromagnetic analysis of the TFM. The accuracy and reliability of the MEC model were verified, both with 3D-FEA and experimental results. The improved MEC model was integrated with a Particle Swarm Optimization (PSO) algorithm to further enhance the capability of the analytical tool for performing rigorous optimization of performance-sensitive machine design parameters to extract the highest torque density for rated speed. A novel concept of integrating the rotary transformer within the proposed TFM design was explored to completely eliminate the use of magnets from the TFM. While keeping the same machine envelope, and without changing the stator or rotor cores, the primary and secondary of a rotary transformer were embedded into the double-sided TFM. The proposed structure allowed for improved flux-weakening capabilities of the TFM for wide speed operations. The electromagnetic design feature of stator pole shaping was used to address the issue of cogging torque and torque ripple in 3-phase TFM. The slant-pole tooth-face in the stator showed significant improvements in cogging torque and torque ripple performance during the 3-phase FEA analysis of the TFM. A detailed structural analysis for the proposed TFM was done prior to the prototype development to validate the structural integrity of the TFM design at rated and maximum speed operation. Vibration performance of the TFM was investigated to determine the structural performance of the TFM under resonance. The prototype for the proposed TFM was developed at the Alternative Energy Laboratory of the University of Akron. The working prototype is a testament to the feasibility of developing and implementing the novel TFM design proposed in this research. Experiments were performed to validate the 3D-FEA electromagnetic and vibration performance result.

The Impact Of Surface Shape Of Chip-Breaker On Machined Surface

NASA Astrophysics Data System (ADS)

Šajgalík, Michal; Czán, Andrej; Martinček, Juraj; Varga, Daniel; Hemžský, Pavel; Pitela, David

2015-12-01

Machined surface is one of the most used indicators of workpiece quality. But machined surface is influenced by several factors such as cutting parameters, cutting material, shape of cutting tool or cutting insert, micro-structure of machined material and other known as technological parameters. By improving of these parameters, we can improve machined surface. In the machining, there is important to identify the characteristics of main product of these processes - workpiece, but also the byproduct - the chip. Size and shape of chip has impact on lifetime of cutting tools and its inappropriate form can influence the machine functionality and lifetime, too. This article deals with elimination of long chip created when machining of shaft in automotive industry and with impact of shape of chip-breaker on shape of chip in various cutting conditions based on production requirements.

Understanding dental CAD/CAM for restorations - dental milling machines from a mechanical engineering viewpoint. Part A: chairside milling machines.

PubMed

Lebon, Nicolas; Tapie, Laurent; Duret, Francois; Attal, Jean-Pierre

2016-01-01

The dental milling machine is an important device in the dental CAD/CAM chain. Nowadays, dental numerical controlled (NC) milling machines are available for dental surgeries (chairside solution). This article provides a mechanical engineering approach to NC milling machines to help dentists understand the involvement of technology in digital dentistry practice. First, some technical concepts and definitions associated with NC milling machines are described from a mechanical engineering viewpoint. The technical and economic criteria of four chairside dental NC milling machines that are available on the market are then described. The technical criteria are focused on the capacities of the embedded technologies of these milling machines to mill both prosthetic materials and types of shape restorations. The economic criteria are focused on investment costs and interoperability with third-party software. The clinical relevance of the technology is assessed in terms of the accuracy and integrity of the restoration.

Study on Gap Flow Field Simulation in Small Hole Machining of Ultrasonic Assisted EDM

NASA Astrophysics Data System (ADS)

Liu, Yu; Chang, Hao; Zhang, Wenchao; Ma, Fujian; Sha, Zhihua; Zhang, Shengfang

2017-12-01

When machining a small hole with high aspect ratio in EDM, it is hard for the flushing liquid entering the bottom gap and the debris could hardly be removed, which results in the accumulation of debris and affects the machining efficiency and machining accuracy. The assisted ultrasonic vibration can improve the removal of debris in the gap. Based on dynamics simulation software Fluent, a 3D model of debris movement in the gap flow field of EDM small hole machining assisted with side flushing and ultrasonic vibration is established in this paper. When depth to ratio is 3, the laws of different amplitudes and frequencies on debris distribution and removal are quantitatively analysed. The research results show that periodic ultrasonic vibration can promote the movement of debris, which is beneficial to the removal of debris in the machining gap. Compared to traditional small hole machining in EDM, the debris in the machining gap is greatly reduced, which ensures the stability of machining process and improves the machining efficiency.

Integration of Machining and Inspection in Aerospace Manufacturing

NASA Astrophysics Data System (ADS)

Simpson, Bart; Dicken, Peter J.

2011-12-01

The main challenge for aerospace manufacturers today is to develop the ability to produce high-quality products on a consistent basis as quickly as possible and at the lowest-possible cost. At the same time, rising material prices are making the cost of scrap higher than ever so making it more important to minimise waste. Proper inspection and quality control methods are no longer a luxury; they are an essential part of every manufacturing operation that wants to grow and be successful. However, simply bolting on some quality control procedures to the existing manufacturing processes is not enough. Inspection must be fully-integrated with manufacturing for the investment to really produce significant improvements. The traditional relationship between manufacturing and inspection is that machining is completed first on the company's machine tools and the components are then transferred to dedicated inspection equipment to be approved or rejected. However, as machining techniques become more sophisticated, and as components become larger and more complex, there are a growing number of cases where closer integration is required to give the highest productivity and the biggest reductions in wastage. Instead of a simple linear progression from CAD to CAM to machining to inspection, a more complicated series of steps is needed, with extra data needed to fill any gaps in the information available at the various stages. These new processes can be grouped under the heading of "adaptive machining". The programming of most machining operations is based around knowing three things: the position of the workpiece on the machine, the starting shape of the material to be machined, and the final shape that needs to be achieved at the end of the operation. Adaptive machining techniques allow successful machining when at least one of those elements is unknown, by using in-process measurement to close the information gaps in the process chain. It also allows any errors to be spotted earlier in the manufacturing process, so helping the problems to be resolved more quickly and at lower cost.

A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue.

PubMed

Chen, Zhenyu; Li, Jianping; Wei, Liwei

2007-10-01

Recently, gene expression profiling using microarray techniques has been shown as a promising tool to improve the diagnosis and treatment of cancer. Gene expression data contain high level of noise and the overwhelming number of genes relative to the number of available samples. It brings out a great challenge for machine learning and statistic techniques. Support vector machine (SVM) has been successfully used to classify gene expression data of cancer tissue. In the medical field, it is crucial to deliver the user a transparent decision process. How to explain the computed solutions and present the extracted knowledge becomes a main obstacle for SVM. A multiple kernel support vector machine (MK-SVM) scheme, consisting of feature selection, rule extraction and prediction modeling is proposed to improve the explanation capacity of SVM. In this scheme, we show that the feature selection problem can be translated into an ordinary multiple parameters learning problem. And a shrinkage approach: 1-norm based linear programming is proposed to obtain the sparse parameters and the corresponding selected features. We propose a novel rule extraction approach using the information provided by the separating hyperplane and support vectors to improve the generalization capacity and comprehensibility of rules and reduce the computational complexity. Two public gene expression datasets: leukemia dataset and colon tumor dataset are used to demonstrate the performance of this approach. Using the small number of selected genes, MK-SVM achieves encouraging classification accuracy: more than 90% for both two datasets. Moreover, very simple rules with linguist labels are extracted. The rule sets have high diagnostic power because of their good classification performance.

Choice Behavior of Nonpathological Women Playing Concurrently Available Slot Machines: Effect of Changes in Payback Percentages

ERIC Educational Resources Information Center

Weatherly, Jeffrey N.; Thompson, Bradley J.; Hodny, Marisa; Meier, Ellen

2009-01-01

In a simulated casino environment, 6 nonpathological women played concurrently available commercial slot machines programmed to pay out at different rates. Participants did not always demonstrate preferences for the higher paying machine. The data suggest that factors other than programmed or obtained rate of reinforcement may control gambling…

49 CFR 214.533 - Schedule of repairs subject to availability of parts.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Maintenance Machines and Hi-Rail Vehicles § 214.533 Schedule of repairs subject to availability of parts. (a... maintenance machine or a hi-rail vehicle by the end of the next business day following the report of the... maintenance machine or hi-rail vehicle within seven calendar days after receiving the necessary part. The...

Power training using pneumatic machines vs. plate-loaded machines to improve muscle power in older adults.

PubMed

Balachandran, Anoop T; Gandia, Kristine; Jacobs, Kevin A; Streiner, David L; Eltoukhy, Moataz; Signorile, Joseph F

2017-11-01

Power training has been shown to be more effective than conventional resistance training for improving physical function in older adults; however, most trials have used pneumatic machines during training. Considering that the general public typically has access to plate-loaded machines, the effectiveness and safety of power training using plate-loaded machines compared to pneumatic machines is an important consideration. The purpose of this investigation was to compare the effects of high-velocity training using pneumatic machines (Pn) versus standard plate-loaded machines (PL). Independently-living older adults, 60years or older were randomized into two groups: pneumatic machine (Pn, n=19) and plate-loaded machine (PL, n=17). After 12weeks of high-velocity training twice per week, groups were analyzed using an intention-to-treat approach. Primary outcomes were lower body power measured using a linear transducer and upper body power using medicine ball throw. Secondary outcomes included lower and upper body muscle muscle strength, the Physical Performance Battery (PPB), gallon jug test, the timed up-and-go test, and self-reported function using the Patient Reported Outcomes Measurement Information System (PROMIS) and an online video questionnaire. Outcome assessors were blinded to group membership. Lower body power significantly improved in both groups (Pn: 19%, PL: 31%), with no significant difference between the groups (Cohen's d=0.4, 95% CI (-1.1, 0.3)). Upper body power significantly improved only in the PL group, but showed no significant difference between the groups (Pn: 3%, PL: 6%). For balance, there was a significant difference between the groups favoring the Pn group (d=0.7, 95% CI (0.1, 1.4)); however, there were no statistically significant differences between groups for PPB, gallon jug transfer, muscle muscle strength, timed up-and-go or self-reported function. No serious adverse events were reported in either of the groups. Pneumatic and plate-loaded machines were effective in improving lower body power and physical function in older adults. The results suggest that power training can be safely and effectively performed by older adults using either pneumatic or plate-loaded machines. Copyright © 2017 Elsevier Inc. All rights reserved.

Process capability improvement through DMAIC for aluminum alloy wheel machining

NASA Astrophysics Data System (ADS)

Sharma, G. V. S. S.; Rao, P. Srinivasa; Babu, B. Surendra

2017-07-01

This paper first enlists the generic problems of alloy wheel machining and subsequently details on the process improvement of the identified critical-to-quality machining characteristic of A356 aluminum alloy wheel machining process. The causal factors are traced using the Ishikawa diagram and prioritization of corrective actions is done through process failure modes and effects analysis. Process monitoring charts are employed for improving the process capability index of the process, at the industrial benchmark of four sigma level, which is equal to the value of 1.33. The procedure adopted for improving the process capability levels is the define-measure-analyze-improve-control (DMAIC) approach. By following the DMAIC approach, the C p, C pk and C pm showed signs of improvement from an initial value of 0.66, -0.24 and 0.27, to a final value of 4.19, 3.24 and 1.41, respectively.

Availability of Vending Machines and School Stores in California Schools.

PubMed

Cisse-Egbuonye, Nafissatou; Liles, Sandy; Schmitz, Katharine E; Kassem, Nada; Irvin, Veronica L; Hovell, Melbourne F

2016-01-01

This study examined the availability of foods sold in vending machines and school stores in United States public and private schools, and associations of availability with students' food purchases and consumption. Descriptive analyses, chi-square tests, and Spearman product-moment correlations were conducted on data collected from 521 students aged 8 to 15 years recruited from orthodontic offices in California. Vending machines were more common in private schools than in public schools, whereas school stores were common in both private and public schools. The food items most commonly available in both vending machines and school stores in all schools were predominately foods of minimal nutritional value (FMNV). Participant report of availability of food items in vending machines and/or school stores was significantly correlated with (1) participant purchase of each item from those sources, except for energy drinks, milk, fruits, and vegetables; and (2) participants' friends' consumption of items at lunch, for 2 categories of FMNV (candy, cookies, or cake; soda or sports drinks). Despite the Child Nutrition and Women, Infants, and Children (WIC) Reauthorization Act of 2004, FMNV were still available in schools, and may be contributing to unhealthy dietary choices and ultimately to health risks. © 2015, American School Health Association.

Availability of vending machines and school stores in California schools

PubMed Central

Liles, Sandy; Schmitz, Katharine E.; Kassem, Nada O.F; Irvin, Veronica L; Hovell, Melbourne F.

2015-01-01

Background This study examined the availability of foods sold in vending machines and school stores in US public and private schools, and associations of availability with students' food purchases and consumption. Methods Descriptive analyses, chi-square tests, and Spearman product-moment correlations were conducted on data collected from 521 students aged 8 to15 years recruited from orthodontic offices in California. Results Vending machines were more common in private schools than in public schools, while school stores were common in both private and public schools. The food items most commonly available in both vending machines and school stores in all schools were predominately foods of minimal nutritional value (FMNV). Participant report of availability of food items in vending machines and/or school stores was significantly correlated with: (1) participant purchase of each item from those sources, except for energy drinks, milk, fruits, and vegetables; and (2) participants' friends' consumption of items at lunch, for two categories of FMNV (candy, cookies, or cake; soda or sports drinks). Conclusions Despite the Child Nutrition and WIC reauthorization Act of 2004, FMNV were still available in schools, and may be contributing to unhealthy dietary choices and ultimately to health risks. PMID:26645420

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning

PubMed Central

Sutphin, George L.; Mahoney, J. Matthew; Sheppard, Keith; Walton, David O.; Korstanje, Ron

2016-01-01

The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species—humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/. PMID:27812085

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning.

PubMed

Sutphin, George L; Mahoney, J Matthew; Sheppard, Keith; Walton, David O; Korstanje, Ron

2016-11-01

The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species-humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/.

A Double-Sided Linear Primary Permanent Magnet Vernier Machine

PubMed Central

2015-01-01

The purpose of this paper is to present a new double-sided linear primary permanent magnet (PM) vernier (DSLPPMV) machine, which can offer high thrust force, low detent force, and improved power factor. Both PMs and windings of the proposed machine are on the short translator, while the long stator is designed as a double-sided simple iron core with salient teeth so that it is very robust to transmit high thrust force. The key of this new machine is the introduction of double stator and the elimination of translator yoke, so that the inductance and the volume of the machine can be reduced. Hence, the proposed machine offers improved power factor and thrust force density. The electromagnetic performances of the proposed machine are analyzed including flux, no-load EMF, thrust force density, and inductance. Based on using the finite element analysis, the characteristics and performances of the proposed machine are assessed. PMID:25874250

A double-sided linear primary permanent magnet vernier machine.

PubMed

Du, Yi; Zou, Chunhua; Liu, Xianxing

2015-01-01

The purpose of this paper is to present a new double-sided linear primary permanent magnet (PM) vernier (DSLPPMV) machine, which can offer high thrust force, low detent force, and improved power factor. Both PMs and windings of the proposed machine are on the short translator, while the long stator is designed as a double-sided simple iron core with salient teeth so that it is very robust to transmit high thrust force. The key of this new machine is the introduction of double stator and the elimination of translator yoke, so that the inductance and the volume of the machine can be reduced. Hence, the proposed machine offers improved power factor and thrust force density. The electromagnetic performances of the proposed machine are analyzed including flux, no-load EMF, thrust force density, and inductance. Based on using the finite element analysis, the characteristics and performances of the proposed machine are assessed.

Relative Performance of Hardwood Sawing Machines

Treesearch

Philip H. Steele; Michael W. Wade; Steven H. Bullard; Philip A. Araman

1991-01-01

Only limited information has been available to hardwood sawmillers on the performance of their sawing machines. This study analyzes a large database of individual machine studies to provide detailed information on 6 machine types. These machine types were band headrig, circular headrig, band linebar resaw, vertical band splitter resaw, single arbor gang resaw and...

Electromagnetic Signal Feedback Control for Proximity Detection Systems

NASA Astrophysics Data System (ADS)

Smith, Adam K.

Coal is the most abundant fossil fuel in the United States and remains an essential source of energy. While more than half of coal production comes from surface mining, nearly twice as many workers are employed by underground operations. One of the key pieces of equipment used in underground coal mining is the continuous mining machine. These large and powerful machines are operated in confined spaces by remote control. Since 1984, 40 mine workers in the U. S. have been killed when struck or pinned by a continuous mining machine. It is estimated that a majority of these accidents could have been prevented with the application of proximity detection systems. While proximity detection systems can significantly increase safety around a continuous mining machine, there are some system limitations. Commercially available proximity warning systems for continuous mining machines use magnetic field generators to detect workers and establish safe work areas around the machines. Several environmental factors, however, can influence and distort the magnetic fields. To minimize these effects, a control system has been developed using electromagnetic field strength and generator current to stabilize and control field drift induced by internal and external environmental factors. A laboratory test set-up was built using a ferrite-core magnetic field generator to produce a stable magnetic field. Previous work based on a field-invariant magnetic flux density model, which generically describes the electromagnetic field, is expanded upon. The analytically established transferable shell-based flux density distribution model is used to experimentally validate the control system. By controlling the current input to the ferrite-core generator, a more reliable and consistent magnetic field is produced. Implementation of this technology will improve accuracy and performance of existing commercial proximity detection systems. These research results will help reduce the risk of traumatic injuries and improve overall safety in the mining workplace.

Declining sex ratio: role of society, technology and government regulation in Faridabad district, Haryana.

PubMed

Bardia, A; Paul, E; Kapoor, S K; Anand, K

2004-01-01

A declining sex ratio at birth has been documented during censuses in India. The decline is especially more in the northern states of Haryana and Punjab. We attempted to assess the role of society (preference for a male child, awareness and acceptability of the practice of sex determination), technology (availability and affordability) and government regulation in the adverse ratio for girls in the Ballabgarh block of Haryana in northern India. The population (about 80 000) in the Ballabgarh block has been under constant demographic surveillance for the past 30 years and the data are stored electronically. This was used to determine the sex ratio at birth in the area since 1990. The data on availability of ultrasound machines was collected from the district authorities, as registration of these machines was made mandatory under the Prenatal Diagnostic Techniques Act, 1994. We interviewed 160 mothers and grandmothers to determine the awareness and acceptability of sex determination methods and practices. The demographic data for the past 10 years showed a declining sex ratio-from 881 in 1990-91 to 833 in 2000-01. The data support the view that in the initial part of this period, ultrasound was used for sex determination of all-order births but subsequently was used more in higher-order births. Our interviews with the mothers and grandmothers of the area showed that the practice of sex determination is prevalent and the attitude of the society is ambivalent. The increased availability of ultrasound machines in the area in the past 10 years corresponded to the decline in sex ratio. When the government made the practice illegal, the sex ratio improved only to fall again as the law was not implemented. Later years saw a more stringent implementation of the law and the sex ratio improved again. There is a 'demand' for sex determination technology and, therefore, this would continue to be 'supplied'. At most the 'supply' can be regulated. Social engineering efforts need to be targeted at reducing the demand if the sex ratio is to be improved.

Relative Kerf and Sawing Variation Values for Some Hardwood Sawing Machines

Treesearch

Philip H. Steele; Michael W. Wade; Steven H. Bullard; Philip A. Araman

1992-01-01

Information on the conversion efficiency of sawing machines is important to those involved in the management, maintenance, and design of sawmills. Little information on the conversion characteristics of hardwood sawing machines has been available. This study, based on 266 studies of 6 machine types, provides an analysis of the machine characteristics of kerf width,...

GRIDVIEW: Recent Improvements in Research and Education Software for Exploring Mars Topography

NASA Technical Reports Server (NTRS)

Roark, J. H.; Masuoka, C. M.; Frey, H. V.

2004-01-01

GRIDVIEW is being developed by the GEODYNAMICS Branch at NASA's Goddard Space Flight Center and can be downloaded on the web at http://geodynamics.gsfc.nasa.gov/gridview/. The program is very mature and has been successfully used for more than four years, but is still under development as we add new features for data analysis and visualization. The software can run on any computer supported by the IDL virtual machine application supplied by RSI. The virtual machine application is currently available for recent versions of MS Windows, MacOS X, Red Hat Linux and UNIX. Minimum system memory requirement is 32 MB, however loading large data sets may require larger amounts of RAM to function adequately.

Reliability of the quench protection system for the LHC superconducting elements

NASA Astrophysics Data System (ADS)

Vergara Fernández, A.; Rodríguez-Mateos, F.

2004-06-01

The Quench Protection System (QPS) is the sole system in the Large Hadron Collider machine monitoring the signals from the superconducting elements (bus bars, current leads, magnets) which form the cold part of the electrical circuits. The basic functions to be accomplished by the QPS during the machine operation will be briefly presented. With more than 4000 internal trigger channels (quench detectors and others), the final QPS design is the result of an optimised balance between on-demand availability and false quench reliability. The built-in redundancy for the different equipment will be presented, focusing on the calculated, expected number of missed quenches and false quenches. Maintenance strategies in order to improve the performance over the years of operation will be addressed.

Observation and analysis of pellet material del B drift on MAST

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garzotti, L.; Baylor, Larry R; Kochi, F.

2010-01-01

Pellet material deposited in a tokamak plasma experiences a drift towards the low field side of the torus induced by the magnetic field gradient. Plasma fuelling in ITER relies on the beneficial effect of this drift to increase the pellet deposition depth and fuelling efficiency. It is therefore important to analyse this phenomenon in present machines to improve the understanding of the del B induced drift and the accuracy of the predictions for ITER. This paper presents a detailed analysis of pellet material drift in MAST pellet injection experiments based on the unique diagnostic capabilities available on this machine andmore » compares the observations with predictions of state-of-the-art ablation and deposition codes.« less

Patient care information systems and physicians: the transition from technology icon to health care instrument.

PubMed

Bria, W F

1993-11-01

We have discussed several important transitions now occurring in PCIS that promise to improve the utility and availability of these systems for the average physician. Charles Babbage developed the first computers as "thinking machines" so that we may extend our ability to grapple with more and more complex problems. If current trends continue, we will finally witness the evolution of patient care computing from information icons of the few to clinical instruments improving the quality of medical decision making and care for all patients.

Design Control Systems of Human Machine Interface in the NTVS-2894 Seat Grinder Machine to Increase the Productivity

NASA Astrophysics Data System (ADS)

Ardi, S.; Ardyansyah, D.

2018-02-01

In the Manufacturing of automotive spare parts, increased sales of vehicles is resulted in increased demand for production of engine valve of the customer. To meet customer demand, we carry out improvement and overhaul of the NTVS-2894 seat grinder machine on a machining line. NTVS-2894 seat grinder machine has been decreased machine productivity, the amount of trouble, and the amount of downtime. To overcome these problems on overhaul the NTVS-2984 seat grinder machine include mechanical and programs, is to do the design and manufacture of HMI (Human Machine Interface) GP-4501T program. Because of the time prior to the overhaul, NTVS-2894 seat grinder machine does not have a backup HMI (Human Machine Interface) program. The goal of the design and manufacture in this program is to improve the achievement of production, and allows an operator to operate beside it easier to troubleshoot the NTVS-2894 seat grinder machine thereby reducing downtime on the NTVS-2894 seat grinder machine. The results after the design are HMI program successfully made it back, machine productivity increased by 34.8%, the amount of trouble, and downtime decreased 40% decrease from 3,160 minutes to 1,700 minutes. The implication of our design, it could facilitate the operator in operating machine and the technician easer to maintain and do the troubleshooting the machine problems.

Improving machine operation management efficiency via improving the vehicle park structure and using the production operation information database

NASA Astrophysics Data System (ADS)

Koptev, V. Yu

2017-02-01

The work represents the results of studying basic interconnected criteria of separate equipment units of the transport network machines fleet, depending on production and mining factors to improve the transport systems management. Justifying the selection of a control system necessitates employing new methodologies and models, augmented with stability and transport flow criteria, accounting for mining work development dynamics on mining sites. A necessary condition is the accounting of technical and operating parameters related to vehicle operation. Modern open pit mining dispatching systems must include such kinds of the information database. An algorithm forming a machine fleet is presented based on multi-variation task solution in connection with defining reasonable operating features of a machine working as a part of a complex. Proposals cited in the work may apply to mining machines (drilling equipment, excavators) and construction equipment (bulldozers, cranes, pile-drivers), city transport and other types of production activities using machine fleet.

Automation of energy demand forecasting

NASA Astrophysics Data System (ADS)

Siddique, Sanzad

Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.

Barriers to radiotherapy access at the University College Hospital in Ibadan, Nigeria.

PubMed

Anakwenze, Chidinma P; Ntekim, Atara; Trock, Bruce; Uwadiae, Iyobosa B; Page, Brandi R

2017-08-01

Nigeria has the biggest gap between radiotherapy availability and need, with one machine per 19.4 million people, compared to one machine per 250,000 people in high-income countries. This study aims to identify its patient-level barriers to radiotherapy access. This was a cross sectional study consisting of patient questionnaires ( n  = 50) conducted in January 2016 to assess patient demographics, types of cancers seen, barriers to receiving radiotherapy, health beliefs and practices, and factors leading to treatment delay. Eighty percent of patients could not afford radiotherapy without financial assistance and only 6% of the patients had federal insurance, which did not cover radiotherapy services. Of the patients who had completed radiotherapy treatment, 91.3% had experienced treatment delay or often cancellation due to healthcare worker strike, power failure, machine breakdown, or prolonged wait time. The timeliness of a patient's radiotherapy care correlated with their employment status and distance from radiotherapy center ( p  < 0.05). Barriers to care at a radiotherapy center in a low- and middle-income country (LMIC) have previously not been well characterized. These findings can be used to inform efforts to expand the availability of radiotherapy and improve current treatment capacity in Nigeria and in other LMICs.

Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology

PubMed Central

Hsin, Kun-Yi; Ghosh, Samik; Kitano, Hiroaki

2013-01-01

Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate. PMID:24391846

Data Mining and Machine Learning in Astronomy

NASA Astrophysics Data System (ADS)

Ball, Nicholas M.; Brunner, Robert J.

We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those in which data mining techniques directly contributed to improving science, and important current and future directions, including probability density functions, parallel algorithms, Peta-Scale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.

Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants.

PubMed

Yousef, Malik; Saçar Demirci, Müşerref Duygu; Khalifa, Waleed; Allmer, Jens

2016-01-01

MicroRNAs (miRNAs) are short RNA sequences involved in posttranscriptional gene regulation. Their experimental analysis is complicated and, therefore, needs to be supplemented with computational miRNA detection. Currently computational miRNA detection is mainly performed using machine learning and in particular two-class classification. For machine learning, the miRNAs need to be parametrized and more than 700 features have been described. Positive training examples for machine learning are readily available, but negative data is hard to come by. Therefore, it seems prerogative to use one-class classification instead of two-class classification. Previously, we were able to almost reach two-class classification accuracy using one-class classifiers. In this work, we employ feature selection procedures in conjunction with one-class classification and show that there is up to 36% difference in accuracy among these feature selection methods. The best feature set allowed the training of a one-class classifier which achieved an average accuracy of ~95.6% thereby outperforming previous two-class-based plant miRNA detection approaches by about 0.5%. We believe that this can be improved upon in the future by rigorous filtering of the positive training examples and by improving current feature clustering algorithms to better target pre-miRNA feature selection.

EffectorP: predicting fungal effector proteins from secretomes using machine learning.

PubMed

Sperschneider, Jana; Gardiner, Donald M; Dodds, Peter N; Tini, Francesco; Covarelli, Lorenzo; Singh, Karam B; Manners, John M; Taylor, Jennifer M

2016-04-01

Eukaryotic filamentous plant pathogens secrete effector proteins that modulate the host cell to facilitate infection. Computational effector candidate identification and subsequent functional characterization delivers valuable insights into plant-pathogen interactions. However, effector prediction in fungi has been challenging due to a lack of unifying sequence features such as conserved N-terminal sequence motifs. Fungal effectors are commonly predicted from secretomes based on criteria such as small size and cysteine-rich, which suffers from poor accuracy. We present EffectorP which pioneers the application of machine learning to fungal effector prediction. EffectorP improves fungal effector prediction from secretomes based on a robust signal of sequence-derived properties, achieving sensitivity and specificity of over 80%. Features that discriminate fungal effectors from secreted noneffectors are predominantly sequence length, molecular weight and protein net charge, as well as cysteine, serine and tryptophan content. We demonstrate that EffectorP is powerful when combined with in planta expression data for predicting high-priority effector candidates. EffectorP is the first prediction program for fungal effectors based on machine learning. Our findings will facilitate functional fungal effector studies and improve our understanding of effectors in plant-pathogen interactions. EffectorP is available at http://effectorp.csiro.au. © 2015 CSIRO New Phytologist © 2015 New Phytologist Trust.

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

PubMed Central

Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian

2015-01-01

Background Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public’s knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Objective Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Methods Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Results Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Conclusions Social media outlets like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics. PMID:26307512

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

PubMed

Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian; Augustson, Erik

2015-08-25

Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public's knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Social media outlets like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics.

Effect of tool material on machinability of TiCp reinforced Al-1100 composite

NASA Astrophysics Data System (ADS)

Harishchandra; Kadadevaramath, R. S.; Anil, K. C.

2016-09-01

In present days MMC's are widely used in most of the industries, like automobiles, aerospace, minerals and marine industries, because of its high specific strength to weight ratio. There are many types of reinforcements are available, selection of reinforcement is depends on availability, cost and desired reinforcement properties. In our study Al-1100 is selected as a primary material and Titanium carbide particle (TiCp) of 44 pm size as reinforcement and synthesized by manual stir casting method, by varying the reinforcement percentage. K2DF6 salt was used as wetting agent in order to improve the wetting behaviour of the reinforcement and same was observed in optical micrographs. Further, prepared composite materials are subjected to machinability studies by using lathe tool dynamometer in order to evaluate the cutting force, surface roughness with respect to reinforcement percentage and tool material. From the results, it is observed that the hardness and surface roughness of a specimen increases with the increasing of reinforcement percentage and Hardness of the tool material respectively.

AP-8 trapped proton environment for solar maximum and solar minimum. [Computer accessible models

NASA Technical Reports Server (NTRS)

Sawyer, D. M.; Vette, J. I.

1976-01-01

Data sets from Ov-3 and Azur indicate a need for improvement in models of the stably trapped proton flux with energies between 0.1 and 400 MeV. Two computer accessible models are described: AP8MAX and AP8MIN. The models are presented in the form of nomographs, B-L plots, R-lambda plots, and equatorial radial profiles. Nomographs of the orbit-integrated fluxes are also discussed. The models are compared with each other, with the data, and with previous AP models. Requirements for future improvements include more complete data coverage and periodic comparisons with new data sets as they become available. The machine-sensible format in which the models are available are described.

Availability of Vending Machines and School Stores in California Schools

ERIC Educational Resources Information Center

Cisse-Egbuonye, Nafissatou; Liles, Sandy; Schmitz, Katharine E.; Kassem, Nada; Irvin, Veronica L.; Hovell, Melbourne F.

2016-01-01

Background: This study examined the availability of foods sold in vending machines and school stores in United States public and private schools, and associations of availability with students' food purchases and consumption. Methods: Descriptive analyses, chi-square tests, and Spearman product-moment correlations were conducted on data collected…

Mechanisation and automation technologies development in work at construction sites

NASA Astrophysics Data System (ADS)

Sobotka, A.; Pacewicz, K.

2017-10-01

Implementing construction work that creates buildings is a very complicated and laborious task and requires the use of various types of machines and equipment. For years there has been a desire for designers and technologists to introduce devices that replace people’s work on machine construction, automation and even robots. Technologies for building construction are still being developed and implemented to limit people’s hard work and improve work efficiency and quality in innovative architectonical and construction solutions. New opportunities for improving work on the construction site include computerisation of technological processes and construction management for projects and processes. The aim of the paper was to analyse the development of mechanisation, automation and computerisation of construction processes and selected building technologies, with special attention paid to 3D printing technology. The state of mechanisation of construction works in Poland and trends in its development in construction technologies are presented. These studies were conducted on the basis of the available literature and a survey of Polish construction companies.

41 CFR 101-25.106 - Servicing of office machines.

Code of Federal Regulations, 2011 CFR

2011-07-01

... inventory in relation to operating needs; i.e., availability of reserve machine in case of breakdown; (9... machines. 101-25.106 Section 101-25.106 Public Contracts and Property Management Federal Property...-General Policies § 101-25.106 Servicing of office machines. (a) The determination as to whether office...

Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

PubMed Central

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

Optimization of processing parameters of UAV integral structural components based on yield response

NASA Astrophysics Data System (ADS)

Chen, Yunsheng

2018-05-01

In order to improve the overall strength of unmanned aerial vehicle (UAV), it is necessary to optimize the processing parameters of UAV structural components, which is affected by initial residual stress in the process of UAV structural components processing. Because machining errors are easy to occur, an optimization model for machining parameters of UAV integral structural components based on yield response is proposed. The finite element method is used to simulate the machining parameters of UAV integral structural components. The prediction model of workpiece surface machining error is established, and the influence of the path of walking knife on residual stress of UAV integral structure is studied, according to the stress of UAV integral component. The yield response of the time-varying stiffness is analyzed, and the yield response and the stress evolution mechanism of the UAV integral structure are analyzed. The simulation results show that this method is used to optimize the machining parameters of UAV integral structural components and improve the precision of UAV milling processing. The machining error is reduced, and the deformation prediction and error compensation of UAV integral structural parts are realized, thus improving the quality of machining.

Designing Computer Agents With Facial Personality To Improve Human-Machine Collaboration

DTIC Science & Technology

2006-05-25

Francis Galton is credited with recognizing the fundamental lexical hypothesis which states that you can identify “the more conspicuous aspects of the...available to describe more important traits. Galton (1884) also surmised that although there are a thousand subtly unique words used to describe character...inconsistent. A hallmark of intelligence , what potentially separates human beings from earlier life forms, is the ability to think about future consequences

Machine learning in heart failure: ready for prime time.

PubMed

Awan, Saqib Ejaz; Sohel, Ferdous; Sanfilippo, Frank Mario; Bennamoun, Mohammed; Dwivedi, Girish

2018-03-01

The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence. Recent studies have shown that the application of machine learning techniques may have the potential to improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Recently developed deep learning methods are expected to yield even better performance than traditional machine learning techniques in performing complex tasks by learning the intricate patterns hidden in big medical data. The review summarizes the recent developments in the application of machine and deep learning methods in heart failure management.

Critical Speed of The Glass Glue Machine's Creep and Influence Factors Analysis

NASA Astrophysics Data System (ADS)

Yang, Jianxi; Huang, Jian; Wang, Liying; Shi, Jintai

When automatic glass glue machine works, two questions of the machine starting vibrating and stick-slip motion are existing. These problems should be solved. According to these questions, a glue machine's model for studying stick-slip is established. Based on the dynamics system describing of the model, mathematical expression is presented. The creep critical speed expression is constructed referring to existing research achievement and a new conclusion is found. The influencing factors of stiffness, dampness, mass, velocity, difference of static and kinetic coefficient of friction are analyzed through Matlab simulation. Research shows that reasonable choice of influence parameters can improve the creep phenomenon. These all supply the theory evidence for improving the machine's motion stability.

Discriminative and informative features for biomolecular text mining with ensemble feature selection.

PubMed

Van Landeghem, Sofie; Abeel, Thomas; Saeys, Yvan; Van de Peer, Yves

2010-09-15

In the field of biomolecular text mining, black box behavior of machine learning systems currently limits understanding of the true nature of the predictions. However, feature selection (FS) is capable of identifying the most relevant features in any supervised learning setting, providing insight into the specific properties of the classification algorithm. This allows us to build more accurate classifiers while at the same time bridging the gap between the black box behavior and the end-user who has to interpret the results. We show that our FS methodology successfully discards a large fraction of machine-generated features, improving classification performance of state-of-the-art text mining algorithms. Furthermore, we illustrate how FS can be applied to gain understanding in the predictions of a framework for biomolecular event extraction from text. We include numerous examples of highly discriminative features that model either biological reality or common linguistic constructs. Finally, we discuss a number of insights from our FS analyses that will provide the opportunity to considerably improve upon current text mining tools. The FS algorithms and classifiers are available in Java-ML (http://java-ml.sf.net). The datasets are publicly available from the BioNLP'09 Shared Task web site (http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/SharedTask/).

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

PubMed

Fang, Xingang; Bagui, Sikha; Bagui, Subhash

2017-08-01

The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Positive-unlabeled learning for disease gene identification

PubMed Central

Yang, Peng; Li, Xiao-Li; Mei, Jian-Ping; Kwoh, Chee-Keong; Ng, See-Kiong

2012-01-01

Background: Identifying disease genes from human genome is an important but challenging task in biomedical research. Machine learning methods can be applied to discover new disease genes based on the known ones. Existing machine learning methods typically use the known disease genes as the positive training set P and the unknown genes as the negative training set N (non-disease gene set does not exist) to build classifiers to identify new disease genes from the unknown genes. However, such kind of classifiers is actually built from a noisy negative set N as there can be unknown disease genes in N itself. As a result, the classifiers do not perform as well as they could be. Result: Instead of treating the unknown genes as negative examples in N, we treat them as an unlabeled set U. We design a novel positive-unlabeled (PU) learning algorithm PUDI (PU learning for disease gene identification) to build a classifier using P and U. We first partition U into four sets, namely, reliable negative set RN, likely positive set LP, likely negative set LN and weak negative set WN. The weighted support vector machines are then used to build a multi-level classifier based on the four training sets and positive training set P to identify disease genes. Our experimental results demonstrate that our proposed PUDI algorithm outperformed the existing methods significantly. Conclusion: The proposed PUDI algorithm is able to identify disease genes more accurately by treating the unknown data more appropriately as unlabeled set U instead of negative set N. Given that many machine learning problems in biomedical research do involve positive and unlabeled data instead of negative data, it is possible that the machine learning methods for these problems can be further improved by adopting PU learning methods, as we have done here for disease gene identification. Availability and implementation: The executable program and data are available at http://www1.i2r.a-star.edu.sg/∼xlli/PUDI/PUDI.html. Contact: xlli@i2r.a-star.edu.sg or yang0293@e.ntu.edu.sg Supplementary information: Supplementary Data are available at Bioinformatics online. PMID:22923290

Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment

NASA Astrophysics Data System (ADS)

Johnson, Nicholas E.; Bonczak, Bartosz; Kontokosta, Constantine E.

2018-07-01

The increased availability and improved quality of new sensing technologies have catalyzed a growing body of research to evaluate and leverage these tools in order to quantify and describe urban environments. Air quality, in particular, has received greater attention because of the well-established links to serious respiratory illnesses and the unprecedented levels of air pollution in developed and developing countries and cities around the world. Though numerous laboratory and field evaluation studies have begun to explore the use and potential of low-cost air quality monitoring devices, the performance and stability of these tools has not been adequately evaluated in complex urban environments, and further research is needed. In this study, we present the design of a low-cost air quality monitoring platform based on the Shinyei PPD42 aerosol monitor and examine the suitability of the sensor for deployment in a dense heterogeneous urban environment. We assess the sensor's performance during a field calibration campaign from February 7th to March 25th 2017 with a reference instrument in New York City, and present a novel calibration approach using a machine learning method that incorporates publicly available meteorological data in order to improve overall sensor performance. We find that while the PPD42 performs well in relation to the reference instrument using linear regression (R2 = 0.36-0.51), a gradient boosting regression tree model can significantly improve device calibration (R2 = 0.68-0.76). We discuss the sensor's performance and reliability when deployed in a dense, heterogeneous urban environment during a period of significant variation in weather conditions, and important considerations when using machine learning techniques to improve the performance of low-cost air quality monitors.

STELAR: An experiment in the electronic distribution of astronomical literature

NASA Technical Reports Server (NTRS)

Warnock, A.; Vansteenburg, M. E.; Brotzman, L. E.; Gass, J.; Kovalsky, D.

1992-01-01

STELAR (Study of Electronic Literature for Astronomical Research) is a Goddard-based project designed to test methods of delivering technical literature in machine readable form. To that end, we have scanned a five year span of the ApJ, ApJ Supp, AJ and PASP, and have obtained abstracts for eight leading academic journals from NASA/STI CASI, which also makes these abstracts available through the NASA RECON system. We have also obtained machine readable versions of some journal volumes from the publishers, although in many instances, the final typeset versions are no longer available. The fundamental data object for the STELAR database is the article, a collection of items associated with a scientific paper - abstract, scanned pages (in a variety of formats), figures, OCR extractions, forward and backward references, errata and versions of the paper in various formats (e.g., TEX, SGML, PostScript, DVI). Articles are uniquely referenced in the database by journal name, volume number and page number. The selection and delivery of articles is accomplished through the WAIS (Wide Area Information Server) client/server models requiring only an Internet connection. Modest modifications to the server code have made it capable of delivering the multiple data types required by STELAR. WAIS is a platform independent and fully open multi-disciplinary delivery system, originally developed by Thinking Machines Corp. and made available free of charge. It is based on the ISO Z39.50 standard communications protocol. WAIS servers run under both UNIX and VMS. WAIS clients run on a wide variety of machines, from UNIX-based Xwindows systems to MS-DOS and macintosh microcomputers. The WAIS system includes full-test indexing and searching of documents, network interface and easy access to a variety of document viewers. ASCII versions of the CASI abstracts have been formatted for display and the full test of the abstracts has been indexed. The entire WAIS database of abstracts is now available for use by the astronomical community. Enhancements of the search and retrieval system are under investigation to include specialized searches (by reference, author or keyword, as opposed to full test searches), improved handling of word stems, improvements in relevancy criteria and other retrieval techniques, such as factor spaces. The STELAR project has been assisted by the full cooperation of the AAS, the ASP, the publishers of the academic journals, librarians from GSFC, NRAO and STScI, the Library of Congress, and the University of North Carolina at Chapel Hill.

Analysis of Availability of Longwall-Shearer Based On Its Working Cycle

NASA Astrophysics Data System (ADS)

Brodny, Jaroslaw; Tutak, Magdalena

2017-12-01

Effective use of any type of devices, particularly machines has very significant meaning for mining enterprises. High costs of their purchase and tenancy cause that these enterprises tend to the best use of own technical potential. However, characteristics of mining production causes that this process not always proceeds without interferences. Practical experiences show that determination of objective measure of utilization of machine in mining company is not simple. In the paper methodology allowing to solve this problem is presented. Longwall-shearer, as the most important machine between longwall mechanical complex. Also it was assumed that the most significant meaning for determination of effectiveness of longwall-shearer has its availability, i.e. its effective time of work related to standard time. Such an approach is conforming to OEE model. However, specification of mining branch causes that determined availability do not give actual state of longwall-shearer’s operation. Therefore, this availability was related to the operation cycle of longwall-shearer. In presented example a longwall-shearer works in unidirectional cycle of mining. It causes that in one direction longwall-shearer mines, moving with operating velocity, and in other direction it does not mine and moves with manoeuvre velocity. Such defined working cycle became a base for determinate availability of longwall-shearer. Using indications of industrial automatic system for each of working shift there were determined number of cycles of longwall-shearer and availability of each one. Accepted of such way of determination of availability of longwall-shearer enabled to perform accurate analysis of losses of its availability. These losses result from non-planned shutdowns of longwall-shearer. Thanks to performed analysis based on the operating cycle of longwall-shearer time of its standstill for particular phase of cycle were determined. Presented methodology of determination of longwall-shearer’s availability enables to obtain information which may be used for optimization of mining process. Knowledge of particular phases of longwall-shearer’s operation, in which reduced availability occurs, allows to direct the repairing actions exactly to these regions. Developed methodology and obtained results create great opportunities for practical application and improvement of effectiveness of underground exploitation.

Scheduling job shop - A case study

NASA Astrophysics Data System (ADS)

Abas, M.; Abbas, A.; Khan, W. A.

2016-08-01

The scheduling in job shop is important for efficient utilization of machines in the manufacturing industry. There are number of algorithms available for scheduling of jobs which depend on machines tools, indirect consumables and jobs which are to be processed. In this paper a case study is presented for scheduling of jobs when parts are treated on available machines. Through time and motion study setup time and operation time are measured as total processing time for variety of products having different manufacturing processes. Based on due dates different level of priority are assigned to the jobs and the jobs are scheduled on the basis of priority. In view of the measured processing time, the times for processing of some new jobs are estimated and for efficient utilization of the machines available an algorithm is proposed and validated.

Food labeling; calorie labeling of articles of food in vending machines. Final rule.

PubMed

2014-12-01

To implement the vending machine food labeling provisions of the Patient Protection and Affordable Care Act of 2010 (ACA), the Food and Drug Administration (FDA or we) is establishing requirements for providing calorie declarations for food sold from certain vending machines. This final rule will ensure that calorie information is available for certain food sold from a vending machine that does not permit a prospective purchaser to examine the Nutrition Facts Panel before purchasing the article, or does not otherwise provide visible nutrition information at the point of purchase. The declaration of accurate and clear calorie information for food sold from vending machines will make calorie information available to consumers in a direct and accessible manner to enable consumers to make informed and healthful dietary choices. This final rule applies to certain food from vending machines operated by a person engaged in the business of owning or operating 20 or more vending machines. Vending machine operators not subject to the rules may elect to be subject to the Federal requirements by registering with FDA.

Automatic extraction of relations between medical concepts in clinical texts

PubMed Central

Harabagiu, Sanda; Roberts, Kirk

2011-01-01

Objective A supervised machine learning approach to discover relations between medical problems, treatments, and tests mentioned in electronic medical records. Materials and methods A single support vector machine classifier was used to identify relations between concepts and to assign their semantic type. Several resources such as Wikipedia, WordNet, General Inquirer, and a relation similarity metric inform the classifier. Results The techniques reported in this paper were evaluated in the 2010 i2b2 Challenge and obtained the highest F1 score for the relation extraction task. When gold standard data for concepts and assertions were available, F1 was 73.7, precision was 72.0, and recall was 75.3. F1 is defined as 2*Precision*Recall/(Precision+Recall). Alternatively, when concepts and assertions were discovered automatically, F1 was 48.4, precision was 57.6, and recall was 41.7. Discussion Although a rich set of features was developed for the classifiers presented in this paper, little knowledge mining was performed from medical ontologies such as those found in UMLS. Future studies should incorporate features extracted from such knowledge sources, which we expect to further improve the results. Moreover, each relation discovery was treated independently. Joint classification of relations may further improve the quality of results. Also, joint learning of the discovery of concepts, assertions, and relations may also improve the results of automatic relation extraction. Conclusion Lexical and contextual features proved to be very important in relation extraction from medical texts. When they are not available to the classifier, the F1 score decreases by 3.7%. In addition, features based on similarity contribute to a decrease of 1.1% when they are not available. PMID:21846787

Performance of a plasma fluid code on the Intel parallel computers

NASA Technical Reports Server (NTRS)

Lynch, V. E.; Carreras, B. A.; Drake, J. B.; Leboeuf, J. N.; Liewer, P.

1992-01-01

One approach to improving the real-time efficiency of plasma turbulence calculations is to use a parallel algorithm. A parallel algorithm for plasma turbulence calculations was tested on the Intel iPSC/860 hypercube and the Touchtone Delta machine. Using the 128 processors of the Intel iPSC/860 hypercube, a factor of 5 improvement over a single-processor CRAY-2 is obtained. For the Touchtone Delta machine, the corresponding improvement factor is 16. For plasma edge turbulence calculations, an extrapolation of the present results to the Intel (sigma) machine gives an improvement factor close to 64 over the single-processor CRAY-2.

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach.

PubMed

Gómez-Bombarelli, Rafael; Aguilera-Iparraguirre, Jorge; Hirzel, Timothy D; Duvenaud, David; Maclaurin, Dougal; Blood-Forsythe, Martin A; Chae, Hyun Sik; Einzinger, Markus; Ha, Dong-Gwang; Wu, Tony; Markopoulos, Georgios; Jeon, Soonok; Kang, Hosuk; Miyazaki, Hiroshi; Numata, Masaki; Kim, Sunghan; Huang, Wenliang; Hong, Seong Ik; Baldo, Marc; Adams, Ryan P; Aspuru-Guzik, Alán

2016-10-01

Virtual screening is becoming a ground-breaking tool for molecular discovery due to the exponential growth of available computer time and constant improvement of simulation and machine learning techniques. We report an integrated organic functional material design process that incorporates theoretical insight, quantum chemistry, cheminformatics, machine learning, industrial expertise, organic synthesis, molecular characterization, device fabrication and optoelectronic testing. After exploring a search space of 1.6 million molecules and screening over 400,000 of them using time-dependent density functional theory, we identified thousands of promising novel organic light-emitting diode molecules across the visible spectrum. Our team collaboratively selected the best candidates from this set. The experimentally determined external quantum efficiencies for these synthesized candidates were as large as 22%.

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

NASA Astrophysics Data System (ADS)

Gómez-Bombarelli, Rafael; Aguilera-Iparraguirre, Jorge; Hirzel, Timothy D.; Duvenaud, David; MacLaurin, Dougal; Blood-Forsythe, Martin A.; Chae, Hyun Sik; Einzinger, Markus; Ha, Dong-Gwang; Wu, Tony; Markopoulos, Georgios; Jeon, Soonok; Kang, Hosuk; Miyazaki, Hiroshi; Numata, Masaki; Kim, Sunghan; Huang, Wenliang; Hong, Seong Ik; Baldo, Marc; Adams, Ryan P.; Aspuru-Guzik, Alán

2016-10-01

Virtual screening is becoming a ground-breaking tool for molecular discovery due to the exponential growth of available computer time and constant improvement of simulation and machine learning techniques. We report an integrated organic functional material design process that incorporates theoretical insight, quantum chemistry, cheminformatics, machine learning, industrial expertise, organic synthesis, molecular characterization, device fabrication and optoelectronic testing. After exploring a search space of 1.6 million molecules and screening over 400,000 of them using time-dependent density functional theory, we identified thousands of promising novel organic light-emitting diode molecules across the visible spectrum. Our team collaboratively selected the best candidates from this set. The experimentally determined external quantum efficiencies for these synthesized candidates were as large as 22%.

A bi-axial active boring tool for chatter mitigation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Redmond, J.M.; Barney, P.S.

This paper summarizes results of metal cutting tests using an actively damped boring bar to suppress regenerative chatter. PZT stack actuators were integrated into a commercially available two-inch diameter boring bar to suppress bending vibrations. Since the modified tool requires no specialized mounting hardware, it can be readily mounted on a variety of machines. A cutting test using the prototype bar to remove metal from a hardened steel workpiece verifies that the authors actively damped tool yields significant vibration reduction and improved surface finish as compared to the open-loop case. In addition, the overall performance of the prototype bar ismore » compared to that of an unmodified bar of pristine geometry, revealing that a significant enlargement of the stable machining envelope is obtained through application of feedback control.« less

Toward Intelligent Machine Learning Algorithms

DTIC Science & Technology

1988-05-01

Machine learning is recognized as a tool for improving the performance of many kinds of systems, yet most machine learning systems themselves are not...directed systems, and with the addition of a knowledge store for organizing and maintaining knowledge to assist learning, a learning machine learning (L...ML) algorithm is possible. The necessary components of L-ML systems are presented along with several case descriptions of existing machine learning systems

Critical Assessment of Small Molecule Identification 2016: automated methods.

PubMed

Schymanski, Emma L; Ruttkies, Christoph; Krauss, Martin; Brouard, Céline; Kind, Tobias; Dührkop, Kai; Allen, Felicity; Vaniya, Arpana; Verdegem, Dries; Böcker, Sebastian; Rousu, Juho; Shen, Huibin; Tsugawa, Hiroshi; Sajed, Tanvir; Fiehn, Oliver; Ghesquière, Bart; Neumann, Steffen

2017-03-27

The fourth round of the Critical Assessment of Small Molecule Identification (CASMI) Contest ( www.casmi-contest.org ) was held in 2016, with two new categories for automated methods. This article covers the 208 challenges in Categories 2 and 3, without and with metadata, from organization, participation, results and post-contest evaluation of CASMI 2016 through to perspectives for future contests and small molecule annotation/identification. The Input Output Kernel Regression (CSI:IOKR) machine learning approach performed best in "Category 2: Best Automatic Structural Identification-In Silico Fragmentation Only", won by Team Brouard with 41% challenge wins. The winner of "Category 3: Best Automatic Structural Identification-Full Information" was Team Kind (MS-FINDER), with 76% challenge wins. The best methods were able to achieve over 30% Top 1 ranks in Category 2, with all methods ranking the correct candidate in the Top 10 in around 50% of challenges. This success rate rose to 70% Top 1 ranks in Category 3, with candidates in the Top 10 in over 80% of the challenges. The machine learning and chemistry-based approaches are shown to perform in complementary ways. The improvement in (semi-)automated fragmentation methods for small molecule identification has been substantial. The achieved high rates of correct candidates in the Top 1 and Top 10, despite large candidate numbers, open up great possibilities for high-throughput annotation of untargeted analysis for "known unknowns". As more high quality training data becomes available, the improvements in machine learning methods will likely continue, but the alternative approaches still provide valuable complementary information. Improved integration of experimental context will also improve identification success further for "real life" annotations. The true "unknown unknowns" remain to be evaluated in future CASMI contests. Graphical abstract .

49 CFR 214.533 - Schedule of repairs subject to availability of parts.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Maintenance Machines and Hi-Rail Vehicles § 214.533 Schedule of repairs subject to availability of parts. (a... 49 Transportation 4 2011-10-01 2011-10-01 false Schedule of repairs subject to availability of... maintenance machine or a hi-rail vehicle by the end of the next business day following the report of the...

MLBCD: a machine learning tool for big clinical data.

PubMed

Luo, Gang

2015-01-01

Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. The paper describes MLBCD's design in detail. By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

IMPROVEMENT OF SMVGEAR II ON VECTOR AND SCALAR MACHINES THROUGH ABSOLUTE ERROR TOLERANCE CONTROL (R823186)

EPA Science Inventory

The computer speed of SMVGEAR II was improved markedly on scalar and vector machines with relatively little loss in accuracy. The improvement was due to a method of frequently recalculating the absolute error tolerance instead of keeping it constant for a given set of chemistry. ...

Reinforcement learning improves behaviour from evaluative feedback

NASA Astrophysics Data System (ADS)

Littman, Michael L.

2015-05-01

Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioural decisions. It has been called the artificial intelligence problem in a microcosm because learning algorithms must act autonomously to perform well and achieve their goals. Partly driven by the increasing availability of rich data, recent years have seen exciting advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology, leading to increasing applicability to real-life problems.

Reinforcement learning improves behaviour from evaluative feedback.

PubMed

Littman, Michael L

2015-05-28

Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioural decisions. It has been called the artificial intelligence problem in a microcosm because learning algorithms must act autonomously to perform well and achieve their goals. Partly driven by the increasing availability of rich data, recent years have seen exciting advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology, leading to increasing applicability to real-life problems.

Defense Logistics: Space-Available Travel Challenges May Be Exacerbated If Eligibility Expands

DTIC Science & Technology

2012-09-10

space-available travelers’ use of terminal facilities results in additional maintenance costs for waiting areas, restrooms, and vending machines ...additional required maintenance. For example, additional travelers’ use of waiting areas, restrooms, and vending machines in the terminals could require

Significant improvements of electrical discharge machining performance by step-by-step updated adaptive control laws

NASA Astrophysics Data System (ADS)

Zhou, Ming; Wu, Jianyang; Xu, Xiaoyi; Mu, Xin; Dou, Yunping

2018-02-01

In order to obtain improved electrical discharge machining (EDM) performance, we have dedicated more than a decade to correcting one essential EDM defect, the weak stability of the machining, by developing adaptive control systems. The instabilities of machining are mainly caused by complicated disturbances in discharging. To counteract the effects from the disturbances on machining, we theoretically developed three control laws from minimum variance (MV) control law to minimum variance and pole placements coupled (MVPPC) control law and then to a two-step-ahead prediction (TP) control law. Based on real-time estimation of EDM process model parameters and measured ratio of arcing pulses which is also called gap state, electrode discharging cycle was directly and adaptively tuned so that a stable machining could be achieved. To this end, we not only theoretically provide three proved control laws for a developed EDM adaptive control system, but also practically proved the TP control law to be the best in dealing with machining instability and machining efficiency though the MVPPC control law provided much better EDM performance than the MV control law. It was also shown that the TP control law also provided a burn free machining.

Candidate gene prioritization by network analysis of differential expression using machine learning approaches

PubMed Central

2010-01-01

Background Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals. To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network. Results We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (Simple Expression Ranking). Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the Heat Kernel Diffusion Ranking leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%. Conclusion In this study we could identify promising candidate genes using network based machine learning approaches even if no knowledge is available about the disease or phenotype. PMID:20840752

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PubMed Central

Kai, Joe; Garibaldi, Jonathan M.; Qureshi, Nadeem

2017-01-01

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others. PMID:28376093

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

PubMed

Weng, Stephen F; Reps, Jenna; Kai, Joe; Garibaldi, Jonathan M; Qureshi, Nadeem

2017-01-01

Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the 'receiver operating curve' (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723-0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739-0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755-0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755-0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759-0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.

Normothermic ex-situ liver preservation: the new gold standard.

PubMed

Laing, Richard W; Mergental, Hynek; Mirza, Darius F

2017-06-01

Normothermic machine perfusion of the liver (NMP-L) is a novel technology recently introduced into the practice of liver transplantation. This review recapitulates benefits of normothermic perfusion over conventional static cold storage and summarizes recent publications in this area. The first clinical trials have demonstrated both safety and feasibility of NMP-L. They have shown that machine perfusion can entirely replace cold storage or be commenced following a period of cold ischaemia. The technology currently allows transplant teams to extend the period of organ preservation for up to 24 h. Results from the first randomized control trial comparing NMP-L with static cold storage will be available soon. One major advantage of NMP-L technology over other parallel technologies is the potential to assess liver function during NMP-L. Several case series have suggested parameters usable for liver viability testing during NMP-L including bile production and clearance of lactic acidosis. NMP-L allows viability testing of high-risk livers. It has shown the potential to increase utilization of donor organs and improve transplant procedure logistics. NMP-L is likely to become an important technology that will improve organ preservation as well as have the potential to improve utilization of extended criteria donor livers.

A randomized, controlled intervention of machine guarding and related safety programs in small metal-fabrication businesses.

PubMed

Parker, David L; Brosseau, Lisa M; Samant, Yogindra; Xi, Min; Pan, Wei; Haugan, David

2009-01-01

Metal fabrication employs an estimated 3.1 million workers in the United States. The absence of machine guarding and related programs such as lockout/tagout may result in serious injury or death. The purpose of this study was to improve machine-related safety in small metal-fabrication businesses. We used a randomized trial with two groups: management only and management-employee. We evaluated businesses for the adequacy of machine guarding (machine scorecard) and related safety programs (safety audit). We provided all businesses with a report outlining deficiencies and prioritizing their remediation. In addition, the management-employee group received four one-hour interactive training sessions from a peer educator. We evaluated 40 metal-fabrication businesses at baseline and 37 (93%) one year later. Of the three nonparticipants, two had gone out of business. More than 40% of devices required for adequate guarding were missing or inadequate, and 35% of required safety programs and practices were absent at baseline. Both measures improved significantly during the course of the intervention. No significant differences in changes occurred between the two intervention groups. Machine-guarding practices and programs improved by up to 13% and safety audit scores by up to 23%. Businesses that added safety committees or those that started with the lowest baseline measures showed the greatest improvements. Simple and easy-to-use assessment tools allowed businesses to significantly improve their safety practices, and safety committees facilitated this process.

Metalworking and machining fluids

DOEpatents

Erdemir, Ali; Sykora, Frank; Dorbeck, Mark

2010-10-12

Improved boron-based metal working and machining fluids. Boric acid and boron-based additives that, when mixed with certain carrier fluids, such as water, cellulose and/or cellulose derivatives, polyhydric alcohol, polyalkylene glycol, polyvinyl alcohol, starch, dextrin, in solid and/or solvated forms result in improved metalworking and machining of metallic work pieces. Fluids manufactured with boric acid or boron-based additives effectively reduce friction, prevent galling and severe wear problems on cutting and forming tools.

Method and apparatus for improving the quality and efficiency of ultrashort-pulse laser machining

DOEpatents

Stuart, Brent C.; Nguyen, Hoang T.; Perry, Michael D.

2001-01-01

A method and apparatus for improving the quality and efficiency of machining of materials with laser pulse durations shorter than 100 picoseconds by orienting and maintaining the polarization of the laser light such that the electric field vector is perpendicular relative to the edges of the material being processed. Its use is any machining operation requiring remote delivery and/or high precision with minimal collateral dames.

Cold machining of high density tungsten and other materials

NASA Technical Reports Server (NTRS)

Ziegelmeier, P.

1969-01-01

Cold machining process, which uses a sub-zero refrigerated cutting fluid, is used for machining refractory or reactive metals and alloys. Special carbide tools for turning and drilling these alloys further improve the cutting performance.

Speech emotion recognition methods: A literature review

NASA Astrophysics Data System (ADS)

Basharirad, Babak; Moradhaseli, Mohammadreza

2017-10-01

Recently, attention of the emotional speech signals research has been boosted in human machine interfaces due to availability of high computation capability. There are many systems proposed in the literature to identify the emotional state through speech. Selection of suitable feature sets, design of a proper classifications methods and prepare an appropriate dataset are the main key issues of speech emotion recognition systems. This paper critically analyzed the current available approaches of speech emotion recognition methods based on the three evaluating parameters (feature set, classification of features, accurately usage). In addition, this paper also evaluates the performance and limitations of available methods. Furthermore, it highlights the current promising direction for improvement of speech emotion recognition systems.

CellSort: a support vector machine tool for optimizing fluorescence-activated cell sorting and reducing experimental effort.

PubMed

Yu, Jessica S; Pertusi, Dante A; Adeniran, Adebola V; Tyo, Keith E J

2017-03-15

High throughput screening by fluorescence activated cell sorting (FACS) is a common task in protein engineering and directed evolution. It can also be a rate-limiting step if high false positive or negative rates necessitate multiple rounds of enrichment. Current FACS software requires the user to define sorting gates by intuition and is practically limited to two dimensions. In cases when multiple rounds of enrichment are required, the software cannot forecast the enrichment effort required. We have developed CellSort, a support vector machine (SVM) algorithm that identifies optimal sorting gates based on machine learning using positive and negative control populations. CellSort can take advantage of more than two dimensions to enhance the ability to distinguish between populations. We also present a Bayesian approach to predict the number of sorting rounds required to enrich a population from a given library size. This Bayesian approach allowed us to determine strategies for biasing the sorting gates in order to reduce the required number of enrichment rounds. This algorithm should be generally useful for improve sorting outcomes and reducing effort when using FACS. Source code available at http://tyolab.northwestern.edu/tools/ . k-tyo@northwestern.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Multimedia systems in ultrasound image boundary detection and measurements

NASA Astrophysics Data System (ADS)

Pathak, Sayan D.; Chalana, Vikram; Kim, Yongmin

1997-05-01

Ultrasound as a medical imaging modality offers the clinician a real-time of the anatomy of the internal organs/tissues, their movement, and flow noninvasively. One of the applications of ultrasound is to monitor fetal growth by measuring biparietal diameter (BPD) and head circumference (HC). We have been working on automatic detection of fetal head boundaries in ultrasound images. These detected boundaries are used to measure BPD and HC. The boundary detection algorithm is based on active contour models and takes 32 seconds on an external high-end workstation, SUN SparcStation 20/71. Our goal has been to make this tool available within an ultrasound machine and at the same time significantly improve its performance utilizing multimedia technology. With the advent of high- performance programmable digital signal processors (DSP), the software solution within an ultrasound machine instead of the traditional hardwired approach or requiring an external computer is now possible. We have integrated our boundary detection algorithm into a programmable ultrasound image processor (PUIP) that fits into a commercial ultrasound machine. The PUIP provides both the high computing power and flexibility needed to support computationally-intensive image processing algorithms within an ultrasound machine. According to our data analysis, BPD/HC measurements made on PUIP lie within the interobserver variability. Hence, the errors in the automated BPD/HC measurements using the algorithm are on the same order as the average interobserver differences. On PUIP, it takes 360 ms to measure the values of BPD/HC on one head image. When processing multiple head images in sequence, it takes 185 ms per image, thus enabling 5.4 BPD/HC measurements per second. Reduction in the overall execution time from 32 seconds to a fraction of a second and making this multimedia system available within an ultrasound machine will help this image processing algorithm and other computer-intensive imaging applications become a practical tool for the sonographers in the feature.

Profits, commercial food supplier involvement, and school vending machine snack food availability: implications for implementing the new competitive foods rule.

PubMed

Terry-McElrath, Yvonne M; Hood, Nancy E; Colabianchi, Natalie; O'Malley, Patrick M; Johnston, Lloyd D

2014-07-01

The 2013-2014 school year involved preparation for implementing the new US Department of Agriculture (USDA) competitive foods nutrition standards. An awareness of associations between commercial supplier involvement, food vending practices, and food vending item availability may assist schools in preparing for the new standards. Analyses used 2007-2012 questionnaire data from administrators of 814 middle and 801 high schools in the nationally representative Youth, Education, and Society study to examine prevalence of profit from and commercial involvement with vending machine food sales, and associations between such measures and food availability. Profits for the school district were associated with decreased low-nutrient, energy-dense (LNED) food availability and increased fruit/vegetable availability. Profits for the school and use of company suppliers were associated with increased LNED availability; company suppliers also were associated with decreased fruit/vegetable availability. Supplier "say" in vending food selection was associated with increased LNED availability and decreased fruit/vegetable availability. Results support (1) increased district involvement with school vending policies and practices, and (2) limited supplier "say" as to what items are made available in student-accessed vending machines. Schools and districts should pay close attention to which food items replace vending machine LNED foods following implementation of the new nutrition standards. © 2014, American School Health Association.

Optimal Control of Induction Machines to Minimize Transient Energy Losses

NASA Astrophysics Data System (ADS)

Plathottam, Siby Jose

Induction machines are electromechanical energy conversion devices comprised of a stator and a rotor. Torque is generated due to the interaction between the rotating magnetic field from the stator, and the current induced in the rotor conductors. Their speed and torque output can be precisely controlled by manipulating the magnitude, frequency, and phase of the three input sinusoidal voltage waveforms. Their ruggedness, low cost, and high efficiency have made them ubiquitous component of nearly every industrial application. Thus, even a small improvement in their energy efficient tend to give a large amount of electrical energy savings over the lifetime of the machine. Hence, increasing energy efficiency (reducing energy losses) in induction machines is a constrained optimization problem that has attracted attention from researchers. The energy conversion efficiency of induction machines depends on both the speed-torque operating point, as well as the input voltage waveform. It also depends on whether the machine is in the transient or steady state. Maximizing energy efficiency during steady state is a Static Optimization problem, that has been extensively studied, with commercial solutions available. On the other hand, improving energy efficiency during transients is a Dynamic Optimization problem that is sparsely studied. This dissertation exclusively focuses on improving energy efficiency during transients. This dissertation treats the transient energy loss minimization problem as an optimal control problem which consists of a dynamic model of the machine, and a cost functional. The rotor field oriented current fed model of the induction machine is selected as the dynamic model. The rotor speed and rotor d-axis flux are the state variables in the dynamic model. The stator currents referred to as d-and q-axis currents are the control inputs. A cost functional is proposed that assigns a cost to both the energy losses in the induction machine, as well as the deviations from desired speed-torque-magnetic flux setpoints. Using Pontryagin's minimum principle, a set of necessary conditions that must be satisfied by the optimal control trajectories are derived. The conditions are in the form a two-point boundary value problem, that can be solved numerically. The conjugate gradient method that was modified using the Hestenes-Stiefel formula was used to obtain the numerical solution of both the control and state trajectories. Using the distinctive shape of the numerical trajectories as inspiration, analytical expressions were derived for the state, and control trajectories. It was shown that the trajectory could be fully described by finding the solution of a one-dimensional optimization problem. The sensitivity of both the optimal trajectory and the optimal energy efficiency to different induction machine parameters were analyzed. A non-iterative solution that can use feedback for generating optimal control trajectories in real time was explored. It was found that an artificial neural network could be trained using the numerical solutions and made to emulate the optimal control trajectories with a high degree of accuracy. Hence a neural network along with a supervisory logic was implemented and used in a real-time simulation to control the Finite Element Method model of the induction machine. The results were compared with three other control regimes and the optimal control system was found to have the highest energy efficiency for the same drive cycle.

Weighted K-means support vector machine for cancer prediction.

PubMed

Kim, SungHwan

2016-01-01

To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org).

The association between the availability of sugar-sweetened beverage in school vending machines and its consumption among adolescents in California: a propensity score matching approach.

PubMed

Shi, Lu

2010-01-01

There is controversy over to what degree banning sugar-sweetened beverage (SSB) sales at schools could decrease the SSB intake. This paper uses the adolescent sample of 2005 California Health Interview Survey to estimate the association between the availability of SSB from school vending machines and the amount of SSB consumption. Propensity score stratification and kernel-based propensity score matching are used to address the selection bias issue in cross-sectional data. Propensity score stratification shows that adolescents who had access to SSB through their school vending machines consumed 0.170 more drinks of SSB than those who did not (P < .05). Kernel-based propensity score matching shows the SSB consumption difference to be 0.158 on the prior day (P < .05). This paper strengthens the evidence for the association between SSB availability via school vending machines and the actual SSB consumption, while future studies are needed to explore changes in other beverages after SSB becomes less available.

Mechanical characterization of Al-2024 reinforced with fly ash and E-glass by stir casting method

NASA Astrophysics Data System (ADS)

Ramesh, B. T.; Swamy, R. P.; Vinayak, Koppad

2018-04-01

The properties of MMCs enhance their handling in automotive and various applications for the reason that of encouraging properties of high stiffness and high strength, low density, high electrical and thermal conductivity, corrosion resistance, improved wear resistance etc. Metal Matrix Composites are a vital family of materials designed at achieving an improved combination of properties. Our paper deals through to fabricate Hybrid Composite by heating Al 2024 in furnace at a temperature of around 4000 C. E-Glass fiber & Fly ash will be added to the molten metal with changing weight fractions and stirred strongly. Then the ensuing composition will poured into the mould to obtain hybrid composite casting. Aluminium alloy (2024) is the matrix metal used in the present investigation. Fly ash and e-glass are used as the reinforced materials to produce the composite by stir casting. Fly ash is selected because of it is less expensive and low density reinforcement available in great quantities as solid disposal from thermal power plants. The Test specimen is prepared as per ASTM standards size by machining operations to conduct Tensile, Compression, Hardness, and wear test. The test specimens are furnished for tensile, compression strength and wear as per ASTM standard E8, E9 and G99 respectively using Universal Testing Machine and pin on disk machine. It is seen that the fabricated MMC obtained has got enhanced mechanical strength.

Improving protein–protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model

PubMed Central

An, Ji‐Yong; Meng, Fan‐Rong; Chen, Xing; Yan, Gui‐Ying; Hu, Ji‐Pu

2016-01-01

Abstract Predicting protein–protein interactions (PPIs) is a challenging task and essential to construct the protein interaction networks, which is important for facilitating our understanding of the mechanisms of biological systems. Although a number of high‐throughput technologies have been proposed to predict PPIs, there are unavoidable shortcomings, including high cost, time intensity, and inherently high false positive rates. For these reasons, many computational methods have been proposed for predicting PPIs. However, the problem is still far from being solved. In this article, we propose a novel computational method called RVM‐BiGP that combines the relevance vector machine (RVM) model and Bi‐gram Probabilities (BiGP) for PPIs detection from protein sequences. The major improvement includes (1) Protein sequences are represented using the Bi‐gram probabilities (BiGP) feature representation on a Position Specific Scoring Matrix (PSSM), in which the protein evolutionary information is contained; (2) For reducing the influence of noise, the Principal Component Analysis (PCA) method is used to reduce the dimension of BiGP vector; (3) The powerful and robust Relevance Vector Machine (RVM) algorithm is used for classification. Five‐fold cross‐validation experiments executed on yeast and Helicobacter pylori datasets, which achieved very high accuracies of 94.57 and 90.57%, respectively. Experimental results are significantly better than previous methods. To further evaluate the proposed method, we compare it with the state‐of‐the‐art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM‐BiGP method is significantly better than the SVM‐based method. In addition, we achieved 97.15% accuracy on imbalance yeast dataset, which is higher than that of balance yeast dataset. The promising experimental results show the efficiency and robust of the proposed method, which can be an automatic decision support tool for future proteomics research. For facilitating extensive studies for future proteomics research, we developed a freely available web server called RVM‐BiGP‐PPIs in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at http://219.219.62.123:8888/BiGP/. PMID:27452983

Measurement value analysis overall equipment effectiveness (OEE) packaging process in line 2 (Case Study of PT. MBI Tbk)

NASA Astrophysics Data System (ADS)

Rimawan, Erry; Kholil, Muhammad; Hendri

2018-03-01

PT. MBI Tbk is engaged in the manufacture of beverage industry, where the company’s production is based on the magnitude of customer demand that is marketing offices that had been scattered in various regions of Indonesia. In the packaging process steps in PT.MBI through the line 3 lines including racking, canning line, bottling line. In the canning process to existing packing on Line 2 (canning line), there are some machines that are used continuously, among other Depalletizer machine, filler machine, can seamer machine, pasteurizer machine, machine FLD, Wrap Around engine, engine Shrink Wrap. Due to the large demand from customers that is relentless, therefore the calculation of overall equipment effectiveness (OEE) as a whole on line 2 (canning line) is needed in order to make improvements continuously (Continuous Improvement) at line 2 (canning line). This study aims to determine the value of overall equipment effectiveness (OEE) and Losses of the most influential of the big six OEE Losses focused on equipment or machinery as a whole into a single unit that is on the line 2, which will then be known root cause of the losses that occur from the research over the field. From the calculation of overall equipment effectiveness (OEE), there are two ratios are still poor and under world-class standards, while the ratio of the availability of 88.85% of the world-class standards by 90% and the performance ratio of 78.51% of the standard world class by 95%, whereas for quality ratio has entered the world-class standard that is equal to 99.90%. Thus the value of OEE on Line 2 line is below world class standards. In this study there were only five losses, which can be identified, and while the losses were very influential, namely the Speed Reduced Losses, losses, these losses accounted for the largest percentage of the value of the rate of 19.12%, of the results of this study losses occurred due to poor surveillance systems (less good) that causes the employee or operator does not perform the work in accordance with a predetermined.

AMS 4.0: consensus prediction of post-translational modifications in protein sequences.

PubMed

Plewczynski, Dariusz; Basu, Subhadip; Saha, Indrajit

2012-08-01

We present here the 2011 update of the AutoMotif Service (AMS 4.0) that predicts the wide selection of 88 different types of the single amino acid post-translational modifications (PTM) in protein sequences. The selection of experimentally confirmed modifications is acquired from the latest UniProt and Phospho.ELM databases for training. The sequence vicinity of each modified residue is represented using amino acids physico-chemical features encoded using high quality indices (HQI) obtaining by automatic clustering of known indices extracted from AAindex database. For each type of the numerical representation, the method builds the ensemble of Multi-Layer Perceptron (MLP) pattern classifiers, each optimising different objectives during the training (for example the recall, precision or area under the ROC curve (AUC)). The consensus is built using brainstorming technology, which combines multi-objective instances of machine learning algorithm, and the data fusion of different training objects representations, in order to boost the overall prediction accuracy of conserved short sequence motifs. The performance of AMS 4.0 is compared with the accuracy of previous versions, which were constructed using single machine learning methods (artificial neural networks, support vector machine). Our software improves the average AUC score of the earlier version by close to 7 % as calculated on the test datasets of all 88 PTM types. Moreover, for the selected most-difficult sequence motifs types it is able to improve the prediction performance by almost 32 %, when compared with previously used single machine learning methods. Summarising, the brainstorming consensus meta-learning methodology on the average boosts the AUC score up to around 89 %, averaged over all 88 PTM types. Detailed results for single machine learning methods and the consensus methodology are also provided, together with the comparison to previously published methods and state-of-the-art software tools. The source code and precompiled binaries of brainstorming tool are available at http://code.google.com/p/automotifserver/ under Apache 2.0 licensing.

Machine Learning.

ERIC Educational Resources Information Center

Kirrane, Diane E.

1990-01-01

As scientists seek to develop machines that can "learn," that is, solve problems by imitating the human brain, a gold mine of information on the processes of human learning is being discovered, expert systems are being improved, and human-machine interactions are being enhanced. (SK)

Probability machines: consistent probability estimation using nonparametric learning machines.

PubMed

Malley, J D; Kruppa, J; Dasgupta, A; Malley, K G; Ziegler, A

2012-01-01

Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.

Improvement in the assessment of wear of total knee replacements using coordinate-measuring machine techniques.

PubMed

Blunt, L A; Bills, P J; Jiang, X-Q; Chakrabarty, G

2008-04-01

Total joint replacement is one of the most common elective surgical procedures performed worldwide, with an estimate of 1.5x 10(6) operations performed annually. Currently joint replacements are expected to function for 10-15 years; however, with an increase in life expectancy, and a greater call for knee replacement due to increased activity levels, there is a requirement to improve their function to offer longer-term improved quality of life for patients. Wear analysis of total joint replacements has long been an important means in determining failure mechanisms and improving longevity of these devices. The effectiveness of the coordinate-measuring machine (CMM) technique for assessing volumetric material loss during simulated life testing of a replacement knee joint has been proved previously by the present authors. The purpose of the current work is to present an improvement to this method for situations where no pre-wear data are available. To validate the method, simulator tests were run and gravimetric measurements taken throughout the test, such that the components measured had a known wear value. The implications of the results are then discussed in terms of assessment of joint functionality and development of standardized CMM-based product standards. The method was then expanded to allow assessment of clinically retrieved bearings so as to ascertain a measure of true clinical wear.

Machine grading of lumber : practical concerns for lumber producers

Treesearch

William L. Galligan; Kent A. McDonald

2000-01-01

Machine lumber grading has been applied in commercial operations in North America since 1963, and research has shown that machine grading can improve the efficient use of wood. However, industry has been reluctant to apply research findings without clear evidence that the change from visual to machine grading will be a profitable one. For instance, mill managers need...

Machine for preparing phosphors for the fluorimetric determination of uranium

USGS Publications Warehouse

Stevens, R.E.; Wood, W.H.; Goetz, K.G.; Horr, C.A.

1956-01-01

The time saved by use of a machine for preparing many phosphors at one time increases the rate of productivity of the fluorimetric method for determining uranium. The machine prepares 18 phosphors at a time and eliminates the tedious and time-consuming step of preparing them by hand, while improving the precision of the method in some localities. The machine consists of a ring burner over which the platinum dishes, containing uranium and flux, are rotated. By placing the machine in an inclined position the molten flux comes into contact with all surfaces within th dish as the dishes rotate over the flame. Precision is improved because the heating and cooling conditions are the same for each of the 18 phosphors in one run as well as for successive runs.

Hypothermic machine perfusion permits extended cold ischemia times with improved early graft function.

PubMed

Guy, Alison; McGrogan, Damian; Inston, Nicholas; Ready, Andrew

2015-04-01

The logistics of deceased-donor renal transplants are largely affected by cold ischemia time. However, to attain successful outcomes, other issues must be considered. Extending cold ischemia time to accommodate these issues would be valuable. We investigated the role of hypothermic machine perfusion to extend cold ischaemia time. Deceased-donor kidneys were allocated to a storage method, depending on predicted time to operation. Kidneys to be transplanted from 8:00 AM to 8:00 PM in the transplant room remained in static cold storage. If predicted operating time was out of hours, the kidney was transferred to hypothermic machine perfusion and transplanted at the earliest opportunity on the dedicated transplant list. There were 74 kidneys transplanted from hypothermic machine perfusion and 101 kidneys from static cold storage. Median cold ischemia time was 23.85 hours in the hypothermic machine perfusion group, compared with 13 hours in the static cold storage group (P ≤ .0001). There were 20 kidneys (27%) from hypothermic machine perfusion that had delayed graft function, compared with 47 kidneys (47%) in the static cold storage group (P = .012). There were no other significant differences in graft or postoperative complications. This study demonstrated that improved early graft outcomes can be achieved following longer cold ischemia time by using hypothermic machine perfusion rather than static cold storage. This effect is likely multifactorial including the inherent effects of hypothermic machine perfusion, improved recipient preparation, and possibly better perioperative conditions.

Machine Learning for Social Services: A Study of Prenatal Case Management in Illinois.

PubMed

Pan, Ian; Nolan, Laura B; Brown, Rashida R; Khan, Romana; van der Boor, Paul; Harris, Daniel G; Ghani, Rayid

2017-06-01

To evaluate the positive predictive value of machine learning algorithms for early assessment of adverse birth risk among pregnant women as a means of improving the allocation of social services. We used administrative data for 6457 women collected by the Illinois Department of Human Services from July 2014 to May 2015 to develop a machine learning model for adverse birth prediction and improve upon the existing paper-based risk assessment. We compared different models and determined the strongest predictors of adverse birth outcomes using positive predictive value as the metric for selection. Machine learning algorithms performed similarly, outperforming the current paper-based risk assessment by up to 36%; a refined paper-based assessment outperformed the current assessment by up to 22%. We estimate that these improvements will allow 100 to 170 additional high-risk pregnant women screened for program eligibility each year to receive services that would have otherwise been unobtainable. Our analysis exhibits the potential for machine learning to move government agencies toward a more data-informed approach to evaluating risk and providing social services. Overall, such efforts will improve the efficiency of allocating resource-intensive interventions.

Machines That Teach.

ERIC Educational Resources Information Center

Sniecinski, Jozef

This paper reviews efforts which have been made to improve the effectiveness of teaching through the development of principles of programed teaching and the construction of teaching machines, concluding that a combination of computer technology and programed teaching principles offers an efficient approach to improving teaching. Three different…

Gradient boosting machine for modeling the energy consumption of commercial buildings

DOE PAGES

Touzani, Samir; Granderson, Jessica; Fernandes, Samuel

2017-11-26

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradientmore » boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.« less

Gradient boosting machine for modeling the energy consumption of commercial buildings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Touzani, Samir; Granderson, Jessica; Fernandes, Samuel

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradientmore » boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.« less

HUMAN DECISIONS AND MACHINE PREDICTIONS.

PubMed

Kleinberg, Jon; Lakkaraju, Himabindu; Leskovec, Jure; Ludwig, Jens; Mullainathan, Sendhil

2018-02-01

Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; and these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals. JEL Codes: C10 (Econometric and statistical methods and methodology), C55 (Large datasets: Modeling and analysis), K40 (Legal procedure, the legal system, and illegal behavior).

HUMAN DECISIONS AND MACHINE PREDICTIONS*

PubMed Central

Kleinberg, Jon; Lakkaraju, Himabindu; Leskovec, Jure; Ludwig, Jens; Mullainathan, Sendhil

2018-01-01

Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; and these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals. JEL Codes: C10 (Econometric and statistical methods and methodology), C55 (Large datasets: Modeling and analysis), K40 (Legal procedure, the legal system, and illegal behavior) PMID:29755141

Improving Simulations of Extreme Flows by Coupling a Physically-based Hydrologic Model with a Machine Learning Model

NASA Astrophysics Data System (ADS)

Mohammed, K.; Islam, A. S.; Khan, M. J. U.; Das, M. K.

2017-12-01

With the large number of hydrologic models presently available along with the global weather and geographic datasets, streamflows of almost any river in the world can be easily modeled. And if a reasonable amount of observed data from that river is available, then simulations of high accuracy can sometimes be performed after calibrating the model parameters against those observed data through inverse modeling. Although such calibrated models can succeed in simulating the general trend or mean of the observed flows very well, more often than not they fail to adequately simulate the extreme flows. This causes difficulty in tasks such as generating reliable projections of future changes in extreme flows due to climate change, which is obviously an important task due to floods and droughts being closely connected to people's lives and livelihoods. We propose an approach where the outputs of a physically-based hydrologic model are used as an input to a machine learning model to try and better simulate the extreme flows. To demonstrate this offline-coupling approach, the Soil and Water Assessment Tool (SWAT) was selected as the physically-based hydrologic model, the Artificial Neural Network (ANN) as the machine learning model and the Ganges-Brahmaputra-Meghna (GBM) river system as the study area. The GBM river system, located in South Asia, is the third largest in the world in terms of freshwater generated and forms the largest delta in the world. The flows of the GBM rivers were simulated separately in order to test the performance of this proposed approach in accurately simulating the extreme flows generated by different basins that vary in size, climate, hydrology and anthropogenic intervention on stream networks. Results show that by post-processing the simulated flows of the SWAT models with ANN models, simulations of extreme flows can be significantly improved. The mean absolute errors in simulating annual maximum/minimum daily flows were minimized from 4967 cusecs to 1294 cusecs for Ganges, from 5695 cusecs to 2115 cusecs for Brahmaputra and from 689 cusecs to 321 cusecs for Meghna. Using this approach, simulations of hydrologic variables other than streamflow can also be improved given that a decent amount of observed data for that variable is available.

Pre-use anesthesia machine check; certified anesthesia technician based quality improvement audit.

PubMed

Al Suhaibani, Mazen; Al Malki, Assaf; Al Dosary, Saad; Al Barmawi, Hanan; Pogoku, Mahdhav

2014-01-01

Quality assurance of providing a work ready machine in multiple theatre operating rooms in a tertiary modern medical center in Riyadh. The aim of the following study is to keep high quality environment for workers and patients in surgical operating rooms. Technicians based audit by using key performance indicators to assure inspection, passing test of machine worthiness for use daily and in between cases and in case of unexpected failure to provide quick replacement by ready to use another anesthetic machine. The anesthetic machines in all operating rooms are daily and continuously inspected and passed as ready by technicians and verified by anesthesiologist consultant or assistant consultant. The daily records of each machines were collected then inspected for data analysis by quality improvement committee department for descriptive analysis and report the degree of staff compliance to daily inspection as "met" items. Replaced machine during use and overall compliance. Distractive statistic using Microsoft Excel 2003 tables and graphs of sums and percentages of item studied in this audit. Audit obtained highest compliance percentage and low rate of replacement of machine which indicate unexpected machine state of use and quick machine switch. The authors are able to conclude that following regular inspection and running self-check recommended by the manufacturers can contribute to abort any possibility of hazard of anesthesia machine failure during operation. Furthermore in case of unexpected reason to replace the anesthesia machine in quick maneuver contributes to high assured operative utilization of man machine inter-phase in modern surgical operating rooms.

On the applicability of brain reading for predictive human-machine interfaces in robotics.

PubMed

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics

PubMed Central

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors. PMID:24358125

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning

PubMed Central

Preuer, Kristina; Lewis, Richard P I; Hochreiter, Sepp; Bender, Andreas; Bulusu, Krishna C; Klambauer, Günter

2018-01-01

Abstract Motivation While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies. Results DeepSynergy was compared to other machine learning methods such as Gradient Boosting Machines, Random Forests, Support Vector Machines and Elastic Nets on the largest publicly available synergy dataset with respect to mean squared error. DeepSynergy significantly outperformed the other methods with an improvement of 7.2% over the second best method at the prediction of novel drug combinations within the space of explored drugs and cell lines. At this task, the mean Pearson correlation coefficient between the measured and the predicted values of DeepSynergy was 0.73. Applying DeepSynergy for classification of these novel drug combinations resulted in a high predictive performance of an AUC of 0.90. Furthermore, we found that all compared methods exhibit low predictive performance when extrapolating to unexplored drugs or cell lines, which we suggest is due to limitations in the size and diversity of the dataset. We envision that DeepSynergy could be a valuable tool for selecting novel synergistic drug combinations. Availability and implementation DeepSynergy is available via www.bioinf.jku.at/software/DeepSynergy. Contact klambauer@bioinf.jku.at Supplementary information Supplementary data are available at Bioinformatics online. PMID:29253077

Machine learning applications in proteomics research: how the past can boost the future.

PubMed

Kelchtermans, Pieter; Bittremieux, Wout; De Grave, Kurt; Degroeve, Sven; Ramon, Jan; Laukens, Kris; Valkenborg, Dirk; Barsnes, Harald; Martens, Lennart

2014-03-01

Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Manipulating Slot Machine Preference in Problem Gamblers through Contextual Control

ERIC Educational Resources Information Center

Nastally, Becky L.; Dixon, Mark R.; Jackson, James W.

2010-01-01

Pathological and nonpathological gamblers completed a task that assessed preference among 2 concurrently available slot machines. Subsequent assessments of choice were conducted after various attempts to transfer contextual functions associated with irrelevant characteristics of the slot machines. Results indicated that the nonproblem gambling…

The influence of Span-20 surfactant and micro-/nano-Chromium (Cr) Powder Mixed Electrical Discharge Machining (PMEDM) on the surface characteristics of AISI D2 hardened steel

NASA Astrophysics Data System (ADS)

Hosni, N. A. J.; Lajis, M. A.

2018-04-01

The application of powder mixed dielectric to improve the efficiency of electrical discharge machining (EDM) has been extensively studied. Therefore, PMEDM have attracted the attention of many researchers since last few decades. Improvement in EDM process has resulted in the use of span-20 surfactant and Cr powder mixed in dielectric fluid, which results in increasing machiniability, better surface quality and faster machining time. However, the study of powder suspension size of surface charateristics in EDM field is still limited. This paper presents the improvement of micro-/nano- Cr powder size on the surface characteristics of the AISI D2 hardened steels in PMEDM. It has found that the reacst layer in PMEDM improved by as high as 41-53 % compared to conventional EDM. Also notably, the combination of added Cr powder and span-20 surfactant reduced the recast layer thickness significantly especially in nano-Cr size. This improvement was great potential adding nano-size Cr powder to dielectric for machining performance.

Exploitation of multi-temporal Earth Observation imagery for monitoring land cover change in mining sites

NASA Astrophysics Data System (ADS)

Petropoulos, G.; Partsinevelos, P.; Mitraka, Z.

2012-04-01

Surface mining has been shown to cause intensive environmental degradation in terms of landscape, vegetation and biological communities. Nowadays, the commercial availability of remote sensing imagery at high spatiotemporal scales, has improved dramatically our ability to monitor surface mining activity and evaluate its impact on the environment and society. In this study we investigate the potential use of Landsat TM imagery combined with diverse classification techniques, namely artificial neural networks and support vector machines for delineating mining exploration and assessing its effect on vegetation in various surface mining sites in the Greek island of Milos. Assessment of the mining impact in the study area is validated through the analysis of available QuickBird imagery acquired nearly concurrently to the TM overpasses. Results indicate the capability of the TM sensor combined with the image analysis applied herein as a potential economically viable solution to provide rapidly and at regular time intervals information on mining activity and its impact to the local environment. KEYWORDS: mining environmental impact, remote sensing, image classification, change detection, land reclamation, support vector machines, neural networks

48 CFR 52.223-13 - Acquisition of EPEAT®-Registered Imaging Equipment.

Code of Federal Regulations, 2014 CFR

2014-10-01

...) Facsimile machine (fax machine)—A commercially available imaging product whose primary functions are... available imaging product with a sole function of the production of hard copy duplicates from graphic hard... functionally integrated components, that performs two or more of the core functions of copying, printing...

Use of Lower-Limb Robotics to Enhance Practice and Participation in Individuals With Neurological Conditions.

PubMed

Jayaraman, Arun; Burt, Sheila; Rymer, William Zev

2017-07-01

To review lower-limb technology currently available for people with neurological disorders, such as spinal cord injury, stroke, or other conditions. We focus on 3 emerging technologies: treadmill-based training devices, exoskeletons, and other wearable robots. Efficacy for these devices remains unclear, although preliminary data indicate that specific patient populations may benefit from robotic training used with more traditional physical therapy. Potential benefits include improved lower-limb function and a more typical gait trajectory. Use of these devices is limited by insufficient data, cost, and in some cases size of the machine. However, robotic technology is likely to become more prevalent as these machines are enhanced and able to produce targeted physical rehabilitation. Therapists should be aware of these technologies as they continue to advance but understand the limitations and challenges posed with therapeutic/mobility robots.

Retrieving Tract Variables From Acoustics: A Comparison of Different Machine Learning Strategies.

PubMed

Mitra, Vikramjit; Nam, Hosung; Espy-Wilson, Carol Y; Saltzman, Elliot; Goldstein, Louis

2010-09-13

Many different studies have claimed that articulatory information can be used to improve the performance of automatic speech recognition systems. Unfortunately, such articulatory information is not readily available in typical speaker-listener situations. Consequently, such information has to be estimated from the acoustic signal in a process which is usually termed "speech-inversion." This study aims to propose and compare various machine learning strategies for speech inversion: Trajectory mixture density networks (TMDNs), feedforward artificial neural networks (FF-ANN), support vector regression (SVR), autoregressive artificial neural network (AR-ANN), and distal supervised learning (DSL). Further, using a database generated by the Haskins Laboratories speech production model, we test the claim that information regarding constrictions produced by the distinct organs of the vocal tract (vocal tract variables) is superior to flesh-point information (articulatory pellet trajectories) for the inversion process.

Upper limb functional electrical stimulation devices and their man-machine interfaces.

PubMed

Venugopalan, L; Taylor, P N; Cobb, J E; Swain, I D

2015-01-01

Functional Electrical Stimulation (FES) is a technique that uses electricity to activate the nerves of a muscle that is paralysed due to hemiplegia, multiple sclerosis, Parkinson's disease or spinal cord injury (SCI). FES has been widely used to restore upper limb functions in people with hemiplegia and C5-C7 tetraplegia and has improved their ability to perform their activities of daily living (ADL). At the time of writing, a detailed literature review of the existing upper limb FES devices and their man-machine interfaces (MMI) showed that only the NESS H200 was commercially available. However, the rigid arm splint doesn't fit everyone and prevents the use of a tenodesis grip. Hence, a robust and versatile upper limb FES device that can be used by a wider group of people is required.

The Glostavent: evolution of an anaesthetic machine for developing countries.

PubMed

Beringer, R M; Eltringham, R J

2008-05-01

The sophisticated anaesthetic machines designed for use in modem hospitals are not appropriate for many parts of the developing world, as they are reliant on regular servicing by skilled engineers and an uninterrupted supply of electricity and compressed gases, which are not always available. The Glostavent has been designed specifically to meet the challenges faced by anaesthetists working in these countries. It is robust, simple to use, economical, easy to service and will continue to run during an interruption of the supply of oxygen or electricity. Feedback from widespread use throughout the developing world over the last 10 years has led to significant improvements to the original design. This article describes the basic components of the original version and the modifications which have been introduced as a result of practical experience in the developing world.

Promoting the purchase of low-calorie foods from school vending machines: a cluster-randomized controlled study.

PubMed

Kocken, Paul L; Eeuwijk, Jennifer; Van Kesteren, Nicole M C; Dusseldorp, Elise; Buijs, Goof; Bassa-Dafesh, Zeina; Snel, Jeltje

2012-03-01

Vending machines account for food sales and revenue in schools. We examined 3 strategies for promoting the sale of lower-calorie food products from vending machines in high schools in the Netherlands. A school-based randomized controlled trial was conducted in 13 experimental schools and 15 control schools. Three strategies were tested within each experimental school: increasing the availability of lower-calorie products in vending machines, labeling products, and reducing the price of lower-calorie products. The experimental schools introduced the strategies in 3 consecutive phases, with phase 3 incorporating all 3 strategies. The control schools remained the same. The sales volumes from the vending machines were registered. Products were grouped into (1) extra foods containing empty calories, for example, candies and potato chips, (2) nutrient-rich basic foods, and (3) beverages. They were also divided into favorable, moderately unfavorable, and unfavorable products. Total sales volumes for experimental and control schools did not differ significantly for the extra and beverage products. Proportionally, the higher availability of lower-calorie extra products in the experimental schools led to higher sales of moderately unfavorable extra products than in the control schools, and to higher sales of favorable extra products in experimental schools where students have to stay during breaks. Together, availability, labeling, and price reduction raised the proportional sales of favorable beverages. Results indicate that when the availability of lower-calorie foods is increased and is also combined with labeling and reduced prices, students make healthier choices without buying more or fewer products from school vending machines. Changes to school vending machines help to create a healthy school environment. © 2012, American School Health Association.

Method and apparatus for characterizing and enhancing the dynamic performance of machine tools

DOEpatents

Barkman, William E; Babelay, Jr., Edwin F

2013-12-17

Disclosed are various systems and methods for assessing and improving the capability of a machine tool. The disclosure applies to machine tools having at least one slide configured to move along a motion axis. Various patterns of dynamic excitation commands are employed to drive the one or more slides, typically involving repetitive short distance displacements. A quantification of a measurable merit of machine tool response to the one or more patterns of dynamic excitation commands is typically derived for the machine tool. Examples of measurable merits of machine tool performance include dynamic one axis positional accuracy of the machine tool, dynamic cross-axis stability of the machine tool, and dynamic multi-axis positional accuracy of the machine tool.

The design and improvement of radial tire molding machine

NASA Astrophysics Data System (ADS)

Wang, Wenhao; Zhang, Tao

2018-04-01

This paper presented that the high accuracy semisteel meridian tire molding machine structure configurations, combining tyre high precision characteristics, the original structure and parameter optimization, technology improvement innovation design period of opening and closing machine rotary shaping drum institutions. This way out of the shaft from the structure to the push-pull type movable shaping drum of thinking limit, compared with the specifications and shaping drum can smaller contraction, is conducive to forming the tire and reduce the tire deformation.

MACHINE COOLANT WASTE REDUCTION BY OPTIMIZING COOLANT LIFE

EPA Science Inventory

Machine shops use coolants to improve the life and function of machine tools. hese coolants become contaminated with oils with use, and this contamination can lead to growth of anaerobic bacteria and shortened coolant life. his project investigated methods to extend coolant life ...

Improving Machining Accuracy of CNC Machines with Innovative Design Methods

NASA Astrophysics Data System (ADS)

Yemelyanov, N. V.; Yemelyanova, I. V.; Zubenko, V. L.

2018-03-01

The article considers achieving the machining accuracy of CNC machines by applying innovative methods in modelling and design of machining systems, drives and machine processes. The topological method of analysis involves visualizing the system as matrices of block graphs with a varying degree of detail between the upper and lower hierarchy levels. This approach combines the advantages of graph theory and the efficiency of decomposition methods, it also has visual clarity, which is inherent in both topological models and structural matrices, as well as the resiliency of linear algebra as part of the matrix-based research. The focus of the study is on the design of automated machine workstations, systems, machines and units, which can be broken into interrelated parts and presented as algebraic, topological and set-theoretical models. Every model can be transformed into a model of another type, and, as a result, can be interpreted as a system of linear and non-linear equations which solutions determine the system parameters. This paper analyses the dynamic parameters of the 1716PF4 machine at the stages of design and exploitation. Having researched the impact of the system dynamics on the component quality, the authors have developed a range of practical recommendations which have enabled one to reduce considerably the amplitude of relative motion, exclude some resonance zones within the spindle speed range of 0...6000 min-1 and improve machining accuracy.

A Personalized Electronic Movie Recommendation System Based on Support Vector Machine and Improved Particle Swarm Optimization

PubMed Central

Wang, Xibin; Luo, Fengji; Qian, Ying; Ranzi, Gianluca

2016-01-01

With the rapid development of ICT and Web technologies, a large an amount of information is becoming available and this is producing, in some instances, a condition of information overload. Under these conditions, it is difficult for a person to locate and access useful information for making decisions. To address this problem, there are information filtering systems, such as the personalized recommendation system (PRS) considered in this paper, that assist a person in identifying possible products or services of interest based on his/her preferences. Among available approaches, collaborative Filtering (CF) is one of the most widely used recommendation techniques. However, CF has some limitations, e.g., the relatively simple similarity calculation, cold start problem, etc. In this context, this paper presents a new regression model based on the support vector machine (SVM) classification and an improved PSO (IPSO) for the development of an electronic movie PRS. In its implementation, a SVM classification model is first established to obtain a preliminary movie recommendation list based on which a SVM regression model is applied to predict movies’ ratings. The proposed PRS not only considers the movie’s content information but also integrates the users’ demographic and behavioral information to better capture the users’ interests and preferences. The efficiency of the proposed method is verified by a series of experiments based on the MovieLens benchmark data set. PMID:27898691

A Personalized Electronic Movie Recommendation System Based on Support Vector Machine and Improved Particle Swarm Optimization.

PubMed

Wang, Xibin; Luo, Fengji; Qian, Ying; Ranzi, Gianluca

2016-01-01

With the rapid development of ICT and Web technologies, a large an amount of information is becoming available and this is producing, in some instances, a condition of information overload. Under these conditions, it is difficult for a person to locate and access useful information for making decisions. To address this problem, there are information filtering systems, such as the personalized recommendation system (PRS) considered in this paper, that assist a person in identifying possible products or services of interest based on his/her preferences. Among available approaches, collaborative Filtering (CF) is one of the most widely used recommendation techniques. However, CF has some limitations, e.g., the relatively simple similarity calculation, cold start problem, etc. In this context, this paper presents a new regression model based on the support vector machine (SVM) classification and an improved PSO (IPSO) for the development of an electronic movie PRS. In its implementation, a SVM classification model is first established to obtain a preliminary movie recommendation list based on which a SVM regression model is applied to predict movies' ratings. The proposed PRS not only considers the movie's content information but also integrates the users' demographic and behavioral information to better capture the users' interests and preferences. The efficiency of the proposed method is verified by a series of experiments based on the MovieLens benchmark data set.

NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets.

PubMed

Nielsen, Morten; Andreatta, Massimo

2016-03-30

Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells. Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining information across both multiple MHC molecules and peptide lengths. This pan-allele/pan-length algorithm significantly outperforms state-of-the-art methods, and captures differences in the length profile of binders to different MHC molecules leading to increased accuracy for ligand identification. Using this model, we demonstrate that percentile ranks in contrast to affinity-based thresholds are optimal for ligand identification due to uniform sampling of the MHC space. We have developed a neural network-based machine-learning algorithm leveraging information across multiple receptor specificities and ligand length scales, and demonstrated how this approach significantly improves the accuracy for prediction of peptide binding and identification of MHC ligands. The method is available at www.cbs.dtu.dk/services/NetMHCpan-3.0 .

Improved Confinement Regimes and the Ignitor Experiment

NASA Astrophysics Data System (ADS)

Bombarda, F.; Coppi, B.; Detragiache, P.

2013-10-01

The Ignitor experiment is the only one designed and planned to reach ignition under controlled DT burning conditions. The machine prameters have been established on the basis of existing knowledge of the confinement properties of high density plasmas. The optimal plasma evolution in order to reach ignition by means of Ohmic heating only, without the contribution of transport barriers has been identified. Improved confinement regimes are expected to be accessible by means of the available ICRH additional heating power and the injection of pellets for density profile control. Moreover, ECRH of the outer edge of the (toroidal) plasma column has been proposed using very high frequency sources developed in Russia. Ignition can then be reached at slightly reduced machine parameters. Significant exploration of the behavior of burning, sub-ignited plasmas can be carried out in less demanding operational conditions than those needed for ignition with plasmas accessing the I or H-regimes. These conditions will be discussed together with the provisions made in order to maintain the required (for ignition) degree of plasma purity. Sponsored in part by the U.S. DOE.

Fabrication and correction of freeform surface based on Zernike polynomials by slow tool servo

NASA Astrophysics Data System (ADS)

Cheng, Yuan-Chieh; Hsu, Ming-Ying; Peng, Wei-Jei; Hsu, Wei-Yao

2017-10-01

Recently, freeform surface widely using to the optical system; because it is have advance of optical image and freedom available to improve the optical performance. For freeform optical fabrication by integrating freeform optical design, precision freeform manufacture, metrology freeform optics and freeform compensate method, to modify the form deviation of surface, due to production process of freeform lens ,compared and provides more flexibilities and better performance. This paper focuses on the fabrication and correction of the free-form surface. In this study, optical freeform surface using multi-axis ultra-precision manufacturing could be upgrading the quality of freeform. It is a machine equipped with a positioning C-axis and has the CXZ machining function which is also called slow tool servo (STS) function. The freeform compensate method of Zernike polynomials results successfully verified; it is correction the form deviation of freeform surface. Finally, the freeform surface are measured experimentally by Ultrahigh Accurate 3D Profilometer (UA3P), compensate the freeform form error with Zernike polynomial fitting to improve the form accuracy of freeform.

Heat-Assisted Machining for Material Removal Improvement

NASA Astrophysics Data System (ADS)

Mohd Hadzley, A. B.; Hafiz, S. Muhammad; Azahar, W.; Izamshah, R.; Mohd Shahir, K.; Abu, A.

2015-09-01

Heat assisted machining (HAM) is a process where an intense heat source is used to locally soften the workpiece material before machined by high speed cutting tool. In this paper, an HAM machine is developed by modification of small CNC machine with the addition of special jig to hold the heat sources in front of the machine spindle. Preliminary experiment to evaluate the capability of HAM machine to produce groove formation for slotting process was conducted. A block AISI D2 tool steel with100mm (width) × 100mm (length) × 20mm (height) size has been cut by plasma heating with different setting of arc current, feed rate and air pressure. Their effect has been analyzed based on distance of cut (DOC).Experimental results demonstrated the most significant factor that contributed to the DOC is arc current, followed by the feed rate and air pressure. HAM improves the slotting process of AISI D2 by increasing distance of cut due to initial cutting groove that formed during thermal melting and pressurized air from the heat source.

Defect Detectability Improvement for Conventional Friction Stir Welds

NASA Technical Reports Server (NTRS)

Hill, Chris

2013-01-01

This research was conducted to evaluate the effects of defect detectability via phased array ultrasound technology in conventional friction stir welds by comparing conventionally prepped post weld surfaces to a machined surface finish. A machined surface is hypothesized to improve defect detectability and increase material strength.

14 CFR 1260.57 - New technology.

Code of Federal Regulations, 2013 CFR

2013-01-01

... operate, in case of a machine or system; and, in each case, under such conditions as to establish that the... items include, but are not limited to, new processes, machines, manufactures, and compositions of matter, and improvements to, or new applications of, existing processes, machines, manufactures, and...

14 CFR 1260.57 - New technology.

Code of Federal Regulations, 2012 CFR

2012-01-01

... operate, in case of a machine or system; and, in each case, under such conditions as to establish that the... items include, but are not limited to, new processes, machines, manufactures, and compositions of matter, and improvements to, or new applications of, existing processes, machines, manufactures, and...

Designing a mathematical model for integrating dynamic cellular manufacturing into supply chain system

NASA Astrophysics Data System (ADS)

Aalaei, Amin; Davoudpour, Hamid

2012-11-01

This article presents designing a new mathematical model for integrating dynamic cellular manufacturing into supply chain system with an extensive coverage of important manufacturing features consideration of multiple plants location, multi-markets allocation, multi-period planning horizons with demand and part mix variation, machine capacity, and the main constraints are demand of markets satisfaction in each period, machine availability, machine time-capacity, worker assignment, available time of worker, production volume for each plant and the amounts allocated to each market. The aim of the proposed model is to minimize holding and outsourcing costs, inter-cell material handling cost, external transportation cost, procurement & maintenance and overhead cost of machines, setup cost, reconfiguration cost of machines installation and removal, hiring, firing and salary worker costs. Aimed to prove the potential benefits of such a design, presented an example is shown using a proposed model.

Vending machine assessment methodology. A systematic review.

PubMed

Matthews, Melissa A; Horacek, Tanya M

2015-07-01

The nutritional quality of food and beverage products sold in vending machines has been implicated as a contributing factor to the development of an obesogenic food environment. How comprehensive, reliable, and valid are the current assessment tools for vending machines to support or refute these claims? A systematic review was conducted to summarize, compare, and evaluate the current methodologies and available tools for vending machine assessment. A total of 24 relevant research studies published between 1981 and 2013 met inclusion criteria for this review. The methodological variables reviewed in this study include assessment tool type, study location, machine accessibility, product availability, healthfulness criteria, portion size, price, product promotion, and quality of scientific practice. There were wide variations in the depth of the assessment methodologies and product healthfulness criteria utilized among the reviewed studies. Of the reviewed studies, 39% evaluated machine accessibility, 91% evaluated product availability, 96% established healthfulness criteria, 70% evaluated portion size, 48% evaluated price, 52% evaluated product promotion, and 22% evaluated the quality of scientific practice. Of all reviewed articles, 87% reached conclusions that provided insight into the healthfulness of vended products and/or vending environment. Product healthfulness criteria and complexity for snack and beverage products was also found to be variable between the reviewed studies. These findings make it difficult to compare results between studies. A universal, valid, and reliable vending machine assessment tool that is comprehensive yet user-friendly is recommended. Copyright © 2015 Elsevier Ltd. All rights reserved.

3-D laser patterning process utilizing horizontal and vertical patterning

DOEpatents

Malba, Vincent; Bernhardt, Anthony F.

2000-01-01

A process which vastly improves the 3-D patterning capability of laser pantography (computer controlled laser direct-write patterning). The process uses commercially available electrodeposited photoresist (EDPR) to pattern 3-D surfaces. The EDPR covers the surface of a metal layer conformally, coating the vertical as well as horizontal surfaces. A laser pantograph then patterns the EDPR, which is subsequently developed in a standard, commercially available developer, leaving patterned trench areas in the EDPR. The metal layer thereunder is now exposed in the trench areas and masked in others, and thereafter can be etched to form the desired pattern (subtractive process), or can be plated with metal (additive process), followed by a resist stripping, and removal of the remaining field metal (additive process). This improved laser pantograph process is simpler, faster, move manufacturable, and requires no micro-machining.

A randomized controlled trial of chair interventions on back and hip pain among sewing machine operators: the los angeles garment study.

PubMed

Wang, Pin-Chieh; Ritz, Beate R; Janowitz, Ira; Harrison, Robert J; Yu, Fei; Chan, Jacqueline; Rempel, David M

2008-03-01

Determine whether an adjustable chair with a curved or a flat seat pan improved monthly back and hip pain scores in sewing machine operators. This 4-month intervention study randomized 293 sewing machine operators with back and hip pain. The participants in the control group received a placebo intervention, and participants in the intervention groups received the placebo intervention and one of the two intervention chairs. Compared with the control group, mean pain improvement for the flat chair intervention was 0.43 points (95% CI = 0.34, 0.51) per month, and mean pain improvement for the curved chair intervention was 0.25 points (95% CI = 0.16, 0.34) per month. A height-adjustable task chair with a swivel function can reduce back and hip pain in sewing machine operators. The findings may be relevant to workers who perform visual- and hand-intensive manufacturing jobs.

40 CFR 63.467 - Recordkeeping requirements.

Code of Federal Regulations, 2011 CFR

2011-07-01

... of a batch vapor or in-line solvent cleaning machine complying with the provisions of § 63.463 shall... for the lifetime of the machine. (1) Owner's manuals, or if not available, written maintenance and operating procedures, for the solvent cleaning machine and control equipment. (2) The date of installation...

40 CFR 63.467 - Recordkeeping requirements.

Code of Federal Regulations, 2012 CFR

2012-07-01

... of a batch vapor or in-line solvent cleaning machine complying with the provisions of § 63.463 shall... for the lifetime of the machine. (1) Owner's manuals, or if not available, written maintenance and operating procedures, for the solvent cleaning machine and control equipment. (2) The date of installation...

40 CFR 63.467 - Recordkeeping requirements.

Code of Federal Regulations, 2014 CFR

2014-07-01

... of a batch vapor or in-line solvent cleaning machine complying with the provisions of § 63.463 shall... for the lifetime of the machine. (1) Owner's manuals, or if not available, written maintenance and operating procedures, for the solvent cleaning machine and control equipment. (2) The date of installation...

40 CFR 63.467 - Recordkeeping requirements.

Code of Federal Regulations, 2013 CFR

2013-07-01

... of a batch vapor or in-line solvent cleaning machine complying with the provisions of § 63.463 shall... for the lifetime of the machine. (1) Owner's manuals, or if not available, written maintenance and operating procedures, for the solvent cleaning machine and control equipment. (2) The date of installation...

Vane Pump Casing Machining of Dumpling Machine Based on CAD/CAM

NASA Astrophysics Data System (ADS)

Huang, Yusen; Li, Shilong; Li, Chengcheng; Yang, Zhen

Automatic dumpling forming machine is also called dumpling machine, which makes dumplings through mechanical motions. This paper adopts the stuffing delivery mechanism featuring the improved and specially-designed vane pump casing, which can contribute to the formation of dumplings. Its 3D modeling in Pro/E software, machining process planning, milling path optimization, simulation based on UG and compiling post program were introduced and verified. The results indicated that adoption of CAD/CAM offers firms the potential to pursue new innovative strategies.

Overview of the Machine-Tool Task Force

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sutton, G.P.

1981-06-08

The Machine Tool Task Force, (MTTF) surveyed the state of the art of machine tool technology for material removal for two and one-half years. This overview gives a brief summary of the approach, specific subjects covered, principal conclusions and some of the key recommendations aimed at improving the technology and advancing the productivity of machine tools. The Task Force consisted of 123 experts from the US and other countries. Their findings are documented in a five-volume report, Technology of Machine Tools.

AN EIGHT WEEK SEMINAR IN AN INTRODUCTION TO NUMERICAL CONTROL ON TWO- AND THREE-AXIS MACHINE TOOLS FOR VOCATIONAL AND TECHNICAL MACHINE TOOL INSTRUCTORS. FINAL REPORT.

ERIC Educational Resources Information Center

BOLDT, MILTON; POKORNY, HARRY

THIRTY-THREE MACHINE SHOP INSTRUCTORS FROM 17 STATES PARTICIPATED IN AN 8-WEEK SEMINAR TO DEVELOP THE SKILLS AND KNOWLEDGE ESSENTIAL FOR TEACHING THE OPERATION OF NUMERICALLY CONTROLLED MACHINE TOOLS. THE SEMINAR WAS GIVEN FROM JUNE 20 TO AUGUST 12, 1966, WITH COLLEGE CREDIT AVAILABLE THROUGH STOUT STATE UNIVERSITY. THE PARTICIPANTS COMPLETED AN…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wessol, D.E.; Wheeler, F.J.; Babcock, R.S.

Several improvements have been developed for the BNCT radiation treatment planning environment (BNCT-Rtpe) during 1994. These improvements have been incorporated into Version 1.0 of BNCT-Rtpe which is currently installed at the INEL, BNL, Japanese Research Center (JRC), and Finland`s Technical Research Center. Platforms supported by this software include Hewlett-Packard (HP), SUN, International Business Machines (IBM), and Silicon Graphics Incorporated (SGI). A draft version of the BNCT-Rtpe user manual is available. Version 1.1 of BNCT-Rtpe is scheduled for release in March 1995. It is anticipated that Version 2.x of BNCT-Rtpe, which includes the nonproprietary NURBS library and data structures, will bemore » released in September 1995.« less

Survey of Commercially Available Computer-Readable Bibliographic Data Bases.

ERIC Educational Resources Information Center

Schneider, John H., Ed.; And Others

This document contains the results of a survey of 94 U. S. organizations, and 36 organizations in other countries that were thought to prepare machine-readable data bases. Of those surveyed, 55 organizations (40 in U. S., 15 in other countries) provided completed camera-ready forms describing 81 commercially available, machine-readable data bases…

47 CFR 76.1700 - Records to be maintained by cable system operators.

Code of Federal Regulations, 2012 CFR

2012-10-01

... inspection file shall be available for public inspection at any time during regular business hours. (c) All... of any material in the public inspection file shall be available for machine reproduction upon.... Requests for machine copies shall be fulfilled at a location specified by the system operator, within a...

Machinability of Green Powder Metallurgy Components: Part I. Characterization of the Influence of Tool Wear

NASA Astrophysics Data System (ADS)

Robert-Perron, Etienne; Blais, Carl; Pelletier, Sylvain; Thomas, Yannig

2007-06-01

The green machining process is an interesting approach for solving the mediocre machining behavior of high-performance powder metallurgy (PM) steels. This process appears as a promising method for extending tool life and reducing machining costs. Recent improvements in binder/lubricant technologies have led to high green strength systems that enable green machining. So far, tool wear has been considered negligible when characterizing the machinability of green PM specimens. This inaccurate assumption may lead to the selection of suboptimum cutting conditions. The first part of this study involves the optimization of the machining parameters to minimize the effects of tool wear on the machinability in turning of green PM components. The second part of our work compares the sintered mechanical properties of components machined in green state with other machined after sintering.

The Association between the Availability of Sugar-Sweetened Beverage in School Vending Machines and Its Consumption among Adolescents in California: A Propensity Score Matching Approach

PubMed Central

Shi, Lu

2010-01-01

There is controversy over to what degree banning sugar-sweetened beverage (SSB) sales at schools could decrease the SSB intake. This paper uses the adolescent sample of 2005 California Health Interview Survey to estimate the association between the availability of SSB from school vending machines and the amount of SSB consumption. Propensity score stratification and kernel-based propensity score matching are used to address the selection bias issue in cross-sectional data. Propensity score stratification shows that adolescents who had access to SSB through their school vending machines consumed 0.170 more drinks of SSB than those who did not (P < .05). Kernel-based propensity score matching shows the SSB consumption difference to be 0.158 on the prior day (P < .05). This paper strengthens the evidence for the association between SSB availability via school vending machines and the actual SSB consumption, while future studies are needed to explore changes in other beverages after SSB becomes less available. PMID:20976298

Using machine learning algorithms to guide rehabilitation planning for home care clients.

PubMed

Zhu, Mu; Zhang, Zhanyang; Hirdes, John P; Stolee, Paul

2007-12-20

Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms - Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) - to guide rehabilitation planning for home care clients. This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP. The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP. Machine learning algorithms achieved superior predictions than the current protocol. Machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.

48 CFR 1852.227-70 - New technology.

Code of Federal Regulations, 2011 CFR

2011-10-01

... method; or to operate, in case of a machine or system; and, in each case, under such conditions as to... contract. Reportable items include, but are not limited to, new processes, machines, manufactures, and compositions of matter, and improvements to, or new applications of, existing processes, machines, manufactures...

Machinery Management. FMO: Fundamentals of Machine Operation. Third Edition.

ERIC Educational Resources Information Center

Bowers, Wendell

This text is intended to provide a basic understanding of selecting, maintaining, and managing farm machinery. The following topics are covered in the individual chapters: dealing with typical problems in farm machinery management; measuring machine capacity; improving field efficiency; matching machine size and capacity; estimating power…

14 CFR § 1260.57 - New technology.

Code of Federal Regulations, 2014 CFR

2014-01-01

... operate, in case of a machine or system; and, in each case, under such conditions as to establish that the... items include, but are not limited to, new processes, machines, manufactures, and compositions of matter, and improvements to, or new applications of, existing processes, machines, manufactures, and...

National Machine Guarding Program: Part 1. Machine safeguarding practices in small metal fabrication businesses.

PubMed

Parker, David L; Yamin, Samuel C; Brosseau, Lisa M; Xi, Min; Gordon, Robert; Most, Ivan G; Stanley, Rodney

2015-11-01

Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures. The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardized checklists to conduct a baseline inspection of machine-related hazards in 221 business. Safeguards at the point of operation were missing or inadequate on 33% of machines. Safeguards for other mechanical hazards were missing on 28% of machines. Older machines were both widely used and less likely than newer machines to be properly guarded. Lockout/tagout procedures were posted at only 9% of machine workstations. The NMGP demonstrates a need for improvement in many aspects of machine safety and lockout in small metal fabrication businesses. © 2015 The Authors. American Journal of Industrial Medicine published by Wiley Periodicals, Inc.

Ergonomics for enhancing detection of machine abnormalities.

PubMed

Illankoon, Prasanna; Abeysekera, John; Singh, Sarbjeet

2016-10-17

Detecting abnormal machine conditions is of great importance in an autonomous maintenance environment. Ergonomic aspects can be invaluable when detection of machine abnormalities using human senses is examined. This research outlines the ergonomic issues involved in detecting machine abnormalities and suggests how ergonomics would improve such detections. Cognitive Task Analysis was performed in a plant in Sri Lanka where Total Productive Maintenance is being implemented to identify sensory types that would be used to detect machine abnormalities and relevant Ergonomic characteristics. As the outcome of this research, a methodology comprising of an Ergonomic Gap Analysis Matrix for machine abnormality detection is presented.

FSW of Aluminum Tailor Welded Blanks across Machine Platforms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hovanski, Yuri; Upadhyay, Piyush; Carlson, Blair

2015-02-16

Development and characterization of friction stir welded aluminum tailor welded blanks was successfully carried out on three separate machine platforms. Each was a commercially available, gantry style, multi-axis machine designed specifically for friction stir welding. Weld parameters were developed to support high volume production of dissimilar thickness aluminum tailor welded blanks at speeds of 3 m/min and greater. Parameters originally developed on an ultra-high stiffness servo driven machine where first transferred to a high stiffness servo-hydraulic friction stir welding machine, and subsequently transferred to a purpose built machine designed to accommodate thin sheet aluminum welding. The inherent beam stiffness, bearingmore » compliance, and control system for each machine were distinctly unique, which posed specific challenges in transferring welding parameters across machine platforms. This work documents the challenges imposed by successfully transferring weld parameters from machine to machine, produced from different manufacturers and with unique control systems and interfaces.« less

Compatibility Problems of Network Interfacing.

ERIC Educational Resources Information Center

Stevens, Mary Elizabeth

From the standpoint of information network technology there is a necessary emphasis upon compatibility requirements which, in turn, will be met at least in part by various techniques of achieving convertibility --- between machine and machine, between man and machine, and between man and man. It may be hoped that improved compatibilities between…

Combining MEDLINE and publisher data to create parallel corpora for the automatic translation of biomedical text

PubMed Central

2013-01-01

Background Most of the institutional and research information in the biomedical domain is available in the form of English text. Even in countries where English is an official language, such as the United States, language can be a barrier for accessing biomedical information for non-native speakers. Recent progress in machine translation suggests that this technique could help make English texts accessible to speakers of other languages. However, the lack of adequate specialized corpora needed to train statistical models currently limits the quality of automatic translations in the biomedical domain. Results We show how a large-sized parallel corpus can automatically be obtained for the biomedical domain, using the MEDLINE database. The corpus generated in this work comprises article titles obtained from MEDLINE and abstract text automatically retrieved from journal websites, which substantially extends the corpora used in previous work. After assessing the quality of the corpus for two language pairs (English/French and English/Spanish) we use the Moses package to train a statistical machine translation model that outperforms previous models for automatic translation of biomedical text. Conclusions We have built translation data sets in the biomedical domain that can easily be extended to other languages available in MEDLINE. These sets can successfully be applied to train statistical machine translation models. While further progress should be made by incorporating out-of-domain corpora and domain-specific lexicons, we believe that this work improves the automatic translation of biomedical texts. PMID:23631733

Combining deep learning and satellite data to inform sustainable development

NASA Astrophysics Data System (ADS)

Lobell, D. B.

2017-12-01

Methods in machine learning, and in particular deep learning, are quickly advancing, in parallel with dramatic increases in the availability of fine resolution satellite data. The combination of both offers the possibility to improve understanding of some of the poorest regions of the world, where traditional data sources are limited. This talk will cover recent applications to track poverty at the village level in Africa, spot the onset of disease outbreaks in agriculture, and identify land use patterns and crop productivity.

Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity.

PubMed

Kim, Hui Kwon; Min, Seonwoo; Song, Myungjae; Jung, Soobin; Choi, Jae Woo; Kim, Younggwang; Lee, Sangeun; Yoon, Sungroh; Kim, Hyongbum Henry

2018-03-01

We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.

Neuroimaging of neurocutaneous diseases.

PubMed

Nandigam, Kaveer; Mechtler, Laszlo L; Smirniotopoulos, James G

2014-02-01

An in-depth knowledge of the imaging characteristics of the common neurocutaneous diseases (NCD) described in this article will help neurologists understand the screening imaging modalities in these patients. The future of neuroimaging is geared towards developing and refining magnetic resonance imaging (MRI) sequences. The detection of tumors in NCD has greatly improved with availability of high-field strength 3T MRI machines. Neuroimaging will remain at the heart and soul of the multidisciplinary care of such complex diagnoses to guide early detection and monitor treatment. Published by Elsevier Inc.

Analysis of NREL Cold-Drink Vending Machines for Energy Savings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deru, M.; Torcellini, P.; Bottom, K.

NREL Staff, as part of Sustainable NREL, an initiative to improve the overall energy and environmental performance of the lab, decided to control how its vending machines used energy. The cold-drink vending machines across the lab were analyzed for potential energy savings opportunities. This report gives the monitoring and the analysis of two energy conservation measures applied to the cold-drink vending machines at NREL.

Electric machine and current source inverter drive system

DOEpatents

Hsu, John S

2014-06-24

A drive system includes an electric machine and a current source inverter (CSI). This integration of an electric machine and an inverter uses the machine's field excitation coil for not only flux generation in the machine but also for the CSI inductor. This integration of the two technologies, namely the U machine motor and the CSI, opens a new chapter for the component function integration instead of the traditional integration by simply placing separate machine and inverter components in the same housing. Elimination of the CSI inductor adds to the CSI volumetric reduction of the capacitors and the elimination of PMs for the motor further improve the drive system cost, weight, and volume.

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dral, Pavlo O.; von Lilienfeld, O. Anatole; Thiel, Walter

2015-05-12

We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempiricalmore » OM2 method using a set of 6095 constitutional isomers C7H10O2, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.« less

Improving orbit prediction accuracy through supervised machine learning

NASA Astrophysics Data System (ADS)

Peng, Hao; Bai, Xiaoli

2018-05-01

Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.

Machine learning of parameters for accurate semiempirical quantum chemical calculations

DOE PAGES

Dral, Pavlo O.; von Lilienfeld, O. Anatole; Thiel, Walter

2015-04-14

We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempiricalmore » OM2 method using a set of 6095 constitutional isomers C 7H 10O 2, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.« less

Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation.

PubMed

Elbouchikhi, Elhoussin; Choqueuse, Vincent; Benbouzid, Mohamed

2016-07-01

Condition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA) has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. This paper focuses on the application of MCSA for the detection of abnormal mechanical conditions that may lead to induction machines failure. In fact, this paper is devoted to the detection of single-point defects in bearings based on parametric spectral estimation. A multi-dimensional MUSIC (MD MUSIC) algorithm has been developed for bearing faults detection based on bearing faults characteristic frequencies. This method has been used to estimate the fundamental frequency and the fault related frequency. Then, an amplitude estimator of the fault characteristic frequencies has been proposed and fault indicator has been derived for fault severity measurement. The proposed bearing faults detection approach is assessed using simulated stator currents data, issued from a coupled electromagnetic circuits approach for air-gap eccentricity emulating bearing faults. Then, experimental data are used for validation purposes. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Defining brain-machine interface applications by matching interface performance with device requirements.

PubMed

Tonet, Oliver; Marinelli, Martina; Citi, Luca; Rossini, Paolo Maria; Rossini, Luca; Megali, Giuseppe; Dario, Paolo

2008-01-15

Interaction with machines is mediated by human-machine interfaces (HMIs). Brain-machine interfaces (BMIs) are a particular class of HMIs and have so far been studied as a communication means for people who have little or no voluntary control of muscle activity. In this context, low-performing interfaces can be considered as prosthetic applications. On the other hand, for able-bodied users, a BMI would only be practical if conceived as an augmenting interface. In this paper, a method is introduced for pointing out effective combinations of interfaces and devices for creating real-world applications. First, devices for domotics, rehabilitation and assistive robotics, and their requirements, in terms of throughput and latency, are described. Second, HMIs are classified and their performance described, still in terms of throughput and latency. Then device requirements are matched with performance of available interfaces. Simple rehabilitation and domotics devices can be easily controlled by means of BMI technology. Prosthetic hands and wheelchairs are suitable applications but do not attain optimal interactivity. Regarding humanoid robotics, the head and the trunk can be controlled by means of BMIs, while other parts require too much throughput. Robotic arms, which have been controlled by means of cortical invasive interfaces in animal studies, could be the next frontier for non-invasive BMIs. Combining smart controllers with BMIs could improve interactivity and boost BMI applications.

AE Monitoring of Diamond Turned Rapidly Soldified Aluminium 443

NASA Astrophysics Data System (ADS)

Onwuka, G.; Abou-El-Hossein, K.; Mkoko, Z.

2017-05-01

The fast replacement of conventional aluminium with rapidly solidified aluminium alloys has become a noticeable trend in the current manufacturing industries involved in the production of optics and optical molding inserts. This is as a result of the improved performance and durability of rapidly solidified aluminium alloys when compared to conventional aluminium. Melt spinning process is vital for manufacturing rapidly solidified aluminium alloys like RSA 905, RSA 6061 and RSA 443 which are common in the industries today. RSA 443 is a newly developed alloy with few research findings and huge research potential. There is no available literature focused on monitoring the machining of RSA 443 alloys. In this research, Acoustic Emission sensing technique was applied to monitor the single point diamond turning of RSA 443 on an ultrahigh precision lathe machine. The machining process was carried out after careful selection of feed, speed and depths of cut. The monitoring process was achieved with a high sampling data acquisition system using different tools while concurrent measurement of the surface roughness and tool wear were initiated after covering a total feed distance of 13km. An increasing trend of raw AE spikes and peak to peak signal were observed with an increase in the surface roughness and tool wear values. Hence, acoustic emission sensing technique proves to be an effective monitoring method for the machining of RSA 443 alloy.

School Vending Machine Purchasing Behavior: Results from the 2005 YouthStyles Survey

ERIC Educational Resources Information Center

Thompson, Olivia M.; Yaroch, Amy L.; Moser, Richard P.; Rutten, Lila J. Finney; Agurs-Collins, Tanya

2010-01-01

Background: Competitive foods are often available in school vending machines. Providing youth with access to school vending machines, and thus competitive foods, is of concern, considering the continued high prevalence of childhood obesity: competitive foods tend to be energy dense and nutrient poor and can contribute to increased energy intake in…

48 CFR 6105.502 - Request for decision [Rule 502].

Code of Federal Regulations, 2014 CFR

2014-10-01

...) Include— (A) The name, address, telephone number, facsimile machine number, and e-mail address, if available, of the official making the request; (B) The name, address, telephone number, facsimile machine... Clerk's facsimile machine number is: (202) 606-0019. The Board's working hours are 8:00 a.m. to 4:30 p.m...

48 CFR 6105.502 - Request for decision [Rule 502].

Code of Federal Regulations, 2012 CFR

2012-10-01

...) Include— (A) The name, address, telephone number, facsimile machine number, and e-mail address, if available, of the official making the request; (B) The name, address, telephone number, facsimile machine... Clerk's facsimile machine number is: (202) 606-0019. The Board's working hours are 8:00 a.m. to 4:30 p.m...

Improving Energy Efficiency in CNC Machining

NASA Astrophysics Data System (ADS)

Pavanaskar, Sushrut S.

We present our work on analyzing and improving the energy efficiency of multi-axis CNC milling process. Due to the differences in energy consumption behavior, we treat 3- and 5-axis CNC machines separately in our work. For 3-axis CNC machines, we first propose an energy model that estimates the energy requirement for machining a component on a specified 3-axis CNC milling machine. Our model makes machine-specific predictions of energy requirements while also considering the geometric aspects of the machining toolpath. Our model - and the associated software tool - facilitate direct comparison of various alternative toolpath strategies based on their energy-consumption performance. Further, we identify key factors in toolpath planning that affect energy consumption in CNC machining. We then use this knowledge to propose and demonstrate a novel toolpath planning strategy that may be used to generate new toolpaths that are inherently energy-efficient, inspired by research on digital micrography -- a form of computational art. For 5-axis CNC machines, the process planning problem consists of several sub-problems that researchers have traditionally solved separately to obtain an approximate solution. After illustrating the need to solve all sub-problems simultaneously for a truly optimal solution, we propose a unified formulation based on configuration space theory. We apply our formulation to solve a problem variant that retains key characteristics of the full problem but has lower dimensionality, allowing visualization in 2D. Given the complexity of the full 5-axis toolpath planning problem, our unified formulation represents an important step towards obtaining a truly optimal solution. With this work on the two types of CNC machines, we demonstrate that without changing the current infrastructure or business practices, machine-specific, geometry-based, customized toolpath planning can save energy in CNC machining.

Compensation strategy for machining optical freeform surfaces by the combined on- and off-machine measurement.

PubMed

Zhang, Xiaodong; Zeng, Zhen; Liu, Xianlei; Fang, Fengzhou

2015-09-21

Freeform surface is promising to be the next generation optics, however it needs high form accuracy for excellent performance. The closed-loop of fabrication-measurement-compensation is necessary for the improvement of the form accuracy. It is difficult to do an off-machine measurement during the freeform machining because the remounting inaccuracy can result in significant form deviations. On the other side, on-machine measurement may hides the systematic errors of the machine because the measuring device is placed in situ on the machine. This study proposes a new compensation strategy based on the combination of on-machine and off-machine measurement. The freeform surface is measured in off-machine mode with nanometric accuracy, and the on-machine probe achieves accurate relative position between the workpiece and machine after remounting. The compensation cutting path is generated according to the calculated relative position and shape errors to avoid employing extra manual adjustment or highly accurate reference-feature fixture. Experimental results verified the effectiveness of the proposed method.

Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.

PubMed

Kunimatsu, Akira; Kunimatsu, Natsuko; Yasaka, Koichiro; Akai, Hiroyuki; Kamiya, Kouhei; Watadani, Takeyuki; Mori, Harushi; Abe, Osamu

2018-05-16

Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system lymphoma (PCNSL) is sometimes confusing. We aimed to evaluate the performance of image classification by support vector machine, a method of traditional machine learning, using texture features computed from contrast-enhanced T 1 -weighted images. This retrospective study on preoperative brain tumor MRI included 76 consecutives, initially treated patients with glioblastoma (n = 55) or PCNSL (n = 21) from one institution, consisting of independent training group (n = 60: 44 glioblastomas and 16 PCNSLs) and test group (n = 16: 11 glioblastomas and 5 PCNSLs) sequentially separated by time periods. A total set of 67 texture features was computed on routine contrast-enhanced T 1 -weighted images of the training group, and the top four most discriminating features were selected as input variables to train support vector machine classifiers. These features were then evaluated on the test group with subsequent image classification. The area under the receiver operating characteristic curves on the training data was calculated at 0.99 (95% confidence interval [CI]: 0.96-1.00) for the classifier with a Gaussian kernel and 0.87 (95% CI: 0.77-0.95) for the classifier with a linear kernel. On the test data, both of the classifiers showed prediction accuracy of 75% (12/16) of the test images. Although further improvement is needed, our preliminary results suggest that machine learning-based image classification may provide complementary diagnostic information on routine brain MRI.

Groundhog Day for Medical Artificial Intelligence.

PubMed

London, Alex John

2018-05-01

Following a boom in investment and overinflated expectations in the 1980s, artificial intelligence entered a period of retrenchment known as the "AI winter." With advances in the field of machine learning and the availability of large datasets for training various types of artificial neural networks, AI is in another cycle of halcyon days. Although medicine is particularly recalcitrant to change, applications of AI in health care have professionals in fields like radiology worried about the future of their careers and have the public tittering about the prospect of soulless machines making life-and-death decisions. Medicine thus appears to be at an inflection point-a kind of Groundhog Day on which either AI will bring a springtime of improved diagnostic and predictive practices or the shadow of public and professional fear will lead to six more metaphorical weeks of winter in medical AI. © 2018 The Hastings Center.

Privacy preserving RBF kernel support vector machine.

PubMed

Li, Haoran; Xiong, Li; Ohno-Machado, Lucila; Jiang, Xiaoqian

2014-01-01

Data sharing is challenging but important for healthcare research. Methods for privacy-preserving data dissemination based on the rigorous differential privacy standard have been developed but they did not consider the characteristics of biomedical data and make full use of the available information. This often results in too much noise in the final outputs. We hypothesized that this situation can be alleviated by leveraging a small portion of open-consented data to improve utility without sacrificing privacy. We developed a hybrid privacy-preserving differentially private support vector machine (SVM) model that uses public data and private data together. Our model leverages the RBF kernel and can handle nonlinearly separable cases. Experiments showed that this approach outperforms two baselines: (1) SVMs that only use public data, and (2) differentially private SVMs that are built from private data. Our method demonstrated very close performance metrics compared to nonprivate SVMs trained on the private data.

Combining satellite imagery and machine learning to predict poverty.

PubMed

Jean, Neal; Burke, Marshall; Xie, Michael; Davis, W Matthew; Lobell, David B; Ermon, Stefano

2016-08-19

Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. Using survey and satellite data from five African countries--Nigeria, Tanzania, Uganda, Malawi, and Rwanda--we show how a convolutional neural network can be trained to identify image features that can explain up to 75% of the variation in local-level economic outcomes. Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries. It also demonstrates how powerful machine learning techniques can be applied in a setting with limited training data, suggesting broad potential application across many scientific domains. Copyright © 2016, American Association for the Advancement of Science.

Local health department translation processes: potential of machine translation technologies to help meet needs.

PubMed

Turner, Anne M; Mandel, Hannah; Capurro, Daniel

2013-01-01

Limited English proficiency (LEP), defined as a limited ability to read, speak, write, or understand English, is associated with health disparities. Despite federal and state requirements to translate health information, the vast majority of health materials are solely available in English. This project investigates barriers to translation of health information and explores new technologies to improve access to multilingual public health materials. We surveyed all 77 local health departments (LHDs) in the Northwest about translation needs, practices, barriers and attitudes towards machine translation (MT). We received 67 responses from 45 LHDs. Translation of health materials is the principle strategy used by LHDs to reach LEP populations. Cost and access to qualified translators are principle barriers to producing multilingual materials. Thirteen LHDs have used online MT tools. Many respondents expressed concerns about the accuracy of MT. Overall, respondents were positive about its potential use, if low costs and quality could be assured.

Local Health Department Translation Processes: Potential of Machine Translation Technologies to Help Meet Needs

PubMed Central

Turner, Anne M.; Mandel, Hannah; Capurro, Daniel

2013-01-01

Limited English proficiency (LEP), defined as a limited ability to read, speak, write, or understand English, is associated with health disparities. Despite federal and state requirements to translate health information, the vast majority of health materials are solely available in English. This project investigates barriers to translation of health information and explores new technologies to improve access to multilingual public health materials. We surveyed all 77 local health departments (LHDs) in the Northwest about translation needs, practices, barriers and attitudes towards machine translation (MT). We received 67 responses from 45 LHDs. Translation of health materials is the principle strategy used by LHDs to reach LEP populations. Cost and access to qualified translators are principle barriers to producing multilingual materials. Thirteen LHDs have used online MT tools. Many respondents expressed concerns about the accuracy of MT. Overall, respondents were positive about its potential use, if low costs and quality could be assured. PMID:24551414

Algorithm of probabilistic assessment of fully-mechanized longwall downtime

NASA Astrophysics Data System (ADS)

Domrachev, A. N.; Rib, S. V.; Govorukhin, Yu M.; Krivopalov, V. G.

2017-09-01

The problem of increasing the load on a long fully-mechanized longwall has several aspects, one of which is the improvement of efficiency in using available stoping equipment due to the increase in coefficient of the machine operating time of a shearer and other mining machines that form an integral part of the longwall set of equipment. The task of predicting the reliability indicators of stoping equipment is solved by the statistical evaluation of parameters of downtime exponential distribution and failure recovery. It is more difficult to solve the problems of downtime accounting in case of accidents in the face workings and, despite the statistical data on accidents in mine workings, no solution has been found to date. The authors have proposed a variant of probability assessment of workings caving using Poisson distribution and the duration of their restoration using normal distribution. The above results confirm the possibility of implementing the approach proposed by the authors.

Predictors of firearm violence in urban communities: A machine-learning approach.

PubMed

Goin, Dana E; Rudolph, Kara E; Ahern, Jennifer

2018-05-01

Interpersonal firearm violence is a leading cause of death and injuries in the United States. Identifying community characteristics associated with firearm violence is important to improve confounder selection and control in health research, to better understand community-level factors that are associated with firearm violence, and to enhance community surveillance and control of firearm violence. The objective of this research was to use machine learning to identify an optimal set of predictors for urban interpersonal firearm violence rates using a broad set of community characteristics. The final list of 18 predictive covariates explain 77.8% of the variance in firearm violence rates, and are publicly available, facilitating their inclusion in analyses relating violence and health. This list includes the black isolation and segregation indices, rates of educational attainment, marital status, indicators of wealth and poverty, longitude, latitude, and temperature. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Cloud-based Approach to Medical NLP

PubMed Central

Chard, Kyle; Russell, Michael; Lussier, Yves A.; Mendonça, Eneida A; Silverstein, Jonathan C.

2011-01-01

Natural Language Processing (NLP) enables access to deep content embedded in medical texts. To date, NLP has not fulfilled its promise of enabling robust clinical encoding, clinical use, quality improvement, and research. We submit that this is in part due to poor accessibility, scalability, and flexibility of NLP systems. We describe here an approach and system which leverages cloud-based approaches such as virtual machines and Representational State Transfer (REST) to extract, process, synthesize, mine, compare/contrast, explore, and manage medical text data in a flexibly secure and scalable architecture. Available architectures in which our Smntx (pronounced as semantics) system can be deployed include: virtual machines in a HIPAA-protected hospital environment, brought up to run analysis over bulk data and destroyed in a local cloud; a commercial cloud for a large complex multi-institutional trial; and within other architectures such as caGrid, i2b2, or NHIN. PMID:22195072

Privacy Preserving RBF Kernel Support Vector Machine

PubMed Central

Xiong, Li; Ohno-Machado, Lucila

2014-01-01

Data sharing is challenging but important for healthcare research. Methods for privacy-preserving data dissemination based on the rigorous differential privacy standard have been developed but they did not consider the characteristics of biomedical data and make full use of the available information. This often results in too much noise in the final outputs. We hypothesized that this situation can be alleviated by leveraging a small portion of open-consented data to improve utility without sacrificing privacy. We developed a hybrid privacy-preserving differentially private support vector machine (SVM) model that uses public data and private data together. Our model leverages the RBF kernel and can handle nonlinearly separable cases. Experiments showed that this approach outperforms two baselines: (1) SVMs that only use public data, and (2) differentially private SVMs that are built from private data. Our method demonstrated very close performance metrics compared to nonprivate SVMs trained on the private data. PMID:25013805

A cloud-based approach to medical NLP.

PubMed

Chard, Kyle; Russell, Michael; Lussier, Yves A; Mendonça, Eneida A; Silverstein, Jonathan C

2011-01-01

Natural Language Processing (NLP) enables access to deep content embedded in medical texts. To date, NLP has not fulfilled its promise of enabling robust clinical encoding, clinical use, quality improvement, and research. We submit that this is in part due to poor accessibility, scalability, and flexibility of NLP systems. We describe here an approach and system which leverages cloud-based approaches such as virtual machines and Representational State Transfer (REST) to extract, process, synthesize, mine, compare/contrast, explore, and manage medical text data in a flexibly secure and scalable architecture. Available architectures in which our Smntx (pronounced as semantics) system can be deployed include: virtual machines in a HIPAA-protected hospital environment, brought up to run analysis over bulk data and destroyed in a local cloud; a commercial cloud for a large complex multi-institutional trial; and within other architectures such as caGrid, i2b2, or NHIN.

Tool path strategy and cutting process monitoring in intelligent machining

NASA Astrophysics Data System (ADS)

Chen, Ming; Wang, Chengdong; An, Qinglong; Ming, Weiwei

2018-06-01

Intelligent machining is a current focus in advanced manufacturing technology, and is characterized by high accuracy and efficiency. A central technology of intelligent machining—the cutting process online monitoring and optimization—is urgently needed for mass production. In this research, the cutting process online monitoring and optimization in jet engine impeller machining, cranio-maxillofacial surgery, and hydraulic servo valve deburring are introduced as examples of intelligent machining. Results show that intelligent tool path optimization and cutting process online monitoring are efficient techniques for improving the efficiency, quality, and reliability of machining.

Alumina additions may improve the damage tolerance of soft machined zirconia-based ceramics.

PubMed

Oilo, Marit; Tvinnereim, Helene M; Gjerdet, Nils Roar

2011-01-01

The aim of this study was to evaluate the damage tolerance of different zirconia-based materials. Bars of one hard machined and one soft machined dental zirconia and an experimental 95% zirconia 5% alumina ceramic were subjected to 100,000 stress cycles (n = 10), indented to provoke cracks on the tensile stress side (n = 10), and left untreated as controls (n = 10). The experimental material demonstrated a higher relative damage tolerance, with a 40% reduction compared to 68% for the hard machined zirconia and 84% for the soft machined zirconia.

Analysis of Machine Learning Techniques for Heart Failure Readmissions.

PubMed

Mortazavi, Bobak J; Downing, Nicholas S; Bucholz, Emily M; Dharmarajan, Kumar; Manhapra, Ajay; Li, Shu-Xia; Negahban, Sahand N; Krumholz, Harlan M

2016-11-01

The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates. © 2016 American Heart Association, Inc.

Method and apparatus for characterizing and enhancing the functional performance of machine tools

DOEpatents

Barkman, William E; Babelay, Jr., Edwin F; Smith, Kevin Scott; Assaid, Thomas S; McFarland, Justin T; Tursky, David A; Woody, Bethany; Adams, David

2013-04-30

Disclosed are various systems and methods for assessing and improving the capability of a machine tool. The disclosure applies to machine tools having at least one slide configured to move along a motion axis. Various patterns of dynamic excitation commands are employed to drive the one or more slides, typically involving repetitive short distance displacements. A quantification of a measurable merit of machine tool response to the one or more patterns of dynamic excitation commands is typically derived for the machine tool. Examples of measurable merits of machine tool performance include workpiece surface finish, and the ability to generate chips of the desired length.

Investigation of influence of errors of cutting machines with CNC on displacement trajectory accuracy of their actuating devices

NASA Astrophysics Data System (ADS)

Fedonin, O. N.; Petreshin, D. I.; Ageenko, A. V.

2018-03-01

In the article, the issue of increasing a CNC lathe accuracy by compensating for the static and dynamic errors of the machine is investigated. An algorithm and a diagnostic system for a CNC machine tool are considered, which allows determining the errors of the machine for their compensation. The results of experimental studies on diagnosing and improving the accuracy of a CNC lathe are presented.

Fractional lasers in dermatology--current status and recommendations.

PubMed

Goel, Apratim; Krupashankar, D S; Aurangabadkar, Sanjeev; Nischal, K C; Omprakash, H M; Mysore, Venkataram

2011-01-01

Fractional laser technology is a new emerging technology to improve scars, fine lines, dyspigmentation, striae and wrinkles. The technique is easy, safe to use and has been used effectively for several clinical and cosmetic indications in Indian skin. Different fractional laser machines, with different wavelengths, both ablative and non-ablative, are now available in India. A detailed understanding of the device being used is recommended. Common indications include resurfacing for acne, chickenpox and surgical scars, periorbital and perioral wrinkles, photoageing changes, facial dyschromias. The use of fractional lasers in stretch marks, melasma and other pigmentary conditions, dermatological conditions such as granuloma annulare has been reported. But further data are needed before adopting them for routine use in such conditions. Any qualified dermatologist may administer fractional laser treatment. He/ she should possess a Master's degree or diploma in dermatology and should have had specific hands-on training in lasers, either during postgraduation or later at a facility which routinely performs laser procedures under a competent dermatologist or plastic surgeon with experience and training in using lasers. Since parameters may vary with different systems, specific training tailored towards the concerned device at either the manufacturer's facility or at another center using the machine is recommended. Fractional lasers can be used in the dermatologist's minor procedure room for the above indications. Detailed counseling with respect to the treatment, desired effects and possible postoperative complications should be provided to the patient. The patient should be provided brochures to study and also adequate opportunity to seek information. A detailed consent form needs to be completed by the patient. Consent form should include information on the machine, possible postoperative course expected and postoperative complications. Preoperative photography should be carried out in all cases of resurfacing. A close-up front and 45-degree lateral photographs of both sides must be taken. There are different machines based on different technologies available. Choice parameters depend on the type of machine, location and type of lesion, and skin color. Physician needs to be familiar with these requirements before using the machine. Fractional laser treatment can be carried out under topical anesthesia with eutectic mixture of lidocaine and prilocaine. Some machines can be used without any anesthesia or only with topical cooling or cryospray. But for maximal patient comfort, a topical anesthetic prior to the procedure is recommended. Proper postoperative care is important in avoiding complications. Post-treatment edema and redness settle in a few hours to a few days. A sunscreen is mandatory, and emollients may be prescribed for the dryness and peeling that could occur.

New developments in operator protection for forest machines

Treesearch

Robert B. Rummer; S. Taylor; M. Veal

2003-01-01

Mechanization of forest operations ha greatly improved saftey of woods work. However, increasing use of machines has introduced new hazards that must be addressed. Two of these hazards are rollover of swing-type forestry machines (currently excluded from standard protection) and the hazard of thrown objects from cutting devices. Ongoing research projects are developing...

Learning About Climate and Atmospheric Models Through Machine Learning

NASA Astrophysics Data System (ADS)

Lucas, D. D.

2017-12-01

From the analysis of ensemble variability to improving simulation performance, machine learning algorithms can play a powerful role in understanding the behavior of atmospheric and climate models. To learn about model behavior, we create training and testing data sets through ensemble techniques that sample different model configurations and values of input parameters, and then use supervised machine learning to map the relationships between the inputs and outputs. Following this procedure, we have used support vector machines, random forests, gradient boosting and other methods to investigate a variety of atmospheric and climate model phenomena. We have used machine learning to predict simulation crashes, estimate the probability density function of climate sensitivity, optimize simulations of the Madden Julian oscillation, assess the impacts of weather and emissions uncertainty on atmospheric dispersion, and quantify the effects of model resolution changes on precipitation. This presentation highlights recent examples of our applications of machine learning to improve the understanding of climate and atmospheric models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein-Ligand Interactions.

PubMed

Li, Yang; Yang, Jianyi

2017-04-24

The prediction of protein-ligand binding affinity has recently been improved remarkably by machine-learning-based scoring functions. For example, using a set of simple descriptors representing the atomic distance counts, the RF-Score improves the Pearson correlation coefficient to about 0.8 on the core set of the PDBbind 2007 database, which is significantly higher than the performance of any conventional scoring function on the same benchmark. A few studies have been made to discuss the performance of machine-learning-based methods, but the reason for this improvement remains unclear. In this study, by systemically controlling the structural and sequence similarity between the training and test proteins of the PDBbind benchmark, we demonstrate that protein structural and sequence similarity makes a significant impact on machine-learning-based methods. After removal of training proteins that are highly similar to the test proteins identified by structure alignment and sequence alignment, machine-learning-based methods trained on the new training sets do not outperform the conventional scoring functions any more. On the contrary, the performance of conventional functions like X-Score is relatively stable no matter what training data are used to fit the weights of its energy terms.

Integrating artificial and human intelligence into tablet production process.

PubMed

Gams, Matjaž; Horvat, Matej; Ožek, Matej; Luštrek, Mitja; Gradišek, Anton

2014-12-01

We developed a new machine learning-based method in order to facilitate the manufacturing processes of pharmaceutical products, such as tablets, in accordance with the Process Analytical Technology (PAT) and Quality by Design (QbD) initiatives. Our approach combines the data, available from prior production runs, with machine learning algorithms that are assisted by a human operator with expert knowledge of the production process. The process parameters encompass those that relate to the attributes of the precursor raw materials and those that relate to the manufacturing process itself. During manufacturing, our method allows production operator to inspect the impacts of various settings of process parameters within their proven acceptable range with the purpose of choosing the most promising values in advance of the actual batch manufacture. The interaction between the human operator and the artificial intelligence system provides improved performance and quality. We successfully implemented the method on data provided by a pharmaceutical company for a particular product, a tablet, under development. We tested the accuracy of the method in comparison with some other machine learning approaches. The method is especially suitable for analyzing manufacturing processes characterized by a limited amount of data.

Downscaling Coarse Scale Microwave Soil Moisture Product using Machine Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, P.; Moradkhani, H.; Yan, H.

2016-12-01

Soil moisture (SM) is a key variable in partitioning and examining the global water-energy cycle, agricultural planning, and water resource management. It is also strongly coupled with climate change, playing an important role in weather forecasting and drought monitoring and prediction, flood modeling and irrigation management. Although satellite retrievals can provide an unprecedented information of soil moisture at a global-scale, the products might be inadequate for basin scale study or regional assessment. To improve the spatial resolution of SM, this work presents a novel approach based on Machine Learning (ML) technique that allows for downscaling of the satellite soil moisture to fine resolution. For this purpose, the SMAP L-band radiometer SM products were used and conditioned on the Variable Infiltration Capacity (VIC) model prediction to describe the relationship between the coarse and fine scale soil moisture data. The proposed downscaling approach was applied to a western US basin and the products were compared against the available SM data from in-situ gauge stations. The obtained results indicated a great potential of the machine learning technique to derive the fine resolution soil moisture information that is currently used for land data assimilation applications.

Analysis of machining accuracy during free form surface milling simulation for different milling strategies

NASA Astrophysics Data System (ADS)

Matras, A.; Kowalczyk, R.

2014-11-01

The analysis results of machining accuracy after the free form surface milling simulations (based on machining EN AW- 7075 alloys) for different machining strategies (Level Z, Radial, Square, Circular) are presented in the work. Particular milling simulations were performed using CAD/CAM Esprit software. The accuracy of obtained allowance is defined as a difference between the theoretical surface of work piece element (the surface designed in CAD software) and the machined surface after a milling simulation. The difference between two surfaces describes a value of roughness, which is as the result of tool shape mapping on the machined surface. Accuracy of the left allowance notifies in direct way a surface quality after the finish machining. Described methodology of usage CAD/CAM software can to let improve a time design of machining process for a free form surface milling by a 5-axis CNC milling machine with omitting to perform the item on a milling machine in order to measure the machining accuracy for the selected strategies and cutting data.

Advanced Direct-Drive Generator for Improved Availability of Oscillating Wave Surge Converter Power Generation Systems Final Technical Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Englebretson, Steven; Ouyang, Wen; Tschida, Colin

This report summarizes the activities conducted under the DOE-EERE funded project DE-EE0006400, where ABB Inc. (ABB), in collaboration with Texas A&M’s Advanced Electric Machines & Power Electronics (EMPE) Lab and Resolute Marine Energy (RME) designed, derisked, developed, and demonstrated a novel magnetically geared electrical generator for direct-drive, low-speed, high torque MHK applications The project objective was to investigate a novel and compact direct-drive electric generator and its system aspects that would enable elimination of hydraulic components in the Power Take-Off (PTO) of a Marine and Hydrokinetic (MHK) system with an oscillating wave surge converter (OWSC), thereby improving the availability ofmore » the MHK system. The scope of this project was limited to the development and dry lab demonstration of a low speed generator to enable future direct drive MHK systems.« less

Analysis of the influence of the milling machine throw-out plate surfacing and structure design on throwing material

NASA Astrophysics Data System (ADS)

Fang, Yuanbin; Sha, Hongwei; Yu, Yunmin; Chen, Bing

2018-03-01

Material composition, hardness and wear properties of the throw-out plate improved are analysed on a road milling machine. At the same time, analyse the tissue and performance of Fe based alloy named Fe60 cladding layer using the plasma surfacing method. And the original and improved throw-out plates are analysed throwing material effect by the dynamic analysis. Then the throw-out plate samples are verified. The results show that Fe60 powder is selected as surface strengthening material. By the improved structure, the hardness of the throw-out plate increases from 14.6HRC to 57.5HRC, and the wear resistance increases from 0.452g-1 to 16.393g-1. At the same time, it increases from 3263 to 3433 to fall into the collecting material number of milling machine. It provides important guidance for structure design and process design of the milling machine throw-out plate.

High-pressure coolant effect on the surface integrity of machining titanium alloy Ti-6Al-4V: a review

NASA Astrophysics Data System (ADS)

Liu, Wentao; Liu, Zhanqiang

2018-03-01

Machinability improvement of titanium alloy Ti-6Al-4V is a challenging work in academic and industrial applications owing to its low thermal conductivity, low elasticity modulus and high chemical affinity at high temperatures. Surface integrity of titanium alloys Ti-6Al-4V is prominent in estimating the quality of machined components. The surface topography (surface defects and surface roughness) and the residual stress induced by machining Ti-6Al-4V occupy pivotal roles for the sustainability of Ti-6Al-4V components. High-pressure coolant (HPC) is a potential choice in meeting the requirements for the manufacture and application of Ti-6Al-4V. This paper reviews the progress towards the improvements of Ti-6Al4V surface integrity under HPC. Various researches of surface integrity characteristics have been reported. In particularly, surface roughness, surface defects, residual stress as well as work hardening are investigated in order to evaluate the machined surface qualities. Several coolant parameters (including coolant type, coolant pressure and the injection position) deserve investigating to provide the guidance for a satisfied machined surface. The review also provides a clear roadmap for applications of HPC in machining Ti-6Al4V. Experimental studies and analysis are reviewed to better understand the surface integrity under HPC machining process. A distinct discussion has been presented regarding the limitations and highlights of the prospective for machining Ti-6Al4V under HPC.

Automated inspection and precision grinding of spiral bevel gears

NASA Technical Reports Server (NTRS)

Frint, Harold

1987-01-01

The results are presented of a four phase MM&T program to define, develop, and evaluate an improved inspection system for spiral bevel gears. The improved method utilizes a multi-axis coordinate measuring machine which maps the working flank of the tooth and compares it to nominal reference values stored in the machine's computer. A unique feature of the system is that corrective grinding machine settings can be automatically calculated and printed out when necessary to correct an errant tooth profile. This new method eliminates most of the subjective decision making involved in the present method, which compares contact patterns obtained when the gear set is run under light load in a rolling test machine. It produces a higher quality gear with significant inspection time and cost savings.

Responsive materials: A novel design for enhanced machine-augmented composites

PubMed Central

Bafekrpour, Ehsan; Molotnikov, Andrey; Weaver, James C.; Brechet, Yves; Estrin, Yuri

2014-01-01

The concept of novel responsive materials with a displacement conversion capability was further developed through the design of new machine-augmented composites (MACs). Embedded converter machines and MACs with improved geometry were designed and fabricated by multi-material 3D printing. This technique proved to be very effective in fabricating these novel composites with tuneable elastic moduli of the matrix and the embedded machines and excellent bonding between them. Substantial improvement in the displacement conversion efficiency of the new MACs over the existing ones was demonstrated. Also, the new design trebled the energy absorption of the MACs. Applications in energy absorbers as well as mechanical sensors and actuators are thus envisaged. A further type of MACs with conversion ability, viz. conversion of compressive displacements to torsional ones, was also proposed. PMID:24445490

Phenolic cutter for machining foam insulation

NASA Technical Reports Server (NTRS)

Blair, T. A.; Miller, A. C.; Price, B. W.; Stiles, W. S.

1970-01-01

Pre-pregged fiber glass is an efficient abrasive for machining polystyrene and polyurethane foams. It bonds easily to any cutter base made of aluminum, steel, or phenolic, is inexpensive, and is readily available.

Gelcasting compositions having improved drying characteristics and machinability

DOEpatents

Janney, Mark A.; Walls, Claudia A. H.

2001-01-01

A gelcasting composition has improved drying behavior, machinability and shelf life in the dried and unfired state. The composition includes an inorganic powder, solvent, monomer system soluble in the solvent, an initiator system for polymerizing the monomer system, and a plasticizer soluble in the solvent. Dispersants and other processing aides to control slurry properties can be added. The plasticizer imparts an ability to dry thick section parts, to store samples in the dried state without cracking under conditions of varying relative humidity, and to machine dry gelcast parts without cracking or chipping. A method of making gelcast parts is also disclosed.

Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model.

PubMed

An, Ji-Yong; Meng, Fan-Rong; You, Zhu-Hong; Chen, Xing; Yan, Gui-Ying; Hu, Ji-Pu

2016-10-01

Predicting protein-protein interactions (PPIs) is a challenging task and essential to construct the protein interaction networks, which is important for facilitating our understanding of the mechanisms of biological systems. Although a number of high-throughput technologies have been proposed to predict PPIs, there are unavoidable shortcomings, including high cost, time intensity, and inherently high false positive rates. For these reasons, many computational methods have been proposed for predicting PPIs. However, the problem is still far from being solved. In this article, we propose a novel computational method called RVM-BiGP that combines the relevance vector machine (RVM) model and Bi-gram Probabilities (BiGP) for PPIs detection from protein sequences. The major improvement includes (1) Protein sequences are represented using the Bi-gram probabilities (BiGP) feature representation on a Position Specific Scoring Matrix (PSSM), in which the protein evolutionary information is contained; (2) For reducing the influence of noise, the Principal Component Analysis (PCA) method is used to reduce the dimension of BiGP vector; (3) The powerful and robust Relevance Vector Machine (RVM) algorithm is used for classification. Five-fold cross-validation experiments executed on yeast and Helicobacter pylori datasets, which achieved very high accuracies of 94.57 and 90.57%, respectively. Experimental results are significantly better than previous methods. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM-BiGP method is significantly better than the SVM-based method. In addition, we achieved 97.15% accuracy on imbalance yeast dataset, which is higher than that of balance yeast dataset. The promising experimental results show the efficiency and robust of the proposed method, which can be an automatic decision support tool for future proteomics research. For facilitating extensive studies for future proteomics research, we developed a freely available web server called RVM-BiGP-PPIs in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at http://219.219.62.123:8888/BiGP/. © 2016 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

Enhanced ultrasonically assisted turning of a β-titanium alloy.

PubMed

Maurotto, Agostino; Muhammad, Riaz; Roy, Anish; Silberschmidt, Vadim V

2013-09-01

Although titanium alloys have outstanding mechanical properties such as high hot hardness, a good strength-to-weight ratio and high corrosion resistance; their low thermal conductivity, high chemical affinity to tool materials severely impair their machinability. Ultrasonically assisted machining (UAM) is an advanced machining technique, which has been shown to improve machinability of a β-titanium alloy, namely, Ti-15-3-3-3, when compared to conventional turning processes. Copyright © 2013 Elsevier B.V. All rights reserved.

National machine guarding program: Part 1. Machine safeguarding practices in small metal fabrication businesses

PubMed Central

Yamin, Samuel C.; Brosseau, Lisa M.; Xi, Min; Gordon, Robert; Most, Ivan G.; Stanley, Rodney

2015-01-01

Background Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures. Methods The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardized checklists to conduct a baseline inspection of machine‐related hazards in 221 business. Results Safeguards at the point of operation were missing or inadequate on 33% of machines. Safeguards for other mechanical hazards were missing on 28% of machines. Older machines were both widely used and less likely than newer machines to be properly guarded. Lockout/tagout procedures were posted at only 9% of machine workstations. Conclusions The NMGP demonstrates a need for improvement in many aspects of machine safety and lockout in small metal fabrication businesses. Am. J. Ind. Med. 58:1174–1183, 2015. © 2015 The Authors. American Journal of Industrial Medicine published by Wiley Periodicals, Inc. PMID:26332060

Development of black scattering coatings for space application (etude de traitements noirs diffusants pour application spatiale)

NASA Astrophysics Data System (ADS)

Mestreau-Garreau, Agnes; Pezant, Christian; Cousin, Bernard; Etcheto, Pierre; Otrio, Georges

2017-11-01

In the context of Research and Technology (R&T), studies have been performed on the coatings of vane edge in the 0.4 to 1 μm spectral range. The main purposes of the study were to improve the diffusing black coatings available on the market and to look for other diffusing black coatings. At the same time, we have also improved the machining technologies of vane edges. The characterisation (thermal tests, radiometric measurements, adhesion tests) of the most promising technologies has been carried out. The results have pointed out the stainless steel vanes with the edge obtained by polishing or by advanced grinding.

Secure Autonomous Automated Scheduling (SAAS). Rev. 1.1

NASA Technical Reports Server (NTRS)

Walke, Jon G.; Dikeman, Larry; Sage, Stephen P.; Miller, Eric M.

2010-01-01

This report describes network-centric operations, where a virtual mission operations center autonomously receives sensor triggers, and schedules space and ground assets using Internet-based technologies and service-oriented architectures. For proof-of-concept purposes, sensor triggers are received from the United States Geological Survey (USGS) to determine targets for space-based sensors. The Surrey Satellite Technology Limited (SSTL) Disaster Monitoring Constellation satellite, the UK-DMC, is used as the space-based sensor. The UK-DMC's availability is determined via machine-to-machine communications using SSTL's mission planning system. Access to/from the UK-DMC for tasking and sensor data is via SSTL's and Universal Space Network's (USN) ground assets. The availability and scheduling of USN's assets can also be performed autonomously via machine-to-machine communications. All communication, both on the ground and between ground and space, uses open Internet standards

Enhanced networked server management with random remote backups

NASA Astrophysics Data System (ADS)

Kim, Song-Kyoo

2003-08-01

In this paper, the model is focused on available server management in network environments. The (remote) backup servers are hooked up by VPN (Virtual Private Network) and replace broken main severs immediately. A virtual private network (VPN) is a way to use a public network infrastructure and hooks up long-distance servers within a single network infrastructure. The servers can be represent as "machines" and then the system deals with main unreliable and random auxiliary spare (remote backup) machines. When the system performs a mandatory routine maintenance, auxiliary machines are being used for backups during idle periods. Unlike other existing models, the availability of auxiliary machines is changed for each activation in this enhanced model. Analytically tractable results are obtained by using several mathematical techniques and the results are demonstrated in the framework of optimized networked server allocation problems.

Documentation for the machine-readable version of the revised new general catalogue of nonstellar astronomical objects

NASA Technical Reports Server (NTRS)

Warren, W. H., Jr.

1982-01-01

The contents and format of the machine-readable version of the cataloque distributed by the Astronomical Data Center are described. Coding for the various scales and abbreviations used in the catalogue are tabulated and certain revisions to the machine version made to improve storage efficiency and notation are discussed.

Nanocomposites for Machining Tools

PubMed Central

Loginov, Pavel; Mishnaevsky, Leon; Levashov, Evgeny

2017-01-01

Machining tools are used in many areas of production. To a considerable extent, the performance characteristics of the tools determine the quality and cost of obtained products. The main materials used for producing machining tools are steel, cemented carbides, ceramics and superhard materials. A promising way to improve the performance characteristics of these materials is to design new nanocomposites based on them. The application of micromechanical modeling during the elaboration of composite materials for machining tools can reduce the financial and time costs for development of new tools, with enhanced performance. This article reviews the main groups of nanocomposites for machining tools and their performance. PMID:29027926

Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units

PubMed Central

McCoy, Andrea

2017-01-01

Introduction Sepsis management is a challenge for hospitals nationwide, as severe sepsis carries high mortality rates and costs the US healthcare system billions of dollars each year. It has been shown that early intervention for patients with severe sepsis and septic shock is associated with higher rates of survival. The Cape Regional Medical Center (CRMC) aimed to improve sepsis-related patient outcomes through a revised sepsis management approach. Methods In collaboration with Dascena, CRMC formed a quality improvement team to implement a machine learning-based sepsis prediction algorithm to identify patients with sepsis earlier. Previously, CRMC assessed all patients for sepsis using twice-daily systemic inflammatory response syndrome screenings, but desired improvements. The quality improvement team worked to implement a machine learning-based algorithm, collect and incorporate feedback, and tailor the system to current hospital workflow. Results Relative to the pre-implementation period, the post-implementation period sepsis-related in-hospital mortality rate decreased by 60.24%, sepsis-related hospital length of stay decreased by 9.55% and sepsis-related 30-day readmission rate decreased by 50.14%. Conclusion The machine learning-based sepsis prediction algorithm improved patient outcomes at CRMC. PMID:29450295

Design and Analysis of Linear Fault-Tolerant Permanent-Magnet Vernier Machines

PubMed Central

Xu, Liang; Liu, Guohai; Du, Yi; Liu, Hu

2014-01-01

This paper proposes a new linear fault-tolerant permanent-magnet (PM) vernier (LFTPMV) machine, which can offer high thrust by using the magnetic gear effect. Both PMs and windings of the proposed machine are on short mover, while the long stator is only manufactured from iron. Hence, the proposed machine is very suitable for long stroke system applications. The key of this machine is that the magnetizer splits the two movers with modular and complementary structures. Hence, the proposed machine offers improved symmetrical and sinusoidal back electromotive force waveform and reduced detent force. Furthermore, owing to the complementary structure, the proposed machine possesses favorable fault-tolerant capability, namely, independent phases. In particular, differing from the existing fault-tolerant machines, the proposed machine offers fault tolerance without sacrificing thrust density. This is because neither fault-tolerant teeth nor the flux-barriers are adopted. The electromagnetic characteristics of the proposed machine are analyzed using the time-stepping finite-element method, which verifies the effectiveness of the theoretical analysis. PMID:24982959

Design and analysis of linear fault-tolerant permanent-magnet vernier machines.

PubMed

Xu, Liang; Ji, Jinghua; Liu, Guohai; Du, Yi; Liu, Hu

2014-01-01

This paper proposes a new linear fault-tolerant permanent-magnet (PM) vernier (LFTPMV) machine, which can offer high thrust by using the magnetic gear effect. Both PMs and windings of the proposed machine are on short mover, while the long stator is only manufactured from iron. Hence, the proposed machine is very suitable for long stroke system applications. The key of this machine is that the magnetizer splits the two movers with modular and complementary structures. Hence, the proposed machine offers improved symmetrical and sinusoidal back electromotive force waveform and reduced detent force. Furthermore, owing to the complementary structure, the proposed machine possesses favorable fault-tolerant capability, namely, independent phases. In particular, differing from the existing fault-tolerant machines, the proposed machine offers fault tolerance without sacrificing thrust density. This is because neither fault-tolerant teeth nor the flux-barriers are adopted. The electromagnetic characteristics of the proposed machine are analyzed using the time-stepping finite-element method, which verifies the effectiveness of the theoretical analysis.

An Evaluation of Online Machine Translation of Arabic into English News Headlines: Implications on Students' Learning Purposes

ERIC Educational Resources Information Center

Kadhim, Kais A.; Habeeb, Luwaytha S.; Sapar, Ahmad Arifin; Hussin, Zaharah; Abdullah, Muhammad Ridhuan Tony Lim

2013-01-01

Nowadays, online Machine Translation (MT) is used widely with translation software, such as Google and Babylon, being easily available and downloadable. This study aims to test the translation quality of these two machine systems in translating Arabic news headlines into English. 40 Arabic news headlines were selected from three online sources,…

Advancing Research in Second Language Writing through Computational Tools and Machine Learning Techniques: A Research Agenda

ERIC Educational Resources Information Center

Crossley, Scott A.

2013-01-01

This paper provides an agenda for replication studies focusing on second language (L2) writing and the use of natural language processing (NLP) tools and machine learning algorithms. Specifically, it introduces a range of the available NLP tools and machine learning algorithms and demonstrates how these could be used to replicate seminal studies…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bates, Robert; McConnell, Elizabeth

Machining methods across many industries generally require multiple operations to machine and process advanced materials, features with micron precision, and complex shapes. The resulting multiple machining platforms can significantly affect manufacturing cycle time and the precision of the final parts, with a resultant increase in cost and energy consumption. Ultrafast lasers represent a transformative and disruptive technology that removes material with micron precision and in a single step manufacturing process. Such precision results from athermal ablation without modification or damage to the remaining material which is the key differentiator between ultrafast laser technologies and traditional laser technologies or mechanical processes.more » Athermal ablation without modification or damage to the material eliminates post-processing or multiple manufacturing steps. Combined with the appropriate technology to control the motion of the work piece, ultrafast lasers are excellent candidates to provide breakthrough machining capability for difficult-to-machine materials. At the project onset in early 2012, the project team recognized that substantial effort was necessary to improve the application of ultrafast laser and precise motion control technologies (for micromachining difficult-to-machine materials) to further the aggregate throughput and yield improvements over conventional machining methods. The project described in this report advanced these leading-edge technologies thru the development and verification of two platforms: a hybrid enhanced laser chassis and a multi-application testbed.« less

A hybrid flowshop scheduling model considering dedicated machines and lot-splitting for the solar cell industry

NASA Astrophysics Data System (ADS)

Wang, Li-Chih; Chen, Yin-Yann; Chen, Tzu-Li; Cheng, Chen-Yang; Chang, Chin-Wei

2014-10-01

This paper studies a solar cell industry scheduling problem, which is similar to traditional hybrid flowshop scheduling (HFS). In a typical HFS problem, the allocation of machine resources for each order should be scheduled in advance. However, the challenge in solar cell manufacturing is the number of machines that can be adjusted dynamically to complete the job. An optimal production scheduling model is developed to explore these issues, considering the practical characteristics, such as hybrid flowshop, parallel machine system, dedicated machines, sequence independent job setup times and sequence dependent job setup times. The objective of this model is to minimise the makespan and to decide the processing sequence of the orders/lots in each stage, lot-splitting decisions for the orders and the number of machines used to satisfy the demands in each stage. From the experimental results, lot-splitting has significant effect on shortening the makespan, and the improvement effect is influenced by the processing time and the setup time of orders. Therefore, the threshold point to improve the makespan can be identified. In addition, the model also indicates that more lot-splitting approaches, that is, the flexibility of allocating orders/lots to machines is larger, will result in a better scheduling performance.

Theoretical and experimental research on machine tool servo system for ultra-precision position compensation on CNC lathe

NASA Astrophysics Data System (ADS)

Ma, Zhichao; Hu, Leilei; Zhao, Hongwei; Wu, Boda; Peng, Zhenxing; Zhou, Xiaoqin; Zhang, Hongguo; Zhu, Shuai; Xing, Lifeng; Hu, Huang

2010-08-01

The theories and techniques for improving machining accuracy via position control of diamond tool's tip and raising resolution of cutting depth on precise CNC lathes have been extremely focused on. A new piezo-driven ultra-precision machine tool servo system is designed and tested to improve manufacturing accuracy of workpiece. The mathematical model of machine tool servo system is established and the finite element analysis is carried out on parallel plate flexure hinges. The output position of diamond tool's tip driven by the machine tool servo system is tested via a contact capacitive displacement sensor. Proportional, integral, derivative (PID) feedback is also implemented to accommodate and compensate dynamical change owing cutting forces as well as the inherent non-linearity factors of the piezoelectric stack during cutting process. By closed loop feedback controlling strategy, the tracking error is limited to 0.8 μm. Experimental results have shown the proposed machine tool servo system could provide a tool positioning resolution of 12 nm, which is much accurate than the inherent CNC resolution magnitude. The stepped shaft of aluminum specimen with a step increment of cutting depth of 1 μm is tested, and the obtained contour illustrates the displacement command output from controller is accurately and real-time reflected on the machined part.

Electronic vending machines for dispensing rapid HIV self-testing kits: a case study.

PubMed

Young, Sean D; Klausner, Jeffrey; Fynn, Risa; Bolan, Robert

2014-02-01

This short report evaluates the feasibility of using electronic vending machines for dispensing oral, fluid, rapid HIV self-testing kits in Los Angeles County. Feasibility criteria that needed to be addressed were defined as: (1) ability to find a manufacturer who would allow dispensing of HIV testing kits and could fit them to the dimensions of a vending machine, (2) ability to identify and address potential initial obstacles, trade-offs in choosing a machine location, and (3) ability to gain community approval for implementing this approach in a community setting. To address these issues, we contracted a vending machine company who could supply a customized, Internet-enabled machine that could dispense HIV kits and partnered with a local health center available to host the machine onsite and provide counseling to participants, if needed. Vending machines appear to be feasible technologies that can be used to distribute HIV testing kits.

Electronic vending machines for dispensing rapid HIV self-testing kits: A case study

PubMed Central

Young, Sean D.; Klausner, Jeffrey; Fynn, Risa; Bolan, Robert

2014-01-01

This short report evaluates the feasibility of using electronic vending machines for dispensing oral, fluid, rapid HIV-self testing kits in Los Angeles County. Feasibility criteria that needed to be addressed were defined as: 1) ability to find a manufacturer who would allow dispensing of HIV testing kits and could fit them to the dimensions of a vending machine, 2) ability to identify and address potential initial obstacles, trade-offs in choosing a machine location, and 3) ability to gain community approval for implementing this approach in a community setting. To address these issues, we contracted a vending machine company who could supply a customized, Internet-enabled machine that could dispense HIV kits and partnered with a local health center available to host the machine onsite and provide counseling to participants, if needed. Vending machines appear to be feasible technologies that can be used to distribute HIV testing kits. PMID:23777528

Energy Saving Melting and Revert Reduction Technology: Aging of Graphitic Cast Irons and Machinability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Richards, Von L.

2012-09-19

The objective of this task was to determine whether ductile iron and compacted graphite iron exhibit age strengthening to a statistically significant extent. Further, this effort identified the mechanism by which gray iron age strengthens and the mechanism by which age-strengthening improves the machinability of gray cast iron. These results were then used to determine whether age strengthening improves the machinability of ductile iron and compacted graphite iron alloys in order to develop a predictive model of alloy factor effects on age strengthening. The results of this work will lead to reduced section sizes, and corresponding weight and energy savings.more » Improved machinability will reduce scrap and enhance casting marketability. Technical Conclusions: Age strengthening was demonstrated to occur in gray iron ductile iron and compacted graphite iron. Machinability was demonstrated to be improved by age strengthening when free ferrite was present in the microstructure, but not in a fully pearlitic microstructure. Age strengthening only occurs when there is residual nitrogen in solid solution in the Ferrite, whether the ferrite is free ferrite or the ferrite lamellae within pearlite. Age strengthening can be accelerated by Mn at about 0.5% in excess of the Mn/S balance Estimated energy savings over ten years is 13.05 trillion BTU, based primarily on yield improvement and size reduction of castings for equivalent service. Also it is estimated that the heavy truck end use of lighter castings for equivalent service requirement will result in a diesel fuel energy savings of 131 trillion BTU over ten years.« less

Color machine vision in industrial process control: case limestone mine

NASA Astrophysics Data System (ADS)

Paernaenen, Pekka H. T.; Lemstrom, Guy F.; Koskinen, Seppo

1994-11-01

An optical sorter technology has been developed to improve profitability of a mine by using color line scan machine vision technology. The new technology adapted longers the expected life time of the limestone mine and improves its efficiency. Also the project has proved that color line scan technology of today can successfully be applied to industrial use in harsh environments.

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

PubMed Central

Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils’ carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms—including the model tuning and predictor selection—were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models’ predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction. PMID:27128736

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

PubMed

Ließ, Mareike; Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

Construction machine control guidance implementation strategy.

DOT National Transportation Integrated Search

2010-07-01

Machine Controlled Guidance (MCG) technology may be used in roadway and bridge construction to improve construction efficiencies, potentially resulting in reduced project costs and accelerated schedules. The technology utilizes a Global Positioning S...

Ryan King | NREL

Science.gov Websites

research focuses on optimization and machine learning applied to complex energy systems and turbulent flows techniques to improve wind plant design and controls and developed a new data-driven machine learning closure

A survey of machine readable data bases

NASA Technical Reports Server (NTRS)

Matlock, P.

1981-01-01

Forty-two of the machine readable data bases available to the technologist and researcher in the natural sciences and engineering are described and compared with the data bases and date base services offered by NASA.

Application of numerical grid generation for improved CFD analysis of multiphase screw machines

NASA Astrophysics Data System (ADS)

Rane, S.; Kovačević, A.

2017-08-01

Algebraic grid generation is widely used for discretization of the working domain of twin screw machines. Algebraic grid generation is fast and has good control over the placement of grid nodes. However, the desired qualities of grid which should be able to handle multiphase flows such as oil injection, may be difficult to achieve at times. In order to obtain fast solution of multiphase screw machines, it is important to further improve the quality and robustness of the computational grid. In this paper, a deforming grid of a twin screw machine is generated using algebraic transfinite interpolation to produce initial mesh upon which an elliptic partial differential equations (PDE) of the Poisson’s form is solved numerically to produce smooth final computational mesh. The quality of numerical cells and their distribution obtained by the differential method is greatly improved. In addition, a similar procedure was introduced to fully smoothen the transition of the partitioning rack curve between the rotors thus improving continuous movement of grid nodes and in turn improve robustness and speed of the Computational Fluid Dynamic (CFD) solver. Analysis of an oil injected twin screw compressor is presented to compare the improvements in grid quality factors in the regions of importance such as interlobe space, radial tip and the core of the rotor. The proposed method that combines algebraic and differential grid generation offer significant improvement in grid quality and robustness of numerical solution.

Health Promotion and Healthier Products Increase Vending Purchases: A Randomized Factorial Trial.

PubMed

Hua, Sophia V; Kimmel, Lisa; Van Emmenes, Michael; Taherian, Rafi; Remer, Geraldine; Millman, Adam; Ickovics, Jeannette R

2017-07-01

The current food environment has a high prevalence of nutrient-sparse foods and beverages, most starkly seen in vending machine offerings. There are currently few studies that explore different interventions that might lead to healthier vending machine purchases. To examine how healthier product availability, price reductions, and/or promotional signs affect sales and revenue of snack and beverage vending machines. A 2×2×2 factorial randomized controlled trial was conducted. Students, staff, and employees on a university campus. All co-located snack and beverage vending machines (n=56, 28 snack and 28 beverage) were randomized into one of eight conditions: availability of healthier products and/or 25% price reduction for healthier items and/or promotional signs on machines. Aggregate sales and revenue data for the 5-month study period (February to June 2015) were compared with data from the same months 1 year prior. Analyses were conducted July 2015. The change in units sold and revenue between February through June 2014 and 2015. Linear regression models (main effects and interaction effects) and t test analyses were performed. The interaction between healthier product guidelines and promotional signs in snack vending machines documented increased revenue (P<0.05). Beverage machines randomized to meet healthier product guidelines documented increased units sold (P<0.05) with no revenue change. Price reductions alone had no effect, nor were there any effects for the three-way interaction of the factors. Examining top-selling products for all vending machines combined, pre- to postintervention, we found an overall shift to healthier purchasing. When healthier vending snacks are available, promotional signs are also important to ensure consumers purchase those items in greater amounts. Mitigating potential loss in profits is essential for sustainability of a healthier food environment. Copyright © 2017 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Predicting drug-target interactions using restricted Boltzmann machines.

PubMed

Wang, Yuhao; Zeng, Jianyang

2013-07-01

In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are available at Bioinformatics online.

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

PubMed Central

2010-01-01

Background Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows. PMID:21034480

An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features.

PubMed

Nandi, Sutanu; Subramanian, Abhishek; Sarkar, Ram Rup

2017-07-25

Prediction of essential genes helps to identify a minimal set of genes that are absolutely required for the appropriate functioning and survival of a cell. The available machine learning techniques for essential gene prediction have inherent problems, like imbalanced provision of training datasets, biased choice of the best model for a given balanced dataset, choice of a complex machine learning algorithm, and data-based automated selection of biologically relevant features for classification. Here, we propose a simple support vector machine-based learning strategy for the prediction of essential genes in Escherichia coli K-12 MG1655 metabolism that integrates a non-conventional combination of an appropriate sample balanced training set, a unique organism-specific genotype, phenotype attributes that characterize essential genes, and optimal parameters of the learning algorithm to generate the best machine learning model (the model with the highest accuracy among all the models trained for different sample training sets). For the first time, we also introduce flux-coupled metabolic subnetwork-based features for enhancing the classification performance. Our strategy proves to be superior as compared to previous SVM-based strategies in obtaining a biologically relevant classification of genes with high sensitivity and specificity. This methodology was also trained with datasets of other recent supervised classification techniques for essential gene classification and tested using reported test datasets. The testing accuracy was always high as compared to the known techniques, proving that our method outperforms known methods. Observations from our study indicate that essential genes are conserved among homologous bacterial species, demonstrate high codon usage bias, GC content and gene expression, and predominantly possess a tendency to form physiological flux modules in metabolism.

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

This factsheet describes a project that developed and demonstrated a new manufacturing-informed design framework that utilizes advanced multi-scale, physics-based process modeling to dramatically improve manufacturing productivity and quality in machining operations while reducing the cost of machined components.

Machine tool task force

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sutton, G.P.

1980-10-22

The Machine Tool Task Force (MTTF) is a multidisciplined team of international experts, whose mission was to investigate the state of the art of machine tool technology, to identify promising future directions of that technology for both the US government and private industry, and to disseminate the findings of its research in a conference and through the publication of a final report. MTTF was a two and one-half year effort that involved the participation of 122 experts in the specialized technologies of machine tools and in the management of machine tool operations. The scope of the MTTF was limited tomore » cutting-type or material-removal-type machine tools, because they represent the major share and value of all machine tools now installed or being built. The activities of the MTTF and the technical, commercial and economic signifiance of recommended activities for improving machine tool technology are discussed. (LCL)« less

Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information.

PubMed

An, Ji-Yong; Zhang, Lei; Zhou, Yong; Zhao, Yu-Jun; Wang, Da-Fu

2017-08-18

Self-interactions Proteins (SIPs) is important for their biological activity owing to the inherent interaction amongst their secondary structures or domains. However, due to the limitations of experimental Self-interactions detection, one major challenge in the study of prediction SIPs is how to exploit computational approaches for SIPs detection based on evolutionary information contained protein sequence. In the work, we presented a novel computational approach named WELM-LAG, which combined the Weighed-Extreme Learning Machine (WELM) classifier with Local Average Group (LAG) to predict SIPs based on protein sequence. The major improvement of our method lies in presenting an effective feature extraction method used to represent candidate Self-interactions proteins by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM); and then employing a reliable and robust WELM classifier to carry out classification. In addition, the Principal Component Analysis (PCA) approach is used to reduce the impact of noise. The WELM-LAG method gave very high average accuracies of 92.94 and 96.74% on yeast and human datasets, respectively. Meanwhile, we compared it with the state-of-the-art support vector machine (SVM) classifier and other existing methods on human and yeast datasets, respectively. Comparative results indicated that our approach is very promising and may provide a cost-effective alternative for predicting SIPs. In addition, we developed a freely available web server called WELM-LAG-SIPs to predict SIPs. The web server is available at http://219.219.62.123:8888/WELMLAG/ .

Comparison of different wind data interpolation methods for a region with complex terrain in Central Asia

NASA Astrophysics Data System (ADS)

Reinhardt, Katja; Samimi, Cyrus

2018-01-01

While climatological data of high spatial resolution are largely available in most developed countries, the network of climatological stations in many other regions of the world still constitutes large gaps. Especially for those regions, interpolation methods are important tools to fill these gaps and to improve the data base indispensible for climatological research. Over the last years, new hybrid methods of machine learning and geostatistics have been developed which provide innovative prospects in spatial predictive modelling. This study will focus on evaluating the performance of 12 different interpolation methods for the wind components \\overrightarrow{u} and \\overrightarrow{v} in a mountainous region of Central Asia. Thereby, a special focus will be on applying new hybrid methods on spatial interpolation of wind data. This study is the first evaluating and comparing the performance of several of these hybrid methods. The overall aim of this study is to determine whether an optimal interpolation method exists, which can equally be applied for all pressure levels, or whether different interpolation methods have to be used for the different pressure levels. Deterministic (inverse distance weighting) and geostatistical interpolation methods (ordinary kriging) were explored, which take into account only the initial values of \\overrightarrow{u} and \\overrightarrow{v} . In addition, more complex methods (generalized additive model, support vector machine and neural networks as single methods and as hybrid methods as well as regression-kriging) that consider additional variables were applied. The analysis of the error indices revealed that regression-kriging provided the most accurate interpolation results for both wind components and all pressure heights. At 200 and 500 hPa, regression-kriging is followed by the different kinds of neural networks and support vector machines and for 850 hPa it is followed by the different types of support vector machine and ordinary kriging. Overall, explanatory variables improve the interpolation results.

Hypothermic machine perfusion in kidney transplantation.

PubMed

De Deken, Julie; Kocabayoglu, Peri; Moers, Cyril

2016-06-01

This article summarizes novel developments in hypothermic machine perfusion (HMP) as an organ preservation modality for kidneys recovered from deceased donors. HMP has undergone a renaissance in recent years. This renewed interest has arisen parallel to a shift in paradigms; not only optimal preservation of an often marginal quality graft is required, but also improved graft function and tools to predict the latter are expected from HMP. The focus of attention in this field is currently drawn to the protection of endothelial integrity by means of additives to the perfusion solution, improvement of the HMP solution, choice of temperature, duration of perfusion, and machine settings. HMP may offer the opportunity to assess aspects of graft viability before transplantation, which can potentially aid preselection of grafts based on characteristics such as perfusate biomarkers, as well as measurement of machine perfusion dynamics parameters. HMP has proven to be beneficial as a kidney preservation method for all types of renal grafts, most notably those retrieved from extended criteria donors. Large numbers of variables during HMP, such as duration, machine settings and additives to the perfusion solution are currently being investigated to improve renal function and graft survival. In addition, the search for biomarkers has become a focus of attention to predict graft function posttransplant.

An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation.

PubMed

Nguyen, Huu-Tho; Md Dawal, Siti Zawiah; Nukman, Yusoff; Aoyama, Hideki; Case, Keith

2015-01-01

Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment.

Assessing Continuous Operator Workload With a Hybrid Scaffolded Neuroergonomic Modeling Approach.

PubMed

Borghetti, Brett J; Giametta, Joseph J; Rusnock, Christina F

2017-02-01

We aimed to predict operator workload from neurological data using statistical learning methods to fit neurological-to-state-assessment models. Adaptive systems require real-time mental workload assessment to perform dynamic task allocations or operator augmentation as workload issues arise. Neuroergonomic measures have great potential for informing adaptive systems, and we combine these measures with models of task demand as well as information about critical events and performance to clarify the inherent ambiguity of interpretation. We use machine learning algorithms on electroencephalogram (EEG) input to infer operator workload based upon Improved Performance Research Integration Tool workload model estimates. Cross-participant models predict workload of other participants, statistically distinguishing between 62% of the workload changes. Machine learning models trained from Monte Carlo resampled workload profiles can be used in place of deterministic workload profiles for cross-participant modeling without incurring a significant decrease in machine learning model performance, suggesting that stochastic models can be used when limited training data are available. We employed a novel temporary scaffold of simulation-generated workload profile truth data during the model-fitting process. A continuous workload profile serves as the target to train our statistical machine learning models. Once trained, the workload profile scaffolding is removed and the trained model is used directly on neurophysiological data in future operator state assessments. These modeling techniques demonstrate how to use neuroergonomic methods to develop operator state assessments, which can be employed in adaptive systems.

New numerical approach for the modelling of machining applied to aeronautical structural parts

NASA Astrophysics Data System (ADS)

Rambaud, Pierrick; Mocellin, Katia

2018-05-01

The manufacturing of aluminium alloy structural aerospace parts involves several steps: forming (rolling, forging …etc), heat treatments and machining. Before machining, the manufacturing processes have embedded residual stresses into the workpiece. The final geometry is obtained during this last step, when up to 90% of the raw material volume is removed by machining. During this operation, the mechanical equilibrium of the part is in constant evolution due to the redistribution of the initial stresses. This redistribution is the main cause for workpiece deflections during machining and for distortions - after unclamping. Both may lead to non-conformity of the part regarding the geometrical and dimensional specifications and therefore to rejection of the part or additional conforming steps. In order to improve the machining accuracy and the robustness of the process, the effect of the residual stresses has to be considered for the definition of the machining process plan and even in the geometrical definition of the part. In this paper, the authors present two new numerical approaches concerning the modelling of machining of aeronautical structural parts. The first deals with the use of an immersed volume framework to model the cutting step, improving the robustness and the quality of the resulting mesh compared to the previous version. The second is about the mechanical modelling of the machining problem. The authors thus show that in the framework of rolled aluminium parts the use of a linear elasticity model is functional in the finite element formulation and promising regarding the reduction of computation times.

Feasibility of retrofitting a university library with active workstations to reduce sedentary behavior.

PubMed

Maeda, Hotaka; Quartiroli, Alessandro; Vos, Paul W; Carr, Lucas J; Mahar, Matthew T

2014-05-01

Libraries are an inherently sedentary environment, but are an understudied setting for sedentary behavior interventions. To investigate the feasibility of incorporating portable pedal machines in a university library to reduce sedentary behaviors. The 11-week intervention targeted students at a university library. Thirteen portable pedal machines were placed in the library. Four forms of prompts (e-mail, library website, advertisement monitors, and poster) encouraging pedal machine use were employed during the first 4 weeks. Pedal machine use was measured via automatic timers on each machine and momentary time sampling. Daily library visits were measured using a gate counter. Individualized data were measured by survey. Data were collected in fall 2012 and analyzed in 2013. Mean (SD) cumulative pedal time per day was 95.5 (66.1) minutes. One or more pedal machines were observed being used 15% of the time (N=589). Pedal machines were used at least once by 7% of students (n=527). Controlled for gate count, no linear change of pedal machine use across days was found (b=-0.1 minutes, p=0.75) and the presence of the prompts did not change daily pedal time (p=0.63). Seven of eight items that assessed attitudes toward the intervention supported intervention feasibility (p<0.05). The unique non-individualized approach of retrofitting a library with pedal machines to reduce sedentary behavior seems feasible, but improvement of its effectiveness is needed. This study could inform future studies aimed at reshaping traditionally sedentary settings to improve public health. Copyright © 2014 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Multiple Cylinder Free-Piston Stirling Machinery

NASA Astrophysics Data System (ADS)

Berchowitz, David M.; Kwon, Yong-Rak

In order to improve the specific power of piston-cylinder type machinery, there is a point in capacity or power where an advantage accrues with increasing number of piston-cylinder assemblies. In the case of Stirling machinery where primary energy is transferred across the casing wall of the machine, this consideration is even more important. This is due primarily to the difference in scaling of basic power and the required heat transfer. Heat transfer is found to be progressively limited as the size of the machine increases. Multiple cylinder machines tend to preserve the surface area to volume ratio at more favorable levels. In addition, the spring effect of the working gas in the so-called alpha configuration is often sufficient to provide a high frequency resonance point that improves the specific power. There are a number of possible multiple cylinder configurations. The simplest is an opposed pair of piston-displacer machines (beta configuration). A three-cylinder machine requires stepped pistons to obtain proper volume phase relationships. Four to six cylinder configurations are also possible. A small demonstrator inline four cylinder alpha machine has been built to demonstrate both cooling operation and power generation. Data from this machine verifies theoretical expectations and is used to extrapolate the performance of future machines. Vibration levels are discussed and it is argued that some multiple cylinder machines have no linear component to the casing vibration but may have a nutating couple. Example applications are discussed ranging from general purpose coolers, computer cooling, exhaust heat power extraction and some high power engines.

Machine learning in autistic spectrum disorder behavioral research: A review and ways forward.

PubMed

Thabtah, Fadi

2018-02-13

Autistic Spectrum Disorder (ASD) is a mental disorder that retards acquisition of linguistic, communication, cognitive, and social skills and abilities. Despite being diagnosed with ASD, some individuals exhibit outstanding scholastic, non-academic, and artistic capabilities, in such cases posing a challenging task for scientists to provide answers. In the last few years, ASD has been investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning to improve diagnostic timing, precision, and quality. Machine learning is a multidisciplinary research topic that employs intelligent techniques to discover useful concealed patterns, which are utilized in prediction to improve decision making. Machine learning techniques such as support vector machines, decision trees, logistic regressions, and others, have been applied to datasets related to autism in order to construct predictive models. These models claim to enhance the ability of clinicians to provide robust diagnoses and prognoses of ASD. However, studies concerning the use of machine learning in ASD diagnosis and treatment suffer from conceptual, implementation, and data issues such as the way diagnostic codes are used, the type of feature selection employed, the evaluation measures chosen, and class imbalances in data among others. A more serious claim in recent studies is the development of a new method for ASD diagnoses based on machine learning. This article critically analyses these recent investigative studies on autism, not only articulating the aforementioned issues in these studies but also recommending paths forward that enhance machine learning use in ASD with respect to conceptualization, implementation, and data. Future studies concerning machine learning in autism research are greatly benefitted by such proposals.

Students' perspectives on promoting healthful food choices from campus vending machines: a qualitative interview study.

PubMed

Ali, Habiba I; Jarrar, Amjad H; Abo-El-Enen, Mostafa; Al Shamsi, Mariam; Al Ashqar, Huda

2015-05-28

Increasing the healthfulness of campus food environments is an important step in promoting healthful food choices among college students. This study explored university students' suggestions on promoting healthful food choices from campus vending machines. It also examined factors influencing students' food choices from vending machines. Peer-led semi-structured individual interviews were conducted with 43 undergraduate students (33 females and 10 males) recruited from students enrolled in an introductory nutrition course in a large national university in the United Arab Emirates. Interviews were audiotaped, transcribed, and coded to generate themes using N-Vivo software. Accessibility, peer influence, and busy schedules were the main factors influencing students' food choices from campus vending machines. Participants expressed the need to improve the nutritional quality of the food items sold in the campus vending machines. Recommendations for students' nutrition educational activities included placing nutrition tips on or beside the vending machines and using active learning methods, such as competitions on nutrition knowledge. The results of this study have useful applications in improving the campus food environment and nutrition education opportunities at the university to assist students in making healthful food choices.

Improving peak detection in high-resolution LC/MS metabolomics data using preexisting knowledge and machine learning approach.

PubMed

Yu, Tianwei; Jones, Dean P

2014-10-15

Peak detection is a key step in the preprocessing of untargeted metabolomics data generated from high-resolution liquid chromatography-mass spectrometry (LC/MS). The common practice is to use filters with predetermined parameters to select peaks in the LC/MS profile. This rigid approach can cause suboptimal performance when the choice of peak model and parameters do not suit the data characteristics. Here we present a method that learns directly from various data features of the extracted ion chromatograms (EICs) to differentiate between true peak regions from noise regions in the LC/MS profile. It utilizes the knowledge of known metabolites, as well as robust machine learning approaches. Unlike currently available methods, this new approach does not assume a parametric peak shape model and allows maximum flexibility. We demonstrate the superiority of the new approach using real data. Because matching to known metabolites entails uncertainties and cannot be considered a gold standard, we also developed a probabilistic receiver-operating characteristic (pROC) approach that can incorporate uncertainties. The new peak detection approach is implemented as part of the apLCMS package available at http://web1.sph.emory.edu/apLCMS/ CONTACT: tyu8@emory.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Developing a Global Database of Historic Flood Events to Support Machine Learning Flood Prediction in Google Earth Engine

NASA Astrophysics Data System (ADS)

Tellman, B.; Sullivan, J.; Kettner, A.; Brakenridge, G. R.; Slayback, D. A.; Kuhn, C.; Doyle, C.

2016-12-01

There is an increasing need to understand flood vulnerability as the societal and economic effects of flooding increases. Risk models from insurance companies and flood models from hydrologists must be calibrated based on flood observations in order to make future predictions that can improve planning and help societies reduce future disasters. Specifically, to improve these models both traditional methods of flood prediction from physically based models as well as data-driven techniques, such as machine learning, require spatial flood observation to validate model outputs and quantify uncertainty. A key dataset that is missing for flood model validation is a global historical geo-database of flood event extents. Currently, the most advanced database of historical flood extent is hosted and maintained at the Dartmouth Flood Observatory (DFO) that has catalogued 4320 floods (1985-2015) but has only mapped 5% of these floods. We are addressing this data gap by mapping the inventory of floods in the DFO database to create a first-of- its-kind, comprehensive, global and historical geospatial database of flood events. To do so, we combine water detection algorithms on MODIS and Landsat 5,7 and 8 imagery in Google Earth Engine to map discrete flood events. The created database will be available in the Earth Engine Catalogue for download by country, region, or time period. This dataset can be leveraged for new data-driven hydrologic modeling using machine learning algorithms in Earth Engine's highly parallelized computing environment, and we will show examples for New York and Senegal.

Modeling the Swift BAT Trigger Algorithm with Machine Learning

NASA Technical Reports Server (NTRS)

Graff, Philip B.; Lien, Amy Y.; Baker, John G.; Sakamoto, Takanori

2015-01-01

To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. (2014) is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of approximately greater than 97% (approximately less than 3% error), which is a significant improvement on a cut in GRB flux which has an accuracy of 89:6% (10:4% error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of eta(sub 0) approximately 0.48(+0.41/-0.23) Gpc(exp -3) yr(exp -1) with power-law indices of eta(sub 1) approximately 1.7(+0.6/-0.5) and eta(sub 2) approximately -5.9(+5.7/-0.1) for GRBs above and below a break point of z(sub 1) approximately 6.8(+2.8/-3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting. The code used in this is analysis is publicly available online.

Quantifying surgical complexity with machine learning: looking beyond patient factors to improve surgical models.

PubMed

Van Esbroeck, Alexander; Rubinfeld, Ilan; Hall, Bruce; Syed, Zeeshan

2014-11-01

To investigate the use of machine learning to empirically determine the risk of individual surgical procedures and to improve surgical models with this information. American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) data from 2005 to 2009 were used to train support vector machine (SVM) classifiers to learn the relationship between textual constructs in current procedural terminology (CPT) descriptions and mortality, morbidity, Clavien 4 complications, and surgical-site infections (SSI) within 30 days of surgery. The procedural risk scores produced by the SVM classifiers were validated on data from 2010 in univariate and multivariate analyses. The procedural risk scores produced by the SVM classifiers achieved moderate-to-high levels of discrimination in univariate analyses (area under receiver operating characteristic curve: 0.871 for mortality, 0.789 for morbidity, 0.791 for SSI, 0.845 for Clavien 4 complications). Addition of these scores also substantially improved multivariate models comprising patient factors and previously proposed correlates of procedural risk (net reclassification improvement and integrated discrimination improvement: 0.54 and 0.001 for mortality, 0.46 and 0.011 for morbidity, 0.68 and 0.022 for SSI, 0.44 and 0.001 for Clavien 4 complications; P < .05 for all comparisons). Similar improvements were noted in discrimination and calibration for other statistical measures, and in subcohorts comprising patients with general or vascular surgery. Machine learning provides clinically useful estimates of surgical risk for individual procedures. This information can be measured in an entirely data-driven manner and substantially improves multifactorial models to predict postoperative complications. Copyright © 2014 Elsevier Inc. All rights reserved.

Application of statistical machine translation to public health information: a feasibility study.

PubMed

Kirchhoff, Katrin; Turner, Anne M; Axelrod, Amittai; Saavedra, Francisco

2011-01-01

Accurate, understandable public health information is important for ensuring the health of the nation. The large portion of the US population with Limited English Proficiency is best served by translations of public-health information into other languages. However, a large number of health departments and primary care clinics face significant barriers to fulfilling federal mandates to provide multilingual materials to Limited English Proficiency individuals. This article presents a pilot study on the feasibility of using freely available statistical machine translation technology to translate health promotion materials. The authors gathered health-promotion materials in English from local and national public-health websites. Spanish versions were created by translating the documents using a freely available machine-translation website. Translations were rated for adequacy and fluency, analyzed for errors, manually corrected by a human posteditor, and compared with exclusively manual translations. Machine translation plus postediting took 15-53 min per document, compared to the reported days or even weeks for the standard translation process. A blind comparison of machine-assisted and human translations of six documents revealed overall equivalency between machine-translated and manually translated materials. The analysis of translation errors indicated that the most important errors were word-sense errors. The results indicate that machine translation plus postediting may be an effective method of producing multilingual health materials with equivalent quality but lower cost compared to manual translations.

Application of statistical machine translation to public health information: a feasibility study

PubMed Central

Turner, Anne M; Axelrod, Amittai; Saavedra, Francisco

2011-01-01

Objective Accurate, understandable public health information is important for ensuring the health of the nation. The large portion of the US population with Limited English Proficiency is best served by translations of public-health information into other languages. However, a large number of health departments and primary care clinics face significant barriers to fulfilling federal mandates to provide multilingual materials to Limited English Proficiency individuals. This article presents a pilot study on the feasibility of using freely available statistical machine translation technology to translate health promotion materials. Design The authors gathered health-promotion materials in English from local and national public-health websites. Spanish versions were created by translating the documents using a freely available machine-translation website. Translations were rated for adequacy and fluency, analyzed for errors, manually corrected by a human posteditor, and compared with exclusively manual translations. Results Machine translation plus postediting took 15–53 min per document, compared to the reported days or even weeks for the standard translation process. A blind comparison of machine-assisted and human translations of six documents revealed overall equivalency between machine-translated and manually translated materials. The analysis of translation errors indicated that the most important errors were word-sense errors. Conclusion The results indicate that machine translation plus postediting may be an effective method of producing multilingual health materials with equivalent quality but lower cost compared to manual translations. PMID:21498805

Etching process for improving the strength of a laser-machined silicon-based ceramic article

DOEpatents

Copley, Stephen M.; Tao, Hongyi; Todd-Copley, Judith A.

1991-01-01

A process for improving the strength of laser-machined articles formed of a silicon-based ceramic material such as silicon nitride, in which the laser-machined surface is immersed in an etching solution of hydrofluoric acid and nitric acid for a duration sufficient to remove substantially all of a silicon film residue on the surface but insufficient to allow the solution to unduly attack the grain boundaries of the underlying silicon nitride substrate. This effectively removes the silicon film as a source of cracks that otherwise could propagate downwardly into the silicon nitride substrate and significantly reduce its strength.

Etching process for improving the strength of a laser-machined silicon-based ceramic article

DOEpatents

Copley, S.M.; Tao, H.; Todd-Copley, J.A.

1991-06-11

A process is disclosed for improving the strength of laser-machined articles formed of a silicon-based ceramic material such as silicon nitride, in which the laser-machined surface is immersed in an etching solution of hydrofluoric acid and nitric acid for a duration sufficient to remove substantially all of a silicon film residue on the surface but insufficient to allow the solution to unduly attack the grain boundaries of the underlying silicon nitride substrate. This effectively removes the silicon film as a source of cracks that otherwise could propagate downwardly into the silicon nitride substrate and significantly reduce its strength. 1 figure.

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

PubMed

Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean

2017-12-04

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.

SU-F-T-226: QA Management for a Large Institution with Multiple Campuses for FMEA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, G; Chan, M; Lovelock, D

2016-06-15

Purpose: To redesign our radiation therapy QA program with the goal to improve quality, efficiency, and consistency among a growing number of campuses at a large institution. Methods: A QA committee was established with at least one physicist representing each of our six campuses (22 linacs). Weekly meetings were scheduled to advise on and update current procedures, to review end-to-end and other test results, and to prepare composite reports for internal and external audits. QA procedures for treatment and imaging equipment were derived from TG Reports 142 and 66, practice guidelines, and feedback from ACR evaluations. The committee focused onmore » reaching a consensus on a single QA program among all campuses using the same type of equipment and reference data. Since the recommendations for tolerances referenced to baseline data were subject to interpretation in some instances, the committee reviewed the characteristics of all machines and quantified any variations before choosing between treatment planning system (i.e. treatment planning system commissioning data that is representative for all machines) or machine-specific values (i.e. commissioning data of the individual machines) as baseline data. Results: The configured QA program will be followed strictly by all campuses. Inventory of available equipment has been compiled, and additional equipment acquisitions for the QA program are made as needed. Dosimetric characteristics are evaluated for all machines using the same methods to ensure consistency of beam data where possible. In most cases, baseline data refer to treatment planning system commissioning data but machine-specific values are used as reference where it is deemed appropriate. Conclusion: With a uniform QA scheme, variations in QA procedures are kept to a minimum. With a centralized database, data collection and analysis are simplified. This program will facilitate uniformity in patient treatments and analysis of large amounts of QA data campus-wide, which will ultimately facilitate FMEA.« less

Virtual Mission Operations of Remote Sensors With Rapid Access To and From Space

NASA Technical Reports Server (NTRS)

Ivancic, William D.; Stewart, Dave; Walke, Jon; Dikeman, Larry; Sage, Steven; Miller, Eric; Northam, James; Jackson, Chris; Taylor, John; Lynch, Scott;

The Availability of Competitive Foods and Beverages to Middle School Students in Appalachian Virginia Before Implementation of the 2014 Smart Snacks in School Standards.

PubMed

Mann, Georgianna; Kraak, Vivica; Serrano, Elena

2015-09-17

The study objective was to examine the nutritional quality of competitive foods and beverages (foods and beverages from vending machines and à la carte foods) available to rural middle school students, before implementation of the US Department of Agriculture's Smart Snacks in School standards in July 2014. In spring 2014, we audited vending machines and à la carte cafeteria foods and beverages in 8 rural Appalachian middle schools in Virginia. Few schools had vending machines. Few à la carte and vending machine foods met Smart Snacks in School standards (36.5%); however, most beverages did (78.2%). The major challenges to meeting standards were fat and sodium content of foods. Most competitive foods (62.2%) did not meet new standards, and rural schools with limited resources will likely require assistance to fully comply.

An application of eddy current damping effect on single point diamond turning of titanium alloys

NASA Astrophysics Data System (ADS)

Yip, W. S.; To, S.

2017-11-01

Titanium alloys Ti6Al4V (TC4) have been popularly applied in many industries. They have superior material properties including an excellent strength-to-weight ratio and corrosion resistance. However, they are regarded as difficult to cut materials; serious tool wear, a high level of cutting vibration and low surface integrity are always involved in machining processes especially in ultra-precision machining (UPM). In this paper, a novel hybrid machining technology using an eddy current damping effect is firstly introduced in UPM to suppress machining vibration and improve the machining performance of titanium alloys. A magnetic field was superimposed on samples during single point diamond turning (SPDT) by exposing the samples in between two permanent magnets. When the titanium alloys were rotated within a magnetic field in the SPDT, an eddy current was generated through a stationary magnetic field inside the titanium alloys. An eddy current generated its own magnetic field with the opposite direction of the external magnetic field leading a repulsive force, compensating for the machining vibration induced by the turning process. The experimental results showed a remarkable improvement in cutting force variation, a significant reduction in adhesive tool wear and an extreme long chip formation in comparison to normal SPDT of titanium alloys, suggesting the enhancement of the machinability of titanium alloys using an eddy current damping effect. An eddy current damping effect was firstly introduced in the area of UPM to deliver the results of outstanding machining performance.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Angers, Crystal Plume; Bottema, Ryan; Buckley, Les

Purpose: Treatment unit uptime statistics are typically used to monitor radiation equipment performance. The Ottawa Hospital Cancer Centre has introduced the use of Quality Control (QC) test success as a quality indicator for equipment performance and overall health of the equipment QC program. Methods: Implemented in 2012, QATrack+ is used to record and monitor over 1100 routine machine QC tests each month for 20 treatment and imaging units ( http://qatrackplus.com/ ). Using an SQL (structured query language) script, automated queries of the QATrack+ database are used to generate program metrics such as the number of QC tests executed and themore » percentage of tests passing, at tolerance or at action. These metrics are compared against machine uptime statistics already reported within the program. Results: Program metrics for 2015 show good correlation between pass rate of QC tests and uptime for a given machine. For the nine conventional linacs, the QC test success rate was consistently greater than 97%. The corresponding uptimes for these units are better than 98%. Machines that consistently show higher failure or tolerance rates in the QC tests have lower uptimes. This points to either poor machine performance requiring corrective action or to problems with the QC program. Conclusions: QATrack+ significantly improves the organization of QC data but can also aid in overall equipment management. Complimenting machine uptime statistics with QC test metrics provides a more complete picture of overall machine performance and can be used to identify areas of improvement in the machine service and QC programs.« less

A single-phase axially-magnetized permanent-magnet oscillating machine for miniature aerospace power sources

NASA Astrophysics Data System (ADS)

Sui, Yi; Zheng, Ping; Cheng, Luming; Wang, Weinan; Liu, Jiaqi

2017-05-01

A single-phase axially-magnetized permanent-magnet (PM) oscillating machine which can be integrated with a free-piston Stirling engine to generate electric power, is investigated for miniature aerospace power sources. Machine structure, operating principle and detent force characteristic are elaborately studied. With the sinusoidal speed characteristic of the mover considered, the proposed machine is designed by 2D finite-element analysis (FEA), and some main structural parameters such as air gap diameter, dimensions of PMs, pole pitches of both stator and mover, and the pole-pitch combinations, etc., are optimized to improve both the power density and force capability. Compared with the three-phase PM linear machines, the proposed single-phase machine features less PM use, simple control and low controller cost. The power density of the proposed machine is higher than that of the three-phase radially-magnetized PM linear machine, but lower than the three-phase axially-magnetized PM linear machine.

Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Siyuan; Hwang, Youngdeok; Khabibrakhmanov, Ildar

With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual modelmore » has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.« less

The upgraded Large Plasma Device, a machine for studying frontier basic plasma physics.

PubMed

Gekelman, W; Pribyl, P; Lucky, Z; Drandell, M; Leneman, D; Maggs, J; Vincena, S; Van Compernolle, B; Tripathi, S K P; Morales, G; Carter, T A; Wang, Y; DeHaas, T

2016-02-01

In 1991 a manuscript describing an instrument for studying magnetized plasmas was published in this journal. The Large Plasma Device (LAPD) was upgraded in 2001 and has become a national user facility for the study of basic plasma physics. The upgrade as well as diagnostics introduced since then has significantly changed the capabilities of the device. All references to the machine still quote the original RSI paper, which at this time is not appropriate. In this work, the properties of the updated LAPD are presented. The strategy of the machine construction, the available diagnostics, the parameters available for experiments, as well as illustrations of several experiments are presented here.

Accuracy of tracking forest machines with GPS

Treesearch

M.W. Veal; S.E. Taylor; T.P. McDonald; D.K. McLemore; M.R. Dunn

2001-01-01

This paper describes the results of a study that measured the accuracy of using GPS to track movement of forest machines. Two different commercially available GPS receivers (Trimble ProXR and GeoExplorer II) were used to track\\r\

ENERGY STAR Certified Vending Machines

EPA Pesticide Factsheets

Certified models meet all ENERGY STAR requirements as listed in the Version 3.0 ENERGY STAR Program Requirements for Refrigerated Beverage Vending Machines that are effective as of March 1, 2013. A detailed listing of key efficiency criteria are available at

Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer.

PubMed

Wang, Jing; Wu, Chen-Jiang; Bao, Mei-Ling; Zhang, Jing; Wang, Xiao-Ning; Zhang, Yu-Dong

2017-10-01

To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa). This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis. For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923-0.976]) than PI-RADS (Az: 0.878 [0.834-0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945-0.988] vs. 0.940 [0.905-0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960-0.995]) and PCa versus TZ (Az: 0.968 [0.940-0.985]). Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa. • Machine-based analysis of MR radiomics outperformed in TZ cancer against PI-RADS. • Adding MR radiomics significantly improved the performance of PI-RADS. • DKI-derived Dapp and Kapp were two strong markers for the diagnosis of PCa.

Effect of cutting parameters on surface finish and machinability of graphite reinforced Al-8011 matrix composite

NASA Astrophysics Data System (ADS)

Anil, K. C.; Vikas, M. G.; Shanmukha Teja, B.; Sreenivas Rao, K. V.

2017-04-01

Many materials such as alloys, composites find their applications on the basis of machinability, cost and availability. In the present work, graphite (Grp) reinforced Aluminium 8011 is synthesized by convention stir casting process and Surface finish & machinability of prepared composite is examined by using lathe tool dynamometer attached with BANKA Lathe by varying the machining parameters like spindle speed, Depth of cut and Feed rate in 3 levels. Also, Roughness Average (Ra) of machined surfaces is measured by using Surface Roughness Tester (Mitutoyo SJ201). From the studies it is cleared that mechanical properties of a composites increases with addition of Grp and The cutting force were decreased with the reinforcement percentage and thus increases the machinability of composites and also results in increased surface finish.

Investigations on high speed machining of EN-353 steel alloy under different machining environments

NASA Astrophysics Data System (ADS)

Venkata Vishnu, A.; Jamaleswara Kumar, P.

2018-03-01

The addition of Nano Particles into conventional cutting fluids enhances its cooling capabilities; in the present paper an attempt is made by adding nano sized particles into conventional cutting fluids. Taguchi Robust Design Methodology is employed in order to study the performance characteristics of different turning parameters i.e. cutting speed, feed rate, depth of cut and type of tool under different machining environments i.e. dry machining, machining with lubricant - SAE 40 and machining with mixture of nano sized particles of Boric acid and base fluid SAE 40. A series of turning operations were performed using L27 (3)13 orthogonal array, considering high cutting speeds and the other machining parameters to measure hardness. The results are compared among the different machining environments, and it is concluded that there is considerable improvement in the machining performance using lubricant SAE 40 and mixture of SAE 40 + boric acid compared with dry machining. The ANOVA suggests that the selected parameters and the interactions are significant and cutting speed has most significant effect on hardness.

Improved Extreme Learning Machine based on the Sensitivity Analysis

NASA Astrophysics Data System (ADS)

Cui, Licheng; Zhai, Huawei; Wang, Benchao; Qu, Zengtang

2018-03-01

Extreme learning machine and its improved ones is weak in some points, such as computing complex, learning error and so on. After deeply analyzing, referencing the importance of hidden nodes in SVM, an novel analyzing method of the sensitivity is proposed which meets people’s cognitive habits. Based on these, an improved ELM is proposed, it could remove hidden nodes before meeting the learning error, and it can efficiently manage the number of hidden nodes, so as to improve the its performance. After comparing tests, it is better in learning time, accuracy and so on.

Coupling for joining a ball nut to a machine tool carriage

DOEpatents

Gerth, Howard L.

1979-01-01

The present invention relates to an improved coupling for joining a lead screw ball nut to a machine tool carriage. The ball nut is coupled to the machine tool carriage by a plurality of laterally flexible bolts which function as hinges during the rotation of the lead screw for substantially reducing lateral carriage movement due to wobble in the lead screw.

Energy Savings and Persistence from an Energy Services Performance Contract at an Army Base

DTIC Science & Technology

2011-10-01

control system upgrades, lighting retrofits, vending machine controls, and cooling tower variable frequency drivers (VFDs). To accomplish the...controls were installed in the vending machines , and for the 87018 thermal plant, cooling tower VFDs were implemented. To develop baseline models...identify the reasons of improved or deteriorated energy performance of the buildings. For example, periodic submetering of the vending machines

Means and method of balancing multi-cylinder reciprocating machines

DOEpatents

Corey, John A.; Walsh, Michael M.

1985-01-01

A virtual balancing axis arrangement is described for multi-cylinder reciprocating piston machines for effectively balancing out imbalanced forces and minimizing residual imbalance moments acting on the crankshaft of such machines without requiring the use of additional parallel-arrayed balancing shafts or complex and expensive gear arrangements. The novel virtual balancing axis arrangement is capable of being designed into multi-cylinder reciprocating piston and crankshaft machines for substantially reducing vibrations induced during operation of such machines with only minimal number of additional component parts. Some of the required component parts may be available from parts already required for operation of auxiliary equipment, such as oil and water pumps used in certain types of reciprocating piston and crankshaft machine so that by appropriate location and dimensioning in accordance with the teachings of the invention, the virtual balancing axis arrangement can be built into the machine at little or no additional cost.

The Impacts of Industrial Robots

DTIC Science & Technology

1981-11-01

plastics, ’and strain gauges are used to measure very small forces at a number of points on the robot’s "end effector. Except for the simplest on-off...devices, tactile sensors are not yet found on commercially available robots. Forces are sensed by using strain gauges or piezoelectric sensors to...tools: deburring, drilling , grinding,milling,routing machines ii. plastic materialsformirg and injection machines iii. metal die casting machines iv

Dual scan CT image recovery from truncated projections

NASA Astrophysics Data System (ADS)

Sarkar, Shubhabrata; Wahi, Pankaj; Munshi, Prabhat

2017-12-01

There are computerized tomography (CT) scanners available commercially for imaging small objects and they are often categorized as mini-CT X-ray machines. One major limitation of these machines is their inability to scan large objects with good image quality because of the truncation of projection data. An algorithm is proposed in this work which enables such machines to scan large objects while maintaining the quality of the recovered image.

PMLB: a large benchmark suite for machine learning evaluation and comparison.

PubMed

Olson, Randal S; La Cava, William; Orzechowski, Patryk; Urbanowicz, Ryan J; Moore, Jason H

2017-01-01

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. From this study, we find that existing benchmarks lack the diversity to properly benchmark machine learning algorithms, and there are several gaps in benchmarking problems that still need to be considered. This work represents another important step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.

Implementing Machine Learning in Radiology Practice and Research.

PubMed

Kohli, Marc; Prevedello, Luciano M; Filice, Ross W; Geis, J Raymond

2017-04-01

The purposes of this article are to describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, supervised as opposed to unsupervised techniques, statistical pitfalls, and data considerations for training and evaluation, and to briefly describe ethical dilemmas and legal risk. Machine learning includes a broad class of computer programs that improve with experience. The complexity of creating, training, and monitoring machine learning indicates that the success of the algorithms will require radiologist involvement for years to come, leading to engagement rather than replacement.

Machine learning and medicine: book review and commentary.

PubMed

Koprowski, Robert; Foster, Kenneth R

2018-02-01

This article is a review of the book "Master machine learning algorithms, discover how they work and implement them from scratch" (ISBN: not available, 37 USD, 163 pages) edited by Jason Brownlee published by the Author, edition, v1.10 http://MachineLearningMastery.com . An accompanying commentary discusses some of the issues that are involved with use of machine learning and data mining techniques to develop predictive models for diagnosis or prognosis of disease, and to call attention to additional requirements for developing diagnostic and prognostic algorithms that are generally useful in medicine. Appendix provides examples that illustrate potential problems with machine learning that are not addressed in the reviewed book.

[Cardiology: is the smartphone era?

PubMed

Mandoli, Giulia Elena; D'Ascenzi, Flavio; Cameli, Matteo; Mondillo, Sergio

2017-12-01

The worldwide spread of smartphones has radically changed the habits of human life, allowing a 24/7 connection with other people. These changes have involved also Medicine with smartphones being able to simplify the clinical practice of physicians. The development of new external devices that can be connected to smartphones has further increased their use with mobile phones converted in portable electrocardiogram or echocardiogram machines. This extraordinary technological improvement seems to be partly in conflict with the classical tools available for the cardiologist, such as the "old" stethoscope that in 2016 had its 200th anniversary. This article focuses on the smartphone as a new tool available for the physicians, describing the most important potential uses and reporting an analysis of pros and cons of the smart-cardiology.

Lean energy analysis of CNC lathe

NASA Astrophysics Data System (ADS)

Liana, N. A.; Amsyar, N.; Hilmy, I.; Yusof, MD

2018-01-01

The industrial sector in Malaysia is one of the main sectors that have high percentage of energy demand compared to other sector and this problem may lead to the future power shortage and increasing the production cost of a company. Suitable initiatives should be implemented by the industrial sectors to solve the issues such as by improving the machining system. In the past, the majority of the energy consumption in industry focus on lighting, HVAC and office section usage. Future trend, manufacturing process is also considered to be included in the energy analysis. A study on Lean Energy Analysis in a machining process is presented. Improving the energy efficiency in a lathe machine by enhancing the cutting parameters of turning process is discussed. Energy consumption of a lathe machine was analyzed in order to identify the effect of cutting parameters towards energy consumption. It was found that the combination of parameters for third run (spindle speed: 1065 rpm, depth of cut: 1.5 mm, feed rate: 0.3 mm/rev) was the most preferred and ideal to be used during the turning machining process as it consumed less energy usage.

Improving the machinability of leaded free cutting steel through process optimization

NASA Astrophysics Data System (ADS)

Sathyamurthy, P.; Vetrivelmurugan, R.; Sooryaprakash, J.

2018-02-01

Free cutting steel grades are high sulphur grades which can be classified under two categories as Leaded and Non-Leaded. These grades are used for manufacturing components like Nuts, bolts, studs, hydraulic fittings, brake pistons where higher machining is required to get intricate shape. Machinability of these grades are affected by hard oxide inclusions and highly deformed manganese sulphide inclusions. At JSW, machinability of leaded free cutting steel is improved by various process modifications namely deoxidation through carbon and manganese, Tellurium (Rare earth element) addition and maintaining the oxygen level at 80- 120ppm. Former one avoids the formation of hard SiO2 and Al2O3 compounds, Tellurium addition forms PbTe compound at the tail of MnS inclusions which resists the deformation of MnS inclusions and increased oxygen level favours the formation of less deformable oxy- sulphide inclusions. Above process modifications have resulted in achieving the low silicate content, better aspect ratio of MnS inclusions in the final rolled product. They are assessed by the characteristics of chip formation and surface roughness of machined part.

Experimental Investigation Nano Particles Influence in NPMEDM to Machine Inconel 800 with Electrolyte Copper Electrode

NASA Astrophysics Data System (ADS)

Karunakaran, K.; Chandrasekaran, M.

2017-05-01

The recent technology of machining hard materials is Powder mix dielectric electrical Discharge Machining (PMEDM). This research investigates nano sized (about 5Nm) powders influence in machining Inconel 800 nickel based super alloy. This work is motivated for a practical need for a manufacturing industry, which processes various kinds of jobs of Inconel 800 material. The conventional EDM machining also considered for investigation for the measure of Nano powders performances. The aluminum, silicon and multi walled Carbon Nano tubes powders were considered in this investigation along with pulse on time, pulse of time and input current to analyze and optimize the responses of Material Removal Rate, Tool Wear Rate and surface roughness. The Taguchi general Full Factorial Design was used to design the experiments. The most advance equipments employed in conducting experiments and measuring equipments to improve the accuracy of the result. The MWCNT powder mix was out performs than other powders which reduce 22% to 50% of the tool wear rate, gives the surface roughness reduction from 29.62% to 41.64% and improved MRR 42.91% to 53.51% than conventional EDM.

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

PubMed Central

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M.; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-01-01

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0. PMID:27892471

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies.

PubMed

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-11-28

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

NASA Astrophysics Data System (ADS)

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M.; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-11-01

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.

System-Level Virtualization for High Performance Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vallee, Geoffroy R; Naughton, III, Thomas J; Engelmann, Christian

2008-01-01

System-level virtualization has been a research topic since the 70's but regained popularity during the past few years because of the availability of efficient solution such as Xen and the implementation of hardware support in commodity processors (e.g. Intel-VT, AMD-V). However, a majority of system-level virtualization projects is guided by the server consolidation market. As a result, current virtualization solutions appear to not be suitable for high performance computing (HPC) which is typically based on large-scale systems. On another hand there is significant interest in exploiting virtual machines (VMs) within HPC for a number of other reasons. By virtualizing themore » machine, one is able to run a variety of operating systems and environments as needed by the applications. Virtualization allows users to isolate workloads, improving security and reliability. It is also possible to support non-native environments and/or legacy operating environments through virtualization. In addition, it is possible to balance work loads, use migration techniques to relocate applications from failing machines, and isolate fault systems for repair. This document presents the challenges for the implementation of a system-level virtualization solution for HPC. It also presents a brief survey of the different approaches and techniques to address these challenges.« less

Improvement of the COP of the LiBr-Water Double-Effect Absorption Cycles

NASA Astrophysics Data System (ADS)

Shitara, Atsushi

Prevention of the global warming has called for a great necessity for energy saving. This applies to the improvement of the COP of absorption chiller-heaters. We started the development of the high efficiency gas-fired double-effect absorption chiller-heater using LiBr-H2O to achieve target performance in short or middle term. To maintain marketability, the volume of the high efficiency machine has been set below the equal to the conventional machine. The absorption cycle technology for improving the COP and the element technology for downsizing the machine is necessary in this development. In this study, the former is investigated. In this report, first of all the target performance has been set at cooling COP of 1.35(on HHV), which is 0.35 higher than the COP of 1.0 for conventional machines in the market. This COP of 1.35 is practically close to the maximum limit achievable by double-effect absorption chiller-heater. Next, the design condition of each element to achieve the target performance and the effect of each mean to improve the COP are investigated. Moreover, as a result of comparing the various flows(series, parallel, reverse)to which the each mean is applied, it has been found the optimum cycle is the parallel flow.

Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome.

PubMed

Liu, Yun; Scirica, Benjamin M; Stultz, Collin M; Guttag, John V

2016-10-06

Frequency domain measures of heart rate variability (HRV) are associated with adverse events after a myocardial infarction. However, patterns in the traditional frequency domain (measured in Hz, or cycles per second) may capture different cardiac phenomena at different heart rates. An alternative is to consider frequency with respect to heartbeats, or beatquency. We compared the use of frequency and beatquency domains to predict patient risk after an acute coronary syndrome. We then determined whether machine learning could further improve the predictive performance. We first evaluated the use of pre-defined frequency and beatquency bands in a clinical trial dataset (N = 2302) for the HRV risk measure LF/HF (the ratio of low frequency to high frequency power). Relative to frequency, beatquency improved the ability of LF/HF to predict cardiovascular death within one year (Area Under the Curve, or AUC, of 0.730 vs. 0.704, p < 0.001). Next, we used machine learning to learn frequency and beatquency bands with optimal predictive power, which further improved the AUC for beatquency to 0.753 (p < 0.001), but not for frequency. Results in additional validation datasets (N = 2255 and N = 765) were similar. Our results suggest that beatquency and machine learning provide valuable tools in physiological studies of HRV.

Findings from the National Machine Guarding Program–A Small Business Intervention: Machine Safety

PubMed Central

Yamin, Samuel C.; Xi, Min; Brosseau, Lisa M.; Gordon, Robert; Most, Ivan G.; Stanley, Rodney

2016-01-01

Objectives The purpose of this nationwide intervention was to improve machine safety in small metal fabrication businesses (3 – 150 employees). The failure to implement machine safety programs related to guarding and lockout/tagout (LOTO) are frequent causes of OSHA citations and may result in serious traumatic injury. Methods Insurance safety consultants conducted a standardized evaluation of machine guarding, safety programs, and LOTO. Businesses received a baseline evaluation, two intervention visits and a twelve-month follow-up evaluation. Results The intervention was completed by 160 businesses. Adding a safety committee was associated with a 10-percentage point increase in business-level machine scores (p< 0.0001) and a 33-percentage point increase in LOTO program scores (p <0.0001). Conclusions Insurance safety consultants proved effective at disseminating a machine safety and LOTO intervention via management-employee safety committees. PMID:26716850

Does machine perfusion decrease ischemia reperfusion injury?

PubMed

Bon, D; Delpech, P-O; Chatauret, N; Hauet, T; Badet, L; Barrou, B

2014-06-01

In 1990's, use of machine perfusion for organ preservation has been abandoned because of improvement of preservation solutions, efficient without perfusion, easy to use and cheaper. Since the last 15 years, a renewed interest for machine perfusion emerged based on studies performed on preclinical model and seems to make consensus in case of expanded criteria donors or deceased after cardiac death donations. We present relevant studies highlighted the efficiency of preservation with hypothermic machine perfusion compared to static cold storage. Machines for organ preservation being in constant evolution, we also summarized recent developments included direct oxygenation of the perfusat. Machine perfusion technology also enables organ reconditioning during the last hours of preservation through a short period of perfusion on hypothermia, subnormothermia or normothermia. We present significant or low advantages for machine perfusion against ischemia reperfusion injuries regarding at least one primary parameter: risk of DFG, organ function or graft survival. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

An iterative learning control method with application for CNC machine tools

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, D.I.; Kim, S.

1996-01-01

A proportional, integral, and derivative (PID) type iterative learning controller is proposed for precise tracking control of industrial robots and computer numerical controller (CNC) machine tools performing repetitive tasks. The convergence of the output error by the proposed learning controller is guaranteed under a certain condition even when the system parameters are not known exactly and unknown external disturbances exist. As the proposed learning controller is repeatedly applied to the industrial robot or the CNC machine tool with the path-dependent repetitive task, the distance difference between the desired path and the actual tracked or machined path, which is one ofmore » the most significant factors in the evaluation of control performance, is progressively reduced. The experimental results demonstrate that the proposed learning controller can improve machining accuracy when the CNC machine tool performs repetitive machining tasks.« less

Findings From the National Machine Guarding Program-A Small Business Intervention: Machine Safety.

PubMed

Parker, David L; Yamin, Samuel C; Xi, Min; Brosseau, Lisa M; Gordon, Robert; Most, Ivan G; Stanley, Rodney

2016-09-01

The purpose of this nationwide intervention was to improve machine safety in small metal fabrication businesses (3 to 150 employees). The failure to implement machine safety programs related to guarding and lockout/tagout (LOTO) are frequent causes of Occupational Safety and Health Administration (OSHA) citations and may result in serious traumatic injury. Insurance safety consultants conducted a standardized evaluation of machine guarding, safety programs, and LOTO. Businesses received a baseline evaluation, two intervention visits, and a 12-month follow-up evaluation. The intervention was completed by 160 businesses. Adding a safety committee was associated with a 10% point increase in business-level machine scores (P < 0.0001) and a 33% point increase in LOTO program scores (P < 0.0001). Insurance safety consultants proved effective at disseminating a machine safety and LOTO intervention via management-employee safety committees.

The influence of machining condition and cutting tool wear on surface roughness of AISI 4340 steel

NASA Astrophysics Data System (ADS)

Natasha, A. R.; Ghani, J. A.; Che Haron, C. H.; Syarif, J.

2018-01-01

Sustainable machining by using cryogenic coolant as the cutting fluid has been proven to enhance some machining outputs. The main objective of the current work was to investigate the influence of machining conditions; dry and cryogenic, as well as the cutting tool wear on the machined surface roughness of AISI 4340 steel. The experimental tests were performed using chemical vapor deposition (CVD) coated carbide inserts. The value of machined surface roughness were measured at 3 cutting intervals; beginning, middle, and end of the cutting based on the readings of the tool flank wear. The results revealed that cryogenic turning had the greatest influence on surface roughness when machined at lower cutting speed and higher feed rate. Meanwhile, the cutting tool wear was also found to influence the surface roughness, either improving it or deteriorating it, based on the severity and the mechanism of the flank wear.

A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking.

PubMed

Ballester, Pedro J; Mitchell, John B O

2010-05-01

Accurately predicting the binding affinities of large sets of diverse protein-ligand complexes is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for analysing the outputs of molecular docking, which in turn is an important technique for drug discovery, chemical biology and structural biology. Each scoring function assumes a predetermined theory-inspired functional form for the relationship between the variables that characterize the complex, which also include parameters fitted to experimental or simulation data and its predicted binding affinity. The inherent problem of this rigid approach is that it leads to poor predictivity for those complexes that do not conform to the modelling assumptions. Moreover, resampling strategies, such as cross-validation or bootstrapping, are still not systematically used to guard against the overfitting of calibration data in parameter estimation for scoring functions. We propose a novel scoring function (RF-Score) that circumvents the need for problematic modelling assumptions via non-parametric machine learning. In particular, Random Forest was used to implicitly capture binding effects that are hard to model explicitly. RF-Score is compared with the state of the art on the demanding PDBbind benchmark. Results show that RF-Score is a very competitive scoring function. Importantly, RF-Score's performance was shown to improve dramatically with training set size and hence the future availability of more high-quality structural and interaction data is expected to lead to improved versions of RF-Score. pedro.ballester@ebi.ac.uk; jbom@st-andrews.ac.uk Supplementary data are available at Bioinformatics online.

Development of the Sealzall Machine : Upgrade to the TTLS (Pavement Crack Sealer)

DOT National Transportation Integrated Search

2009-10-31

The AHMCT Research Center, together with Caltrans, has been leading a multi-year research effort to develop : innovative high production crack sealing equipment, which improves safety while reducing costs. The Sealzall : Machine development project i...

GAME: GAlaxy Machine learning for Emission lines

NASA Astrophysics Data System (ADS)

Ucci, G.; Ferrara, A.; Pallottini, A.; Gallerani, S.

2018-06-01

We present an updated, optimized version of GAME (GAlaxy Machine learning for Emission lines), a code designed to infer key interstellar medium physical properties from emission line intensities of ultraviolet /optical/far-infrared galaxy spectra. The improvements concern (a) an enlarged spectral library including Pop III stars, (b) the inclusion of spectral noise in the training procedure, and (c) an accurate evaluation of uncertainties. We extensively validate the optimized code and compare its performance against empirical methods and other available emission line codes (PYQZ and HII-CHI-MISTRY) on a sample of 62 SDSS stacked galaxy spectra and 75 observed HII regions. Very good agreement is found for metallicity. However, ionization parameters derived by GAME tend to be higher. We show that this is due to the use of too limited libraries in the other codes. The main advantages of GAME are the simultaneous use of all the measured spectral lines and the extremely short computational times. We finally discuss the code potential and limitations.

Detecting Vessels Carrying Migrants Using Machine Learning

NASA Astrophysics Data System (ADS)

Sfyridis, A.; Cheng, T.; Vespe, M.

2017-10-01

Political instability, conflicts and inequalities result into significant flows of people worldwide, moving to different countries in search of a better life, safety or to be reunited with their families. Irregular crossings into Europe via sea routes, despite not being new, have recently increased together with the loss of lives of people in the attempt to reach EU shores. This highlights the need to find ways to improve the understanding of what is happening at sea. This paper, intends to expand the knowledge available on practices among smugglers and contribute to early warning and maritime situational awareness. By identifying smuggling techniques and based on anomaly detection methods, behaviours of interest are modelled and one class support vector machines are used to classify unlabelled data and detect potential smuggling vessels. Nine vessels are identified as potentially carrying irregular migrants and refugees. Though, further inspection of the results highlights possible misclassifications caused by data gaps and limited knowledge on smuggling tactics. Accepted classifications are considered subject to further investigation by the authorities.

Small communal laundries in block of flats: Planning, Equipment, Handicap Adaption

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pedersen, B.

1980-01-01

The primary requirements which must be made for a communal laundry is that it must be adapted to the laundry quantities, laundry needs, and available time of the households. In addition, the equipment must be such that the work involved and the and water are kept as low as possible. It is also important that the laundry facility be regarded as an attractive work environment. The following topics are discussed: Small communal laundries offer many advantages (In the same building, Possibilities for unscheduled laundering, Economically advantageous, Easy to agree on laundering times); Calculation of laundry capacity; Equipment in the laundrymore » (Washing machines, Spin dryer, Tumbler dryer and drying cabinets, Work table, Sink unit, Cold mangle); Information on equipment; Energy conservation measures (Heat exchanger, Outdoor drying); Location of equipment; Work areas which also suit the physically handicapped; Work postures are improved if the machines are placed on a higher level; Layouts; Standards for laundries.« less

Machine learning applications in genetics and genomics.

PubMed

Libbrecht, Maxwell W; Noble, William Stafford

2015-06-01

The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

Machine Learning in Radiology: Applications Beyond Image Interpretation.

PubMed

Lakhani, Paras; Prater, Adam B; Hutson, R Kent; Andriole, Kathy P; Dreyer, Keith J; Morey, Jose; Prevedello, Luciano M; Clark, Toshi J; Geis, J Raymond; Itri, Jason N; Hawkins, C Matthew

2018-02-01

Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional "machine radiologist" is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Prostate Cancer Probability Prediction By Machine Learning Technique.

PubMed

Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

2017-11-26

The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation

PubMed Central

Nguyen, Huu-Tho; Md Dawal, Siti Zawiah; Nukman, Yusoff; Aoyama, Hideki; Case, Keith

2015-01-01

Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment. PMID:26368541

One or 4 h of "in-house" reconditioning by machine perfusion after cold storage improve reperfusion parameters in porcine kidneys.

PubMed

Gallinat, Anja; Efferz, Patrik; Paul, Andreas; Minor, Thomas

2014-11-01

In-house machine perfusion after cold storage (hypothermic reconditioning) has been proposed as convenient tool to improve kidney graft function. This study investigated the role of machine perfusion duration for early reperfusion parameters in porcine kidneys. Kidney function after cold preservation (4 °C, 18 h) and subsequent reconditioning by one or 4 h of pulsatile, nonoxygenated hypothermic machine perfusion (HMP) was studied in an isolated kidney perfusion model in pigs (n = 6, respectively) and compared with simply cold-stored grafts (CS). Compared with CS alone, one or 4 h of subsequent HMP similarly and significantly improved renal flow and kidney function (clearance and sodium reabsorption) upon warm reperfusion, along with reduced perfusate concentrations of endothelin-1 and increased vascular release of nitric oxide. Molecular effects of HMP comprised a significant (vs CS) mRNA increase in the endothelial transcription factor KLF2 and lower expression of endothelin that were observed already at the end of one-hour HMP after CS. Reconditioning of cold-stored kidneys is possible, even if clinical logistics only permit one hour of therapy, while limited extension of the overall storage time by in-house machine perfusion might also allow for postponing of transplantation from night to early day work. © 2014 Steunstichting ESOT.

Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms.

PubMed

Ozcift, Akin; Gulten, Arif

2011-12-01

Improving accuracies of machine learning algorithms is vital in designing high performance computer-aided diagnosis (CADx) systems. Researches have shown that a base classifier performance might be enhanced by ensemble classification strategies. In this study, we construct rotation forest (RF) ensemble classifiers of 30 machine learning algorithms to evaluate their classification performances using Parkinson's, diabetes and heart diseases from literature. While making experiments, first the feature dimension of three datasets is reduced using correlation based feature selection (CFS) algorithm. Second, classification performances of 30 machine learning algorithms are calculated for three datasets. Third, 30 classifier ensembles are constructed based on RF algorithm to assess performances of respective classifiers with the same disease data. All the experiments are carried out with leave-one-out validation strategy and the performances of the 60 algorithms are evaluated using three metrics; classification accuracy (ACC), kappa error (KE) and area under the receiver operating characteristic (ROC) curve (AUC). Base classifiers succeeded 72.15%, 77.52% and 84.43% average accuracies for diabetes, heart and Parkinson's datasets, respectively. As for RF classifier ensembles, they produced average accuracies of 74.47%, 80.49% and 87.13% for respective diseases. RF, a newly proposed classifier ensemble algorithm, might be used to improve accuracy of miscellaneous machine learning algorithms to design advanced CADx systems. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Ergonomic evaluation of conventional and improved methods of aonla pricking with women workers.

PubMed

Rai, Arpana; Gandhi, Sudesh; Sharma, D K

2012-01-01

Conventional and improved methods of aonla pricking were evaluated ergonomically on an experiment conducted for 20 minute with women workers. The working heart rate, energy expenditure rate, total cardiac cost of work and physiological cost of work with conventional tools varied from 93-102 beats.min-1, 6-7.5 kJ.min-1, 285-470 beats, 14 -23 beats.min-1 while with machine varied from 96-105 beats.min-1, 6.5-8 kJ.min-1 , 336-540 beats, 16-27 beats.min-1 respectively. OWAS score for conventional method was 2 indicating corrective measures in near future while with machine was 1 indicating no corrective measures. Result of Nordic Musculoskeletal Questionnaire revealed that subjects complaint of pain in back, neck, right shoulder and right hand due to unnatural body posture and repetitive movement with hand tool. Moreover pricking was carried out in improper lighting conditions (200-300 lux) resulting into finger injuries from sharp edges of hand tool, whereas with machine no such problems were observed. Output with machine increased thrice than hand pricking in a given time. Machine was found useful in terms of saving time, increased productivity, enhanced safety and comfort as involved improved posture, was easy to handle and operate, thus increasing efficiency of the worker leading to better quality of life.

Photoelectron studies of machined brass surfaces

NASA Astrophysics Data System (ADS)

Potts, A. W.; Merrison, J. P.; Tournas, A. D.; Yacoot, A.

UV photoelectron spectroscopy has been used to determine the surface composition of machined brass. The results show a considerable change between the photoelectron surface composition and the bulk composition of the same sample determined by energy-dispersive X-ray fluorescence. On the surface the lead composition is increased by ˜900 G. This is consistent with the important part that lead is believed to play in improving the machinability of this alloy.

Applications of machine learning in cancer prediction and prognosis.

PubMed

Cruz, Joseph A; Wishart, David S

2007-02-11

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on "older" technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Applications of Machine Learning in Cancer Prediction and Prognosis

PubMed Central

Cruz, Joseph A.; Wishart, David S.

2006-01-01

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression. PMID:19458758

Joint FAM/Line Management Assessment Report on LLNL Machine Guarding Safety Program

DOE Office of Scientific and Technical Information (OSTI.GOV)

Armstrong, J. J.

2016-07-19

The LLNL Safety Program for Machine Guarding is implemented to comply with requirements in the ES&H Manual Document 11.2, "Hazards-General and Miscellaneous," Section 13 Machine Guarding (Rev 18, issued Dec. 15, 2015). The primary goal of this LLNL Safety Program is to ensure that LLNL operations involving machine guarding are managed so that workers, equipment and government property are adequately protected. This means that all such operations are planned and approved using the Integrated Safety Management System to provide the most cost effective and safest means available to support the LLNL mission.

The dynamic analysis of drum roll lathe for machining of rollers

NASA Astrophysics Data System (ADS)

Qiao, Zheng; Wu, Dongxu; Wang, Bo; Li, Guo; Wang, Huiming; Ding, Fei

2014-08-01

An ultra-precision machine tool for machining of the roller has been designed and assembled, and due to the obvious impact which dynamic characteristic of machine tool has on the quality of microstructures on the roller surface, the dynamic characteristic of the existing machine tool is analyzed in this paper, so is the influence of circumstance that a large scale and slender roller is fixed in the machine on dynamic characteristic of the machine tool. At first, finite element model of the machine tool is built and simplified, and based on that, the paper carries on with the finite element mode analysis and gets the natural frequency and shaking type of four steps of the machine tool. According to the above model analysis results, the weak stiffness systems of machine tool can be further improved and the reasonable bandwidth of control system of the machine tool can be designed. In the end, considering the shock which is caused by Z axis as a result of fast positioning frequently to feeding system and cutting tool, transient analysis is conducted by means of ANSYS analysis in this paper. Based on the results of transient analysis, the vibration regularity of key components of machine tool and its impact on cutting process are explored respectively.

Evaluation of I-FIT results and machine variability using MnRoad test track mixtures.

DOT National Transportation Integrated Search

2017-06-01

The Illinois Flexibility Index Test (I-FIT) was developed to distinguish between different mixtures in terms of potential cracking. Several : machines were manufactured and are currently available to perform the I-FIT. This report presents the result...

Shedding Light on Synergistic Chemical Genetic Connections with Machine Learning.

PubMed

Ekins, Sean; Siqueira-Neto, Jair Lage

2015-12-23

Machine learning can be used to predict compounds acting synergistically, and this could greatly expand the universe of available potential treatments for diseases that are currently hidden in the dark chemical matter. Copyright © 2015 Elsevier Inc. All rights reserved.

Distributed communications and control network for robotic mining

NASA Technical Reports Server (NTRS)

Schiffbauer, William H.

1989-01-01

The application of robotics to coal mining machines is one approach pursued to increase productivity while providing enhanced safety for the coal miner. Toward that end, a network composed of microcontrollers, computers, expert systems, real time operating systems, and a variety of program languages are being integrated that will act as the backbone for intelligent machine operation. Actual mining machines, including a few customized ones, have been given telerobotic semiautonomous capabilities by applying the described network. Control devices, intelligent sensors and computers onboard these machines are showing promise of achieving improved mining productivity and safety benefits. Current research using these machines involves navigation, multiple machine interaction, machine diagnostics, mineral detection, and graphical machine representation. Guidance sensors and systems employed include: sonar, laser rangers, gyroscopes, magnetometers, clinometers, and accelerometers. Information on the network of hardware/software and its implementation on mining machines are presented. Anticipated coal production operations using the network are discussed. A parallelism is also drawn between the direction of present day underground coal mining research to how the lunar soil (regolith) may be mined. A conceptual lunar mining operation that employs a distributed communication and control network is detailed.

Machining of Fibre Reinforced Plastic Composite Materials.

PubMed

Caggiano, Alessandra

2018-03-18

Fibre reinforced plastic composite materials are difficult to machine because of the anisotropy and inhomogeneity characterizing their microstructure and the abrasiveness of their reinforcement components. During machining, very rapid cutting tool wear development is experienced, and surface integrity damage is often produced in the machined parts. An accurate selection of the proper tool and machining conditions is therefore required, taking into account that the phenomena responsible for material removal in cutting of fibre reinforced plastic composite materials are fundamentally different from those of conventional metals and their alloys. To date, composite materials are increasingly used in several manufacturing sectors, such as the aerospace and automotive industry, and several research efforts have been spent to improve their machining processes. In the present review, the key issues that are concerning the machining of fibre reinforced plastic composite materials are discussed with reference to the main recent research works in the field, while considering both conventional and unconventional machining processes and reporting the more recent research achievements. For the different machining processes, the main results characterizing the recent research works and the trends for process developments are presented.

Machining of Fibre Reinforced Plastic Composite Materials

PubMed Central

2018-01-01

Fibre reinforced plastic composite materials are difficult to machine because of the anisotropy and inhomogeneity characterizing their microstructure and the abrasiveness of their reinforcement components. During machining, very rapid cutting tool wear development is experienced, and surface integrity damage is often produced in the machined parts. An accurate selection of the proper tool and machining conditions is therefore required, taking into account that the phenomena responsible for material removal in cutting of fibre reinforced plastic composite materials are fundamentally different from those of conventional metals and their alloys. To date, composite materials are increasingly used in several manufacturing sectors, such as the aerospace and automotive industry, and several research efforts have been spent to improve their machining processes. In the present review, the key issues that are concerning the machining of fibre reinforced plastic composite materials are discussed with reference to the main recent research works in the field, while considering both conventional and unconventional machining processes and reporting the more recent research achievements. For the different machining processes, the main results characterizing the recent research works and the trends for process developments are presented. PMID:29562635

Simultaneous Scheduling of Jobs, AGVs and Tools Considering Tool Transfer Times in Multi Machine FMS By SOS Algorithm

NASA Astrophysics Data System (ADS)

Sivarami Reddy, N.; Ramamurthy, D. V., Dr.; Prahlada Rao, K., Dr.

2017-08-01

This article addresses simultaneous scheduling of machines, AGVs and tools where machines are allowed to share the tools considering transfer times of jobs and tools between machines, to generate best optimal sequences that minimize makespan in a multi-machine Flexible Manufacturing System (FMS). Performance of FMS is expected to improve by effective utilization of its resources, by proper integration and synchronization of their scheduling. Symbiotic Organisms Search (SOS) algorithm is a potent tool which is a better alternative for solving optimization problems like scheduling and proven itself. The proposed SOS algorithm is tested on 22 job sets with makespan as objective for scheduling of machines and tools where machines are allowed to share tools without considering transfer times of jobs and tools and the results are compared with the results of existing methods. The results show that the SOS has outperformed. The same SOS algorithm is used for simultaneous scheduling of machines, AGVs and tools where machines are allowed to share tools considering transfer times of jobs and tools to determine the best optimal sequences that minimize makespan.

Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data

PubMed Central

Yang, Yang; Niehaus, Katherine E; Walker, Timothy M; Iqbal, Zamin; Walker, A Sarah; Wilson, Daniel J; Peto, Tim E A; Crook, Derrick W; Smith, E Grace; Zhu, Tingting; Clifton, David A

2018-01-01

Abstract Motivation Correct and rapid determination of Mycobacterium tuberculosis (MTB) resistance against available tuberculosis (TB) drugs is essential for the control and management of TB. Conventional molecular diagnostic test assumes that the presence of any well-studied single nucleotide polymorphisms is sufficient to cause resistance, which yields low sensitivity for resistance classification. Summary Given the availability of DNA sequencing data from MTB, we developed machine learning models for a cohort of 1839 UK bacterial isolates to classify MTB resistance against eight anti-TB drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, ciprofloxacin, moxifloxacin, ofloxacin, streptomycin) and to classify multi-drug resistance. Results Compared to previous rules-based approach, the sensitivities from the best-performing models increased by 2-4% for isoniazid, rifampicin and ethambutol to 97% (P < 0.01), respectively; for ciprofloxacin and multi-drug resistant TB, they increased to 96%. For moxifloxacin and ofloxacin, sensitivities increased by 12 and 15% from 83 and 81% based on existing known resistance alleles to 95% and 96% (P < 0.01), respectively. Particularly, our models improved sensitivities compared to the previous rules-based approach by 15 and 24% to 84 and 87% for pyrazinamide and streptomycin (P < 0.01), respectively. The best-performing models increase the area-under-the-ROC curve by 10% for pyrazinamide and streptomycin (P < 0.01), and 4–8% for other drugs (P < 0.01). Availability and implementation The details of source code are provided at http://www.robots.ox.ac.uk/~davidc/code.php. Contact david.clifton@eng.ox.ac.uk Supplementary information Supplementary data are available at Bioinformatics online. PMID:29240876

The Cooling and Lubrication Performance of Graphene Platelets in Micro-Machining Environments

NASA Astrophysics Data System (ADS)

Chu, Bryan

The research presented in this thesis is aimed at investigating the use of graphene platelets (GPL) to address the challenges of excessive tool wear, reduced part quality, and high specific power consumption encountered in micro-machining processes. There are two viable methods of introducing GPL into micro-machining environments, viz., the embedded delivery method, where the platelets are embedded into the part being machined, and the external delivery method, where graphene is carried into the cutting zone by jetting or atomizing a carrier fluid. The study involving the embedded delivery method is focused on the micro-machining performance of hierarchical graphene composites. The results of this study show that the presence of graphene in the epoxy matrix improves the machinability of the composite. In general, the tool wear, cutting forces, surface roughness, and extent of delamination are all seen to be lower for the hierarchical composite when compared to the conventional two-phase glass fiber composite. These improvements are attributed to the fact that graphene platelets improve the thermal conductivity of the matrix, provide lubrication at the tool-chip interface and also improve the interface strength between the glass fibers and the matrix. The benefits of graphene are seen to also carry over to the external delivery method. The platelets provide improved cooling and lubrication performance to both environmentally-benign cutting fluids as well as to semi-synthetic cutting fluids used in micro-machining. The cutting performance is seen to be a function of the geometry (i.e., lateral size and thickness) and extent of oxygen-functionalization of the platelet. Ultrasonically exfoliated platelets (with 2--3 graphene layers and lowest in-solution characteristic lateral length of 120 nm) appear to be the most favorable for micro-machining applications. Even at the lowest concentration of 0.1 wt%, they are capable of providing a 51% reduction in the cutting temperature and a 25% reduction in the surface roughness value over that of the baseline semi-synthetic cutting fluid. For the thermally-reduced platelets (with 4--8 graphene layers and in-solution characteristic lateral length of 562--2780 nm), a concentration of 0.2 wt% appears to be optimal. An investigation into the impingement dynamics of the graphene-laden colloidal solutions on a heated substrate reveals that the most important criterion dictating their machining performance is their ability to form uniform, submicron thick films of the platelets upon evaporation of the carrier fluid. As such, the characterization of the residual platelet film left behind on a heated substrate may be an effective technique for evaluating different graphene colloidal solutions for cutting fluids applications in micromachining. Graphene platelets have also recently been shown to reduce the aggressive chemical wear of diamond tools during the machining of transition metal alloys. However, the specific mechanisms responsible for this improvement are currently unknown. The modeling work presented in this thesis uses molecular dynamics techniques to shed light on the wear mitigation mechanisms that are active during the diamond cutting of steel when in the presence of graphene platelets. The dual mechanisms responsible for graphene-induced chemical wear mitigation are: 1) The formation of a physical barrier between the metal and tool atoms, preventing graphitization; and 2) The preferential transfer of carbon from the graphene platelet rather than from the diamond tool. The results of the simulations also provide new insight into the behavior of the 2D graphene platelets in the cutting zone, specifically illustrating the mechanisms of cleaving and interlayer sliding in graphene platelets under the high pressures in cutting zones.

Development of the Sealzall Machine : upgrade to the TTLS (pavement crack sealer).

DOT National Transportation Integrated Search

2009-10-01

The AHMCT Research Center, together with Caltrans, has been leading a multi-year research effort to develop innovative high production crack sealing equipment, which improves safety while reducing costs. The Sealzall Machine development project is th...

An Electronic Cigarette Vaping Machine for the Characterization of Aerosol Delivery and Composition.

PubMed

Havel, Christopher M; Benowitz, Neal L; Jacob, Peyton; St Helen, Gideon

2017-10-01

Characterization of aerosols generated by electronic cigarettes (e-cigarettes) is one method used to evaluate the safety of e-cigarettes. While some researchers have modified smoking machines for e-cigarette aerosol generation, these machines are either not readily available, not automated for e-cigarette testing or have not been adequately described. The objective of this study was to build an e-cigarette vaping machine that can be used to test, under standard conditions, e-liquid aerosolization and nicotine and toxicant delivery. The vaping machine was assembled from commercially available parts, including a puff controller, vacuum pump, power supply, switch to control current flow to the atomizer, three-way value to direct air flow to the atomizer, and three gas dispersion tubes for aerosol trapping. To validate and illustrate its use, the variation in aerosol generation was assessed within and between KangerTech Mini ProTank 3 clearomizers, and the effect of voltage on aerosolization and toxic aldehyde generation were assessed. When using one ProTank 3 clearomizer and different e-liquid flavors, the coefficient of variation (CV) of aerosol generated ranged between 11.5% and 19.3%. The variation in aerosol generated between ProTank 3 clearomizers with different e-liquid flavors and voltage settings ranged between 8.3% and 16.3% CV. Aerosol generation increased linearly at 3-6V across e-liquids and clearomizer brands. Acetaldehyde, acrolein, and formaldehyde generation increased markedly at voltages at or above 5V. The vaping machine that we describe reproducibly aerosolizes e-liquids from e-cigarette atomizers under controlled conditions and is useful for testing of nicotine and toxicant delivery. This study describes an electronic cigarette vaping machine that was assembled from commercially available parts. The vaping machine can be replicated by researchers and used under standard conditions to generate e-cigarette aerosols and characterize nicotine and toxicant delivery. © The Author 2016. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning.

PubMed

McGovern, Amy; Gagne, David J; Williams, John K; Brown, Rodger A; Basara, Jeffrey B

Severe weather, including tornadoes, thunderstorms, wind, and hail annually cause significant loss of life and property. We are developing spatiotemporal machine learning techniques that will enable meteorologists to improve the prediction of these events by improving their understanding of the fundamental causes of the phenomena and by building skillful empirical predictive models. In this paper, we present significant enhancements of our Spatiotemporal Relational Probability Trees that enable autonomous discovery of spatiotemporal relationships as well as learning with arbitrary shapes. We focus our evaluation on two real-world case studies using our technique: predicting tornadoes in Oklahoma and predicting aircraft turbulence in the United States. We also discuss how to evaluate success for a machine learning algorithm in the severe weather domain, which will enable new methods such as ours to transfer from research to operations, provide a set of lessons learned for embedded machine learning applications, and discuss how to field our technique.

Toolpath strategy for cutter life improvement in plunge milling of AISI H13 tool steel

NASA Astrophysics Data System (ADS)

Adesta, E. Y. T.; Avicenna; hilmy, I.; Daud, M. R. H. C.

2018-01-01

Machinability of AISI H13 tool steel is a prominent issue since the material has the characteristics of high hardenability, excellent wear resistance, and hot toughness. A method of improving cutter life of AISI H13 tool steel plunge milling by alternating the toolpath and cutting conditions is proposed. Taguchi orthogonal array with L9 (3^4) resolution will be employed with one categorical factor of toolpath strategy (TS) and three numeric factors of cutting speed (Vc), radial depth of cut (ae ), and chip load (fz ). It is expected that there are significant differences for each application of toolpath strategy and each cutting condition factor toward the cutting force and tool wear mechanism of the machining process, and medial axis transform toolpath could provide a better tool life improvement by a reduction of cutting force during machining.

Muxstep: an open-source C ++ multiplex HMM library for making inferences on multiple data types.

PubMed

Veličković, Petar; Liò, Pietro

2016-08-15

With the development of experimental methods and technology, we are able to reliably gain access to data in larger quantities, dimensions and types. This has great potential for the improvement of machine learning (as the learning algorithms have access to a larger space of information). However, conventional machine learning approaches used thus far on single-dimensional data inputs are unlikely to be expressive enough to accurately model the problem in higher dimensions; in fact, it should generally be most suitable to represent our underlying models as some form of complex networksng;nsio with nontrivial topological features. As the first step in establishing such a trend, we present MUXSTEP: , an open-source library utilising multiplex networks for the purposes of binary classification on multiple data types. The library is designed to be used out-of-the-box for developing models based on the multiplex network framework, as well as easily modifiable to suit problem modelling needs that may differ significantly from the default approach described. The full source code is available on GitHub: https://github.com/PetarV-/muxstep petar.velickovic@cl.cam.ac.uk Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Small mammal MRI imaging in spinal cord injury: a novel practical technique for using a 1.5 T MRI.

PubMed

Levene, Howard B; Mohamed, Feroze B; Faro, Scott H; Seshadri, Asha B; Loftus, Christopher M; Tuma, Ronald F; Jallo, Jack I

2008-07-30

The field of spinal cord injury research is an active one. The pathophysiology of SCI is not yet entirely revealed. As such, animal models are required for the exploration of new therapies and treatments. We present a novel technique using available hospital MRI machines to examine SCI in a mouse SCI model. The model is a 60 kdyne direct contusion injury in a mouse thoracic spine. No new electronic equipment is required. A 1.5T MRI machine with a human wrist coil is employed. A standard multisection 2D fast spin-echo (FSE) T2-weighted sequence is used for imaging the mouse. The contrast-to-noise ratio (CNR) between the injured and normal area of the spinal cord showed a three-fold increase in the contrast between these two regions. The MRI findings could be correlated with kinematic outcome scores of ambulation, such as BBB or BMS. The ability to follow a SCI in the same animal over time should improve the quality of data while reducing the quantity of animals required in SCI research. It is the aim of the authors to share this non-invasive technique and to make it available to the scientific research community.

GREAT: a web portal for Genome Regulatory Architecture Tools

PubMed Central

Bouyioukos, Costas; Bucchini, François; Elati, Mohamed; Képès, François

2016-01-01

GREAT (Genome REgulatory Architecture Tools) is a novel web portal for tools designed to generate user-friendly and biologically useful analysis of genome architecture and regulation. The online tools of GREAT are freely accessible and compatible with essentially any operating system which runs a modern browser. GREAT is based on the analysis of genome layout -defined as the respective positioning of co-functional genes- and its relation with chromosome architecture and gene expression. GREAT tools allow users to systematically detect regular patterns along co-functional genomic features in an automatic way consisting of three individual steps and respective interactive visualizations. In addition to the complete analysis of regularities, GREAT tools enable the use of periodicity and position information for improving the prediction of transcription factor binding sites using a multi-view machine learning approach. The outcome of this integrative approach features a multivariate analysis of the interplay between the location of a gene and its regulatory sequence. GREAT results are plotted in web interactive graphs and are available for download either as individual plots, self-contained interactive pages or as machine readable tables for downstream analysis. The GREAT portal can be reached at the following URL https://absynth.issb.genopole.fr/GREAT and each individual GREAT tool is available for downloading. PMID:27151196

Machine Learning, Sentiment Analysis, and Tweets: An Examination of Alzheimer's Disease Stigma on Twitter.

PubMed

Oscar, Nels; Fox, Pamela A; Croucher, Racheal; Wernick, Riana; Keune, Jessica; Hooker, Karen

2017-09-01

Social scientists need practical methods for harnessing large, publicly available datasets that inform the social context of aging. We describe our development of a semi-automated text coding method and use a content analysis of Alzheimer's disease (AD) and dementia portrayal on Twitter to demonstrate its use. The approach improves feasibility of examining large publicly available datasets. Machine learning techniques modeled stigmatization expressed in 31,150 AD-related tweets collected via Twitter's search API based on 9 AD-related keywords. Two researchers manually coded 311 random tweets on 6 dimensions. This input from 1% of the dataset was used to train a classifier against the tweet text and code the remaining 99% of the dataset. Our automated process identified that 21.13% of the AD-related tweets used AD-related keywords to perpetuate public stigma, which could impact stereotypes and negative expectations for individuals with the disease and increase "excess disability". This technique could be applied to questions in social gerontology related to how social media outlets reflect and shape attitudes bearing on other developmental outcomes. Recommendations for the collection and analysis of large Twitter datasets are discussed. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Wind utilization in remote regions: An economic study. [for comparison with diesel engines

NASA Technical Reports Server (NTRS)

Vansant, J. H.

1973-01-01

A wind driven generator was considered as a supplement to a diesel group, for the purpose of economizing fuel when wind power is available. A specific location on Hudson's Bay, Povognituk, was selected. Technical and economic data available for a wind machine of 10-kilowatt nominal capacity and available wind data for that region were used for the study. After subtracting the yearly wind machine costs from savings in fuel costs, a net savings of $1400 per year is realized. These values are approximate, but are though to be highly conservative.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

PubMed Central

Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources. PMID:28114334

Tomographic Imaging on a Cobalt Radiotherapy Machine

NASA Astrophysics Data System (ADS)

Marsh, Matthew Brendon

Cancer is a global problem, and many people in low-income countries do not have access to the treatment options, such as radiation therapy, that are available in wealthy countries. Where radiation therapy is available, it is often delivered using older Co-60 equipment that has not been updated to modern standards. Previous research has indicated that an updated Co-60 radiation therapy machine could deliver treatments that are equivalent to those performed with modern linear accelerators. Among the key features of these modern treatments is a tightly conformal dose distribution-- the radiation dose is shaped in three dimensions to closely match the tumour, with minimal irradiation of surrounding normal tissues. Very accurate alignment of the patient in the beam is therefore necessary to avoid missing the tumour, so all modern radiotherapy machines include imaging systems to verify the patient's position before treatment. Imaging with the treatment beam is relatively cost-effective, as it avoids the need for a second radiation source and the associated control systems. The dose rate from a Co-60 therapy source, though, is more than an order of magnitude too high to use for computed tomography (CT) imaging of a patient. Digital tomosynthesis (DT), a limited-arc imaging method that can be thought of as a hybrid of CT and conventional radiography, allows some of the three-dimensional selectivity of CT but with shorter imaging times and a five- to fifteen-fold reduction in dose. In the present work, a prototype Co-60 DT imaging system was developed and characterized. A class of clinically useful Co-60 DT protocols has been identified, based on the filtered backprojection algorithm originally designed for CT, with images acquired over a relatively small arc. Parts of the reconstruction algorithm must be modified for the DT case, and a way to reduce the beam intensity will be necessary to reduce the imaging dose to acceptable levels. Some additional study is required to determine whether improvements made to the DT imaging protocol translate to improvements in the accuracy of the image guidance process, but it is clear that Co-60 DT is feasible and will probably be practical for clinical use.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

PubMed

Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

The Python Spectral Analysis Tool (PySAT): A Powerful, Flexible, Preprocessing and Machine Learning Library and Interface

NASA Astrophysics Data System (ADS)

Anderson, R. B.; Finch, N.; Clegg, S. M.; Graff, T. G.; Morris, R. V.; Laura, J.; Gaddis, L. R.

2017-12-01

Machine learning is a powerful but underutilized approach that can enable planetary scientists to derive meaningful results from the rapidly-growing quantity of available spectral data. For example, regression methods such as Partial Least Squares (PLS) and Least Absolute Shrinkage and Selection Operator (LASSO), can be used to determine chemical concentrations from ChemCam and SuperCam Laser-Induced Breakdown Spectroscopy (LIBS) data [1]. Many scientists are interested in testing different spectral data processing and machine learning methods, but few have the time or expertise to write their own software to do so. We are therefore developing a free open-source library of software called the Python Spectral Analysis Tool (PySAT) along with a flexible, user-friendly graphical interface to enable scientists to process and analyze point spectral data without requiring significant programming or machine-learning expertise. A related but separately-funded effort is working to develop a graphical interface for orbital data [2]. The PySAT point-spectra tool includes common preprocessing steps (e.g. interpolation, normalization, masking, continuum removal, dimensionality reduction), plotting capabilities, and capabilities to prepare data for machine learning such as creating stratified folds for cross validation, defining training and test sets, and applying calibration transfer so that data collected on different instruments or under different conditions can be used together. The tool leverages the scikit-learn library [3] to enable users to train and compare the results from a variety of multivariate regression methods. It also includes the ability to combine multiple "sub-models" into an overall model, a method that has been shown to improve results and is currently used for ChemCam data [4]. Although development of the PySAT point-spectra tool has focused primarily on the analysis of LIBS spectra, the relevant steps and methods are applicable to any spectral data. The tool is available at https://github.com/USGS-Astrogeology/PySAT_Point_Spectra_GUI. [1] Clegg, S.M., et al. (2017) Spectrochim Acta B. 129, 64-85. [2] Gaddis, L. et al. (2017) 3rd Planetary Data Workshop, #1986. [3] http://scikit-learn.org/ [4] Anderson, R.B., et al. (2017) Spectrochim. Acta B. 129, 49-57.

Quadrilateral Micro-Hole Array Machining on Invar Thin Film: Wet Etching and Electrochemical Fusion Machining

PubMed Central

Choi, Woong-Kirl; Kim, Seong-Hyun; Choi, Seung-Geon; Lee, Eun-Sang

2018-01-01

Ultra-precision products which contain a micro-hole array have recently shown remarkable demand growth in many fields, especially in the semiconductor and display industries. Photoresist etching and electrochemical machining are widely known as precision methods for machining micro-holes with no residual stress and lower surface roughness on the fabricated products. The Invar shadow masks used for organic light-emitting diodes (OLEDs) contain numerous micro-holes and are currently machined by a photoresist etching method. However, this method has several problems, such as uncontrollable hole machining accuracy, non-etched areas, and overcutting. To solve these problems, a machining method that combines photoresist etching and electrochemical machining can be applied. In this study, negative photoresist with a quadrilateral hole array pattern was dry coated onto 30-µm-thick Invar thin film, and then exposure and development were carried out. After that, photoresist single-side wet etching and a fusion method of wet etching-electrochemical machining were used to machine micro-holes on the Invar. The hole machining geometry, surface quality, and overcutting characteristics of the methods were studied. Wet etching and electrochemical fusion machining can improve the accuracy and surface quality. The overcutting phenomenon can also be controlled by the fusion machining. Experimental results show that the proposed method is promising for the fabrication of Invar film shadow masks. PMID:29351235

Contributions a la caracterisation et a l'amelioration de l'usinabilite de pieces d'acier elaborees par metallurgie des poudres

NASA Astrophysics Data System (ADS)

Boilard, Patrick

Even though powder metallurgy (P/M) is a near net shape process, a large number of parts still require one or more machining operations during the course of their elaboration and/or their finishing. The main objectives of the work presented in this thesis are centered on the elaboration of blends with enhanced machinability, as well as helping with the definition and in the characterization of the machinability of P/M parts. Enhancing machinability can be done in various ways, through the use of machinability additives and by decreasing the amount of porosity of the parts. These different ways of enhancing machinability have been investigated thoroughly, by systematically planning and preparing series of samples in order to obtain valid and repeatable results leading to meaningful conclusions relevant to the P/M domain. Results obtained during the course of the work are divided into three main chapters: (1) the effect of machining parameters on machinability, (2) the effect of additives on machinability, and (3) the development and the characterization of high density parts obtained by liquid phase sintering. Regarding the effect of machining parameters on machinability, studies were performed on parameters such as rotating speed, feed, tool position and diameter of the tool. Optimal cutting parameters are found for drilling operations performed on a standard FC-0208 blend, for different machinability criteria. Moreover, study of material removal rates shows the sensitivity of the machinability criteria for different machining parameters and indicates that thrust force is more regular than tool wear and slope of the drillability curve in the characterization of machinability. The chapter discussing the effect of various additives on machinability reveals many interesting results. First, work carried out on MoS2 additions reveals the dissociation of this additive and the creation of metallic sulphides (namely CuxS sulphides) when copper is present. Results also show that it is possible to reduce the amount of MoS2 in the blend so as to lower the dimensional change and the cost (blend Mo8A), while enhancing machinability and keeping hardness values within the same range (70 HRB). Second, adding enstatite (MgO·SiO2) permits the observation of the mechanisms occurring with the use of this additive. It is found that the stability of enstatite limits the diffusion of graphite during sintering, leading to the presence of free graphite in the pores, thus enhancing machinability. Furthermore, a lower amount of graphite in the matrix leads to a lower hardness, which is also beneficial to machinability. It is also found that the presence of copper enhances the diffusion of graphite, through the formation of a liquid phase during sintering. With the objective of improving machinability by reaching higher densities, blends were developed for densification through liquid phase sintering. High density samples are obtained (>7.5 g/cm3) for blends prepared with Fe-C-P constituents, namely with 0.5%P and 2.4%C. By systematically studying the effect of different parameters, the importance of the chemical composition (mainly the carbon content) and the importance of the sintering cycle (particularly the cooling rate) are demonstrated. Moreover, various heat treatments studied illustrate the different microstructures achievable for this system, showing various amounts of cementite, pearlite and free graphite. Although the machinability is limited for samples containing large amounts of cementite, it can be greatly improved with very slow cooling, leading to graphitization of the carbon in presence of phosphorus. Adequate control of the sintering cycle on samples made from FGS1625 powder leads to the obtention of high density (≥7.0 g/cm 3) microstructures containing various amounts of pearlite, ferrite and free graphite. Obtaining ferritic microstructures with free graphite designed for very high machinability (tool wear <1.0%) or fine pearlitic microstructures with excellent mechanical properties (transverse rupture strength >1600 MPa) is therefore possible. These results show that improvement of machinability through higher densities is limited by microstructure. Indeed, for the studied samples, microstructure is dominant in the determination of machinability, far more important than density, judging by the influence of cementite or of the volume fraction of free graphite on machinability for example. (Abstract shortened by UMI.)

ALE: automated label extraction from GEO metadata.

PubMed

Giles, Cory B; Brown, Chase A; Ripperger, Michael; Dennis, Zane; Roopnarinesingh, Xiavan; Porter, Hunter; Perz, Aleksandra; Wren, Jonathan D

2017-12-28

NCBI's Gene Expression Omnibus (GEO) is a rich community resource containing millions of gene expression experiments from human, mouse, rat, and other model organisms. However, information about each experiment (metadata) is in the format of an open-ended, non-standardized textual description provided by the depositor. Thus, classification of experiments for meta-analysis by factors such as gender, age of the sample donor, and tissue of origin is not feasible without assigning labels to the experiments. Automated approaches are preferable for this, primarily because of the size and volume of the data to be processed, but also because it ensures standardization and consistency. While some of these labels can be extracted directly from the textual metadata, many of the data available do not contain explicit text informing the researcher about the age and gender of the subjects with the study. To bridge this gap, machine-learning methods can be trained to use the gene expression patterns associated with the text-derived labels to refine label-prediction confidence. Our analysis shows only 26% of metadata text contains information about gender and 21% about age. In order to ameliorate the lack of available labels for these data sets, we first extract labels from the textual metadata for each GEO RNA dataset and evaluate the performance against a gold standard of manually curated labels. We then use machine-learning methods to predict labels, based upon gene expression of the samples and compare this to the text-based method. Here we present an automated method to extract labels for age, gender, and tissue from textual metadata and GEO data using both a heuristic approach as well as machine learning. We show the two methods together improve accuracy of label assignment to GEO samples.

Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set.

PubMed

Lenselink, Eelke B; Ten Dijke, Niels; Bongers, Brandon; Papadatos, George; van Vlijmen, Herman W T; Kowalczyk, Wojtek; IJzerman, Adriaan P; van Westen, Gerard J P

2017-08-14

The increase of publicly available bioactivity data in recent years has fueled and catalyzed research in chemogenomics, data mining, and modeling approaches. As a direct result, over the past few years a multitude of different methods have been reported and evaluated, such as target fishing, nearest neighbor similarity-based methods, and Quantitative Structure Activity Relationship (QSAR)-based protocols. However, such studies are typically conducted on different datasets, using different validation strategies, and different metrics. In this study, different methods were compared using one single standardized dataset obtained from ChEMBL, which is made available to the public, using standardized metrics (BEDROC and Matthews Correlation Coefficient). Specifically, the performance of Naïve Bayes, Random Forests, Support Vector Machines, Logistic Regression, and Deep Neural Networks was assessed using QSAR and proteochemometric (PCM) methods. All methods were validated using both a random split validation and a temporal validation, with the latter being a more realistic benchmark of expected prospective execution. Deep Neural Networks are the top performing classifiers, highlighting the added value of Deep Neural Networks over other more conventional methods. Moreover, the best method ('DNN_PCM') performed significantly better at almost one standard deviation higher than the mean performance. Furthermore, Multi-task and PCM implementations were shown to improve performance over single task Deep Neural Networks. Conversely, target prediction performed almost two standard deviations under the mean performance. Random Forests, Support Vector Machines, and Logistic Regression performed around mean performance. Finally, using an ensemble of DNNs, alongside additional tuning, enhanced the relative performance by another 27% (compared with unoptimized 'DNN_PCM'). Here, a standardized set to test and evaluate different machine learning algorithms in the context of multi-task learning is offered by providing the data and the protocols. Graphical Abstract .

Laser Induced Damage of Potassium Dihydrogen Phosphate (KDP) Optical Crystal Machined by Water Dissolution Ultra-Precision Polishing Method

PubMed Central

Gao, Hang; Wang, Xu; Guo, Dongming; Liu, Ziyuan

2018-01-01

Laser induced damage threshold (LIDT) is an important optical indicator for nonlinear Potassium Dihydrogen Phosphate (KDP) crystal used in high power laser systems. In this study, KDP optical crystals are initially machined with single point diamond turning (SPDT), followed by water dissolution ultra-precision polishing (WDUP) and then tested with 355 nm nanosecond pulsed-lasers. Power spectral density (PSD) analysis shows that WDUP process eliminates the laser-detrimental spatial frequencies band of micro-waviness on SPDT machined surface and consequently decreases its modulation effect on the laser beams. The laser test results show that LIDT of WDUP machined crystal improves and its stability has a significant increase by 72.1% compared with that of SPDT. Moreover, a subsequent ultrasonic assisted solvent cleaning process is suggested to have a positive effect on the laser performance of machined KDP crystal. Damage crater investigation indicates that the damage morphologies exhibit highly thermal explosion features of melted cores and brittle fractures of periphery material, which can be described with the classic thermal explosion model. The comparison result demonstrates that damage mechanisms for SPDT and WDUP machined crystal are the same and WDUP process reveals the real bulk laser resistance of KDP optical crystal by removing the micro-waviness and subsurface damage on SPDT machined surface. This improvement of WDUP method makes the LIDT more accurate and will be beneficial to the laser performance of KDP crystal. PMID:29534032

Geometric improvement of electrochemical discharge micro-drilling using an ultrasonic-vibrated electrolyte

NASA Astrophysics Data System (ADS)

Han, Min-Seop; Min, Byung-Kwon; Lee, Sang Jo

2009-06-01

Electrochemical discharge machining (ECDM) is a spark-based micromachining method especially suitable for the fabrication of various microstructures on nonconductive materials, such as glass and some engineering ceramics. However, since the spark discharge frequency is drastically reduced as the machining depth increases ECDM microhole drilling has confronted difficulty in achieving uniform geometry for machined holes. One of the primary reasons for this is the difficulty of sustaining an adequate electrolyte flow in the narrow gap between the tool and the workpiece, which results in a widened taper at the hole entrance, as well as a significant reduction of the machining depth. In this paper, ultrasonic electrolyte vibration was used to enhance the machining depth of the ECDM drilling process by assuring an adequate electrolyte flow, thus helping to maintain consistent spark generation. Moreover, the stability of the gas film formation, as well as the surface quality of the hole entrance, was improved with the aid of a side-insulated electrode and a pulse-power generator. The side-insulated electrode prevented stray electrolysis and concentrated the spark discharge at the tool tip, while the pulse voltage reduced thermal damage to the workpiece surface by introducing a periodic pulse-off time. Microholes were fabricated in order to investigate the effects of ultrasonic assistance on the overcut and machining depth of the holes. The experimental results demonstrated that the possibility of consistent spark generation and the machinability of microholes were simultaneously enhanced.

Acceleration of saddle-point searches with machine learning.

PubMed

Peterson, Andrew A

2016-08-21

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the number of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.

Acceleration of saddle-point searches with machine learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peterson, Andrew A., E-mail: andrew-peterson@brown.edu

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the numbermore » of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.« less

Ergonomics Perspective in Agricultural Research: A User-Centred Approach Using CAD and Digital Human Modeling (DHM) Technologies

NASA Astrophysics Data System (ADS)

Patel, Thaneswer; Sanjog, J.; Karmakar, Sougata

2016-09-01

Computer-aided Design (CAD) and Digital Human Modeling (DHM) (specialized CAD software for virtual human representation) technologies endow unique opportunities to incorporate human factors pro-actively in design development. Challenges of enhancing agricultural productivity through improvement of agricultural tools/machineries and better human-machine compatibility can be ensured by adoption of these modern technologies. Objectives of present work are to provide the detailed scenario of CAD and DHM applications in agricultural sector; and finding out means for wide adoption of these technologies for design and development of cost-effective, user-friendly, efficient and safe agricultural tools/equipment and operator's workplace. Extensive literature review has been conducted for systematic segregation and representation of available information towards drawing inferences. Although applications of various CAD software have momentum in agricultural research particularly for design and manufacturing of agricultural equipment/machinery, use of DHM is still at its infancy in this sector. Current review discusses about reasons of less adoption of these technologies in agricultural sector and steps to be taken for their wide adoption. It also suggests possible future research directions to come up with better ergonomic design strategies for improvement of agricultural equipment/machines and workstations through application of CAD and DHM.

Learning clinically useful information from images: Past, present and future.

PubMed

Rueckert, Daniel; Glocker, Ben; Kainz, Bernhard

2016-10-01

Over the last decade, research in medical imaging has made significant progress in addressing challenging tasks such as image registration and image segmentation. In particular, the use of model-based approaches has been key in numerous, successful advances in methodology. The advantage of model-based approaches is that they allow the incorporation of prior knowledge acting as a regularisation that favours plausible solutions over implausible ones. More recently, medical imaging has moved away from hand-crafted, and often explicitly designed models towards data-driven, implicit models that are constructed using machine learning techniques. This has led to major improvements in all stages of the medical imaging pipeline, from acquisition and reconstruction to analysis and interpretation. As more and more imaging data is becoming available, e.g., from large population studies, this trend is likely to continue and accelerate. At the same time new developments in machine learning, e.g., deep learning, as well as significant improvements in computing power, e.g., parallelisation on graphics hardware, offer new potential for data-driven, semantic and intelligent medical imaging. This article outlines the work of the BioMedIA group in this area and highlights some of the challenges and opportunities for future work. Copyright © 2016 Elsevier B.V. All rights reserved.

Studying depression using imaging and machine learning methods.

PubMed

Patel, Meenal J; Khalaf, Alexander; Aizenstein, Howard J

2016-01-01

Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

Array servo scanning micro EDM of 3D micro cavities

NASA Astrophysics Data System (ADS)

Tong, Hao; Li, Yong; Yi, Futing

2011-05-01

Micro electro discharge machining (Micro EDM) is a non-traditional processing technology with the special advantages of low set-up cost and few cutting force in machining any conductive materials regardless of their hardness. As well known, die-sinking EDM is unsuitable for machining the complex 3D micro cavity less than 1mm due to the high-priced fabrication of 3D microelectrode itself and its serous wear during EDM process. In our former study, a servo scanning 3D micro-EDM (3D SSMEDM) method was put forward, and our experiments showed it was available to fabricate complex 3D micro-cavities. In this study, in order to improve machining efficiency and consistency accuracy for array 3D micro-cavities, an array-servo-scanning 3D micro EDM (3D ASSMEDM) method is presented considering the complementary advantages of the 3D SSMEDM and the array micro electrodes with simple cross-section. During 3D ASSMEDM process, the array cavities designed by CAD / CAM system can be batch-manufactured by servo scanning layer by layer using array-rod-like micro tool electrodes, and the axial wear of the array electrodes is compensated in real time by keeping discharge gap. To verify the effectiveness of the 3D ASSMEDM, the array-triangle-micro cavities (side length 630 μm) are batch-manufactured on P-doped silicon by applying the array-micro-electrodes with square-cross-section fabricated by LIGA process. Our exploratory experiment shows that the 3D ASSMEDM provides a feasible approach for the batch-manufacture of 3D array-micro-cavities of conductive materials.

Machine Translation: The Alternative for the 21st Century?

ERIC Educational Resources Information Center

Cribb, V. Michael

2000-01-01

Outlines a scenario for the future of Teaching English as a Second or Other Languages that has seldom, if ever been considered in academic discussion: that advances in and availability of quality machine translation could mitigate the need for English language learning. (Author/VWL)

Research on intelligent machine self-perception method based on LSTM

NASA Astrophysics Data System (ADS)

Wang, Qiang; Cheng, Tao

2018-05-01

In this paper, we use the advantages of LSTM in feature extraction and processing high-dimensional and complex nonlinear data, and apply it to the autonomous perception of intelligent machines. Compared with the traditional multi-layer neural network, this model has memory, can handle time series information of any length. Since the multi-physical domain signals of processing machines have a certain timing relationship, and there is a contextual relationship between states and states, using this deep learning method to realize the self-perception of intelligent processing machines has strong versatility and adaptability. The experiment results show that the method proposed in this paper can obviously improve the sensing accuracy under various working conditions of the intelligent machine, and also shows that the algorithm can well support the intelligent processing machine to realize self-perception.

The methodic of calculation for the need of basic construction machines on construction site when developing organizational and technological documentation

NASA Astrophysics Data System (ADS)

Zhadanovsky, Boris; Sinenko, Sergey

2018-03-01

Economic indicators of construction work, particularly in high-rise construction, are directly related to the choice of optimal number of machines. The shortage of machinery makes it impossible to complete the construction & installation work on scheduled time. Rates of performance of construction & installation works and labor productivity during high-rise construction largely depend on the degree of provision of construction project with machines (level of work mechanization). During calculation of the need for machines in construction projects, it is necessary to ensure that work is completed on scheduled time, increased level of complex mechanization, increased productivity and reduction of manual work, and improved usage and maintenance of machine fleet. The selection of machines and determination of their numbers should be carried out by using formulas presented in this work.

Identification of Technological Parameters of Ni-Alloys When Machining by Monolithic Ceramic Milling Tool

NASA Astrophysics Data System (ADS)

Czán, Andrej; Kubala, Ondrej; Danis, Igor; Czánová, Tatiana; Holubják, Jozef; Mikloš, Matej

2017-12-01

The ever-increasing production and the usage of hard-to-machine progressive materials are the main cause of continual finding of new ways and methods of machining. One of these ways is the ceramic milling tool, which combines the pros of conventional ceramic cutting materials and pros of conventional coating steel-based insert. These properties allow to improve cutting conditions and so increase the productivity with preserved quality known from conventional tools usage. In this paper, there is made the identification of properties and possibilities of this tool when machining of hard-to-machine materials such as nickel alloys using in airplanes engines. This article is focused on the analysis and evaluation ordinary technological parameters and surface quality, mainly roughness of surface and quality of machined surface and tool wearing.

Machinability of experimental Ti-Ag alloys.

PubMed

Kikuchi, Masafumi; Takahashi, Masatoshi; Okuno, Osamu

2008-03-01

This study investigated the machinability of experimental Ti-Ag alloys (5, 10, 20, and 30 mass% Ag) as a new dental titanium alloy candidate for CAD/CAM use. The alloys were slotted with a vertical milling machine and carbide square end mills under two cutting conditions. Machinability was evaluated through cutting force using a three-component force transducer fixed on the table of the milling machine. The horizontal cutting force of the Ti-Ag alloys tended to decrease as the concentration of silver increased. Values of the component of the horizontal cutting force perpendicular to the feed direction for Ti-20% Ag and Ti-30% Ag were more than 20% lower than those for titanium under both cutting conditions. Alloying with silver significantly improved the machinability of titanium in terms of cutting force under the present cutting conditions.

Investigation of a tubular dual-stator flux-switching permanent-magnet linear generator for free-piston energy converter

NASA Astrophysics Data System (ADS)

Sui, Yi; Zheng, Ping; Tong, Chengde; Yu, Bin; Zhu, Shaohong; Zhu, Jianguo

2015-05-01

This paper describes a tubular dual-stator flux-switching permanent-magnet (PM) linear generator for free-piston energy converter. The operating principle, topology, and design considerations of the machine are investigated. Combining the motion characteristic of free-piston Stirling engine, a tubular dual-stator PM linear generator is designed by finite element method. Some major structural parameters, such as the outer and inner radii of the mover, PM thickness, mover tooth width, tooth width of the outer and inner stators, etc., are optimized to improve the machine performances like thrust capability and power density. In comparison with conventional single-stator PM machines like moving-magnet linear machine and flux-switching linear machine, the proposed dual-stator flux-switching PM machine shows advantages in higher mass power density, higher volume power density, and lighter mover.

A tubular hybrid Halbach/axially-magnetized permanent-magnet linear machine

NASA Astrophysics Data System (ADS)

Sui, Yi; Liu, Yong; Cheng, Luming; Liu, Jiaqi; Zheng, Ping

2017-05-01

A single-phase tubular permanent-magnet linear machine (PMLM) with hybrid Halbach/axially-magnetized PM arrays is proposed for free-piston Stirling power generation system. Machine topology and operating principle are elaborately illustrated. With the sinusoidal speed characteristic of the free-piston Stirling engine considered, the proposed machine is designed and calculated by finite-element analysis (FEA). The main structural parameters, such as outer radius of the mover, radial length of both the axially-magnetized PMs and ferromagnetic poles, axial length of both the middle and end radially-magnetized PMs, etc., are optimized to improve both the force capability and power density. Compared with the conventional PMLMs, the proposed machine features high mass and volume power density, and has the advantages of simple control and low converter cost. The proposed machine topology is applicable to tubular PMLMs with any phases.

Analysis of total productive maintenance (TPM) implementation using overall equipment effectiveness (OEE) and six big losses: A case study

NASA Astrophysics Data System (ADS)

Martomo, Zenithia Intan; Laksono, Pringgo Widyo

2018-02-01

In improving the productivity of the machine, the management of the decision or maintenance policy must be appropriate. In Spinning II unit at PT Apac Inti Corpora, there are 124 ring frame machines that often have breakdown and cause a high downtime so that the production target is not achieved, so this research was conducted on the ring frame machine. This study aims to measure the value of equipment effectiveness, find the root cause of the problem and provide suggestions for improvement. This research begins with measuring the achievement of overall equipment effectiveness (OEE) value, then identifying the six big losses that occur. The results show that the average value of OEE in the ring frame machine is 79.96%, the effectiveness value is quite low because the standard of OEE value for world class company ideally is 85%. The biggest factor that influences the low value of OEE is performance rate with percentage factor six big losses at reduced speed losses of 17.303% of all time loss. Proposed improvement actions are the application of autonomous maintenance, providing training for operators and maintenance technicians and supervising operators in the workplace.

Buying a Laser - Tips and Pearls

PubMed Central

Aurangabadkar, Sanjeev J; Mysore, Venkataram; Ahmed, E Suhail

2014-01-01

Lasers and aesthetic procedures have transformed dermatology practice. They have aided in the treatment of hitherto untreatable conditions and allowed better financial remuneration to the physician. The availability of a variety of laser devices of different makes, specifications and pricing has lead to confusion and dilemma in the mind of the buying physician. There are presently no guidelines available for buying a laser. Since purchase of a laser involves large investments, careful consideration to laser specifications, training, costing, warranty, availability of spares, and reliability of service are important prerequisites. This article describes various factors that are needed to be considered and also attempts to lay down criteria to be assessed while buying a laser system that will be useful to physicians before investing in a laser machine. Practice points Meticulous planning of the type of machine, specifications, financial aspects, maintenance and warranties is important.It is wise to sign a contract or agreement between the buyer and seller before purchase of a laser which covers key aspects of installation, after sales service and maintenance of the machine.Adequate training is essential; understanding laser physics and laser-tissue interaction goes a long way in getting the best out of the machine.The credibility of the dealer and company should be ascertained in order to be assured of after-sales service.Buying used machines, sharing of equipment to offset high initial investments is a good option but even more care is required to ensure proper functioning and maintenance. PMID:25136218

Buying a laser - tips and pearls.

PubMed

Aurangabadkar, Sanjeev J; Mysore, Venkataram; Ahmed, E Suhail

2014-04-01

Lasers and aesthetic procedures have transformed dermatology practice. They have aided in the treatment of hitherto untreatable conditions and allowed better financial remuneration to the physician. The availability of a variety of laser devices of different makes, specifications and pricing has lead to confusion and dilemma in the mind of the buying physician. There are presently no guidelines available for buying a laser. Since purchase of a laser involves large investments, careful consideration to laser specifications, training, costing, warranty, availability of spares, and reliability of service are important prerequisites. This article describes various factors that are needed to be considered and also attempts to lay down criteria to be assessed while buying a laser system that will be useful to physicians before investing in a laser machine. Meticulous planning of the type of machine, specifications, financial aspects, maintenance and warranties is important.It is wise to sign a contract or agreement between the buyer and seller before purchase of a laser which covers key aspects of installation, after sales service and maintenance of the machine.Adequate training is essential; understanding laser physics and laser-tissue interaction goes a long way in getting the best out of the machine.The credibility of the dealer and company should be ascertained in order to be assured of after-sales service.Buying used machines, sharing of equipment to offset high initial investments is a good option but even more care is required to ensure proper functioning and maintenance.

Processing of Novel Nanoparticle Dispersion Strengthened Ceramics for Improved Mechanical Performance

DTIC Science & Technology

1992-12-14

the composite . The top and bottom surfaces of each disc were removed to eliminate any reaction layer, and the discs were machined ’ to produce bars...l.It is postulated that during grinding of the composite , compressive stresses and machining flaws are introduced into the surface. The compressive...two materials considered would react differently to the annealing step. It can be expected that machining flaws will heal in the composite samples

Sealing intersecting vane machines

DOEpatents

Martin, Jedd N.; Chomyszak, Stephen M.

2005-06-07

The invention provides a toroidal intersecting vane machine incorporating intersecting rotors to form primary and secondary chambers whose porting configurations minimize friction and maximize efficiency. Specifically, it is an object of the invention to provide a toroidal intersecting vane machine that greatly reduces the frictional losses through intersecting surfaces without the need for external gearing by modifying the width of one or both tracks at the point of intermeshing. The inventions described herein relate to these improvements.

Sealing intersecting vane machines

DOEpatents

Martin, Jedd N [Providence, RI; Chomyszak, Stephen M [Attleboro, MA

2007-06-05

The invention provides a toroidal intersecting vane machine incorporating intersecting rotors to form primary and secondary chambers whose porting configurations minimize friction and maximize efficiency. Specifically, it is an object of the invention to provide a toroidal intersecting vane machine that greatly reduces the frictional losses through intersecting surfaces without the need for external gearing by modifying the width of one or both tracks at the point of intermeshing. The inventions described herein relate to these improvements.

Augmentation of machine structure to improve its diagnosability

NASA Technical Reports Server (NTRS)

Hsieh, L.

1973-01-01

Two methods of augmenting the structure of a sequential machine so that it is diagnosable are presented. The checkable (checking sequences) and repeated symbol distinguishing sequences (RDS) are discussed. It was found that as few as twice the number of outputs of the given machine is sufficient for constructing a state-output augmentation with RDS. Techniques for minimizing the number of states in resolving convergences and in resolving equivalent and nonreduced cycles are developed.

External validation of ADO, DOSE, COTE and CODEX at predicting death in primary care patients with COPD using standard and machine learning approaches.

PubMed

Morales, Daniel R; Flynn, Rob; Zhang, Jianguo; Trucco, Emmanuel; Quint, Jennifer K; Zutis, Kris

2018-05-01

Several models for predicting the risk of death in people with chronic obstructive pulmonary disease (COPD) exist but have not undergone large scale validation in primary care. The objective of this study was to externally validate these models using statistical and machine learning approaches. We used a primary care COPD cohort identified using data from the UK Clinical Practice Research Datalink. Age-standardised mortality rates were calculated for the population by gender and discrimination of ADO (age, dyspnoea, airflow obstruction), COTE (COPD-specific comorbidity test), DOSE (dyspnoea, airflow obstruction, smoking, exacerbations) and CODEX (comorbidity, dyspnoea, airflow obstruction, exacerbations) at predicting death over 1-3 years measured using logistic regression and a support vector machine learning (SVM) method of analysis. The age-standardised mortality rate was 32.8 (95%CI 32.5-33.1) and 25.2 (95%CI 25.4-25.7) per 1000 person years for men and women respectively. Complete data were available for 54879 patients to predict 1-year mortality. ADO performed the best (c-statistic of 0.730) compared with DOSE (c-statistic 0.645), COTE (c-statistic 0.655) and CODEX (c-statistic 0.649) at predicting 1-year mortality. Discrimination of ADO and DOSE improved at predicting 1-year mortality when combined with COTE comorbidities (c-statistic 0.780 ADO + COTE; c-statistic 0.727 DOSE + COTE). Discrimination did not change significantly over 1-3 years. Comparable results were observed using SVM. In primary care, ADO appears superior at predicting death in COPD. Performance of ADO and DOSE improved when combined with COTE comorbidities suggesting better models may be generated with additional data facilitated using novel approaches. Copyright © 2018. Published by Elsevier Ltd.

An in-silico method for identifying aggregation rate enhancer and mitigator mutations in proteins.

PubMed

Rawat, Puneet; Kumar, Sandeep; Michael Gromiha, M

2018-06-24

Newly synthesized polypeptides must pass stringent quality controls in cells to ensure appropriate folding and function. However, mutations, environmental stresses and aging can reduce efficiencies of these controls, leading to accumulation of protein aggregates, amyloid fibrils and plaques. In-vitro experiments have shown that even single amino acid substitutions can drastically enhance or mitigate protein aggregation kinetics. In this work, we have collected a dataset of 220 unique mutations in 25 proteins and classified them as enhancers or mitigators on the basis of their effect on protein aggregation rate. The data were analyzed via machine learning to identify features capable of distinguishing between aggregation rate enhancers and mitigators. Our initial Support Vector Machine (SVM) model separated such mutations with an overall accuracy of 69%. When local secondary structures at the mutation sites were considered, the accuracies further improved by 13-15%. The machine-learnt features are distinct for each secondary structure class at mutation sites. Protein stability and flexibility changes are important features for mutations in α-helices. β-strand propensity, polarity and charge become important when mutations occur in β-strands and ability to form secondary structure, helical tendency and aggregation propensity are important for mutations lying in coils. These results have been incorporated into a sequence-based algorithm (available at http://www.iitm.ac.in/bioinfo/aggrerate-disc/) capable of predicting whether a mutation will enhance or mitigate a protein's aggregation rate. This algorithm will find several applications towards understanding protein aggregation in human diseases, enable in-silico optimization of biopharmaceuticals and enzymes for improved biophysical attributes and de novo design of bio-nanomaterials. Copyright © 2018. Published by Elsevier B.V.

Combining snow depth and innovative skier flow measurements in order to improve snow grooming techniques

NASA Astrophysics Data System (ADS)

Carmagnola, Carlo Maria; Albrecht, Stéphane; Hargoaa, Olivier

2017-04-01

In the last decades, ski resort managers have massively improved their snow management practices, in order to adapt their strategies to the inter-annual variability in snow conditions and to the effects of climate change. New real-time informations, such as snow depth measurements carried out on the ski slopes by grooming machines during their daily operations, have become available, allowing high saving, efficiency and optimization gains (reducing for instance the groomer fuel consumption and operation time and the need for machine-made snow production). In order to take a step forward in improving the grooming techniques, it would be necessary to keep into account also the snow erosion by skiers, which depends mostly on the snow surface properties and on the skier attendance. Today, however, most ski resort managers have only a vague idea of the evolution of the skier flows on each slope during the winter season. In this context, we have developed a new sensor (named Skiflux) able to measure the skier attendance using an infrared beam crossing the slopes. Ten Skiflux sensors have been deployed during the 2016/17 winter season at Val Thorens ski area (French Alps), covering a whole sector of the resort. A dedicated software showing the number of skier passages in real time as been developed as well. Combining this new Skiflux dataset with the snow depth measurements from grooming machines (Snowsat System) and the snow and meteorological conditions measured in-situ (Liberty System from Technoalpin), we were able to create a "real-time skiability index" accounting for the quality of the surface snow and its evolution during the day. Moreover, this new framework allowed us to improve the preparation of ski slopes, suggesting new strategies for adapting the grooming working schedule to the snow quality and the skier attendance. In the near future, this work will benefit from the advances made within the H2020 PROSNOW project ("Provision of a prediction system allowing for management and optimization of snow in Alpine ski resorts"), which has been funded for the period 2017-2020.

Improving air traffic control: Proving new tools or approving the joint human-machine system?

NASA Technical Reports Server (NTRS)

Gaillard, Irene; Leroux, Marcel

1994-01-01

From the description of a field problem (i.e., designing decision aids for air traffic controllers), this paper points out how a cognitive engineering approach provides the milestones for the evaluation of future joint human-machine systems.

Reinforcement for Stretch Formed Sheet Metal

NASA Technical Reports Server (NTRS)

Lea, J. B.; Baxter, C. R.

1983-01-01

Tearing of aluminum sheet metal durinng stretch forming prevented by flame spraying layer of aluminum on edges held in stretch-forming machine. Technique improves grip of machine on metal and reinforced sheet better able to with stand concentration of force in vicinity of grips.

Early experiences in developing and managing the neuroscience gateway.

PubMed

Sivagnanam, Subhashini; Majumdar, Amit; Yoshimoto, Kenneth; Astakhov, Vadim; Bandrowski, Anita; Martone, MaryAnn; Carnevale, Nicholas T

2015-02-01

The last few decades have seen the emergence of computational neuroscience as a mature field where researchers are interested in modeling complex and large neuronal systems and require access to high performance computing machines and associated cyber infrastructure to manage computational workflow and data. The neuronal simulation tools, used in this research field, are also implemented for parallel computers and suitable for high performance computing machines. But using these tools on complex high performance computing machines remains a challenge because of issues with acquiring computer time on these machines located at national supercomputer centers, dealing with complex user interface of these machines, dealing with data management and retrieval. The Neuroscience Gateway is being developed to alleviate and/or hide these barriers to entry for computational neuroscientists. It hides or eliminates, from the point of view of the users, all the administrative and technical barriers and makes parallel neuronal simulation tools easily available and accessible on complex high performance computing machines. It handles the running of jobs and data management and retrieval. This paper shares the early experiences in bringing up this gateway and describes the software architecture it is based on, how it is implemented, and how users can use this for computational neuroscience research using high performance computing at the back end. We also look at parallel scaling of some publicly available neuronal models and analyze the recent usage data of the neuroscience gateway.

Early experiences in developing and managing the neuroscience gateway

PubMed Central

Sivagnanam, Subhashini; Majumdar, Amit; Yoshimoto, Kenneth; Astakhov, Vadim; Bandrowski, Anita; Martone, MaryAnn; Carnevale, Nicholas. T.

2015-01-01

SUMMARY The last few decades have seen the emergence of computational neuroscience as a mature field where researchers are interested in modeling complex and large neuronal systems and require access to high performance computing machines and associated cyber infrastructure to manage computational workflow and data. The neuronal simulation tools, used in this research field, are also implemented for parallel computers and suitable for high performance computing machines. But using these tools on complex high performance computing machines remains a challenge because of issues with acquiring computer time on these machines located at national supercomputer centers, dealing with complex user interface of these machines, dealing with data management and retrieval. The Neuroscience Gateway is being developed to alleviate and/or hide these barriers to entry for computational neuroscientists. It hides or eliminates, from the point of view of the users, all the administrative and technical barriers and makes parallel neuronal simulation tools easily available and accessible on complex high performance computing machines. It handles the running of jobs and data management and retrieval. This paper shares the early experiences in bringing up this gateway and describes the software architecture it is based on, how it is implemented, and how users can use this for computational neuroscience research using high performance computing at the back end. We also look at parallel scaling of some publicly available neuronal models and analyze the recent usage data of the neuroscience gateway. PMID:26523124

Design and implementation of a system for laser assisted milling of advanced materials

NASA Astrophysics Data System (ADS)

Wu, Xuefeng; Feng, Gaocheng; Liu, Xianli

2016-09-01

Laser assisted machining is an effective method to machine advanced materials with the added benefits of longer tool life and increased material removal rates. While extensive studies have investigated the machining properties for laser assisted milling(LAML), few attempts have been made to extend LAML to machining parts with complex geometric features. A methodology for continuous path machining for LAML is developed by integration of a rotary and movable table into an ordinary milling machine with a laser beam system. The machining strategy and processing path are investigated to determine alignment of the machining path with the laser spot. In order to keep the material removal temperatures above the softening temperature of silicon nitride, the transformation is coordinated and the temperature interpolated, establishing a transient thermal model. The temperatures of the laser center and cutting zone are also carefully controlled to achieve optimal machining results and avoid thermal damage. These experiments indicate that the system results in no surface damage as well as good surface roughness, validating the application of this machining strategy and thermal model in the development of a new LAML system for continuous path processing of silicon nitride. The proposed approach can be easily applied in LAML system to achieve continuous processing and improve efficiency in laser assisted machining.

Defect detection and classification of machined surfaces under multiple illuminant directions

NASA Astrophysics Data System (ADS)

Liao, Yi; Weng, Xin; Swonger, C. W.; Ni, Jun

2010-08-01

Continuous improvement of product quality is crucial to the successful and competitive automotive manufacturing industry in the 21st century. The presence of surface porosity located on flat machined surfaces such as cylinder heads/blocks and transmission cases may allow leaks of coolant, oil, or combustion gas between critical mating surfaces, thus causing damage to the engine or transmission. Therefore 100% inline inspection plays an important role for improving product quality. Although the techniques of image processing and machine vision have been applied to machined surface inspection and well improved in the past 20 years, in today's automotive industry, surface porosity inspection is still done by skilled humans, which is costly, tedious, time consuming and not capable of reliably detecting small defects. In our study, an automated defect detection and classification system for flat machined surfaces has been designed and constructed. In this paper, the importance of the illuminant direction in a machine vision system was first emphasized and then the surface defect inspection system under multiple directional illuminations was designed and constructed. After that, image processing algorithms were developed to realize 5 types of 2D or 3D surface defects (pore, 2D blemish, residue dirt, scratch, and gouge) detection and classification. The steps of image processing include: (1) image acquisition and contrast enhancement (2) defect segmentation and feature extraction (3) defect classification. An artificial machined surface and an actual automotive part: cylinder head surface were tested and, as a result, microscopic surface defects can be accurately detected and assigned to a surface defect class. The cycle time of this system can be sufficiently fast that implementation of 100% inline inspection is feasible. The field of view of this system is 150mm×225mm and the surfaces larger than the field of view can be stitched together in software.

Zooniverse: Combining Human and Machine Classifiers for the Big Survey Era

NASA Astrophysics Data System (ADS)

Fortson, Lucy; Wright, Darryl; Beck, Melanie; Lintott, Chris; Scarlata, Claudia; Dickinson, Hugh; Trouille, Laura; Willi, Marco; Laraia, Michael; Boyer, Amy; Veldhuis, Marten; Zooniverse

2018-01-01

Many analyses of astronomical data sets, ranging from morphological classification of galaxies to identification of supernova candidates, have relied on humans to classify data into distinct categories. Crowdsourced galaxy classifications via the Galaxy Zoo project provided a solution that scaled visual classification for extant surveys by harnessing the combined power of thousands of volunteers. However, the much larger data sets anticipated from upcoming surveys will require a different approach. Automated classifiers using supervised machine learning have improved considerably over the past decade but their increasing sophistication comes at the expense of needing ever more training data. Crowdsourced classification by human volunteers is a critical technique for obtaining these training data. But several improvements can be made on this zeroth order solution. Efficiency gains can be achieved by implementing a “cascade filtering” approach whereby the task structure is reduced to a set of binary questions that are more suited to simpler machines while demanding lower cognitive loads for humans.Intelligent subject retirement based on quantitative metrics of volunteer skill and subject label reliability also leads to dramatic improvements in efficiency. We note that human and machine classifiers may retire subjects differently leading to trade-offs in performance space. Drawing on work with several Zooniverse projects including Galaxy Zoo and Supernova Hunter, we will present recent findings from experiments that combine cohorts of human and machine classifiers. We show that the most efficient system results when appropriate subsets of the data are intelligently assigned to each group according to their particular capabilities.With sufficient online training, simple machines can quickly classify “easy” subjects, leaving more difficult (and discovery-oriented) tasks for volunteers. We also find humans achieve higher classification purity while samples produced by machines are typically more complete. These findings set the stage for further investigations, with the ultimate goal of efficiently and accurately labeling the wide range of data classes that will arise from the planned large astronomical surveys.

Interfacing with the brain using organic electronics (Presentation Recording)

NASA Astrophysics Data System (ADS)

Malliaras, George G.

2015-10-01

Implantable electrodes are being used for diagnostic purposes, for brain-machine interfaces, and for delivering electrical stimulation to alleviate the symptoms of diseases such as Parkinson's. The field of organic electronics made available devices with a unique combination of attractive properties, including mixed ionic/electronic conduction, mechanical flexibility, enhanced biocompatibility, and capability for drug delivery. I will present examples of organic electrodes, transistors and other devices for recording and stimulation of brain activity and discuss how they can improve our understanding of brain physiology and pathology, and how they can be used to deliver new therapies.

Multitasking runtime systems for the Cedar Multiprocessor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guzzi, M.D.

1986-07-01

The programming of a MIMD machine is more complex than for SISD and SIMD machines. The multiple computational resources of the machine must be made available to the programming language compiler and to the programmer so that multitasking programs may be written. This thesis will explore the additional complexity of programming a MIMD machine, the Cedar Multiprocessor specifically, and the multitasking runtime system necessary to provide multitasking resources to the user. First, the problem will be well defined: the Cedar machine, its operating system, the programming language, and multitasking concepts will be described. Second, a solution to the problem, calledmore » macrotasking, will be proposed. This solution provides multitasking facilities to the programmer at a very coarse level with many visible machine dependencies. Third, an alternate solution, called microtasking, will be proposed. This solution provides multitasking facilities of a much finer grain. This solution does not depend so rigidly on the specific architecture of the machine. Finally, the two solutions will be compared for effectiveness. 12 refs., 16 figs.« less

Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data.

PubMed

Yang, Yang; Niehaus, Katherine E; Walker, Timothy M; Iqbal, Zamin; Walker, A Sarah; Wilson, Daniel J; Peto, Tim E A; Crook, Derrick W; Smith, E Grace; Zhu, Tingting; Clifton, David A

2018-05-15

Correct and rapid determination of Mycobacterium tuberculosis (MTB) resistance against available tuberculosis (TB) drugs is essential for the control and management of TB. Conventional molecular diagnostic test assumes that the presence of any well-studied single nucleotide polymorphisms is sufficient to cause resistance, which yields low sensitivity for resistance classification. Given the availability of DNA sequencing data from MTB, we developed machine learning models for a cohort of 1839 UK bacterial isolates to classify MTB resistance against eight anti-TB drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, ciprofloxacin, moxifloxacin, ofloxacin, streptomycin) and to classify multi-drug resistance. Compared to previous rules-based approach, the sensitivities from the best-performing models increased by 2-4% for isoniazid, rifampicin and ethambutol to 97% (P < 0.01), respectively; for ciprofloxacin and multi-drug resistant TB, they increased to 96%. For moxifloxacin and ofloxacin, sensitivities increased by 12 and 15% from 83 and 81% based on existing known resistance alleles to 95% and 96% (P < 0.01), respectively. Particularly, our models improved sensitivities compared to the previous rules-based approach by 15 and 24% to 84 and 87% for pyrazinamide and streptomycin (P < 0.01), respectively. The best-performing models increase the area-under-the-ROC curve by 10% for pyrazinamide and streptomycin (P < 0.01), and 4-8% for other drugs (P < 0.01). The details of source code are provided at http://www.robots.ox.ac.uk/~davidc/code.php. david.clifton@eng.ox.ac.uk. Supplementary data are available at Bioinformatics online.

Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining

PubMed Central

Liang, Qiaokang; Zhang, Dan; Wu, Wanneng; Zou, Kunlin

2016-01-01

Multi-component cutting force sensing systems in manufacturing processes applied to cutting tools are gradually becoming the most significant monitoring indicator. Their signals have been extensively applied to evaluate the machinability of workpiece materials, predict cutter breakage, estimate cutting tool wear, control machine tool chatter, determine stable machining parameters, and improve surface finish. Robust and effective sensing systems with capability of monitoring the cutting force in machine operations in real time are crucial for realizing the full potential of cutting capabilities of computer numerically controlled (CNC) tools. The main objective of this paper is to present a brief review of the existing achievements in the field of multi-component cutting force sensing systems in modern manufacturing. PMID:27854322

Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining.

PubMed

Liang, Qiaokang; Zhang, Dan; Wu, Wanneng; Zou, Kunlin

2016-11-16

Multi-component cutting force sensing systems in manufacturing processes applied to cutting tools are gradually becoming the most significant monitoring indicator. Their signals have been extensively applied to evaluate the machinability of workpiece materials, predict cutter breakage, estimate cutting tool wear, control machine tool chatter, determine stable machining parameters, and improve surface finish. Robust and effective sensing systems with capability of monitoring the cutting force in machine operations in real time are crucial for realizing the full potential of cutting capabilities of computer numerically controlled (CNC) tools. The main objective of this paper is to present a brief review of the existing achievements in the field of multi-component cutting force sensing systems in modern manufacturing.

Mining chemical information from open patents

PubMed Central

2011-01-01

Linked Open Data presents an opportunity to vastly improve the quality of science in all fields by increasing the availability and usability of the data upon which it is based. In the chemical field, there is a huge amount of information available in the published literature, the vast majority of which is not available in machine-understandable formats. PatentEye, a prototype system for the extraction and semantification of chemical reactions from the patent literature has been implemented and is discussed. A total of 4444 reactions were extracted from 667 patent documents that comprised 10 weeks' worth of publications from the European Patent Office (EPO), with a precision of 78% and recall of 64% with regards to determining the identity and amount of reactants employed and an accuracy of 92% with regards to product identification. NMR spectra reported as product characterisation data are additionally captured. PMID:21999425

Controlling corrosion rate of Magnesium alloy using powder mixed electrical discharge machining

NASA Astrophysics Data System (ADS)

Razak, M. A.; Rani, A. M. A.; Saad, N. M.; Littlefair, G.; Aliyu, A. A.

2018-04-01

Biomedical implant can be divided into permanent and temporary employment. The duration of a temporary implant applied to children and adult is different due to different bone healing rate among the children and adult. Magnesium and its alloys are compatible for the biodegradable implanting application. Nevertheless, it is difficult to control the degradation rate of magnesium alloy to suit the application on both the children and adult. Powder mixed electrical discharge machining (PM-EDM) method, a modified EDM process, has high capability to improve the EDM process efficiency and machined surface quality. The objective of this paper is to establish a formula to control the degradation rate of magnesium alloy using the PM-EDM method. The different corrosion rate of machined surface is hypothesized to be obtained by having different combinations of PM-EDM operation inputs. PM-EDM experiments are conducted using an opened-loop PM-EDM system and the in-vitro corrosion tests are carried out on the machined surface of each specimen. There are four operation inputs investigated in this study which are zinc powder concentration, peak current, pulse on-time and pulse off-time. The results indicate that zinc powder concentration is significantly affecting the response with 2 g/l of zinc powder concentration obtaining the lowest corrosion rate. The high localized temperature at the cutting zone in spark erosion process causes some of the zinc particles get deposited on the machined surface, hence improving the surface characteristics. The suspended zinc particles in the dielectric fluid have also improve the sparking efficiency and the uniformity of sparks distribution. From the statistical analysis, a formula was developed to control the corrosion rate of magnesium alloy within the range from 0.000183 mm/year to 0.001528 mm/year.

CP Violation and the Future of Flavor Physics

NASA Astrophysics Data System (ADS)

Kiesling, Christian

2009-12-01

With the nearing completion of the first-generation experiments at asymmetric e+e- colliders running at the Υ(4S) resonance ("B-Factories") a new era of high luminosity machines is at the horizon. We report here on the plans at KEK in Japan to upgrade the KEKB machine ("SuperKEKB") with the goal of achieving an instantaneous luminosity exceeding 8×1035 cm-2 s-1, which is almost two orders of magnitude higher than KEKB. Together with the machine, the Belle detector will be upgraded as well ("Belle-II"), with significant improvements to increase its background tolerance as well as improving its physics performance. The new generation of experiments is scheduled to take first data in the year 2013.

Diagnosis of the Computer-Controlled Milling Machine, Definition of the Working Errors and Input Corrections on the Basis of Mathematical Model

NASA Astrophysics Data System (ADS)

Starikov, A. I.; Nekrasov, R. Yu; Teploukhov, O. J.; Soloviev, I. V.; Narikov, K. A.

2016-10-01

Manufactures, machinery and equipment improve of constructively as science advances and technology, and requirements are improving of quality and longevity. That is, the requirements for surface quality and precision manufacturing, oil and gas equipment parts are constantly increasing. Production of oil and gas engineering products on modern machine tools with computer numerical control - is a complex synthesis of technical and electrical equipment parts, as well as the processing procedure. Technical machine part wears during operation and in the electrical part are accumulated mathematical errors. Thus, the above-mentioned disadvantages of any of the following parts of metalworking equipment affect the manufacturing process of products in general, and as a result lead to the flaw.

Mechanical properties of a new mica-based machinable glass ceramic for CAD/CAM restorations.

PubMed

Thompson, J Y; Bayne, S C; Heymann, H O

1996-12-01

Machinable ceramics (Vita Mark II and Dicor MGC) exhibit good short-term clinical performance, but long-term in vivo fracture resistance is still being monitored. The relatively low fracture toughness of currently available machinable ceramics restricts their use to conservative inlays and onlays. A new machinable glass ceramic (MGC-F) has been developed (Corning Inc.) with enhanced fluorescence and machinability. The purpose of this study was to characterize and compare key mechanical properties of MGC-F to Dicor MGC-Light, Dicor MGC-Dark, and Vita Mark II glass ceramics. The mean fracture toughness and indented biaxial flexure strength of MGC-F were each significantly greater (p < or = 0.01) than that of Dicor MGC-Light, Dicor MGC-Dark, and Vita Mark II ceramic materials. The results of this study indicate the potential for better in vivo fracture resistance of MGC-F compared with existing machinable ceramic materials for CAD/CAM restorations.

16 CFR 423.8 - Exemptions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... washing and drycleaning procedures can safely be used on a product: (1) Machine washing in hot water; (2) Machine drying at a high setting; (3) Ironing at a hot setting; (4) Bleaching with all commercially... National Archives and Records Administration (NARA). For information on the availability of this material...

A COMPARATIVE STUDY OF VIDEO TAPE RECORDINGS.

ERIC Educational Resources Information Center

WIENS, JACOB H.

THE COMPARATIVE EFFECTIVENESS OF PRESENTLY AVAILABLE VIDEO TAPE MACHINES IS REPORTED, FOR THE CONVENIENCE OF SCHOOL ADMINISTRATORS PLANNING TO USE SUCH EQUIPMENT IN EDUCATIONAL PROGRAMS. TESTS WERE CONDUCTED AT THE WIENS ELECTRONIC LABORATORIES. MACHINE BRANDS TESTED WERE AMPEX, CONCORD, MACHTRONICS, PRECISION, RCA, SONY, AND WOLLENSAK. A DETAILED…

16 CFR 423.8 - Exemptions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... washing and drycleaning procedures can safely be used on a product: (1) Machine washing in hot water; (2) Machine drying at a high setting; (3) Ironing at a hot setting; (4) Bleaching with all commercially... National Archives and Records Administration (NARA). For information on the availability of this material...

Surface wind characteristics of some Aleutian Islands. [for selection of windpowered machine sites

NASA Technical Reports Server (NTRS)

Wentink, T., Jr.

1973-01-01

The wind power potential of Alaska is assessed in order to determine promising windpower sites for construction of wind machines and for shipment of wind derived energy. Analyses of near surface wind data from promising Aleutian sites accessible by ocean transport indicate probable velocity regimes and also present deficiencies in available data. It is shown that winds for some degree of power generation are available 77 percent of the time in the Aleutians with peak velocities depending on location.

Assessing the effects of different dielectrics on environmentally conscious powder-mixed EDM of difficult-to-machine material (WC-Co)

NASA Astrophysics Data System (ADS)

Singh, Jagdeep; Sharma, Rajiv Kumar

2016-12-01

Electrical discharge machining (EDM) is a well-known nontraditional manufacturing process to machine the difficult-to-machine (DTM) materials which have unique hardness properties. Researchers have successfully performed hybridization to improve this process by incorporating powders into the EDM process known as powder-mixed EDM process. This process drastically improves process efficiency by increasing material removal rate, micro-hardness, as well as reducing the tool wear rate and surface roughness. EDM also has some input parameters, including pulse-on time, dielectric levels and its type, current setting, flushing pressure, and so on, which have a significant effect on EDM performance. However, despite their positive influence, investigating the effects of these parameters on environmental conditions is necessary. Most studies demonstrate the use of kerosene oil as dielectric fluid. Nevertheless, in this work, the authors highlight the findings with respect to three different dielectric fluids, including kerosene oil, EDM oil, and distilled water using one-variable-at-a-time approach for machining as well as environmental aspects. The hazard and operability analysis is employed to identify the inherent safety factors associated with powder-mixed EDM of WC-Co.

Electric machine differential for vehicle traction control and stability control

NASA Astrophysics Data System (ADS)

Kuruppu, Sandun Shivantha

Evolving requirements in energy efficiency and tightening regulations for reliable electric drivetrains drive the advancement of the hybrid electric (HEV) and full electric vehicle (EV) technology. Different configurations of EV and HEV architectures are evaluated for their performance. The future technology is trending towards utilizing distinctive properties in electric machines to not only to improve efficiency but also to realize advanced road adhesion controls and vehicle stability controls. Electric machine differential (EMD) is such a concept under current investigation for applications in the near future. Reliability of a power train is critical. Therefore, sophisticated fault detection schemes are essential in guaranteeing reliable operation of a complex system such as an EMD. The research presented here emphasize on implementation of a 4kW electric machine differential, a novel single open phase fault diagnostic scheme, an implementation of a real time slip optimization algorithm and an electric machine differential based yaw stability improvement study. The proposed d-q current signature based SPO fault diagnostic algorithm detects the fault within one electrical cycle. The EMD based extremum seeking slip optimization algorithm reduces stopping distance by 30% compared to hydraulic braking based ABS.

Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles.

PubMed

Bisgin, Halil; Bera, Tanmay; Ding, Hongjian; Semey, Howard G; Wu, Leihong; Liu, Zhichao; Barnes, Amy E; Langley, Darryl A; Pava-Ripoll, Monica; Vyas, Himansu J; Tong, Weida; Xu, Joshua

2018-04-25

Insect pests, such as pantry beetles, are often associated with food contaminations and public health risks. Machine learning has the potential to provide a more accurate and efficient solution in detecting their presence in food products, which is currently done manually. In our previous research, we demonstrated such feasibility where Artificial Neural Network (ANN) based pattern recognition techniques could be implemented for species identification in the context of food safety. In this study, we present a Support Vector Machine (SVM) model which improved the average accuracy up to 85%. Contrary to this, the ANN method yielded ~80% accuracy after extensive parameter optimization. Both methods showed excellent genus level identification, but SVM showed slightly better accuracy  for most species. Highly accurate species level identification remains a challenge, especially in distinguishing between species from the same genus which may require improvements in both imaging and machine learning techniques. In summary, our work does illustrate a new SVM based technique and provides a good comparison with the ANN model in our context. We believe such insights will pave better way forward for the application of machine learning towards species identification and food safety.

Reliability Evaluation and Improvement Approach of Chemical Production Man - Machine - Environment System

NASA Astrophysics Data System (ADS)

Miao, Yongchun; Kang, Rongxue; Chen, Xuefeng

2017-12-01

In recent years, with the gradual extension of reliability research, the study of production system reliability has become the hot topic in various industries. Man-machine-environment system is a complex system composed of human factors, machinery equipment and environment. The reliability of individual factor must be analyzed in order to gradually transit to the research of three-factor reliability. Meanwhile, the dynamic relationship among man-machine-environment should be considered to establish an effective blurry evaluation mechanism to truly and effectively analyze the reliability of such systems. In this paper, based on the system engineering, fuzzy theory, reliability theory, human error, environmental impact and machinery equipment failure theory, the reliabilities of human factor, machinery equipment and environment of some chemical production system were studied by the method of fuzzy evaluation. At last, the reliability of man-machine-environment system was calculated to obtain the weighted result, which indicated that the reliability value of this chemical production system was 86.29. Through the given evaluation domain it can be seen that the reliability of man-machine-environment integrated system is in a good status, and the effective measures for further improvement were proposed according to the fuzzy calculation results.

Testing of Anesthesia Machines and Defibrillators in Healthcare Institutions.

PubMed

Gurbeta, Lejla; Dzemic, Zijad; Bego, Tamer; Sejdic, Ervin; Badnjevic, Almir

2017-09-01

To improve the quality of patient treatment by improving the functionality of medical devices in healthcare institutions. To present the results of the safety and performance inspection of patient-relevant output parameters of anesthesia machines and defibrillators defined by legal metrology. This study covered 130 anesthesia machines and 161 defibrillators used in public and private healthcare institutions, during a period of two years. Testing procedures were carried out according to international standards and legal metrology legislative procedures in Bosnia and Herzegovina. The results show that in 13.84% of tested anesthesia machine and 14.91% of defibrillators device performance is not in accordance with requirements and should either have its results be verified, or the device removed from use or scheduled for corrective maintenance. Research emphasizes importance of independent safety and performance inspections, and gives recommendations for the frequency of inspection based on measurements. Results offer implications for adequacy of preventive and corrective maintenance performed in healthcare institutions. Based on collected data, the first digital electronical database of anesthesia machines and defibrillators used in healthcare institutions in Bosnia and Herzegovina is created. This database is a useful tool for tracking each device's performance over time.

An incremental anomaly detection model for virtual machines.

PubMed

Zhang, Hancui; Chen, Shuyu; Liu, Jun; Zhou, Zhen; Wu, Tianshu

2017-01-01

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.

An incremental anomaly detection model for virtual machines

PubMed Central

Zhang, Hancui; Chen, Shuyu; Liu, Jun; Zhou, Zhen; Wu, Tianshu

2017-01-01

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform. PMID:29117245

Optical character recognition reading aid for the visually impaired.

PubMed

Grandin, Juan Carlos; Cremaschi, Fabian; Lombardo, Elva; Vitu, Ed; Dujovny, Manuel

2008-06-01

An optical character recognition (OCR) reading machine is a significant help for visually impaired patients. An OCR reading machine is used. This instrument can provide a significant help in order to improve the quality of life of patients with low vision or blindness.

[The testing system for OCP of the digital X-ray machine].

PubMed

Wang, Yan; Mo, Guoming; Wang, Juru; Zhou, Tao; Yu, Jianguo

2011-09-01

In this paper, we designed a testing system for operator control panel of a high-voltage and high-frequency X-ray machine, and an online testing software for functional components, in order to help the testing engineers to improve their work efficiency.

A Survey of Statistical Machine Translation

DTIC Science & Technology

2007-04-01

methods are notoriously sen- sitive to domain differences, however, so the move to informal text is likely to present many interesting challenges ...Och, Christoph Tillman, and Hermann Ney. Improved alignment models for statistical machine translation. In Proc. of EMNLP- VLC , pages 20–28, Jun 1999

Hardware Acceleration of Adaptive Neural Algorithms.

DOE Office of Scientific and Technical Information (OSTI.GOV)

James, Conrad D.

As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - worldmore » conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.« less

Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

PubMed

Piccinini, Filippo; Balassa, Tamas; Szkalisity, Abel; Molnar, Csaba; Paavolainen, Lassi; Kujala, Kaisa; Buzas, Krisztina; Sarazova, Marie; Pietiainen, Vilja; Kutay, Ulrike; Smith, Kevin; Horvath, Peter

2017-06-28

High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. Copyright © 2017 Elsevier Inc. All rights reserved.

Study on electroplating technology of diamond tools for machining hard and brittle materials

NASA Astrophysics Data System (ADS)

Cui, Ying; Chen, Jian Hua; Sun, Li Peng; Wang, Yue

2016-10-01

With the development of the high speed cutting, the ultra-precision machining and ultrasonic vibration technique in processing hard and brittle material , the requirement of cutting tools is becoming higher and higher. As electroplated diamond tools have distinct advantages, such as high adaptability, high durability, long service life and good dimensional stability, the cutting tools are effective and extensive used in grinding hard and brittle materials. In this paper, the coating structure of electroplating diamond tool is described. The electroplating process flow is presented, and the influence of pretreatment on the machining quality is analyzed. Through the experimental research and summary, the reasonable formula of the electrolyte, the electroplating technologic parameters and the suitable sanding method were determined. Meanwhile, the drilling experiment on glass-ceramic shows that the electroplating process can effectively improve the cutting performance of diamond tools. It has laid a good foundation for further improving the quality and efficiency of the machining of hard and brittle materials.

Intraoperative performance and postoperative outcome comparison of longitudinal, torsional, and transversal phacoemulsification machines.

PubMed

Christakis, Panos G; Braga-Mele, Rosa M

2012-02-01

To compare the intraoperative performance and postoperative outcomes of 3 phacoemulsification machines that use different modes. Kensington Eye Institute, Toronto, Ontario, Canada. Comparative case series. This chart and video review comprised consecutive eligible patients who had phacoemulsification by the same surgeon using a Whitestar Signature Ellips-FX (transversal), Infiniti-Ozil-IP (torsional), or Stellaris (longitudinal) machine. The review included 98 patients. Baseline characteristics in the groups were similar; the mean nuclear sclerosis grade was 2.0 ± 0.8. There were no significant intraoperative complications. The torsional machine averaged less phacoemulsification needle time (83 ± 33 seconds) than the transversal (99 ± 40 seconds; P=.21) or longitudinal (110 ± 45 seconds; P=.02) machines; the difference was accentuated in cases with high-grade nuclear sclerosis. The torsional machine had less chatter and better followability than the transversal or longitudinal machines (P<.001). The torsional and longitudinal machines had better anterior chamber stability than the transversal machine (P<.001). Postoperatively, the torsional machine yielded less central corneal edema than the transversal (P<.001) and longitudinal (P=.04) machines, corresponding to a smaller increase in mean corneal thickness (torsional 5%, transversal 10%, longitudinal 12%; P=.04). Also, the torsional machine had better 1-day postoperative visual acuities (P<.001). All 3 phacoemulsification machines were effective with no significant intraoperative complications. The torsional machine outperformed the transversal and longitudinal machines, with a lower mean needle time, less chatter, and improved followability. This corresponded to less corneal edema 1 day postoperatively and better visual acuity. Copyright © 2011 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Effects of retrofit emission controls and work practices on perchloroethylene exposures in small dry-cleaning shops.

PubMed

Ewers, Lynda M; Ruder, Avima M; Petersen, Martin R; Earnest, G Scott; Goldenhar, Linda M

2002-02-01

The effectiveness of commercially available interventions for reducing workers' perchloroethylene exposures in three small dry-cleaning shops was evaluated. Depending upon machine configuration, the intervention consisted of the addition of either a refrigerated condenser or a closed-loop carbon adsorber to the existing dry-cleaning machine. These relatively inexpensive (less than $5000) engineering controls were designed to reduce perchloroethylene emissions when dry-cleaning machine doors were opened for loading or unloading. Effectiveness of the interventions was judged by comparing pre- and postintervention perchloroethylene exposures using three types of measurements in each shop: (1) full-shift, personal breathing zone, air monitoring, (2) next-morning, end-exhaled worker breath concentrations of perchloroethylene, and (3) differences in the end-exhaled breath perchloroethylene concentrations before and after opening the dry-cleaning machine door. In general, measurements supported the hypothesis that machine operators' exposures to perchloroethylene can be reduced. However, work practices, especially maintenance practices, influenced exposures more than was originally anticipated. Only owners of dry-cleaning machines in good repair, with few leaks, should consider retrofitting them, and only after consultation with their machine's manufacturer. If machines are in poor condition, a new machine or alternative technology should be considered. Shop owners and employees should never circumvent safety features on dry-cleaning machines.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

PubMed Central

Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

PubMed

Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

Evaluation of machine learning algorithms for improved risk assessment for Down's syndrome.

PubMed

Koivu, Aki; Korpimäki, Teemu; Kivelä, Petri; Pahikkala, Tapio; Sairanen, Mikko

2018-05-04

Prenatal screening generates a great amount of data that is used for predicting risk of various disorders. Prenatal risk assessment is based on multiple clinical variables and overall performance is defined by how well the risk algorithm is optimized for the population in question. This article evaluates machine learning algorithms to improve performance of first trimester screening of Down syndrome. Machine learning algorithms pose an adaptive alternative to develop better risk assessment models using the existing clinical variables. Two real-world data sets were used to experiment with multiple classification algorithms. Implemented models were tested with a third, real-world, data set and performance was compared to a predicate method, a commercial risk assessment software. Best performing deep neural network model gave an area under the curve of 0.96 and detection rate of 78% with 1% false positive rate with the test data. Support vector machine model gave area under the curve of 0.95 and detection rate of 61% with 1% false positive rate with the same test data. When compared with the predicate method, the best support vector machine model was slightly inferior, but an optimized deep neural network model was able to give higher detection rates with same false positive rate or similar detection rate but with markedly lower false positive rate. This finding could further improve the first trimester screening for Down syndrome, by using existing clinical variables and a large training data derived from a specific population. Copyright © 2018 Elsevier Ltd. All rights reserved.