Build your own time machine

CERN Document Server

Clegg, Brian

2012-01-01

There is no physical law to prevent time travel nothing in physics to say it is impossible. So who is to say it can't be done? In Build Your Own Time Machine, acclaimed science writer Brian Clegg takes inspiration from his childhood heroes, Doctor Who and H. G. Wells, to explain the nature of time. How do we understand it and why measure it the way we do? How did the theories of one man change the way time was perceived by the world? Why wouldn't H. G. Wells's time machine have worked? And what would we need to do to make a real one? Build Your Own Time Machine explores the amazing possib

Automated Classification of Heritage Buildings for As-Built Bim Using Machine Learning Techniques

Science.gov (United States)

Bassier, M.; Vergauwen, M.; Van Genechten, B.

2017-08-01

Semantically rich three dimensional models such as Building Information Models (BIMs) are increasingly used in digital heritage. They provide the required information to varying stakeholders during the different stages of the historic buildings life cyle which is crucial in the conservation process. The creation of as-built BIM models is based on point cloud data. However, manually interpreting this data is labour intensive and often leads to misinterpretations. By automatically classifying the point cloud, the information can be proccesed more effeciently. A key aspect in this automated scan-to-BIM process is the classification of building objects. In this research we look to automatically recognise elements in existing buildings to create compact semantic information models. Our algorithm efficiently extracts the main structural components such as floors, ceilings, roofs, walls and beams despite the presence of significant clutter and occlusions. More specifically, Support Vector Machines (SVM) are proposed for the classification. The algorithm is evaluated using real data of a variety of existing buildings. The results prove that the used classifier recognizes the objects with both high precision and recall. As a result, entire data sets are reliably labelled at once. The approach enables experts to better document and process heritage assets.

An Automatic Assembling System for Sealing Rings Based on Machine Vision

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Mingyu Gao

2017-01-01

Full Text Available In order to grab and place the sealing rings of battery lid quickly and accurately, an automatic assembling system for sealing rings based on machine vision is developed in this paper. The whole system is composed of the light sources, cameras, industrial control units, and a 4-degree-of-freedom industrial robot. Specifically, the sealing rings are recognized and located automatically with the machine vision module. Then industrial robot is controlled for grabbing the sealing rings dynamically under the joint work of multiple control units and visual feedback. Furthermore, the coordinates of the fast-moving battery lid are tracked by the machine vision module. Finally the sealing rings are placed on the sealing ports of battery lid accurately and automatically. Experimental results demonstrate that the proposed system can grab the sealing rings and place them on the sealing port of the fast-moving battery lid successfully. More importantly, the proposed system can improve the efficiency of the battery production line obviously.

Automatic tea service machine with instantaneous cooling unit; Shunkan reikyaku kiko tosai jido kyuchaki

Energy Technology Data Exchange (ETDEWEB)

Sato, T.; Nishiyama, A. [Fuji Denki Reiki Co. Ltd., Tokyo (Japan)

1999-08-10

The market of automatic tea service machines demands sanitation, reduction in time required for pouring tea, feeding the material, cleaning the inside of the machine, the maintenance and service of the machine, and excellent design. To meet these demands, Fuji Electric has changed the former configuration and components for functions and has developed a new series of instantaneous automatic tea service machines based on a new concept. This paper outlines the advantages of the instantaneous cooling tank and improvements in handling. (author)

Automatic testing in the integration phase of mobile work machine (TINAT) - MASIT31

Energy Technology Data Exchange (ETDEWEB)

Multanen, P.; Hyvoenen, M. (Tampere University of Technology, Department of Intelligent Hydraulics and Automation, Tampere (Finland)); Ellman, A. (Tampere University of Technology, Department of Mechanics and Design, Tampere (Finland)); Rantala, S.; Alanen, J. (VTT Technical Research Centre of Finland, Espoo (Finland))

2008-07-01

Abstract The performance and reliability of mobile work machines are significantly affected by control systems of machines and their characteristics. Currently the testing of control systems and verification of their properties is often carried out just in the integration phase of controls and mechanical structure of machine. This is very time consuming, requires a lot of test personnel and is not extensive enough. In TINAT project a test concept will be developed for the testing of entire control systems of work machines without real the mechanical structures of the machines by utilizing modelling and real-time hardware-in-the-loop simulation. The simulator system enables automatic generation of test scenarios and automatic analysis and reporting of test results. (orig.)

An Automatic Decision-Making Mechanism for Virtual Machine Live Migration in Private Clouds

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Ming-Tsung Kao

2014-01-01

Full Text Available Due to the increasing number of computer hosts deployed in an enterprise, automatic management of electronic applications is inevitable. To provide diverse services, there will be increases in procurement, maintenance, and electricity costs. Virtualization technology is getting popular in cloud computing environment, which enables the efficient use of computing resources and reduces the operating cost. In this paper, we present an automatic mechanism to consolidate virtual servers and shut down the idle physical machines during the off-peak hours, while activating more machines at peak times. Through the monitoring of system resources, heavy system loads can be evenly distributed over physical machines to achieve load balancing. By integrating the feature of load balancing with virtual machine live migration, we successfully develop an automatic private cloud management system. Experimental results demonstrate that, during the off-peak hours, we can save power consumption of about 69 W by consolidating the idle virtual servers. And the load balancing implementation has shown that two machines with 80% and 40% CPU loads can be uniformly balanced to 60% each. And, through the use of preallocated virtual machine images, the proposed mechanism can be easily applied to a large amount of physical machines.

Design of electric control system for automatic vegetable bundling machine

Science.gov (United States)

Bao, Yan

2017-06-01

A design can meet the requirements of automatic bale food structure and has the advantages of simple circuit, and the volume is easy to enhance the electric control system of machine carrying bunch of dishes and low cost. The bundle of vegetable machine should meet the sensor to detect and control, in order to meet the control requirements; binding force can be adjusted by the button to achieve; strapping speed also can be adjusted, by the keys to set; sensors and mechanical line connection, convenient operation; can be directly connected with the plug, the 220V power supply can be connected to a power source; if, can work, by the transmission signal sensor, MCU to control the motor, drive and control procedures for small motor. The working principle of LED control circuit and temperature control circuit is described. The design of electric control system of automatic dish machine.

Automatic 3d Building Model Generations with Airborne LiDAR Data

Science.gov (United States)

Yastikli, N.; Cetin, Z.

2017-11-01

LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D) modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified that automatic 3D

AUTOMATIC 3D BUILDING MODEL GENERATIONS WITH AIRBORNE LiDAR DATA

Directory of Open Access Journals (Sweden)

N. Yastikli

2017-11-01

Full Text Available LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified

Interface unit and software of X-ray television automatic machine

International Nuclear Information System (INIS)

Molodykh, V.A.; Yamanaev, M.S.

1983-01-01

Description of the interface unit and specialized software of X-ray television automatic machine is presented. An algorithm for automatic defect survey, measuring of defect geometric parameters with a successive estimate of control quality in accordance with technical norms is proposed. Experimental investigation results on the quality of welded joints of steel tubes obtained using the above system are summarized

Novel Automatic Filter-Class Feature Selection for Machine Learning Regression

DEFF Research Database (Denmark)

Wollsen, Morten Gill; Hallam, John; Jørgensen, Bo Nørregaard

2017-01-01

With the increased focus on application of Big Data in all sectors of society, the performance of machine learning becomes essential. Efficient machine learning depends on efficient feature selection algorithms. Filter feature selection algorithms are model-free and therefore very fast, but require...... model in the feature selection process. PCA is often used in machine learning litterature and can be considered the default feature selection method. RDESF outperformed PCA in both experiments in both prediction error and computational speed. RDESF is a new step into filter-based automatic feature...

An obstacle to building a time machine

International Nuclear Information System (INIS)

Carroll, S.M.; Farhi, E.; Guth, A.H.

1992-01-01

Gott has shown that a spacetime with two infinite parallel cosmic strings passing each other with sufficient velocity contains closed timelike curves. We discuss an attempt to build such a time machine. Using the energy-momentum conservation laws in the equivalent (2+1)-dimensional theory, we explicitly construct the spacetime representing the decay of one gravitating particle into two. We find that there is never enough mass in an open universe to build the time machine from the products of decays of stationary particles. More generally, the Gott time machine cannot exist in any open (2+1)-dimensional universe for which the total momentum is timelike

Towards an automatic model transformation mechanism from UML state machines to DEVS models

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Ariel González

2015-08-01

Full Text Available The development of complex event-driven systems requires studies and analysis prior to deployment with the goal of detecting unwanted behavior. UML is a language widely used by the software engineering community for modeling these systems through state machines, among other mechanisms. Currently, these models do not have appropriate execution and simulation tools to analyze the real behavior of systems. Existing tools do not provide appropriate libraries (sampling from a probability distribution, plotting, etc. both to build and to analyze models. Modeling and simulation for design and prototyping of systems are widely used techniques to predict, investigate and compare the performance of systems. In particular, the Discrete Event System Specification (DEVS formalism separates the modeling and simulation; there are several tools available on the market that run and collect information from DEVS models. This paper proposes a model transformation mechanism from UML state machines to DEVS models in the Model-Driven Development (MDD context, through the declarative QVT Relations language, in order to perform simulations using tools, such as PowerDEVS. A mechanism to validate the transformation is proposed. Moreover, examples of application to analyze the behavior of an automatic banking machine and a control system of an elevator are presented.

Crowd-sourced data collection to support automatic classification of building footprint data

Science.gov (United States)

Hecht, Robert; Kalla, Matthias; Krüger, Tobias

2018-05-01

Human settlements are mainly formed by buildings with their different characteristics and usage. Despite the importance of buildings for the economy and society, complete regional or even national figures of the entire building stock and its spatial distribution are still hardly available. Available digital topographic data sets created by National Mapping Agencies or mapped voluntarily through a crowd via Volunteered Geographic Information (VGI) platforms (e.g. OpenStreetMap) contain building footprint information but often lack additional information on building type, usage, age or number of floors. For this reason, predictive modeling is becoming increasingly important in this context. The capabilities of machine learning allow for the prediction of building types and other building characteristics and thus, the efficient classification and description of the entire building stock of cities and regions. However, such data-driven approaches always require a sufficient amount of ground truth (reference) information for training and validation. The collection of reference data is usually cost-intensive and time-consuming. Experiences from other disciplines have shown that crowdsourcing offers the possibility to support the process of obtaining ground truth data. Therefore, this paper presents the results of an experimental study aiming at assessing the accuracy of non-expert annotations on street view images collected from an internet crowd. The findings provide the basis for a future integration of a crowdsourcing component into the process of land use mapping, particularly the automatic building classification.

An automatic taxonomy of galaxy morphology using unsupervised machine learning

Science.gov (United States)

Hocking, Alex; Geach, James E.; Sun, Yi; Davey, Neil

2018-01-01

We present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy we use no pre-selection or pre-filtering of target galaxy type to identify galaxies that are similar. We demonstrate the technique on the Hubble Space Telescope (HST) Frontier Fields. By training the algorithm using galaxies from one field (Abell 2744) and applying the result to another (MACS 0416.1-2403), we show how the algorithm can cleanly separate early and late type galaxies without any form of pre-directed training for what an 'early' or 'late' type galaxy is. We then apply the technique to the HST Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) fields, creating a catalogue of approximately 60 000 classifications. We show how the automatic classification groups galaxies of similar morphological (and photometric) type and make the classifications public via a catalogue, a visual catalogue and galaxy similarity search. We compare the CANDELS machine-based classifications to human-classifications from the Galaxy Zoo: CANDELS project. Although there is not a direct mapping between Galaxy Zoo and our hierarchical labelling, we demonstrate a good level of concordance between human and machine classifications. Finally, we show how the technique can be used to identify rarer objects and present lensed galaxy candidates from the CANDELS imaging.

a Method for the Seamlines Network Automatic Selection Based on Building Vector

Science.gov (United States)

Li, P.; Dong, Y.; Hu, Y.; Li, X.; Tan, P.

2018-04-01

In order to improve the efficiency of large scale orthophoto production of city, this paper presents a method for automatic selection of seamlines network in large scale orthophoto based on the buildings' vector. Firstly, a simple model of the building is built by combining building's vector, height and DEM, and the imaging area of the building on single DOM is obtained. Then, the initial Voronoi network of the measurement area is automatically generated based on the positions of the bottom of all images. Finally, the final seamlines network is obtained by optimizing all nodes and seamlines in the network automatically based on the imaging areas of the buildings. The experimental results show that the proposed method can not only get around the building seamlines network quickly, but also remain the Voronoi network' characteristics of projection distortion minimum theory, which can solve the problem of automatic selection of orthophoto seamlines network in image mosaicking effectively.

Integrating Automatic Speech Recognition and Machine Translation for Better Translation Outputs

DEFF Research Database (Denmark)

Liyanapathirana, Jeevanthi

translations, combining machine translation with computer assisted translation has drawn attention in current research. This combines two prospects: the opportunity of ensuring high quality translation along with a significant performance gain. Automatic Speech Recognition (ASR) is another important area......, which caters important functionalities in language processing and natural language understanding tasks. In this work we integrate automatic speech recognition and machine translation in parallel. We aim to avoid manual typing of possible translations as dictating the translation would take less time...... to the n-best list rescoring, we also use word graphs with the expectation of arriving at a tighter integration of ASR and MT models. Integration methods include constraining ASR models using language and translation models of MT, and vice versa. We currently develop and experiment different methods...

Automatic Modulation Recognition by Support Vector Machines Using Wavelet Kernel

Energy Technology Data Exchange (ETDEWEB)

Feng, X Z; Yang, J; Luo, F L; Chen, J Y; Zhong, X P [College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha (China)

2006-10-15

Automatic modulation identification plays a significant role in electronic warfare, electronic surveillance systems and electronic counter measure. The task of modulation recognition of communication signals is to determine the modulation type and signal parameters. In fact, automatic modulation identification can be range to an application of pattern recognition in communication field. The support vector machines (SVM) is a new universal learning machine which is widely used in the fields of pattern recognition, regression estimation and probability density. In this paper, a new method using wavelet kernel function was proposed, which maps the input vector xi into a high dimensional feature space F. In this feature space F, we can construct the optimal hyperplane that realizes the maximal margin in this space. That is to say, we can use SVM to classify the communication signals into two groups, namely analogue modulated signals and digitally modulated signals. In addition, computer simulation results are given at last, which show good performance of the method.

Automatic Modulation Recognition by Support Vector Machines Using Wavelet Kernel

International Nuclear Information System (INIS)

Feng, X Z; Yang, J; Luo, F L; Chen, J Y; Zhong, X P

2006-01-01

Automatic modulation identification plays a significant role in electronic warfare, electronic surveillance systems and electronic counter measure. The task of modulation recognition of communication signals is to determine the modulation type and signal parameters. In fact, automatic modulation identification can be range to an application of pattern recognition in communication field. The support vector machines (SVM) is a new universal learning machine which is widely used in the fields of pattern recognition, regression estimation and probability density. In this paper, a new method using wavelet kernel function was proposed, which maps the input vector xi into a high dimensional feature space F. In this feature space F, we can construct the optimal hyperplane that realizes the maximal margin in this space. That is to say, we can use SVM to classify the communication signals into two groups, namely analogue modulated signals and digitally modulated signals. In addition, computer simulation results are given at last, which show good performance of the method

Building machine learning systems with Python

CERN Document Server

Richert, Willi

2013-01-01

This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to pro

Machine Learning Classification of Buildings for Map Generalization

Directory of Open Access Journals (Sweden)

Jaeeun Lee

2017-10-01

Full Text Available A critical problem in mapping data is the frequent updating of large data sets. To solve this problem, the updating of small-scale data based on large-scale data is very effective. Various map generalization techniques, such as simplification, displacement, typification, elimination, and aggregation, must therefore be applied. In this study, we focused on the elimination and aggregation of the building layer, for which each building in a large scale was classified as “0-eliminated,” “1-retained,” or “2-aggregated.” Machine-learning classification algorithms were then used for classifying the buildings. The data of 1:1000 scale and 1:25,000 scale digital maps obtained from the National Geographic Information Institute were used. We applied to these data various machine-learning classification algorithms, including naive Bayes (NB, decision tree (DT, k-nearest neighbor (k-NN, and support vector machine (SVM. The overall accuracies of each algorithm were satisfactory: DT, 88.96%; k-NN, 88.27%; SVM, 87.57%; and NB, 79.50%. Although elimination is a direct part of the proposed process, generalization operations, such as simplification and aggregation of polygons, must still be performed for buildings classified as retained and aggregated. Thus, these algorithms can be used for building classification and can serve as preparatory steps for building generalization.

Automatic Generation of Machine Emulators: Efficient Synthesis of Robust Virtual Machines for Legacy Software Migration

DEFF Research Database (Denmark)

Franz, Michael; Gal, Andreas; Probst, Christian

2006-01-01

As older mainframe architectures become obsolete, the corresponding le- gacy software is increasingly executed via platform emulators running on top of more modern commodity hardware. These emulators are virtual machines that often include a combination of interpreters and just-in-time compilers....... Implementing interpreters and compilers for each combination of emulated and target platform independently of each other is a redundant and error-prone task. We describe an alternative approach that automatically synthesizes specialized virtual-machine interpreters and just-in-time compilers, which...... then execute on top of an existing software portability platform such as Java. The result is a considerably reduced implementation effort....

Component simulation in problems of calculated model formation of automatic machine mechanisms

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Telegin Igor

2017-01-01

Full Text Available The paper deals with the problems of the component simulation method application in the problems of the automation of the mechanical system model formation with the further possibility of their CAD-realization. The purpose of the investigations mentioned consists in the automation of the CAD-model formation of high-speed mechanisms in automatic machines and in the analysis of dynamic processes occurred in their units taking into account their elasto-inertial properties, power dissipation, gaps in kinematic pairs, friction forces, design and technological loads. As an example in the paper there are considered a formalization of stages in the computer model formation of the cutting mechanism in cold stamping automatic machine AV1818 and methods of for the computation of their parameters on the basis of its solid-state model.

Trends in the structures development of the regional machine-building complex

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Ershova I.V.

2017-01-01

Full Text Available In the process of market reforms of the Russian machine-building complex several distinct periods can be revealed. In this article the authors define periods of mass disintegration and spontaneous integration (since the beginning of the reforms until the financial crisis of 1996, post-crisis stabilization, directional specialization (2000-2008 and evolutionary development (since 2010. The economic consequences of the enterprises mergers and divisions are shown on the example of machine-building enterprises of the Middle Urals. The aim of this study is to substantiate the methodical approach to the selection of the optimal organizational structure for the machine-building business. The necessity of taking into account the extent of the personnel diversification and the production volume has been revealed for the optimum organizational structure determination in the machine-building associations. The authors have analyzed sales profitability of the 2745 machine-building enterprises, depending on the production scale and industry sector. The factors affecting the development of cooperative ties and outsourcing have been defined. The authors have made a conclusion that it is necessary to form technological chains as a new kind of business associations.

System approach to machine building enterprise innovative activity management

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І.V. Levytska

2016-12-01

Full Text Available The company, which operates in a challenging competitive environment should focus on new products and provide innovative services that enhance their innovation to maintain the company’s market position. The article deals with the peculiarities of such an activity in the company. The authors analyze the various approaches used in the management, and supply the advantages and disadvantages of each. It is determine that the most optimal approach among them is a system approach. The definition of the consepts "a system" and "a systematic approach to innovative activity management" are suggested. The article works out the system of machine building enterprise innovative activity management, the organization of machine building enterprise innovative activity; the planning of machine building enterprise innovative activity; the control in the system of machine building enterprise innovative activity management; the elements of the control subsystem. The properties, typical for the system of innovative management, are supplied. The managers, engaged in enterprise innovative activity management, must perform a number of the suggested tasks, which affect the efficiency of the enterprise as a whole. These exact tasks are performed using the systematic approach, providing the enterprise competitive operation and quick adaptation to changes in the external environment.

Automatic Detection of Acromegaly From Facial Photographs Using Machine Learning Methods.

Science.gov (United States)

Kong, Xiangyi; Gong, Shun; Su, Lijuan; Howard, Newton; Kong, Yanguo

2018-01-01

Automatic early detection of acromegaly is theoretically possible from facial photographs, which can lessen the prevalence and increase the cure probability. In this study, several popular machine learning algorithms were used to train a retrospective development dataset consisting of 527 acromegaly patients and 596 normal subjects. We firstly used OpenCV to detect the face bounding rectangle box, and then cropped and resized it to the same pixel dimensions. From the detected faces, locations of facial landmarks which were the potential clinical indicators were extracted. Frontalization was then adopted to synthesize frontal facing views to improve the performance. Several popular machine learning methods including LM, KNN, SVM, RT, CNN, and EM were used to automatically identify acromegaly from the detected facial photographs, extracted facial landmarks, and synthesized frontal faces. The trained models were evaluated using a separate dataset, of which half were diagnosed as acromegaly by growth hormone suppression test. The best result of our proposed methods showed a PPV of 96%, a NPV of 95%, a sensitivity of 96% and a specificity of 96%. Artificial intelligence can automatically early detect acromegaly with a high sensitivity and specificity. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction.

Science.gov (United States)

Luo, Gang

2016-01-01

Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. This paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. We did a computer coding implementation of the method. Using the electronic medical record data set from the Practice Fusion diabetes classification competition containing patient records from all 50 states in the United States, we demonstrated the method on predicting type 2 diabetes diagnosis within the next year. For the champion machine learning model of the competition, our method explained prediction results for 87.4 % of patients who were correctly predicted by the model to have type 2 diabetes diagnosis within the next year. Our demonstration showed the feasibility of automatically explaining results for any machine learning predictive model without degrading accuracy.

Process acceptance and adjustment techniques for Swiss automatic screw machine parts. Final report

International Nuclear Information System (INIS)

Robb, J.M.

1976-01-01

Product tolerance requirements for small, cylindrical, piece parts produced on swiss automatic screw machines have progressed to the reliability limits of inspection equipment. The miniature size, configuration, and tolerance requirements (plus or minus 0.0001 in.) (0.00254 mm) of these parts preclude the use of screening techniques to accept product or adjust processes during setup and production runs; therefore, existing means of product acceptance and process adjustment must be refined or new techniques must be developed. The purpose of this endeavor has been to determine benefits gained through the implementation of a process acceptance technique (PAT) to swiss automatic screw machine processes. PAT is a statistical approach developed for the purpose of accepting product and centering processes for parts produced by selected, controlled processes. Through this endeavor a determination has been made of the conditions under which PAT can benefit a controlled process and some specific types of screw machine processes upon which PAT could be applied. However, it was also determined that PAT, if used indiscriminately, may become a record keeping burden when applied to more than one dimension at a given machining operation

A tape-controlled remote automatic diameter measurement machine

International Nuclear Information System (INIS)

Jennison, W.; Salmon, A.M.

1978-01-01

The machine is designed for the automatic measurement of fuel pins after irradiation in the fast reactors and is a modified version of a machine which has been in use for several years. These modifications consist of mechanical improvements and solid state control circuitry but the design criteria are unchanged. Irradiated fuel pins with diameters up to 0.875 in. are measured at fixed axial positions and angular intervals. Axial stepping of either 1 cm or 1 in. with a standard deviation of 5 x 10 -4 in. and angular rotation by multiples of 18 0 with a non-cumulative error of 1 0 can be selected. Data on axial position to 0.1 in. or 0.1 cm and fuel element diameter to 5 x 10 -5 in. are both punched and printed out for computer evaluation. The standard deviation of a single measurement on cylindrical specimens with an eccentricity of up to at least 0.1 in. should be no worse than 1 x 10 -4 in. No operator attention is required after the pin is positioned in the machine and 40 sets of 10 diameter readings at 36 0 intervals can be performed in an hour. Switches can be set between 1 and 99 to terminate an examination when power is switched off with the machine in its rest position. (author)

Semi-Automatic Modelling of Building FAÇADES with Shape Grammars Using Historic Building Information Modelling

Science.gov (United States)

Dore, C.; Murphy, M.

2013-02-01

This paper outlines a new approach for generating digital heritage models from laser scan or photogrammetric data using Historic Building Information Modelling (HBIM). HBIM is a plug-in for Building Information Modelling (BIM) software that uses parametric library objects and procedural modelling techniques to automate the modelling stage. The HBIM process involves a reverse engineering solution whereby parametric interactive objects representing architectural elements are mapped onto laser scan or photogrammetric survey data. A library of parametric architectural objects has been designed from historic manuscripts and architectural pattern books. These parametric objects were built using an embedded programming language within the ArchiCAD BIM software called Geometric Description Language (GDL). Procedural modelling techniques have been implemented with the same language to create a parametric building façade which automatically combines library objects based on architectural rules and proportions. Different configurations of the façade are controlled by user parameter adjustment. The automatically positioned elements of the façade can be subsequently refined using graphical editing while overlaying the model with orthographic imagery. Along with this semi-automatic method for generating façade models, manual plotting of library objects can also be used to generate a BIM model from survey data. After the 3D model has been completed conservation documents such as plans, sections, elevations and 3D views can be automatically generated for conservation projects.

Criteria of the effectiveness of a liaison center for the machine building industry

International Nuclear Information System (INIS)

Hofer, P.; Wolff, H.; Franzen, D.

1977-06-01

The study aimed at working out a catalogue of criteria for the effective work of a liaison center for the machine building industry within the planned system of information and documentation of the German Government. By selecting this objective, the investigation methodically demanded a continuous change between theoretical analysis (study of literature, analytical deduction of the framework for a user-oriented information system) and empirical observation of information behaviour in the machine building industry (personal interviews with enterprises and important information sources for the machine building industries). An information system keyed to the information needs of the machine building industry must cover three main phases: Collection and documentation of information, selecting and procuring the needed information as well as encouraging and consulting the clients on the use of information. These different tasks complement one another, they correspond to different functions within enterprises: management, staff, and information function. Central point of a liaison center must be the task of selecting the required information (making information available, selling good information, analysing the information needs of clients), completed by fields of activity in documenting information (specifically for the machine building industry) and consulting clients on the use of information (agency for contacts, drawing the clients' attention to the complexity of machine building problems). A concrete catalogue of criteria for an effective conception of a liaison center for the machine building industry has been worked out. (orig.) [de

Criteria of the effectiveness of a liaison center for the machine building industry

International Nuclear Information System (INIS)

Hofer, P.; Wolff, H.; Franzen, D.; Schlichting, J.; Weidig, I.

1978-04-01

The study aimed at working out a catalogue of criteria for the effective work of a liaison center for the machine building industry within the planned system of information and documentation of the German Government. By selecting this objective, the investigation methodically demanded a continuous change between theoretical analysis (study of literature, analytical deduction of the framework for a user-oriented information system) and empirical observation of information behaviour in the machine building industry (personal interviews with enterprises and important information sources for the machine building industries). An information system keyed to the information needs of the machine building industry must cover three main phases: Collection and documentation of information, selecting and procuring the needed information as well as encouraging and consulting the clients on the use of information. These different tasks complement one another, they correspond to different functions within enterprises: management, staff, and information function. Central point of a liaison center must be the task of selecting the required information (making information available, selling good information, analysing the information needs of clients), completed by fields of activity in documenting information (specifically for the machine building industry) and consulting clients on the use of information (agency for contacts, drawing the client's attention to the complexity of machine building problems). A concrete catalogue of criteria for an effective conception of a liaison center for the machine building industry has been worked out. (orig.) [de

A Multiple Sensor Machine Vision System for Automatic Hardwood Feature Detection

Science.gov (United States)

D. Earl Kline; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman; Robert L. Brisbin

1993-01-01

A multiple sensor machine vision prototype is being developed to scan full size hardwood lumber at industrial speeds for automatically detecting features such as knots holes, wane, stain, splits, checks, and color. The prototype integrates a multiple sensor imaging system, a materials handling system, a computer system, and application software. The prototype provides...

COMPONENTS PROVISION MANAGEMENT FOR MACHINE BUILDING MANUFACTURER

Directory of Open Access Journals (Sweden)

Ekaterina P. Bochkareva

2014-01-01

Full Text Available In the paper is given an approach to themanagement of components provision formachine building manufacturer based uponinternational standards and best practicesof leading international companies. Thecomplex expertise methods are used forthe development of the proposed machinebuilding manufacturer suppliers’ operational management method. At a strategic level is proposed a tool for planning the suppliers’portfolio and a machine building manufacturer supplier development methodology.

Automatic Anthropometric System Development Using Machine Learning

Directory of Open Access Journals (Sweden)

Long The Nguyen

2016-08-01

Full Text Available The contactless automatic anthropometric system is proposed for the reconstruction of the 3D-model of the human body using the conventional smartphone. Our approach involves three main steps. The first step is the extraction of 12 anthropological features. Then we determine the most important features. Finally, we employ these features to build the 3D model of the human body and classify them according to gender and the commonly used sizes. 

Possibilities for Automatic Control of Hydro-Mechanical Transmission and Birotating Electric Machine

Directory of Open Access Journals (Sweden)

V. V. Mikhailov

2014-01-01

Full Text Available The paper presents mathematical models and results of virtual investigations pertaining to the selected motion parameters of a mobile machine equipped with hydro mechanical and modernized transmissions. The machine has been tested in similar technological cycles and it has been equipped with a universal automatic control system. Changes in structure and type of power transmission have been obtained with the help of a control algorithm including an extra reversible electric machine which is switched in at some operational modes.Implementation of the proposed  concept makes it possible to obtain and check the improved C-code of the control system and enhance operational parameters of the transmission and machine efficiency, reduce slippage and tire wear while using braking energy for its later beneficial use which is usually considered as a consumable element.

PECULIARITIES OF THE TECHNOLOGY OF CONTINUOUS CASTING OF SLUGS OF MACHINE- AND MACHINE-TOOL-BUILDING

OpenAIRE

E. B. Demchenko; E. I. Marukovich

2006-01-01

The peculiarities of technology of continuous casting of ingots of machine- and machine tool building are shown. At development of technology it is necessary to subject the nomenclature of ingots to analysis in order to reveal expediency of their production by means of continuous casting.

A topological insight into restricted Boltzmann machines

NARCIS (Netherlands)

Mocanu, D.C.; Mocanu, E.; Nguyen, H.P.; Gibescu, M.; Liotta, A.

Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep artificial neural networks for automatic features extraction, unsupervised weights initialization, but also as density estimators. Thus, their generative and discriminative

A Development of Automatic Audit System for Written Informed Consent using Machine Learning.

Science.gov (United States)

Yamada, Hitomi; Takemura, Tadamasa; Asai, Takahiro; Okamoto, Kazuya; Kuroda, Tomohiro; Kuwata, Shigeki

2015-01-01

In Japan, most of all the university and advanced hospitals have implemented both electronic order entry systems and electronic charting. In addition, all medical records are subjected to inspector audit for quality assurance. The record of informed consent (IC) is very important as this provides evidence of consent from the patient or patient's family and health care provider. Therefore, we developed an automatic audit system for a hospital information system (HIS) that is able to evaluate IC automatically using machine learning.

Welding process automation in power machine building

International Nuclear Information System (INIS)

Mel'bard, S.N.; Shakhnov, A.F.; Shergov, I.V.

1977-01-01

The level of welding automation operations in power engineering and ways of its enhancement are highlighted. Used as the examples of comlex automation are an apparatus for the horizontal welding of turbine rotors, remotely controlled automatic machine for welding ring joint of large-sized vessels, equipment for the electron-beam welding of steam turbine assemblies of alloyed steels. The prospects of industrial robots are noted. The importance of the complex automation of technological process, including stocking, assemblying, transportation and auxiliary operations, is emphasized

Comparative Analysis of Automatic Exudate Detection between Machine Learning and Traditional Approaches

Science.gov (United States)

Sopharak, Akara; Uyyanonvara, Bunyarit; Barman, Sarah; Williamson, Thomas

To prevent blindness from diabetic retinopathy, periodic screening and early diagnosis are neccessary. Due to lack of expert ophthalmologists in rural area, automated early exudate (one of visible sign of diabetic retinopathy) detection could help to reduce the number of blindness in diabetic patients. Traditional automatic exudate detection methods are based on specific parameter configuration, while the machine learning approaches which seems more flexible may be computationally high cost. A comparative analysis of traditional and machine learning of exudates detection, namely, mathematical morphology, fuzzy c-means clustering, naive Bayesian classifier, Support Vector Machine and Nearest Neighbor classifier are presented. Detected exudates are validated with expert ophthalmologists' hand-drawn ground-truths. The sensitivity, specificity, precision, accuracy and time complexity of each method are also compared.

A modern automatic Carriage-Trolley Position Control System for Dhruva fuelling machine

International Nuclear Information System (INIS)

Agrawal, Ankit; Hari Balakrishna; Narvekar, J.P.; Sanadhya, Vivek

2014-01-01

A fully automatic absolute encoder based position control system has been designed developed implemented and commissioned for the Dhruva Fuelling Machine A (FM/A). This supports both the coarse and fine positioning modes. Provision for fully manual positioning as a standby system has been retained. This system replaces the ageing peg counting based incremental positioner used briefly during the early period after the Dhruva FM/A was commissioned. The older system suffered from peg detection skipping problems; hence it was not being used. Only full manual positioning was being carried out. This paper describes the automatic Carriage Trolley Position Control System (CTPCS). (author)

Automatic Generation of 3D Building Models with Multiple Roofs

Institute of Scientific and Technical Information of China (English)

Kenichi Sugihara; Yoshitugu Hayashi

2008-01-01

Based on building footprints (building polygons) on digital maps, we are proposing the GIS and CG integrated system that automatically generates 3D building models with multiple roofs. Most building polygons' edges meet at right angles (orthogonal polygon). The integrated system partitions orthogonal building polygons into a set of rectangles and places rectangular roofs and box-shaped building bodies on these rectangles. In order to partition an orthogonal polygon, we proposed a useful polygon expression in deciding from which vertex a dividing line is drawn. In this paper, we propose a new scheme for partitioning building polygons and show the process of creating 3D roof models.

Machine learning for the automatic detection of anomalous events

Science.gov (United States)

Fisher, Wendy D.

In this dissertation, we describe our research contributions for a novel approach to the application of machine learning for the automatic detection of anomalous events. We work in two different domains to ensure a robust data-driven workflow that could be generalized for monitoring other systems. Specifically, in our first domain, we begin with the identification of internal erosion events in earth dams and levees (EDLs) using geophysical data collected from sensors located on the surface of the levee. As EDLs across the globe reach the end of their design lives, effectively monitoring their structural integrity is of critical importance. The second domain of interest is related to mobile telecommunications, where we investigate a system for automatically detecting non-commercial base station routers (BSRs) operating in protected frequency space. The presence of non-commercial BSRs can disrupt the connectivity of end users, cause service issues for the commercial providers, and introduce significant security concerns. We provide our motivation, experimentation, and results from investigating a generalized novel data-driven workflow using several machine learning techniques. In Chapter 2, we present results from our performance study that uses popular unsupervised clustering algorithms to gain insights to our real-world problems, and evaluate our results using internal and external validation techniques. Using EDL passive seismic data from an experimental laboratory earth embankment, results consistently show a clear separation of events from non-events in four of the five clustering algorithms applied. Chapter 3 uses a multivariate Gaussian machine learning model to identify anomalies in our experimental data sets. For the EDL work, we used experimental data from two different laboratory earth embankments. Additionally, we explore five wavelet transform methods for signal denoising. The best performance is achieved with the Haar wavelets. We achieve up to 97

Model design and simulation of automatic sorting machine using proximity sensor

Directory of Open Access Journals (Sweden)

Bankole I. Oladapo

2016-09-01

Full Text Available The automatic sorting system has been reported to be complex and a global problem. This is because of the inability of sorting machines to incorporate flexibility in their design concept. This research therefore designed and developed an automated sorting object of a conveyor belt. The developed automated sorting machine is able to incorporate flexibility and separate species of non-ferrous metal objects and at the same time move objects automatically to the basket as defined by the regulation of the Programmable Logic Controllers (PLC with a capacitive proximity sensor to detect a value range of objects. The result obtained shows that plastic, wood, and steel were sorted into their respective and correct position with an average, sorting, time of 9.903 s, 14.072 s and 18.648 s respectively. The proposed developed model of this research could be adopted at any institution or industries, whose practices are based on mechatronics engineering systems. This is to guide the industrial sector in sorting of object and teaching aid to institutions and hence produce the list of classified materials according to the enabled sorting program commands.

Automatic Task Classification via Support Vector Machine and Crowdsourcing

Directory of Open Access Journals (Sweden)

Hyungsik Shin

2018-01-01

Full Text Available Automatic task classification is a core part of personal assistant systems that are widely used in mobile devices such as smartphones and tablets. Even though many industry leaders are providing their own personal assistant services, their proprietary internals and implementations are not well known to the public. In this work, we show through real implementation and evaluation that automatic task classification can be implemented for mobile devices by using the support vector machine algorithm and crowdsourcing. To train our task classifier, we collected our training data set via crowdsourcing using the Amazon Mechanical Turk platform. Our classifier can classify a short English sentence into one of the thirty-two predefined tasks that are frequently requested while using personal mobile devices. Evaluation results show high prediction accuracy of our classifier ranging from 82% to 99%. By using large amount of crowdsourced data, we also illustrate the relationship between training data size and the prediction accuracy of our task classifier.

Automatic Construction by Contour Crafting Technology

Directory of Open Access Journals (Sweden)

Mohammad Reza Khorramshahi

2017-07-01

Full Text Available Contour Crafting is a novel technology in construction industry based on 3D printing that uses robotics to construct free form building structures by repeatedly laying down layers of material such as concrete. It is actually an approach to scale up automatic fabrication from building small industrial parts to constructing buildings. However, there are little information about contour crafting (CC in current use; present paper aims to describe the operational steps of creating a whole building by the machine reviewing relevant literature. Furthermore, it will represent the advantages of CC usage compared to traditional construction methods, as well as its applicability in construction industry.

Estimating building energy consumption using extreme learning machine method

International Nuclear Information System (INIS)

Naji, Sareh; Keivani, Afram; Shamshirband, Shahaboddin; Alengaram, U. Johnson; Jumaat, Mohd Zamin; Mansor, Zulkefli; Lee, Malrey

2016-01-01

The current energy requirements of buildings comprise a large percentage of the total energy consumed around the world. The demand of energy, as well as the construction materials used in buildings, are becoming increasingly problematic for the earth's sustainable future, and thus have led to alarming concern. The energy efficiency of buildings can be improved, and in order to do so, their operational energy usage should be estimated early in the design phase, so that buildings are as sustainable as possible. An early energy estimate can greatly help architects and engineers create sustainable structures. This study proposes a novel method to estimate building energy consumption based on the ELM (Extreme Learning Machine) method. This method is applied to building material thicknesses and their thermal insulation capability (K-value). For this purpose up to 180 simulations are carried out for different material thicknesses and insulation properties, using the EnergyPlus software application. The estimation and prediction obtained by the ELM model are compared with GP (genetic programming) and ANNs (artificial neural network) models for accuracy. The simulation results indicate that an improvement in predictive accuracy is achievable with the ELM approach in comparison with GP and ANN. - Highlights: • Buildings consume huge amounts of energy for operation. • Envelope materials and insulation influence building energy consumption. • Extreme learning machine is used to estimate energy usage of a sample building. • The key effective factors in this study are insulation thickness and K-value.

Building a Mechanism for the Function of an Innovation-Oriented Machine-Building Enterprise in Operating the Development

Directory of Open Access Journals (Sweden)

Boiarynova Кateryna О.

2017-07-01

Full Text Available The article is aimed at developing and substantiating a mechanism for the function of an innovation-oriented machine-building enterprise in operating the development in order to increase effectuality in the space of ecosystem of operation and to satisfy the economic interests of development. The article proposes a mechanism for the function of an innovation-oriented machine-building enterprise in operating the development, its structure and building based on a business model; operating the development through responsibility centers; functional transformation of economic interest of actors in the ecosystem of function into the economic interest of enterprise; use of economic-institutional regulators and management technologies. Implementation of the mechanism would ensure the value of enterprise, the ability to manage development with the prolonged satisfaction of the joint economic interest, the ability to operate the economic interest of actors in the ecosystem, and maintenance the development mode in the operating process. Prospect for further research will be development of technologies for implementation of the proposed mechanism in the machine-building enterprises.

A topological insight into restricted Boltzmann machines (extented abstract)

NARCIS (Netherlands)

Mocanu, D.C.; Mocanu, E.; Nguyen, H.P.; Gibescu, M.; Liotta, A.

2016-01-01

Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep neural networks for automatic features extraction, unsupervised weights initialization, but also as standalone models for density estimation, activity recognition and so on.

USAGE OF CONSTRUCTION-ORIENTED SOFTWARE SCAD FOR ANALYSIS OF WORK OF MACHINE-BUILDING STRUCTURES

Directory of Open Access Journals (Sweden)

D. О. Bannikov

2018-02-01

Full Text Available Purpose. In the case of analysis of work of the machine-building structures, the direct usage of construction-oriented software developments is impossible, since ideology and methodology for solving various tasks in construction and machine-building are different. Therefore, in the conducting of practical calculations, there is a need for a certain adjustment of the approaches put in the program complexes and their adaptation to the engineering industry. The presentation of the author's experience of the construction-oriented software SCAD usage for Windows for analyzing the work of various machine-building structures, their components and assemblies is the immediate purpose of the publication. Methodology. During a long period of time the author was engaged in analyzing the work of building, mainly thin-walled, steel structures using the Finite Element Method based on the SCAD for Windows software package. At the same time, a considerable number of machine-building structures were considered, including railroad rolling stock units. Most of these tasks grew into a scientific and research problem that needed to be thoroughly researched and analyzed before giving design recommendations. Findings. The publication presents more than a dozen different tasks, typical for the machine-building industry, which the author had to deal with. Static and quasi-static problems, the problem of motion in time, the contact problem, the problem of the cracks deve-lopment, the physical and geometric non-linearity are among them. Accordingly, for each of these problems the main challenges, features and practical techniques developed during the work are presented, as well as the constructed finite element models are presented as an illustration. Originality. The experience of construction-oriented software product usage on the basis of the Finite Element Method for analyzing of the work of machine-building structures is generalized. A number of practical methods and

Point card compatible automatic vending machine for canned drink; Point card taio kan jido hanbaiki

Energy Technology Data Exchange (ETDEWEB)

NONE

2000-01-10

A point card compatible automatic vending machine for canned drinks is developed, which provides drink manufacturers with a powerful tool to acquire selling sites and attract consumers. Since the machine is equipped with a device to handle point cards, regular customers have increased and sales have picked up. A point card issuing device is also installed, and the new machine issues a point card whenever a customer wants. The drink manufacturers are evaluating high of the vending machine because it will contribute to the diffusion of the point card system and because a sales promotion campaign may be conducted through the vending machine for instance by exchanging a fully marked card with a giveaway on the spot. In the future, a bill validator (paper money identifier) will be integrated even with small size machines for the diffusion of point card compatible machines. (translated by NEDO)

Design and construction of automatic sorting station with machine vision

Directory of Open Access Journals (Sweden)

Oscar D. Velasco-Delgado

2014-01-01

Full Text Available This article presents the design, construction and testing of an automatic product sorting system in belt conveyor with machine vision that integrates Free and Open Source Software technology and Allen Bradley commercial equipment. Requirements are defined to determine features such as: mechanics of manufacturing station, an app of product sorting with machine vision and for automation system. For the app of machine vision a library is used for optical digital image processing Open CV, for the mechanical design of the manufacturing station is used the CAD tool Solid Edge and for the design and implementation of automation ISA standards are used along with an automation engineering project methodology integrating a PLC, an inverter, a Panel View and a DeviceNet Network. Performance tests are shown by classifying bottles and PVC pieces in four established types, the behavior of the integrated system is checked so as the efficiency of the same. The processing time on machine vision is 0.290 s on average for a piece of PVC, a capacity of 206 accessories per minute, for bottles was obtained a processing time of 0.267 s, a capacity of 224 bottles per minute. A maximum mechanical performance is obtained with 32 products per minute (1920 products/hour with the conveyor to 22 cm/s and 40 cm of distance between products obtaining an average error of 0.8%.

Intelligent buildings, automatic fire alarm and fire-protection control system

International Nuclear Information System (INIS)

Tian Deyuan

1999-01-01

The author describes in brief the intelligent buildings, and the automatic fire alarm and fire-protection control system. On the basis of the four-bus, three-bus and two-bus, a new transfer technique was developed

Connection of control circuits of machine for automatic measurement of radioactive samples

International Nuclear Information System (INIS)

Vorlicek, J.

1984-01-01

A windowless through-flow gas detector is used for measurement. The automatic machine is controlled by four flip-flops defining the following states: the dish replacement in the measuring space, washing, measurement, measured value print-out, and resetting. The first and second outputs of the first, second and third flip-flops are connected to six inputs of a block whose four outputs provide counter reset and stop-watch reset, washing, measurement, and print-out. Such machine control eliminates measurement errors by disabling sample measurement until air is removed from the measurement space, introduced on an unwashed dish or on several dishes passed under the detector. The elimination of this error is also guaranteed in manual operation. (M.D.)

THE FORMATION OF THE COMPETITIVENESS OF THE ENTERPRISES OF MACHINE-BUILDING COMPLEX OF UKRAINE

Directory of Open Access Journals (Sweden)

Oksana Zbyrannyk

2016-11-01

Full Text Available The purpose is to analyse the existing approaches to determine the value of the production of innovative products and innovation in enterprises of machine-building complex of Ukraine in order to improve their level of competitiveness. Methodology. Statistical analysis and generalization of scientific approaches to the formation of the competitiveness of machine-building enterprises. Results of the of the analyzed approaches allowed to identify the cause of the imperfection of innovation policy in engineering. According to the research, the number of machine-building enterprises engaged in innovation activity, constantly shrinks; the share of innovative products in the total is only 3.5-4%, and the volume of imports of high-tech products exceeding the size of own production; the level of knowledge-intensive industrial production is only 0.3%. All this slows down the process of creating competitive products and as a result, the failure to provide highly own products to other industries, take a niche world of mechanical engineering. Practical implications. Ensure accelerated economic growth of the country as the defining condition for implementation of the European integration aspirations of Ukraine in the short term requires the intensification of innovative activity of the machine-building enterprises. The current state of innovation activity of enterprises in Ukraine is characterized by a number of negative factors: the internal environment of the majority of machine-building enterprises does not correspond to the market conditions of managing: high energy productions, the growth of the degree of wear and tear of fixed assets and reduce investment to update them, the lack of introduction of advanced production and resource-saving technologies, reducing innovation activity due to lack of financial resources significantly affect the level of the competitive machine-building enterprises. Value/ originality systematic approaches to determining the

Automated mapping of building facades by machine learning

DEFF Research Database (Denmark)

Höhle, Joachim

2014-01-01

Facades of buildings contain various types of objects which have to be recorded for information systems. The article describes a solution for this task focussing on automated classification by means of machine learning techniques. Stereo pairs of oblique images are used to derive 3D point clouds...

Another Method of Building 2D Entropy to Realize Automatic Segmentation

International Nuclear Information System (INIS)

Zhang, Y F; Zhang, Y

2006-01-01

2D entropy formed during the process of building 2D histogram can realize automatic segmentation. Traditional method utilizes central pixel grey value and the others or all of pixels grey mean value in 4-neighbor to build 2D histogram. In fact, the change of the greyscale value between two ''invariable position vectors'' cannot represent the total characteristics among neighbour pixels very well. A new method is proposed which makes use of minimum grey value in the 4-neighbor and of maximum grey value in the 3x3 neighbour except pixels of the 4-neighbor. New method and traditional one are used in contrast to realize image automatic segmentation. The experimental results of the classical image prove the new method is effective

Automated Biometric Voice-Based Access Control in Automatic Teller Machine (ATM)

OpenAIRE

Yekini N.A.; Itegboje A.O.; Oyeyinka I.K.; Akinwole A.K.

2012-01-01

An automatic teller machine requires a user to pass an identity test before any transaction can be granted. The current method available for access control in ATM is based on smartcard. Efforts were made to conduct an interview with structured questions among the ATM users and the result proofed that a lot of problems was associated with ATM smartcard for access control. Among the problems are; it is very difficult to prevent another person from attaining and using a legitimate persons card, ...

Possibilities of radiation technique application in machine-building industry of Bulgaria

International Nuclear Information System (INIS)

Petrov, A.; Avramov, D.; Kostov, St.

1979-01-01

In last ten years, in development of machine-building industry, tendency has been outlined for creation of machines and constructions having minimum weight and elevated reliability from one side due to improvement of design and technology of production and from the other side due to application of materials with improved parameters. Solution of these problems is closely connected with application of the radiation methods. State-of-art of the radiation technology application in the machine-building industry is analyzed and mainly for investigation of wear resistance of friction machineparts. Use of spatial radioactive labelling in investigation of materials and application of radiation methods for optimization of technological processes in metallurgy, foundry and so on is considered. Estimation is give of perspectives of further growth of introduction of radiation methods in Bulgaria [ru

An artificial molecular machine that builds an asymmetric catalyst

Science.gov (United States)

De Bo, Guillaume; Gall, Malcolm A. Y.; Kuschel, Sonja; De Winter, Julien; Gerbaux, Pascal; Leigh, David A.

2018-05-01

Biomolecular machines perform types of complex molecular-level tasks that artificial molecular machines can aspire to. The ribosome, for example, translates information from the polymer track it traverses (messenger RNA) to the new polymer it constructs (a polypeptide)1. The sequence and number of codons read determines the sequence and number of building blocks incorporated into the biomachine-synthesized polymer. However, neither control of sequence2,3 nor the transfer of length information from one polymer to another (which to date has only been accomplished in man-made systems through template synthesis)4 is easily achieved in the synthesis of artificial macromolecules. Rotaxane-based molecular machines5-7 have been developed that successively add amino acids8-10 (including β-amino acids10) to a growing peptide chain by the action of a macrocycle moving along a mono-dispersed oligomeric track derivatized with amino-acid phenol esters. The threaded macrocycle picks up groups that block its path and links them through successive native chemical ligation reactions11 to form a peptide sequence corresponding to the order of the building blocks on the track. Here, we show that as an alternative to translating sequence information, a rotaxane molecular machine can transfer the narrow polydispersity of a leucine-ester-derivatized polystyrene chain synthesized by atom transfer radical polymerization12 to a molecular-machine-made homo-leucine oligomer. The resulting narrow-molecular-weight oligomer folds to an α-helical secondary structure13 that acts as an asymmetric catalyst for the Juliá-Colonna epoxidation14,15 of chalcones.

Component simulation in problems of calculated model formation of automatic machine mechanisms

OpenAIRE

Telegin Igor; Kozlov Alexander; Zhirkov Alexander

2017-01-01

The paper deals with the problems of the component simulation method application in the problems of the automation of the mechanical system model formation with the further possibility of their CAD-realization. The purpose of the investigations mentioned consists in the automation of the CAD-model formation of high-speed mechanisms in automatic machines and in the analysis of dynamic processes occurred in their units taking into account their elasto-inertial properties, power dissipation, gap...

The testing report of the development for the lithium grains and lithium rod automatic machine

International Nuclear Information System (INIS)

Qian Zongkui; Kong Xianghong; Huang Yong

2008-06-01

With the development of lithium industry, the lithium grains and lithium rod, as additive or catalyzer, having a big comparatively acreage and a strong activated feature, have a broad application. The lithium grains and lithium rod belong to the kind of final machining materials. The principle of the lithium grains and lithium rod that how to take shape through the procedures of extrusion, cutting, anti-conglutination, threshing and so on are analysed, A sort of lithium grains and lithium rod automatic machine is developed. (authors)

LARA. Localization of an automatized refueling machine by acoustical sounding in breeder reactors - implementation of artificial intelligence techniques

International Nuclear Information System (INIS)

Lhuillier, C.; Malvache, P.

1987-01-01

The automatic control of the machine which handles the nuclear subassemblies in fast neutron reactors requires autonomous perception and decision tools. An acoustical device allows the machine to position in the work area. Artificial intelligence techniques are implemented to interpret the data: pattern recognition, scene analysis. The localization process is managed by an expert system. 6 refs.; 8 figs

SEMI-AUTOMATIC CO-REGISTRATION OF PHOTOGRAMMETRIC AND LIDAR DATA USING BUILDINGS

Directory of Open Access Journals (Sweden)

C. Armenakis

2012-07-01

Full Text Available In this work, the co-registration steps between LiDAR and photogrammetric DSM 3Ddata are analyzed and a solution based on automated plane matching is proposed and implemented. For a robust 3D geometric transformation both planes and points are used. Initially planes are chosen as the co-registration primitives. To confine the search space for the plane matching a sequential automatic building matching is performed first. For matching buildings from the LiDAR and the photogrammetric data, a similarity objective function is formed based on the roof height difference (RHD, the 3D histogram of the building attributes, and the building boundary area of a building. A region growing algorithm based on a Triangulated Irregular Network (TIN is implemented to extract planes from both datasets. Next, an automatic successive process for identifying and matching corresponding planes from the two datasets has been developed and implemented. It is based on the building boundary region and determines plane pairs through a robust matching process thus eliminating outlier pairs. The selected correct plane pairs are the input data for the geometric transformation process. The 3D conformal transformation method in conjunction with the attitude quaternion is applied to obtain the transformation parameters using the normal vectors of the corresponding plane pairs. Following the mapping of one dataset onto the coordinate system of the other, the Iterative Closest Point (ICP algorithm is then applied, using the corresponding building point clouds to further refine the transformation solution. The results indicate that the combination of planes and points improve the co-registration outcomes.

Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning.

Science.gov (United States)

Go, Taesik; Byeon, Hyeokjun; Lee, Sang Joon

2018-04-30

Cell types of erythrocytes should be identified because they are closely related to their functionality and viability. Conventional methods for classifying erythrocytes are time consuming and labor intensive. Therefore, an automatic and accurate erythrocyte classification system is indispensable in healthcare and biomedical fields. In this study, we proposed a new label-free sensor for automatic identification of erythrocyte cell types using a digital in-line holographic microscopy (DIHM) combined with machine learning algorithms. A total of 12 features, including information on intensity distributions, morphological descriptors, and optical focusing characteristics, is quantitatively obtained from numerically reconstructed holographic images. All individual features for discocytes, echinocytes, and spherocytes are statistically different. To improve the performance of cell type identification, we adopted several machine learning algorithms, such as decision tree model, support vector machine, linear discriminant classification, and k-nearest neighbor classification. With the aid of these machine learning algorithms, the extracted features are effectively utilized to distinguish erythrocytes. Among the four tested algorithms, the decision tree model exhibits the best identification performance for the training sets (n = 440, 98.18%) and test sets (n = 190, 97.37%). This proposed methodology, which smartly combined DIHM and machine learning, would be helpful for sensing abnormal erythrocytes and computer-aided diagnosis of hematological diseases in clinic. Copyright © 2017 Elsevier B.V. All rights reserved.

CERN awards "Gold CMS Award" to Savyolovsk machine-building Plant

CERN Multimedia

2007-01-01

"The contribution pf Savyolovsk machine-building Plant OJSC into the international program to develop an CMS unit was recognized by the European Nuclear Research Center with "Gold Prize"for 2007. (1 small paragraph)

Raw Milk Hygiene at Local Markets and Automatic Milk Dispenser Machines

Directory of Open Access Journals (Sweden)

Gheorghe Şteţca

2014-11-01

Full Text Available In Romania, direct sales of raw milk to the final consumer is developed based on the local regulations. These are in accordance to European Regulation that must meet some quality requirements for the total number of germs, somatic cells, without antibiotics, coming from healthy animals who did not suffer from diseases that can be transmitted to humans through milk. Raw milk is sold in Romania in local markets and by automatic milk dispenser machines. Based on these regulations, a study regarding the quality and security to human health of raw milk was conducted on the commercialized milk in local markets and automatic milk dispensers. During May-June 2014 samples of raw milk were collected from Cluj-Napoca local markets and automatic milk dispensers. All samples were kept to refrigeration conditions until the moment of analyze which took place at the sampling day. The following parameters were taken into account: fat content, protein, casein, lactose, nonfat dry matter, pH, milk freezing point, added water, antibiotics residues, milk urea, number of germ cells and somatic cells. All obtained results were verified by the validated methods applied. Our research can be forward conducted in order to verify the hygiene and composition of milk from the whole dairy chain. 

Machine learning: Trends, perspectives, and prospects.

Science.gov (United States)

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.

Automatic Detection of P and S Phases by Support Vector Machine

Science.gov (United States)

Jiang, Y.; Ning, J.; Bao, T.

2017-12-01

Many methods in seismology rely on accurately picked phases. A well performed program on automatically phase picking will assure the application of these methods. Related researches before mostly focus on finding different characteristics between noise and phases, which are all not enough successful. We have developed a new method which mainly based on support vector machine to detect P and S phases. In it, we first input some waveform pieces into the support vector machine, then employ it to work out a hyper plane which can divide the space into two parts: respectively noise and phase. We further use the same method to find a hyper plane which can separate the phase space into P and S parts based on the three components' cross-correlation matrix. In order to further improve the ability of phase detection, we also employ array data. At last, we show that the overall effect of our method is robust by employing both synthetic and real data.

Study on granulated material automatic weighing machine%颗粒状物料自动称量机研究

Institute of Scientific and Technical Information of China (English)

贾丽娜; 张辉; 陈文庆

2012-01-01

In order to solve the problems of the granulated material automatic weighing, according to the characteristics of granulated material, the granulated material automatic weighing machine based on PLC was established. A method was presented to improve the automatic weighing machine speed and accuracy effectively, that used frequency conversion motor driving synchronous belt rough charging and vibrating feeder fine dosing. The weighing experiments were evaluated on the granulated material automatic weighing machine, the several kinds of drug were tested. The experimental results show that the equipment has high weighing accuracy and weighing speed, the characteristics of the operation is stable and reliable, and the equipment can satisfy different granulated drug automatic weighing requirements.%为了解决颗粒状物料自动称量的问题,根据颗粒状物料特性,研制了一种基于PLC的颗粒状物料自动称量机,该系统采用变频电机驱动同步带进行粗加料和振动给料机精加料结合的方式,有效提高了自动称量机的速度和精度.对不同种类的颗粒状药品进行了称量试验,试验结果表明,该称量设备具有精度高、称量速度快、运行稳定可靠的特点,且可以满足不同颗粒药品的自动称量要求.

Issues of Application of Machine Learning Models for Virtual and Real-Life Buildings

Directory of Open Access Journals (Sweden)

Young Min Kim

2016-06-01

Full Text Available The current Building Energy Performance Simulation (BEPS tools are based on first principles. For the correct use of BEPS tools, simulationists should have an in-depth understanding of building physics, numerical methods, control logics of building systems, etc. However, it takes significant time and effort to develop a first principles-based simulation model for existing buildings—mainly due to the laborious process of data gathering, uncertain inputs, model calibration, etc. Rather than resorting to an expert’s effort, a data-driven approach (so-called “inverse” approach has received growing attention for the simulation of existing buildings. This paper reports a cross-comparison of three popular machine learning models (Artificial Neural Network (ANN, Support Vector Machine (SVM, and Gaussian Process (GP for predicting a chiller’s energy consumption in a virtual and a real-life building. The predictions based on the three models are sufficiently accurate compared to the virtual and real measurements. This paper addresses the following issues for the successful development of machine learning models: reproducibility, selection of inputs, training period, outlying data obtained from the building energy management system (BEMS, and validation of the models. From the result of this comparative study, it was found that SVM has a disadvantage in computation time compared to ANN and GP. GP is the most sensitive to a training period among the three models.

Automatic pellet density checking machine using vision technique

International Nuclear Information System (INIS)

Kumar, Suman; Raju, Y.S.; Raj Kumar, J.V.; Sairam, S.; Sheela; Hemantha Rao, G.V.S.

2012-01-01

Uranium di-oxide powder prepared through chemical process is converted to green pellets through the powder metallurgy route of precompaction and final compaction operations. These green pellets are kept in a molybdenum boat, which consists of a molybdenum base and a shroud. The boats are passed through the high temperature sintering furnaces to achieve required density of pellets. At present MIL standard 105 E is followed for measuring density of sintered pellets in the boat. As per AQL 2.5 of MIL standard, five pellets are collected from each boat, which contains approximately 800 nos of pellets. The densities of these collected pellets are measured. If anyone pellet density is less than the required value, the entire boat of pellets are rejected and sent back for dissolution for further processing. An Automatic Pellet Density Checking Machine (APDCM) was developed to salvage the acceptable density pellets from the rejected boat of pellets

Analysis on machine tool systems using spindle vibration monitoring for automatic tool changer

OpenAIRE

Shang-Liang Chen; Yin-Ting Cheng; Chin-Fa Su

2015-01-01

Recently, the intelligent systems of technology have become one of the major items in the development of machine tools. One crucial technology is the machinery status monitoring function, which is required for abnormal warnings and the improvement of cutting efficiency. During processing, the mobility act of the spindle unit determines the most frequent and important part such as automatic tool changer. The vibration detection system includes the development of hardware and software, such as ...

Building machines that learn and think like people.

Science.gov (United States)

Lake, Brenden M; Ullman, Tomer D; Tenenbaum, Joshua B; Gershman, Samuel J

2017-01-01

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

The application of automatic chemiluminescence machine in rapid immune detection

International Nuclear Information System (INIS)

Lin Aizhen; Li Xuanwei; Chen Binhong; Li Zhenqian; Chen Zhaoxuan

2004-01-01

Objective: To provide high-quality, rapid and dependable result for clinical practice, and give satisfactory service to patients of different economical status by supplementation with other labeling immune examination. With an innovative attitude, we carried out efficient technical reform on ACS180 automatic chemiluminescence machine, making it possible for patients to complete the whole process including examination, check-out, diagnosis and getting drugs. The reported will be issued within an hour, thus a rapid immune detection service was established in out-patients department. Methods: 1. ACS-180 automatic chemiluminescence machine is used based on the principle of chemiluminescence immune methods. 2. The reagents are provided by Ciba-Comig Company of USA, composed of anti acridinium ester antibody of liquid phase and particulate antigen of solid phase wrapped in magnetic powder. 3. Calibration and quality control: high and low concentration are set for each calibration fluid with attached standard curve. Product for quality controlling includes three concentration of low, moderate and high. Results: 1. rapid machine detection for sample: serum is replaced with plasma coagulated by heparin, and comparison among series of methods using serum or plasma suggest no significant difference exists. 2. The problem about fasting detection: chemiluminescence machine measure optical density directly, with the results hardly being influenced by turbidity. But attention should be paid to the treatment of lipid turbid samples. 3. Other innovations: (1) direct placement of sample tube on machine: a cushion is placed on sample plate to transfer sample to machine directly after centrifugation, saving time and reducing the accident in sample transference. (2) for HCG quantification in blood and urine, 'gold criteria' is used firstly in screening to determine approximately the dilution range, with an advantage of saving time and reagent as well as accuracy. (3) we design a

A method for the automatic separation of the images of galaxies and stars from measurements made with the COSMOS machine

International Nuclear Information System (INIS)

MacGillivray, H.T.; Martin, R.; Pratt, N.M.; Reddish, V.C.; Seddon, H.; Alexander, L.W.G.; Walker, G.S.; Williams, P.R.

1976-01-01

A method has been developed which allows the computer to distinguish automatically between the images of galaxies and those of stars from measurements made with the COSMOS automatic plate-measuring machine at the Royal Observatory, Edinburgh. Results have indicated that a 90 to 95 per cent separation between galaxies and stars is possible. (author)

AUTOMATIC BUILDING OUTLINING FROM MULTI-VIEW OBLIQUE IMAGES

Directory of Open Access Journals (Sweden)

J. Xiao

2012-07-01

Full Text Available Automatic building detection plays an important role in many applications. Multiple overlapped airborne images as well as lidar point clouds are among the most popular data sources used for this purpose. Multi-view overlapped oblique images bear both height and colour information, and additionally we explicitly have access to the vertical extent of objects, therefore we explore the usability of this data source solely to detect and outline buildings in this paper. The outline can then be used for further 3D modelling. In the previous work, building hypotheses are generated using a box model based on detected façades from four directions. In each viewing direction, façade edges extracted from images and height information by stereo matching from an image pair is used for the façade detection. Given that many façades were missing due to occlusion or lack of texture whilst building roofs can be viewed in most images, this work mainly focuses on improve the building box outline by adding roof information. Stereo matched point cloud generated from oblique images are combined with the features from images. Initial roof patches are located in the point cloud. Then AdaBoost is used to integrate geometric and radiometric attributes extracted from oblique image on grid pixel level with the aim to refine the roof area. Generalized contours of the roof pixels are taken as building outlines. The preliminary test has been done by training with five buildings and testing around sixty building clusters. The proposed method performs well concerning covering the irregular roofs as well as improve the sides location of slope roof buildings. Outline result comparing with cadastral map shows almost all above 70% completeness and correctness in an area-based assessment, as well as 20% to 40% improvement in correctness with respect to our previous work.

Design and Assessment of a Machine Vision System for Automatic Vehicle Wheel Alignment

Directory of Open Access Journals (Sweden)

Rocco Furferi

2013-05-01

Full Text Available Abstract Wheel alignment, consisting of properly checking the wheel characteristic angles against vehicle manufacturers' specifications, is a crucial task in the automotive field since it prevents irregular tyre wear and affects vehicle handling and safety. In recent years, systems based on Machine Vision have been widely studied in order to automatically detect wheels' characteristic angles. In order to overcome the limitations of existing methodologies, due to measurement equipment being mounted onto the wheels, the present work deals with design and assessment of a 3D machine vision-based system for the contactless reconstruction of vehicle wheel geometry, with particular reference to characteristic planes. Such planes, properly referred to as a global coordinate system, are used for determining wheel angles. The effectiveness of the proposed method was tested against a set of measurements carried out using a commercial 3D scanner; the absolute average error in measuring toe and camber angles with the machine vision system resulted in full compatibility with the expected accuracy of wheel alignment systems.

MO-F-CAMPUS-J-02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning

Energy Technology Data Exchange (ETDEWEB)

Chang, X; Yang, D [Washington University in St Louis, St Louis, MO (United States)

2015-06-15

Purpose: To investigate the method to automatically recognize the treatment site in the X-Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup. Methods: The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down-sampled (from 1024×768 to 128×96) by using bi-cubic interpolation algorithm. An appearance-based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was used to reduce the vector dimensions. A multi-class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross-validation experiments were performed to evaluate the performance. Results: MATLAB image processing Toolbox and scikit-learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images. Conclusion: The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance. The work was partially supported by a research grant from Varian Medical System.

MO-F-CAMPUS-J-02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning

International Nuclear Information System (INIS)

Chang, X; Yang, D

2015-01-01

Purpose: To investigate the method to automatically recognize the treatment site in the X-Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup. Methods: The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down-sampled (from 1024×768 to 128×96) by using bi-cubic interpolation algorithm. An appearance-based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was used to reduce the vector dimensions. A multi-class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross-validation experiments were performed to evaluate the performance. Results: MATLAB image processing Toolbox and scikit-learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images. Conclusion: The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance. The work was partially supported by a research grant from Varian Medical System

Alan Turing's Automatic Computing Engine The Master Codebreaker's Struggle to build the Modern Computer

CERN Document Server

Copeland, B Jack

2005-01-01

The mathematical genius Alan Turing (1912-1954) was one of the greatest scientists and thinkers of the 20th century. Now well known for his crucial wartime role in breaking the ENIGMA code, he was the first to conceive of the fundamental principle of the modern computer-the idea of controlling a computing machine's operations by means of a program of coded instructions, stored in the machine's 'memory'. In 1945 Turing drew up his revolutionary design for an electronic computingmachine-his Automatic Computing Engine ('ACE'). A pilot model of the ACE ran its first program in 1950 and the product

Innovative model of business process reengineering at machine building enterprises

Science.gov (United States)

Nekrasov, R. Yu; Tempel, Yu A.; Tempel, O. A.

2017-10-01

The paper provides consideration of business process reengineering viewed as amanagerial innovation accepted by present day machine building enterprises, as well as waysto improve its procedure. A developed innovative model of reengineering measures isdescribed and is based on the process approach and other principles of company management.

Formation of competitive potential of the machine-building complex of the region

Directory of Open Access Journals (Sweden)

Oleg Ivanovich Botkin

2014-03-01

Full Text Available The article describes the features of competitive potential of regional machine-building complex in a globalized world economy. The purpose of the research is the development of theoretically reasonable economic basis of the machine-building complex considering  the  features of business in the conditions of the WTO. In the work, the hypothesis of a special role of the external economic factors locates in development of the enterprises of regional industrial complexes. The study of the theoretical provisions defining the development of the region revealed the factors determining influence of the international trade agreements on spatial localization of the industry. The main attention is paid to an analytical assessment of the current state and the trends, which have developed in the period of post-crisis economic recovery. Analysis of the main indicators of attractiveness has revealed the weak position of local industrial enterprises in the WTO. The directions of strengthening of the competitive capacity of the local industrial enterprises are defined. The obtained results allow us to increase the sustainability of the industry by means of effective management mechanism improvements and to create favorable operating conditions of a machine-building complex of the region

Automatic generation of smart earthquake-resistant building system: Hybrid system of base-isolation and building-connection

Directory of Open Access Journals (Sweden)

M. Kasagi

2016-02-01

Full Text Available A base-isolated building may sometimes exhibit an undesirable large response to a long-duration, long-period earthquake ground motion and a connected building system without base-isolation may show a large response to a near-fault (rather high-frequency earthquake ground motion. To overcome both deficiencies, a new hybrid control system of base-isolation and building-connection is proposed and investigated. In this new hybrid building system, a base-isolated building is connected to a stiffer free wall with oil dampers. It has been demonstrated in a preliminary research that the proposed hybrid system is effective both for near-fault (rather high-frequency and long-duration, long-period earthquake ground motions and has sufficient redundancy and robustness for a broad range of earthquake ground motions.An automatic generation algorithm of this kind of smart structures of base-isolation and building-connection hybrid systems is presented in this paper. It is shown that, while the proposed algorithm does not work well in a building without the connecting-damper system, it works well in the proposed smart hybrid system with the connecting damper system.

Convective Heat Transfer Coefficients of Automatic Transmission Fluid Jets with Implications for Electric Machine Thermal Management: Preprint

Energy Technology Data Exchange (ETDEWEB)

Bennion, Kevin; Moreno, Gilberto

2015-09-29

Thermal management for electric machines (motors/ generators) is important as the automotive industry continues to transition to more electrically dominant vehicle propulsion systems. Cooling of the electric machine(s) in some electric vehicle traction drive applications is accomplished by impinging automatic transmission fluid (ATF) jets onto the machine's copper windings. In this study, we provide the results of experiments characterizing the thermal performance of ATF jets on surfaces representative of windings, using Ford's Mercon LV ATF. Experiments were carried out at various ATF temperatures and jet velocities to quantify the influence of these parameters on heat transfer coefficients. Fluid temperatures were varied from 50 degrees C to 90 degrees C to encompass potential operating temperatures within an automotive transaxle environment. The jet nozzle velocities were varied from 0.5 to 10 m/s. The experimental ATF heat transfer coefficient results provided in this report are a useful resource for understanding factors that influence the performance of ATF-based cooling systems for electric machines.

Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning

Science.gov (United States)

Nguyen, Tan H.; Sridharan, Shamira; Macias, Virgilia; Kajdacsy-Balla, Andre; Melamed, Jonathan; Do, Minh N.; Popescu, Gabriel

2017-03-01

We present an approach for automatic diagnosis of tissue biopsies. Our methodology consists of a quantitative phase imaging tissue scanner and machine learning algorithms to process these data. We illustrate the performance by automatic Gleason grading of prostate specimens. The imaging system operates on the principle of interferometry and, as a result, reports on the nanoscale architecture of the unlabeled specimen. We use these data to train a random forest classifier to learn textural behaviors of prostate samples and classify each pixel in the image into different classes. Automatic diagnosis results were computed from the segmented regions. By combining morphological features with quantitative information from the glands and stroma, logistic regression was used to discriminate regions with Gleason grade 3 versus grade 4 cancer in prostatectomy tissue. The overall accuracy of this classification derived from a receiver operating curve was 82%, which is in the range of human error when interobserver variability is considered. We anticipate that our approach will provide a clinically objective and quantitative metric for Gleason grading, allowing us to corroborate results across instruments and laboratories and feed the computer algorithms for improved accuracy.

Automatic Training of Rat Cyborgs for Navigation.

Science.gov (United States)

Yu, Yipeng; Wu, Zhaohui; Xu, Kedi; Gong, Yongyue; Zheng, Nenggan; Zheng, Xiaoxiang; Pan, Gang

2016-01-01

A rat cyborg system refers to a biological rat implanted with microelectrodes in its brain, via which the outer electrical stimuli can be delivered into the brain in vivo to control its behaviors. Rat cyborgs have various applications in emergency, such as search and rescue in disasters. Prior to a rat cyborg becoming controllable, a lot of effort is required to train it to adapt to the electrical stimuli. In this paper, we build a vision-based automatic training system for rat cyborgs to replace the time-consuming manual training procedure. A hierarchical framework is proposed to facilitate the colearning between rats and machines. In the framework, the behavioral states of a rat cyborg are visually sensed by a camera, a parameterized state machine is employed to model the training action transitions triggered by rat's behavioral states, and an adaptive adjustment policy is developed to adaptively adjust the stimulation intensity. The experimental results of three rat cyborgs prove the effectiveness of our system. To the best of our knowledge, this study is the first to tackle automatic training of animal cyborgs.

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

Science.gov (United States)

Imbalzano, Giulio; Anelli, Andrea; Giofré, Daniele; Klees, Sinja; Behler, Jörg; Ceriotti, Michele

2018-06-01

Machine learning of atomic-scale properties is revolutionizing molecular modeling, making it possible to evaluate inter-atomic potentials with first-principles accuracy, at a fraction of the costs. The accuracy, speed, and reliability of machine learning potentials, however, depend strongly on the way atomic configurations are represented, i.e., the choice of descriptors used as input for the machine learning method. The raw Cartesian coordinates are typically transformed in "fingerprints," or "symmetry functions," that are designed to encode, in addition to the structure, important properties of the potential energy surface like its invariances with respect to rotation, translation, and permutation of like atoms. Here we discuss automatic protocols to select a number of fingerprints out of a large pool of candidates, based on the correlations that are intrinsic to the training data. This procedure can greatly simplify the construction of neural network potentials that strike the best balance between accuracy and computational efficiency and has the potential to accelerate by orders of magnitude the evaluation of Gaussian approximation potentials based on the smooth overlap of atomic positions kernel. We present applications to the construction of neural network potentials for water and for an Al-Mg-Si alloy and to the prediction of the formation energies of small organic molecules using Gaussian process regression.

Cleaning and can end chamfering special machine MSCS-04

International Nuclear Information System (INIS)

Negulescu, D.; Rusu, A.; Dragomir, I.; Turcanu, V.; Bailescu, V.; Burcea, Gh.; Chitu, I.

2001-01-01

The MSCS-04 machine executes cleaning and can end chamfering of the CANDU 6 fuel element can through the following technologic chain: - manual positioning of the workpiece in the transporter feeding location; - the transport of the workpiece in front of the cleaning machine and workpiece orientation checking; - automatic loading of the workpiece in the cleaning machine; - bonding the workpiece in the cleaning machine; - cleaning the ends of the workpiece with graphite dust aspiration; - automatic disconnection of the workpiece from the cleaning machine; - automatic unloading of the cleaning machine; - disposal of the workpiece on the transporter in front of cleaning machine; workpiece's transport in front of the chamfering machine; - automatic checking of the workpiece orientation; - automatic loading of the workpiece in the chamfering machine; - axial positioning and bounding of the workpiece in the chamfering machine; chamfering the workpiece's ends with graphite dust and splinter aspiration; - disconnecting the workpiece from the chamfering machine; - automatic unloading of the workpiece from the chamfering machine with splinter blow from the workpiece interior; - workpiece disposal on transporter and the piece transport to the outlet. Details about the technological system, transport system, manipulators, cleaning and chamfering machines are given. Novel elements are highlighted and the technical characteristics are presented

Analysis on machine tool systems using spindle vibration monitoring for automatic tool changer

Directory of Open Access Journals (Sweden)

Shang-Liang Chen

2015-12-01

Full Text Available Recently, the intelligent systems of technology have become one of the major items in the development of machine tools. One crucial technology is the machinery status monitoring function, which is required for abnormal warnings and the improvement of cutting efficiency. During processing, the mobility act of the spindle unit determines the most frequent and important part such as automatic tool changer. The vibration detection system includes the development of hardware and software, such as vibration meter, signal acquisition card, data processing platform, and machine control program. Meanwhile, based on the difference between the mechanical configuration and the desired characteristics, it is difficult for a vibration detection system to directly choose the commercially available kits. For this reason, it was also selected as an item for self-development research, along with the exploration of a significant parametric study that is sufficient to represent the machine characteristics and states. However, we also launched the development of functional parts of the system simultaneously. Finally, we entered the conditions and the parameters generated from both the states and the characteristics into the developed system to verify its feasibility.

Automatic bad channel detection in intracranial electroencephalographic recordings using ensemble machine learning.

Science.gov (United States)

Tuyisenge, Viateur; Trebaul, Lena; Bhattacharjee, Manik; Chanteloup-Forêt, Blandine; Saubat-Guigui, Carole; Mîndruţă, Ioana; Rheims, Sylvain; Maillard, Louis; Kahane, Philippe; Taussig, Delphine; David, Olivier

2018-03-01

Intracranial electroencephalographic (iEEG) recordings contain "bad channels", which show non-neuronal signals. Here, we developed a new method that automatically detects iEEG bad channels using machine learning of seven signal features. The features quantified signals' variance, spatial-temporal correlation and nonlinear properties. Because the number of bad channels is usually much lower than the number of good channels, we implemented an ensemble bagging classifier known to be optimal in terms of stability and predictive accuracy for datasets with imbalanced class distributions. This method was applied on stereo-electroencephalographic (SEEG) signals recording during low frequency stimulations performed in 206 patients from 5 clinical centers. We found that the classification accuracy was extremely good: It increased with the number of subjects used to train the classifier and reached a plateau at 99.77% for 110 subjects. The classification performance was thus not impacted by the multicentric nature of data. The proposed method to automatically detect bad channels demonstrated convincing results and can be envisaged to be used on larger datasets for automatic quality control of iEEG data. This is the first method proposed to classify bad channels in iEEG and should allow to improve the data selection when reviewing iEEG signals. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Improving Automation Routines for Automatic Heating Load Detection in Buildings

Directory of Open Access Journals (Sweden)

Stephen Timlin

2012-11-01

Full Text Available Energy managers use weather compensation data and heating system cut off routines to reduce heating energy consumption in buildings and improve user comfort. These routines are traditionally based on the calculation of an estimated building load that is inferred from the external dry bulb temperature at any point in time. While this method does reduce heating energy consumption and accidental overheating, it can be inaccurate under some weather conditions and therefore has limited effectiveness. There remains considerable scope to improve on the accuracy and relevance of the traditional method by expanding the calculations used to include a larger range of environmental metrics. It is proposed that weather compensation and automatic shut off routines that are commonly used could be improved notably with little additional cost by the inclusion of additional weather metrics. This paper examines the theoretical relationship between various external metrics and building heating loads. Results of the application of an advanced routine to a recently constructed building are examined, and estimates are made of the potential savings that can be achieved through the use of the routines proposed.

Automatic inspection of textured surfaces by support vector machines

Science.gov (United States)

Jahanbin, Sina; Bovik, Alan C.; Pérez, Eduardo; Nair, Dinesh

2009-08-01

Automatic inspection of manufactured products with natural looking textures is a challenging task. Products such as tiles, textile, leather, and lumber project image textures that cannot be modeled as periodic or otherwise regular; therefore, a stochastic modeling of local intensity distribution is required. An inspection system to replace human inspectors should be flexible in detecting flaws such as scratches, cracks, and stains occurring in various shapes and sizes that have never been seen before. A computer vision algorithm is proposed in this paper that extracts local statistical features from grey-level texture images decomposed with wavelet frames into subbands of various orientations and scales. The local features extracted are second order statistics derived from grey-level co-occurrence matrices. Subsequently, a support vector machine (SVM) classifier is trained to learn a general description of normal texture from defect-free samples. This algorithm is implemented in LabVIEW and is capable of processing natural texture images in real-time.

Automatic Generation of Structural Building Descriptions from 3D Point Cloud Scans

DEFF Research Database (Denmark)

Ochmann, Sebastian; Vock, Richard; Wessel, Raoul

2013-01-01

We present a new method for automatic semantic structuring of 3D point clouds representing buildings. In contrast to existing approaches which either target the outside appearance like the facade structure or rather low-level geometric structures, we focus on the building’s interior using indoor...... scans to derive high-level architectural entities like rooms and doors. Starting with a registered 3D point cloud, we probabilistically model the affiliation of each measured point to a certain room in the building. We solve the resulting clustering problem using an iterative algorithm that relies...

MDSplus automated build and distribution system

Energy Technology Data Exchange (ETDEWEB)

Fredian, T., E-mail: twf@psfc.mit.edu [Massachusetts Institute of Technology, 175 Albany Street, Cambridge, MA 02139 (United States); Stillerman, J. [Massachusetts Institute of Technology, 175 Albany Street, Cambridge, MA 02139 (United States); Manduchi, G. [Consorzio RFX, Euratom-ENEA Association, Corso Stati Uniti 4, Padova 35127 (Italy)

2014-05-15

Support of the MDSplus data handling system has been enhanced by the addition of an automated build system which does nightly builds of MDSplus for many computer platforms producing software packages which can now be downloaded using a web browser or via package repositories suitable for automatic updating. The build system was implemented using an extensible continuous integration server product called Hudson which schedules software builds on a collection of VMware based virtual machines. New releases are created based on updates via the MDSplus cvs code repository and versioning are managed using cvs tags and branches. Currently stable, beta and alpha releases of MDSplus are maintained for eleven different platforms including Windows, MacOSX, RedHat Enterprise Linux, Fedora, Ubuntu and Solaris. For some of these platforms, MDSplus packaging has been broken into functional modules so users can pick and choose which MDSplus features they want to install. An added feature to the latest Linux based platforms is the use of package dependencies. When installing MDSplus from the package repositories, any additional required packages used by MDSplus will be installed automatically greatly simplifying the installation of MDSplus. This paper will describe the MDSplus package automated build and distribution system.

MDSplus automated build and distribution system

International Nuclear Information System (INIS)

Fredian, T.; Stillerman, J.; Manduchi, G.

2014-01-01

Support of the MDSplus data handling system has been enhanced by the addition of an automated build system which does nightly builds of MDSplus for many computer platforms producing software packages which can now be downloaded using a web browser or via package repositories suitable for automatic updating. The build system was implemented using an extensible continuous integration server product called Hudson which schedules software builds on a collection of VMware based virtual machines. New releases are created based on updates via the MDSplus cvs code repository and versioning are managed using cvs tags and branches. Currently stable, beta and alpha releases of MDSplus are maintained for eleven different platforms including Windows, MacOSX, RedHat Enterprise Linux, Fedora, Ubuntu and Solaris. For some of these platforms, MDSplus packaging has been broken into functional modules so users can pick and choose which MDSplus features they want to install. An added feature to the latest Linux based platforms is the use of package dependencies. When installing MDSplus from the package repositories, any additional required packages used by MDSplus will be installed automatically greatly simplifying the installation of MDSplus. This paper will describe the MDSplus package automated build and distribution system

Micro-CernVM: slashing the cost of building and deploying virtual machines

International Nuclear Information System (INIS)

Blomer, J; Berzano, D; Buncic, P; Charalampidis, I; Ganis, G; Lestaris, G; Meusel, R; Nicolaou, V

2014-01-01

The traditional virtual machine (VM) building and and deployment process is centered around the virtual machine hard disk image. The packages comprising the VM operating system are carefully selected, hard disk images are built for a variety of different hypervisors, and images have to be distributed and decompressed in order to instantiate a virtual machine. Within the HEP community, the CernVM File System (CernVM-FS) has been established in order to decouple the distribution from the experiment software from the building and distribution of the VM hard disk images. We show how to get rid of such pre-built hard disk images altogether. Due to the high requirements on POSIX compliance imposed by HEP application software, CernVM-FS can also be used to host and boot a Linux operating system. This allows the use of a tiny bootable CD image that comprises only a Linux kernel while the rest of the operating system is provided on demand by CernVM-FS. This approach speeds up the initial instantiation time and reduces virtual machine image sizes by an order of magnitude. Furthermore, security updates can be distributed instantaneously through CernVM-FS. By leveraging the fact that CernVM-FS is a versioning file system, a historic analysis environment can be easily re-spawned by selecting the corresponding CernVM-FS file system snapshot.

Automatic assessment of average diaphragm motion trajectory from 4DCT images through machine learning.

Science.gov (United States)

Li, Guang; Wei, Jie; Huang, Hailiang; Gaebler, Carl Philipp; Yuan, Amy; Deasy, Joseph O

2015-12-01

To automatically estimate average diaphragm motion trajectory (ADMT) based on four-dimensional computed tomography (4DCT), facilitating clinical assessment of respiratory motion and motion variation and retrospective motion study. We have developed an effective motion extraction approach and a machine-learning-based algorithm to estimate the ADMT. Eleven patients with 22 sets of 4DCT images (4DCT1 at simulation and 4DCT2 at treatment) were studied. After automatically segmenting the lungs, the differential volume-per-slice (dVPS) curves of the left and right lungs were calculated as a function of slice number for each phase with respective to the full-exhalation. After 5-slice moving average was performed, the discrete cosine transform (DCT) was applied to analyze the dVPS curves in frequency domain. The dimensionality of the spectrum data was reduced by using several lowest frequency coefficients ( f v ) to account for most of the spectrum energy (Σ f v 2 ). Multiple linear regression (MLR) method was then applied to determine the weights of these frequencies by fitting the ground truth-the measured ADMT, which are represented by three pivot points of the diaphragm on each side. The 'leave-one-out' cross validation method was employed to analyze the statistical performance of the prediction results in three image sets: 4DCT1, 4DCT2, and 4DCT1 + 4DCT2. Seven lowest frequencies in DCT domain were found to be sufficient to approximate the patient dVPS curves ( R = 91%-96% in MLR fitting). The mean error in the predicted ADMT using leave-one-out method was 0.3 ± 1.9 mm for the left-side diaphragm and 0.0 ± 1.4 mm for the right-side diaphragm. The prediction error is lower in 4DCT2 than 4DCT1, and is the lowest in 4DCT1 and 4DCT2 combined. This frequency-analysis-based machine learning technique was employed to predict the ADMT automatically with an acceptable error (0.2 ± 1.6 mm). This volumetric approach is not affected by the presence of the lung tumors

Automatic inspection of surface defects in die castings after machining

Directory of Open Access Journals (Sweden)

S. J. Świłło

2011-07-01

Full Text Available A new camera based machine vision system for the automatic inspection of surface defects in aluminum die casting was developed by the authors. The problem of surface defects in aluminum die casting is widespread throughout the foundry industry and their detection is of paramount importance in maintaining product quality. The casting surfaces are the most highly loaded regions of materials and components. Mechanical and thermal loads as well as corrosion or irradiation attacks are directed primarily at the surface of the castings. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks or tears, inclusions due to chemical reactions or foreign material in the molten metal, and pores that greatly influence the material ability to withstand these loads. Surface defects may act as a stress concentrator initiating a fracture point. If a pressure is applied in this area, the casting can fracture. The human visual system is well adapted to perform in areas of variety and change; the visual inspection processes, on the other hand, require observing the same type of image repeatedly to detect anomalies. Slow, expensive, erratic inspection usually is the result. Computer based visual inspection provides a viable alternative to human inspectors. Developed by authors machine vision system uses an image processing algorithm based on modified Laplacian of Gaussian edge detection method to detect defects with different sizes and shapes. The defect inspection algorithm consists of three parameters. One is a parameter of defects sensitivity, the second parameter is a threshold level and the third parameter is to identify the detected defects size and shape. The machine vision system has been successfully tested for the different types of defects on the surface of castings.

Tool set for distributed real-time machine control

Science.gov (United States)

Carrott, Andrew J.; Wright, Christopher D.; West, Andrew A.; Harrison, Robert; Weston, Richard H.

1997-01-01

Demands for increased control capabilities require next generation manufacturing machines to comprise intelligent building elements, physically located at the point where the control functionality is required. Networks of modular intelligent controllers are increasingly designed into manufacturing machines and usable standards are slowly emerging. To implement a control system using off-the-shelf intelligent devices from multi-vendor sources requires a number of well defined activities, including (a) the specification and selection of interoperable control system components, (b) device independent application programming and (c) device configuration, management, monitoring and control. This paper briefly discusses the support for the above machine lifecycle activities through the development of an integrated computing environment populated with an extendable software toolset. The toolset supports machine builder activities such as initial control logic specification, logic analysis, machine modeling, mechanical verification, application programming, automatic code generation, simulation/test, version control, distributed run-time support and documentation. The environment itself consists of system management tools and a distributed object-oriented database which provides storage for the outputs from machine lifecycle activities and specific target control solutions.

Alignment of Custom Standards by Machine Learning Algorithms

Directory of Open Access Journals (Sweden)

Adela Sirbu

2010-09-01

Full Text Available Building an efficient model for automatic alignment of terminologies would bring a significant improvement to the information retrieval process. We have developed and compared two machine learning based algorithms whose aim is to align 2 custom standards built on a 3 level taxonomy, using kNN and SVM classifiers that work on a vector representation consisting of several similarity measures. The weights utilized by the kNN were optimized with an evolutionary algorithm, while the SVM classifier's hyper-parameters were optimized with a grid search algorithm. The database used for train was semi automatically obtained by using the Coma++ tool. The performance of our aligners is shown by the results obtained on the test set.

Automatic Test Pattern Generator for Fuzzing Based on Finite State Machine

Directory of Open Access Journals (Sweden)

Ming-Hung Wang

2017-01-01

Full Text Available With the rapid development of the Internet, several emerging technologies are adopted to construct fancy, interactive, and user-friendly websites. Among these technologies, HTML5 is a popular one and is widely used in establishing modern sites. However, the security issues in the new web technologies are also raised and are worthy of investigation. For vulnerability investigation, many previous studies used fuzzing and focused on generation-based approaches to produce test cases for fuzzing; however, these methods require a significant amount of knowledge and mental efforts to develop test patterns for generating test cases. To decrease the entry barrier of conducting fuzzing, in this study, we propose a test pattern generation algorithm based on the concept of finite state machines. We apply graph analysis techniques to extract paths from finite state machines and use these paths to construct test patterns automatically. According to the proposal, fuzzing can be completed through inputting a regular expression corresponding to the test target. To evaluate the performance of our proposal, we conduct an experiment in identifying vulnerabilities of the input attributes in HTML5. According to the results, our approach is not only efficient but also effective for identifying weak validators in HTML5.

Transducer-actuator systems and methods for performing on-machine measurements and automatic part alignment

Science.gov (United States)

Barkman, William E.; Dow, Thomas A.; Garrard, Kenneth P.; Marston, Zachary

2016-07-12

Systems and methods for performing on-machine measurements and automatic part alignment, including: a measurement component operable for determining the position of a part on a machine; and an actuation component operable for adjusting the position of the part by contacting the part with a predetermined force responsive to the determined position of the part. The measurement component consists of a transducer. The actuation component consists of a linear actuator. Optionally, the measurement component and the actuation component consist of a single linear actuator operable for contacting the part with a first lighter force for determining the position of the part and with a second harder force for adjusting the position of the part. The actuation component is utilized in a substantially horizontal configuration and the effects of gravitational drop of the part are accounted for in the force applied and the timing of the contact.

Semi-Automatic Registration of Airborne and Terrestrial Laser Scanning Data Using Building Corner Matching with Boundaries as Reliability Check

Directory of Open Access Journals (Sweden)

Liang Cheng

2013-11-01

Full Text Available Data registration is a prerequisite for the integration of multi-platform laser scanning in various applications. A new approach is proposed for the semi-automatic registration of airborne and terrestrial laser scanning data with buildings without eaves. Firstly, an automatic calculation procedure for thresholds in density of projected points (DoPP method is introduced to extract boundary segments from terrestrial laser scanning data. A new algorithm, using a self-extending procedure, is developed to recover the extracted boundary segments, which then intersect to form the corners of buildings. The building corners extracted from airborne and terrestrial laser scanning are reliably matched through an automatic iterative process in which boundaries from two datasets are compared for the reliability check. The experimental results illustrate that the proposed approach provides both high reliability and high geometric accuracy (average error of 0.44 m/0.15 m in horizontal/vertical direction for corresponding building corners for the final registration of airborne laser scanning (ALS and tripod mounted terrestrial laser scanning (TLS data.

Modeling of industrial stream and resources of machine-building enterpriser complex of wood preparation

Science.gov (United States)

Sereda, T. G.; Kostarev, S. N.

2018-03-01

Theoretical bases of linkage of material streams of the machine-building enterprise and the automated system of decision-making are developed. The process of machine-building manufacture is submitted by the existential system. The equation of preservation of movement is based on calculation of volume of manufacture. The basis of resource variables includes capacities and operators of the equipment. Indignations such as a defect and failure are investigated in the existential basis. The equation of a stream of details on a manufacturing route is made. The received analytical expression expresses a condition of a stream of movement of details in view of influence of work of the equipment and traumatism of the personnel.

Automatic optical detection and classification of marine animals around MHK converters using machine vision

Energy Technology Data Exchange (ETDEWEB)

Brunton, Steven [Univ. of Washington, Seattle, WA (United States)

2018-01-15

Optical systems provide valuable information for evaluating interactions and associations between organisms and MHK energy converters and for capturing potentially rare encounters between marine organisms and MHK device. The deluge of optical data from cabled monitoring packages makes expert review time-consuming and expensive. We propose algorithms and a processing framework to automatically extract events of interest from underwater video. The open-source software framework consists of background subtraction, filtering, feature extraction and hierarchical classification algorithms. This principle classification pipeline was validated on real-world data collected with an experimental underwater monitoring package. An event detection rate of 100% was achieved using robust principal components analysis (RPCA), Fourier feature extraction and a support vector machine (SVM) binary classifier. The detected events were then further classified into more complex classes – algae | invertebrate | vertebrate, one species | multiple species of fish, and interest rank. Greater than 80% accuracy was achieved using a combination of machine learning techniques.

Giro form reading machine

Science.gov (United States)

Minh Ha, Thien; Niggeler, Dieter; Bunke, Horst; Clarinval, Jose

1995-08-01

Although giro forms are used by many people in daily life for money remittance in Switzerland, the processing of these forms at banks and post offices is only partly automated. We describe an ongoing project for building an automatic system that is able to recognize various items printed or written on a giro form. The system comprises three main components, namely, an automatic form feeder, a camera system, and a computer. These components are connected in such a way that the system is able to process a bunch of forms without any human interactions. We present two real applications of our system in the field of payment services, which require the reading of both machine printed and handwritten information that may appear on a giro form. One particular feature of giro forms is their flexible layout, i.e., information items are located differently from one form to another, thus requiring an additional analysis step to localize them before recognition. A commercial optical character recognition software package is used for recognition of machine-printed information, whereas handwritten information is read by our own algorithms, the details of which are presented. The system is implemented by using a client/server architecture providing a high degree of flexibility to change. Preliminary results are reported supporting our claim that the system is usable in practice.

Automatic SLEEP staging: From young aduslts to elderly patients using multi-class support vector machine

DEFF Research Database (Denmark)

Kempfner, Jacob; Jennum, Poul; Sorensen, Helge B. D.

2013-01-01

an automatic sleep stage detector, which can separate wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep using only EEG and EOG. Most sleep events, which define the sleep stages, are reduced with age. This is addressed by focusing on the amplitude of the clinical EEG bands......Aging is a process that is inevitable, and makes our body vulnerable to age-related diseases. Age is the most consistent factor affecting the sleep structure. Therefore, new automatic sleep staging methods, to be used in both of young and elderly patients, are needed. This study proposes......, and not the affected sleep events. The age-related influences are then reduced by robust subject-specific scaling. The classification of the three sleep stages are achieved by a multi-class support vector machine using the one-versus-rest scheme. It was possible to obtain a high classification accuracy of 0...

Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation.

Science.gov (United States)

Pereira, Sérgio; Meier, Raphael; McKinley, Richard; Wiest, Roland; Alves, Victor; Silva, Carlos A; Reyes, Mauricio

2018-02-01

Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes". In this paper, we propose a methodology to enhance the interpretability of automatically extracted machine learning features. The proposed system is composed of a Restricted Boltzmann Machine for unsupervised feature learning, and a Random Forest classifier, which are combined to jointly consider existing correlations between imaging data, features, and target variables. We define two levels of interpretation: global and local. The former is devoted to understanding if the system learned the relevant relations in the data correctly, while the later is focused on predictions performed on a voxel- and patient-level. In addition, we propose a novel feature importance strategy that considers both imaging data and target variables, and we demonstrate the ability of the approach to leverage the interpretability of the obtained representation for the task at hand. We evaluated the proposed methodology in brain tumor segmentation and penumbra estimation in ischemic stroke lesions. We show the ability of the proposed methodology to unveil information regarding relationships between imaging modalities and extracted features and their usefulness for the task at hand. In both clinical scenarios, we demonstrate that the proposed methodology enhances the interpretability of automatically learned features, highlighting specific learning patterns that resemble how an expert extracts relevant data from medical images. Copyright © 2017 Elsevier B.V. All rights reserved.

Automatic vetting of planet candidates from ground based surveys: Machine learning with NGTS

Science.gov (United States)

Armstrong, David J.; Günther, Maximilian N.; McCormac, James; Smith, Alexis M. S.; Bayliss, Daniel; Bouchy, François; Burleigh, Matthew R.; Casewell, Sarah; Eigmüller, Philipp; Gillen, Edward; Goad, Michael R.; Hodgkin, Simon T.; Jenkins, James S.; Louden, Tom; Metrailler, Lionel; Pollacco, Don; Poppenhaeger, Katja; Queloz, Didier; Raynard, Liam; Rauer, Heike; Udry, Stéphane; Walker, Simon R.; Watson, Christopher A.; West, Richard G.; Wheatley, Peter J.

2018-05-01

State of the art exoplanet transit surveys are producing ever increasing quantities of data. To make the best use of this resource, in detecting interesting planetary systems or in determining accurate planetary population statistics, requires new automated methods. Here we describe a machine learning algorithm that forms an integral part of the pipeline for the NGTS transit survey, demonstrating the efficacy of machine learning in selecting planetary candidates from multi-night ground based survey data. Our method uses a combination of random forests and self-organising-maps to rank planetary candidates, achieving an AUC score of 97.6% in ranking 12368 injected planets against 27496 false positives in the NGTS data. We build on past examples by using injected transit signals to form a training set, a necessary development for applying similar methods to upcoming surveys. We also make the autovet code used to implement the algorithm publicly accessible. autovet is designed to perform machine learned vetting of planetary candidates, and can utilise a variety of methods. The apparent robustness of machine learning techniques, whether on space-based or the qualitatively different ground-based data, highlights their importance to future surveys such as TESS and PLATO and the need to better understand their advantages and pitfalls in an exoplanetary context.

From Point Clouds to Building Information Models: 3D Semi-Automatic Reconstruction of Indoors of Existing Buildings

Directory of Open Access Journals (Sweden)

Hélène Macher

2017-10-01

Full Text Available The creation of as-built Building Information Models requires the acquisition of the as-is state of existing buildings. Laser scanners are widely used to achieve this goal since they permit to collect information about object geometry in form of point clouds and provide a large amount of accurate data in a very fast way and with a high level of details. Unfortunately, the scan-to-BIM (Building Information Model process remains currently largely a manual process which is time consuming and error-prone. In this paper, a semi-automatic approach is presented for the 3D reconstruction of indoors of existing buildings from point clouds. Several segmentations are performed so that point clouds corresponding to grounds, ceilings and walls are extracted. Based on these point clouds, walls and slabs of buildings are reconstructed and described in the IFC format in order to be integrated into BIM software. The assessment of the approach is proposed thanks to two datasets. The evaluation items are the degree of automation, the transferability of the approach and the geometric quality of results of the 3D reconstruction. Additionally, quality indexes are introduced to inspect the results in order to be able to detect potential errors of reconstruction.

Determining the Efficiency of Adaptation of Foreign Economic Activity of Machine-Building Enterprises in Conditions of Deepening the European Integration Process of Ukraine

Directory of Open Access Journals (Sweden)

Semeniuk Iryna Yu.

2018-02-01

Full Text Available The article determines that introduction and implementation of the mechanism for foreign economic adaptation of machine-building enterprises to the conditions of the European integration processes requires constant monitoring of the processes of export-import operations and the adaptation activities to identify current problems and avoid risks. It has been found that one of the monitoring instruments is the system of indicators, which provides to evaluate the efficiency of use of the mechanism for foreign economic adaptation of a machine-building enterprise by comparing the values of the obtained indicators after accomplishing adaptation changes with the values of the indicators of previous periods. It is suggested to determine efficiency of adaptation of foreign economic activity of machine-building enterprises to conditions of deepening of the European integration process of Ukraine by means of: index of change of volume of exported production of a machine-building enterprise to the EU countries; weighted average of the change in the share of the European market, which is covered by the enterprise’s products; indicator of efficiency of exports of production of a machine-building enterprise to the European Union countries; indicator of the index of changes in the volume of permanent orders from European partners; integral indicator of efficiency of use of adaptive potential of a machine-building enterprise in conditions of integration processes.

Automatic generation of medium-detailed 3D models of buildings based on CAD data

NARCIS (Netherlands)

Dominguez-Martin, B.; Van Oosterom, P.; Feito-Higueruela, F.R.; Garcia-Fernandez, A.L.; Ogayar-Anguita, C.J.

2015-01-01

We present the preliminary results of a work in progress which aims to obtain a software system able to automatically generate a set of diverse 3D building models with a medium level of detail, that is, more detailed that a mere parallelepiped, but not as detailed as a complete geometric

Machine-Building for Fuel and Energy Complex: Perspective Forms of Interaction

Science.gov (United States)

Nikitenko, S. M.; Goosen, E. V.; Pakhomova, E. A.; Rozhkova, O. V.; Mesyats, M. A.

2017-10-01

The article is devoted to the study of the existing forms of cooperation between the authorities, business and science in the fuel and energy complex and the machine-building industry at the regional level. The possibilities of applying the concept of the “triple helix” and its multi-helix modifications for the implementation of the import substitution program for high- tech products have been considered.

Automatic turbot fish cutting using machine vision

OpenAIRE

Martín Rodríguez, Fernando; Barral Martínez, Mónica

2015-01-01

This paper is about the design of an automated machine to cut turbot fish specimens. Machine vision is a key part of this project as it is used to compute a cutting curve for specimen’s head. This task is impossible to be carried out by mechanical means. Machine vision is used to detect head boundary and a robot is used to cut the head. Afterwards mechanical systems are used to slice fish to get an easy presentation for end consumer (as fish fillets than can be easily marketed ...

AUTOMATIC INTERPRETATION OF HIGH RESOLUTION SAR IMAGES: FIRST RESULTS OF SAR IMAGE SIMULATION FOR SINGLE BUILDINGS

Directory of Open Access Journals (Sweden)

J. Tao

2012-09-01

Full Text Available Due to the all-weather data acquisition capabilities, high resolution space borne Synthetic Aperture Radar (SAR plays an important role in remote sensing applications like change detection. However, because of the complex geometric mapping of buildings in urban areas, SAR images are often hard to interpret. SAR simulation techniques ease the visual interpretation of SAR images, while fully automatic interpretation is still a challenge. This paper presents a method for supporting the interpretation of high resolution SAR images with simulated radar images using a LiDAR digital surface model (DSM. Line features are extracted from the simulated and real SAR images and used for matching. A single building model is generated from the DSM and used for building recognition in the SAR image. An application for the concept is presented for the city centre of Munich where the comparison of the simulation to the TerraSAR-X data shows a good similarity. Based on the result of simulation and matching, special features (e.g. like double bounce lines, shadow areas etc. can be automatically indicated in SAR image.

Automatic fitting of Gaussian peaks using abductive machine learning

Science.gov (United States)

Abdel-Aal, R. E.

1998-02-01

Analytical techniques have been used for many years for fitting Gaussian peaks in nuclear spectroscopy. However, the complexity of the approach warrants looking for machine-learning alternatives where intensive computations are required only once (during training), while actual analysis on individual spectra is greatly simplified and quickened. This should allow the use of simple portable systems for fast and automated analysis of large numbers of spectra, particularly in situations where accuracy may be traded for speed and simplicity. This paper proposes the use of abductive networks machine learning for this purpose. The Abductory Induction Mechanism (AIM) tool was used to build models for analyzing both single and double Gaussian peaks in the presence of noise depicting statistical uncertainties in collected spectra. AIM networks were synthesized by training on 1000 representative simulated spectra and evaluated on 500 new spectra. A classifier network determines the multiplicity of single/double peaks with an accuracy of 5.8%. With statistical uncertainties corresponding to a peak count of 100, average percentage absolute errors for the height, position, and width of single peaks are 4.9, 2.9, and 4.2%, respectively. For double peaks, these average errors are within 7.0, 3.1, and 5.9%, respectively. Models have been developed which account for the effect of a linear background on a single peak. Performance is compared with a neural network application and with an analytical curve-fitting routine, and the new technique is applied to actual data of an alpha spectrum.

Automatic fitting of Gaussian peaks using abductive machine learning

International Nuclear Information System (INIS)

Abdel-Aal, R.E.

1998-01-01

Analytical techniques have been used for many years for fitting Gaussian peaks in nuclear spectroscopy. However, the complexity of the approach warrants looking for machine-learning alternatives where intensive computations are required only once (during training), while actual analysis on individual spectra is greatly simplified and quickened. This should allow the use of simple portable systems for fast and automated analysis of large numbers of spectra, particularly in situations where accuracy may be traded for speed and simplicity. This paper proposes the use of abductive networks machine learning for this purpose. The Abductory Induction Mechanism (AIM) tool was used to build models for analyzing both single and double Gaussian peaks in the presence of noise depicting statistical uncertainties in collected spectra. AIM networks were synthesized by training on 1,000 representative simulated spectra and evaluated on 500 new spectra. A classifier network determines the multiplicity of single/double peaks with an accuracy of 98%. With statistical uncertainties corresponding to a peak count of 100, average percentage absolute errors for the height, position, and width of single peaks are 4.9, 2.9, and 4.2%, respectively. For double peaks, these average errors are within 7.0, 3.1, and 5.9%, respectively. Models have been developed which account for the effect of a linear background on a single peak. Performance is compared with a neural network application and with an analytical curve-fitting routine, and the new technique is applied to actual data of an alpha spectrum

Using Standard-Sole Cost Method for Performance Gestion Accounting and Calculation Cost in the Machine Building Industry

Directory of Open Access Journals (Sweden)

Cleopatra Sendroiu

2006-07-01

Full Text Available The main purpose of improving and varying cost calculation methods in the machine building industry is to make them more operational and efficient in supplying the information necessary to the management in taking its decisions. The present cost calculation methods used in the machine building plants - global method and the method per orders - by which a historical cost is determined a posteriori used in deducting and post factum justification of manufacturing expenses does not offer the management the possibility to fully satisfy its need for information. We are talking about a change of conception in applying certain systems, methods and work techniques, according to the needs of efficient administration of production and the plant seen as a whole. The standard-cost method best answers to the needs of the effective management of the value side of the manufacturing process and raising economic efficiency. We consider that, in the machine building industry, these objectives can be achieved by using the standard - sole cost alternative of the standard-cost method.

Using Standard-Sole Cost Method for Performance Gestion Accounting and Calculation Cost in the Machine Building Industry

Directory of Open Access Journals (Sweden)

Aureliana Geta Roman

2006-09-01

Full Text Available The main purpose of improving and varying cost calculation methods in the machine building industry is to make them more operational and efficient in supplying the information necessary to the management in taking its decisions. The present cost calculation methods used in the machine building plants – global method and the method per orders – by which a historical cost is determined a posteriori used in deducting and post factum justification of manufacturing expenses does not offer the management the possibility to fully satisfy its need for information. We are talking about a change of conception in applying certain systems, methods and work techniques, according to the needs of efficient administration of production and the plant seen as a whole. The standard-cost method best answers to the needs of the effective management of the value side of the manufacturing process and raising economic efficiency. We consider that, in the machine building industry, these objectives can be achieved by using the standard - sole cost alternative of the standard-cost method.

Grinding Parts For Automatic Welding

Science.gov (United States)

Burley, Richard K.; Hoult, William S.

1989-01-01

Rollers guide grinding tool along prospective welding path. Skatelike fixture holds rotary grinder or file for machining large-diameter rings or ring segments in preparation for welding. Operator grasps handles to push rolling fixture along part. Rollers maintain precise dimensional relationship so grinding wheel cuts precise depth. Fixture-mounted grinder machines surface to quality sufficient for automatic welding; manual welding with attendant variations and distortion not necessary. Developed to enable automatic welding of parts, manual welding of which resulted in weld bead permeated with microscopic fissures.

Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper

Science.gov (United States)

Luo, Gang

2017-01-01

For user-friendliness, many software systems offer progress indicators for long-duration tasks. A typical progress indicator continuously estimates the remaining task execution time as well as the portion of the task that has been finished. Building a machine learning model often takes a long time, but no existing machine learning software supplies a non-trivial progress indicator. Similarly, running a data mining algorithm often takes a long time, but no existing data mining software provides a nontrivial progress indicator. In this article, we consider the problem of offering progress indicators for machine learning model building and data mining algorithm execution. We discuss the goals and challenges intrinsic to this problem. Then we describe an initial framework for implementing such progress indicators and two advanced, potential uses of them, with the goal of inspiring future research on this topic. PMID:29177022

Machine Learning and Radiology

Science.gov (United States)

Wang, Shijun; Summers, Ronald M.

2012-01-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

Evaluation of cleaning and disinfection performance of automatic washer disinfectors machines in programs presenting different cycle times and temperatures

OpenAIRE

Bergo,Maria do Carmo Noronha Cominato

2006-01-01

Thermal washer-disinfectors represent a technology that brought about great advantages such as, establishment of protocols, standard operating procedures, reduction in occupational risk of a biological and environmental nature. The efficacy of the cleaning and disinfection obtained by automatic washer disinfectors machines in running programs with different times and temperatures determined by the different official agencies was validated according to recommendations from ISO Standards 15883-...

Electricity of machine tool

International Nuclear Information System (INIS)

Gijeon media editorial department

1977-10-01

This book is divided into three parts. The first part deals with electricity machine, which can taints from generator to motor, motor a power source of machine tool, electricity machine for machine tool such as switch in main circuit, automatic machine, a knife switch and pushing button, snap switch, protection device, timer, solenoid, and rectifier. The second part handles wiring diagram. This concludes basic electricity circuit of machine tool, electricity wiring diagram in your machine like milling machine, planer and grinding machine. The third part introduces fault diagnosis of machine, which gives the practical solution according to fault diagnosis and the diagnostic method with voltage and resistance measurement by tester.

Principles of control automation of soil compacting machine operating mechanism

Science.gov (United States)

Anatoly Fedorovich, Tikhonov; Drozdov, Anatoly

2018-03-01

The relevance of the qualitative compaction of soil bases in the erection of embankment and foundations in building and structure construction is given.The quality of the compactible gravel and sandy soils provides the bearing capability and, accordingly, the strength and durability of constructed buildings.It has been established that the compaction quality depends on many external actions, such as surface roughness and soil moisture; granulometry, chemical composition and degree of elasticity of originalfilled soil for compaction.The analysis of technological processes of soil bases compaction of foreign and domestic information sources showed that the solution of such important problem as a continuous monitoring of soil compaction actual degree in the process of machine operation carry out only with the use of modern means of automation. An effective vibrodynamic method of gravel and sand material sealing for the building structure foundations for various applications was justified and suggested.The method of continuous monitoring the soil compaction by measurement of the amplitudes and frequencies of harmonic oscillations on the compactible surface was determined, which allowed to determine the basic elements of facilities of soil compacting machine monitoring system of operating, etc. mechanisms: an accelerometer, a bandpass filter, a vibro-harmonics, an on-board microcontroller. Adjustable parameters have been established to improve the soil compaction degree and the soil compacting machine performance, and the adjustable parameter dependences on the overall indexhave been experimentally determined, which is the soil compaction degree.A structural scheme of automatic control of the soil compacting machine control mechanism and theoperation algorithm has been developed.

Automatic Evaluation of Machine Translation

DEFF Research Database (Denmark)

Martinez, Mercedes Garcia; Koglin, Arlene; Mesa-Lao, Bartolomé

2015-01-01

The availability of systems capable of producing fairly accurate translations has increased the popularity of machine translation (MT). The translation industry is steadily incorporating MT in their workflows engaging the human translator to post-edit the raw MT output in order to comply with a s...

Automatic reel controls filler wire in welding machines

Science.gov (United States)

Millett, A. V.

1966-01-01

Automatic reel on automatic welding equipment takes up slack in the reel-fed filler wire when welding operation is terminated. The reel maintains constant, adjustable tension on the wire during the welding operation and rewinds the wire from the wire feed unit when the welding is completed.

Innovative technologies of outsourcing at the machine-building enterprises of Sverdlovsk area

Directory of Open Access Journals (Sweden)

P. P. Krylatkov

2010-09-01

Full Text Available The analysis of the basic problems connected with infringements of integrity of the machine-building enterprises of Sverdlovsk area is lead in the article. The specified problems are considered from a position of the complete approach developed by the author. Display of infringements of integrity of some the enterprises are illustrated by data of their inspection. As the effective tool of increase of integrity of the enterprises — expansion use of attitudes of outsourcing is offered. The comparative given applications of outsourcing in developed the country of the West and at the domestic enterprises, and also its benefits and risks are cited. Outsourcing, insoursing and subcontracting are considered by the author as the innovative tool of the decision of many serious problems of the machine-building enterprises of region. As an example practice of work of «The Sverdlovsk regional center of industrial cooperation» on coordination of attitudes of outsourcing, subcontracting and cooperation is offered. The author recommends the tabulated form, with the instruction of kinds of works for which performance the method of allocation or attraction outsourcing of the companies can be used.

Study on the Feasibility of RGB Substitute CIR for Automatic Removal Vegetation Occlusion Based on Ground Close-Range Building Images

Science.gov (United States)

Li, C.; Li, F.; Liu, Y.; Li, X.; Liu, P.; Xiao, B.

2012-07-01

Building 3D reconstruction based on ground remote sensing data (image, video and lidar) inevitably faces the problem that buildings are always occluded by vegetation, so how to automatically remove and repair vegetation occlusion is a very important preprocessing work for image understanding, compute vision and digital photogrammetry. In the traditional multispectral remote sensing which is achieved by aeronautics and space platforms, the Red and Near-infrared (NIR) bands, such as NDVI (Normalized Difference Vegetation Index), are useful to distinguish vegetation and clouds, amongst other targets. However, especially in the ground platform, CIR (Color Infra Red) is little utilized by compute vision and digital photogrammetry which usually only take true color RBG into account. Therefore whether CIR is necessary for vegetation segmentation or not has significance in that most of close-range cameras don't contain such NIR band. Moreover, the CIE L*a*b color space, which transform from RGB, seems not of much interest by photogrammetrists despite its powerfulness in image classification and analysis. So, CIE (L, a, b) feature and support vector machine (SVM) is suggested for vegetation segmentation to substitute for CIR. Finally, experimental results of visual effect and automation are given. The conclusion is that it's feasible to remove and segment vegetation occlusion without NIR band. This work should pave the way for texture reconstruction and repair for future 3D reconstruction.

SEMI-AUTOMATIC BUILDING MODELS AND FAÇADE TEXTURE MAPPING FROM MOBILE PHONE IMAGES

Directory of Open Access Journals (Sweden)

J. Jeong

2016-06-01

Full Text Available Research on 3D urban modelling has been actively carried out for a long time. Recently the need of 3D urban modelling research is increased rapidly due to improved geo-web services and popularized smart devices. Nowadays 3D urban models provided by, for example, Google Earth use aerial photos for 3D urban modelling but there are some limitations: immediate update for the change of building models is difficult, many buildings are without 3D model and texture, and large resources for maintaining and updating are inevitable. To resolve the limitations mentioned above, we propose a method for semi-automatic building modelling and façade texture mapping from mobile phone images and analyze the result of modelling with actual measurements. Our method consists of camera geometry estimation step, image matching step, and façade mapping step. Models generated from this method were compared with actual measurement value of real buildings. Ratios of edge length of models and measurements were compared. Result showed 5.8% average error of length ratio. Through this method, we could generate a simple building model with fine façade textures without expensive dedicated tools and dataset.

Integrated Multi-Scale Data Analytics and Machine Learning for the Distribution Grid and Building-to-Grid Interface

Energy Technology Data Exchange (ETDEWEB)

Stewart, Emma M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Hendrix, Val [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Deka, Deepjyoti [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-03-16

This white paper introduces the application of advanced data analytics to the modernized grid. In particular, we consider the field of machine learning and where it is both useful, and not useful, for the particular field of the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper we consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid and the building-to-grid interface. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. Machine learning is a subfield of computer science that studies and constructs algorithms that can learn from data and make predictions and improve forecasts. Incorporation of machine learning in grid monitoring and analysis tools may have the potential to solve data and operational challenges that result from increasing penetration of distributed and behind-the-meter energy resources. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors – such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals – such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

Science.gov (United States)

Lajnef, Tarek; Chaibi, Sahbi; Ruby, Perrine; Aguera, Pierre-Emmanuel; Eichenlaub, Jean-Baptiste; Samet, Mounir; Kachouri, Abdennaceur; Jerbi, Karim

2015-07-30

Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis. The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection. Copyright © 2015 Elsevier B.V. All rights reserved.

Empirical Studies On Machine Learning Based Text Classification Algorithms

OpenAIRE

Shweta C. Dharmadhikari; Maya Ingle; Parag Kulkarni

2011-01-01

Automatic classification of text documents has become an important research issue now days. Properclassification of text documents requires information retrieval, machine learning and Natural languageprocessing (NLP) techniques. Our aim is to focus on important approaches to automatic textclassification based on machine learning techniques viz. supervised, unsupervised and semi supervised.In this paper we present a review of various text classification approaches under machine learningparadig...

A relevance vector machine technique for the automatic detection of clustered microcalcifications (Honorable Mention Poster Award)

Science.gov (United States)

Wei, Liyang; Yang, Yongyi; Nishikawa, Robert M.

2005-04-01

Microcalcification (MC) clusters in mammograms can be important early signs of breast cancer in women. Accurate detection of MC clusters is an important but challenging problem. In this paper, we propose the use of a recently developed machine learning technique -- relevance vector machine (RVM) -- for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called relevance vectors. We formulate MC detection as a supervised-learning problem, and use RVM to classify if an MC object is present or not at each location in a mammogram image. MC clusters are then identified by grouping the detected MC objects. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier which we developed previously. The detection performance is evaluated using the free-response receiver operating characteristic (FROC) curves. It is demonstrated that the RVM classifier matches closely with the SVM classifier in detection performance, and does so with a much sparser kernel representation than the SVM classifier. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time processing of MC clusters in mammograms.

Towards Automatic Classification of Exoplanet-Transit-Like Signals: A Case Study on Kepler Mission Data

Science.gov (United States)

Valizadegan, Hamed; Martin, Rodney; McCauliff, Sean D.; Jenkins, Jon Michael; Catanzarite, Joseph; Oza, Nikunj C.

2015-08-01

Building new catalogues of planetary candidates, astrophysical false alarms, and non-transiting phenomena is a challenging task that currently requires a reviewing team of astrophysicists and astronomers. These scientists need to examine more than 100 diagnostic metrics and associated graphics for each candidate exoplanet-transit-like signal to classify it into one of the three classes. Considering that the NASA Explorer Program's TESS mission and ESA's PLATO mission survey even a larger area of space, the classification of their transit-like signals is more time-consuming for human agents and a bottleneck to successfully construct the new catalogues in a timely manner. This encourages building automatic classification tools that can quickly and reliably classify the new signal data from these missions. The standard tool for building automatic classification systems is the supervised machine learning that requires a large set of highly accurate labeled examples in order to build an effective classifier. This requirement cannot be easily met for classifying transit-like signals because not only are existing labeled signals very limited, but also the current labels may not be reliable (because the labeling process is a subjective task). Our experiments with using different supervised classifiers to categorize transit-like signals verifies that the labeled signals are not rich enough to provide the classifier with enough power to generalize well beyond the observed cases (e.g. to unseen or test signals). That motivated us to utilize a new category of learning techniques, so-called semi-supervised learning, that combines the label information from the costly labeled signals, and distribution information from the cheaply available unlabeled signals in order to construct more effective classifiers. Our study on the Kepler Mission data shows that semi-supervised learning can significantly improve the result of multiple base classifiers (e.g. Support Vector Machines, Ada

Machine learning and radiology.

Science.gov (United States)

Wang, Shijun; Summers, Ronald M

2012-07-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Data

Directory of Open Access Journals (Sweden)

Ole Marius Hoel Rindal

2017-12-01

Full Text Available The automatic classification of sub-techniques in classical cross-country skiing provides unique possibilities for analyzing the biomechanical aspects of outdoor skiing. This is currently possible due to the miniaturization and flexibility of wearable inertial measurement units (IMUs that allow researchers to bring the laboratory to the field. In this study, we aimed to optimize the accuracy of the automatic classification of classical cross-country skiing sub-techniques by using two IMUs attached to the skier’s arm and chest together with a machine learning algorithm. The novelty of our approach is the reliable detection of individual cycles using a gyroscope on the skier’s arm, while a neural network machine learning algorithm robustly classifies each cycle to a sub-technique using sensor data from an accelerometer on the chest. In this study, 24 datasets from 10 different participants were separated into the categories training-, validation- and test-data. Overall, we achieved a classification accuracy of 93.9% on the test-data. Furthermore, we illustrate how an accurate classification of sub-techniques can be combined with data from standard sports equipment including position, altitude, speed and heart rate measuring systems. Combining this information has the potential to provide novel insight into physiological and biomechanical aspects valuable to coaches, athletes and researchers.

Automatic Generation of Structural Building Descriptions from 3D Point Cloud Scans

DEFF Research Database (Denmark)

Ochmann, Sebastian; Vock, Richard; Wessel, Raoul

2013-01-01

We present a new method for automatic semantic structuring of 3D point clouds representing buildings. In contrast to existing approaches which either target the outside appearance like the facade structure or rather low-level geometric structures, we focus on the building’s interior using indoor...... scans to derive high-level architectural entities like rooms and doors. Starting with a registered 3D point cloud, we probabilistically model the affiliation of each measured point to a certain room in the building. We solve the resulting clustering problem using an iterative algorithm that relies...... on the estimated visibilities between any two locations within the point cloud. With the segmentation into rooms at hand, we subsequently determine the locations and extents of doors between adjacent rooms. In our experiments, we demonstrate the feasibility of our method by applying it to synthetic as well...

Spread of `Eco-vendor`, an energy-saving type automatic vending machine for beverages with a peak cut function; Shoenegata seiryo inryoyo jido hanbaiki (peak cut kino tsuki) `eko benda` no fukyu ni tsuite

Energy Technology Data Exchange (ETDEWEB)

NONE

1996-05-01

`Eco-vendor`, an energy-saving type automatic vending machine for beverages with a peak cut function has been developed in 1995 through cooperation by Tokyo Electric Power Company, Coca-Cola Japan, and Fuji Electric Co., Ltd. It can cut 90 percent of the power consumption during 13:00 to 16:00 on weekdays in summer, and can save 10 to 15 percent of annual power consumption. Since the heat insulation performance has been improved to keep beverages at the set temperature during the peak cut operation, the energy-saving effect for cold products in summer and for hot products in winter has been enhanced. For the manufacturing of `Eco-vendor`, Fuji Electric Co., Ltd. has offered its technical know-how to five manufacturers of automatic vending machine for beverages without compensation, and each manufacturer has accepted the offer. Electric power companies of Japan have cooperated to pay incident of 10,000 yen per an `Eco-vendor` to the manufacturers. Japan`s automatic vending machine industries and nationwide beverage industries have positively cooperated. Consequently, Tokyo Electric Power Company has successfully spread 2,600 machines up to the end of fiscal 1995, and aims at the spread of 16,000 machines in fiscal 1996. 2 figs.

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

Science.gov (United States)

Wang, Jun Yi; Ngo, Michael M; Hessl, David; Hagerman, Randi J; Rivera, Susan M

2016-01-01

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as

Energy management systems in buildings

Energy Technology Data Exchange (ETDEWEB)

Lush, D. M.

1979-07-01

An investigation is made of the range of possibilities available from three types of systems (automatic control devices, building envelope, and the occupants) in buildings. The following subjects are discussed: general (buildings, design and personnel); new buildings (envelope, designers, energy and load calculations, plant design, general design parameters); existing buildings (conservation measures, general energy management, air conditioned buildings, industrial buildings); man and motivation (general, energy management and documentation, maintenance, motivation); automatic energy management systems (thermostatic controls, optimized plant start up, air conditioned and industrial buildings, building automatic systems). (MCW)

Self-Improving CNC Milling Machine

OpenAIRE

Spilling, Torjus

2014-01-01

This thesis is a study of the ability of a CNC milling machine to create parts for itself, and an evaluation of whether or not the machine is able to improve itself by creating new machine parts. This will be explored by using off-the-shelf parts to build an initial machine, using 3D printing/rapid prototyping to create any special parts needed for the initial build. After an initial working machine is completed, the design of the machine parts will be adjusted so that the machine can start p...

Building a semi-automatic ontology learning and construction system for geosciences

Science.gov (United States)

Babaie, H. A.; Sunderraman, R.; Zhu, Y.

2013-12-01

We are developing an ontology learning and construction framework that allows continuous, semi-automatic knowledge extraction, verification, validation, and maintenance by potentially a very large group of collaborating domain experts in any geosciences field. The system brings geoscientists from the side-lines to the center stage of ontology building, allowing them to collaboratively construct and enrich new ontologies, and merge, align, and integrate existing ontologies and tools. These constantly evolving ontologies can more effectively address community's interests, purposes, tools, and change. The goal is to minimize the cost and time of building ontologies, and maximize the quality, usability, and adoption of ontologies by the community. Our system will be a domain-independent ontology learning framework that applies natural language processing, allowing users to enter their ontology in a semi-structured form, and a combined Semantic Web and Social Web approach that lets direct participation of geoscientists who have no skill in the design and development of their domain ontologies. A controlled natural language (CNL) interface and an integrated authoring and editing tool automatically convert syntactically correct CNL text into formal OWL constructs. The WebProtege-based system will allow a potentially large group of geoscientists, from multiple domains, to crowd source and participate in the structuring of their knowledge model by sharing their knowledge through critiquing, testing, verifying, adopting, and updating of the concept models (ontologies). We will use cloud storage for all data and knowledge base components of the system, such as users, domain ontologies, discussion forums, and semantic wikis that can be accessed and queried by geoscientists in each domain. We will use NoSQL databases such as MongoDB as a service in the cloud environment. MongoDB uses the lightweight JSON format, which makes it convenient and easy to build Web applications using

Evaluation of automatic vacuum- assisted compaction solutions

Directory of Open Access Journals (Sweden)

M. Brzeziński

2011-01-01

Full Text Available Currently on the mould-making machines market the companies like: DiSA, KUENKEL WAGNER, HAFLINGER, HEINRICH WAGNER SINTO, HUNTER, SAVELLI AND TECHNICAL play significant role. These companies are the manufacturers of various solutions in machines and instalations applied in foundry engineering. Automatic foundry machines for compaction of green sand have the major role in mechanisation and automation processes of making the mould. The concept of operation of automatic machines is based on the static and dynamic methods of compacting the green sand. The method which gains the importance is the compacting method by using the energy of the air pressure. It's the initial stage or the supporting process of compacting the green sand. However in the automatic mould making machines using this method it's essential to use the additional compaction of the mass in order to receive the final parameters of the form. In the constructional solutions of the machines there is the additional division which concerns the method of putting the sand into the mould box. This division distinquishes the transport of the sand with simultaneous compaction or the putting of the sand without the pre-compaction. As the solutions of the major manufacturers are often the subject for application in various foundries, the authors of the paper would like/have the confidence to present their own evaluation process confirmed by their own researches and independent analysis of the producers' solutions.

Low Speed Control for Automatic Welding

Science.gov (United States)

Iceland, W. E.

1982-01-01

Amplifier module allows rotating positioner of automatic welding machine to operate at speeds below normal range. Low speeds are precisely regulated by a servomechanism as are normal-range speeds. Addition of module to standard welding machine makes it unnecessary to purchase new equipment for low-speed welding.

Diagnostic information system dynamics in the evaluation of machine learning algorithms for the supervision of energy efficiency of district heating-supplied buildings

International Nuclear Information System (INIS)

Kiluk, Sebastian

2017-01-01

Highlights: • Energy efficiency classification sustainability benefits from knowledge prediction. • Diagnostic classification can be validated with its dynamics and current data. • Diagnostic classification dynamics provides novelty extraction for knowledge update. • Data mining comparison can be performed with knowledge dynamics and uncertainty. • Diagnostic information refinement benefits form comparing classifiers dynamics. - Abstract: Modern ways of exploring the diagnostic knowledge provided by data mining and machine learning raise some concern about the ways of evaluating the quality of output knowledge, usually represented by information systems. Especially in district heating, the stationarity of efficiency models, and thus the relevance of diagnostic classification system, cannot be ensured due to the impact of social, economic or technological changes, which are hard to identify or predict. Therefore, data mining and machine learning have become an attractive strategy for automatically and continuously absorbing such dynamics. This paper presents a new method of evaluation and comparison of diagnostic information systems gathered algorithmically in district heating efficiency supervision based on exploring the evolution of information system and analyzing its dynamic features. The process of data mining and knowledge discovery was applied to the data acquired from district heating substations’ energy meters to provide the automated discovery of diagnostic knowledge base necessary for the efficiency supervision of district heating-supplied buildings. The implemented algorithm consists of several steps of processing the billing data, including preparation, segmentation, aggregation and knowledge discovery stage, where classes of abstract models representing energy efficiency constitute an information system representing diagnostic knowledge about the energy efficiency of buildings favorably operating under similar climate conditions and

Automatic Indoor Building Reconstruction from Mobile Laser Scanning Data

Science.gov (United States)

Xie, L.; Wang, R.

2017-09-01

Indoor reconstruction from point clouds is a hot topic in photogrammetry, computer vision and computer graphics. Reconstructing indoor scene from point clouds is challenging due to complex room floorplan and line-of-sight occlusions. Most of existing methods deal with stationary terrestrial laser scanning point clouds or RGB-D point clouds. In this paper, we propose an automatic method for reconstructing indoor 3D building models from mobile laser scanning point clouds. The method includes 2D floorplan generation, 3D building modeling, door detection and room segmentation. The main idea behind our approach is to separate wall structure into two different types as the inner wall and the outer wall based on the observation of point distribution. Then we utilize a graph cut based optimization method to solve the labeling problem and generate the 2D floorplan based on the optimization result. Subsequently, we leverage an ?-shape based method to detect the doors on the 2D projected point clouds and utilize the floorplan to segment the individual room. The experiments show that this door detection method can achieve a recognition rate at 97% and the room segmentation method can attain the correct segmentation results. We also evaluate the reconstruction accuracy on the synthetic data, which indicates the accuracy of our method is comparable to the state-of-the art.

The Organizational-Economic Provision of Reengineering of Marketing Activity of Ukrainian Machine-Building Enterprises

Directory of Open Access Journals (Sweden)

Kobyzskyi Denys S.

2018-02-01

Full Text Available The article is aimed at developing an organizational mechanism to provide reengineering of the marketing activities of machine-building enterprise for further development of the appropriate methodical recommendations. The meaning and role of organizational structure in the sphere of reengineering are disclosed, the key aspects and principles of its construction are defined; the key elements, in particular business processes, and their role in organizational structure as well as properties of the organizational system are researched; content of the basic components of the organizational mechanism of the provision, their role and peculiarities of communication between them are analyzed. The new attitude to the principles of construction, functional content and content of the constituents of organization of enterprises allows to realize the wide functional potential of organizational possibilities within the terms of reengineering, as well as to form an organizational mechanism of post-reengineering company. Certain aspects of development of the organizational mechanism create the preconditions and disclose a potential instrumentarium for effective and efficient methodical recommendations as to reengineering of marketing activities of Ukrainian machine-building enterprises.

Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis

International Nuclear Information System (INIS)

Chen, Yixing; Hong, Tianzhen; Piette, Mary Ann

2017-01-01

Highlights: •Developed methods and used data models to integrate city’s public building records. •Shading from neighborhood buildings strongly influences urban building performance. •A case study demonstrated the workflow, simulation and analysis of building retrofits. •CityBES retrofit analysis feature provides actionable information for decision making. •Discussed significance and challenges of urban building energy modeling. -- Abstract: Buildings in cities consume 30–70% of total primary energy, and improving building energy efficiency is one of the key strategies towards sustainable urbanization. Urban building energy models (UBEM) can support city managers to evaluate and prioritize energy conservation measures (ECMs) for investment and the design of incentive and rebate programs. This paper presents the retrofit analysis feature of City Building Energy Saver (CityBES) to automatically generate and simulate UBEM using EnergyPlus based on cities’ building datasets and user-selected ECMs. CityBES is a new open web-based tool to support city-scale building energy efficiency strategic plans and programs. The technical details of using CityBES for UBEM generation and simulation are introduced, including the workflow, key assumptions, and major databases. Also presented is a case study that analyzes the potential retrofit energy use and energy cost savings of five individual ECMs and two measure packages for 940 office and retail buildings in six city districts in northeast San Francisco, United States. The results show that: (1) all five measures together can save 23–38% of site energy per building; (2) replacing lighting with light-emitting diode lamps and adding air economizers to existing heating, ventilation and air-conditioning (HVAC) systems are most cost-effective with an average payback of 2.0 and 4.3 years, respectively; and (3) it is not economical to upgrade HVAC systems or replace windows in San Francisco due to the city’s mild

Automatic recognition of damaged town buildings caused by earthquake using remote sensing information: Taking the 2001 Bhuj, India, earthquake and the 1976 Tangshan, China, earthquake as examples

Science.gov (United States)

Liu, Jia-Hang; Shan, Xin-Jian; Yin, Jing-Yuan

2004-11-01

In the high-resolution images, the undamaged buildings generally show a natural textural feature, while the damaged or semi-damaged buildings always exhibit some low-grayscale blocks because of their coarsely damaged sections. If we use a proper threshold to classify the grayscale of image, some independent holes will appear in the damaged regions. By using such statistical information as the number of holes in every region, or the ratio between the area of holes and that of the region, etc, the damaged buildings can be separated from the undamaged, thus automatic detection of damaged buildings can be realized. Based on these characteristics, a new method to automatically detect the damage buildings by using regional structure and statistical information of texture is presented in the paper. In order to test its validity, 1-m-resolution iKonos merged image of the 2001 Bhuj earthquake and grayscale aerial photos of the 1976 Tangshan earthquake are selected as two examples to automatically detect the damaged buildings. Satisfied results are obtained.

Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions.

Science.gov (United States)

Zdravevski, Eftim; Risteska Stojkoska, Biljana; Standl, Marie; Schulz, Holger

2017-01-01

Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position. The study used diarized jogging periods and the corresponding accelerometer data from thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus study. The data was obtained from two accelerometers placed at the hip and ankle. Automated feature engineering technique was performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine learning algorithms were used for classification: Logistic regression, Support Vector Machines, Random Forest and Extremely Randomized Trees. Classification was performed using only data from the hip accelerometer, using only data from ankle accelerometer and using data from both accelerometers. The reported jogging periods were verified by visual inspection and used as golden standard. After the feature selection and tuning of the classification algorithms, all options provided a classification accuracy of at least 0.99, independent of the applied segmentation strategy with sliding windows of either 60s or 180s. The best matching ratio, i.e. the length of correctly identified jogging periods related to the total time including the missed ones, was up to 0.875. It could be additionally improved up to 0.967 by application of post-classification rules, which considered the duration of breaks and jogging periods. There was no obvious benefit of using two accelerometers, rather almost the same performance could be achieved from

Automatic machine-learning based identification of jogging periods from accelerometer measurements of adolescents under field conditions.

Directory of Open Access Journals (Sweden)

Eftim Zdravevski

Full Text Available Assessment of health benefits associated with physical activity depend on the activity duration, intensity and frequency, therefore their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are: to develop an algorithm for automatic identification of intended jogging periods; and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared to when using only one at either position.The study used diarized jogging periods and the corresponding accelerometer data from thirty-nine, 15-year-old adolescents, collected under field conditions, as part of the GINIplus study. The data was obtained from two accelerometers placed at the hip and ankle. Automated feature engineering technique was performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine learning algorithms were used for classification: Logistic regression, Support Vector Machines, Random Forest and Extremely Randomized Trees. Classification was performed using only data from the hip accelerometer, using only data from ankle accelerometer and using data from both accelerometers.The reported jogging periods were verified by visual inspection and used as golden standard. After the feature selection and tuning of the classification algorithms, all options provided a classification accuracy of at least 0.99, independent of the applied segmentation strategy with sliding windows of either 60s or 180s. The best matching ratio, i.e. the length of correctly identified jogging periods related to the total time including the missed ones, was up to 0.875. It could be additionally improved up to 0.967 by application of post-classification rules, which considered the duration of breaks and jogging periods. There was no obvious benefit of using two accelerometers, rather almost the same performance could be

Automatic Generation of Building Models with Levels of Detail 1-3

Science.gov (United States)

Nguatem, W.; Drauschke, M.; Mayer, H.

2016-06-01

We present a workflow for the automatic generation of building models with levels of detail (LOD) 1 to 3 according to the CityGML standard (Gröger et al., 2012). We start with orienting unsorted image sets employing (Mayer et al., 2012), we compute depth maps using semi-global matching (SGM) (Hirschmüller, 2008), and fuse these depth maps to reconstruct dense 3D point clouds (Kuhn et al., 2014). Based on planes segmented from these point clouds, we have developed a stochastic method for roof model selection (Nguatem et al., 2013) and window model selection (Nguatem et al., 2014). We demonstrate our workflow up to the export into CityGML.

Using support vector machines to improve elemental ion identification in macromolecular crystal structures

Energy Technology Data Exchange (ETDEWEB)

Morshed, Nader [University of California, Berkeley, CA 94720 (United States); Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); Echols, Nathaniel, E-mail: nechols@lbl.gov [Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); Adams, Paul D., E-mail: nechols@lbl.gov [Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); University of California, Berkeley, CA 94720 (United States)

2015-05-01

A method to automatically identify possible elemental ions in X-ray crystal structures has been extended to use support vector machine (SVM) classifiers trained on selected structures in the PDB, with significantly improved sensitivity over manually encoded heuristics. In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific knowledge of metal-binding chemistry and scattering properties and is prone to error. A method has previously been described to identify ions based on manually chosen criteria for a number of elements. Here, the use of support vector machines (SVMs) to automatically classify isolated atoms as either solvent or one of various ions is described. Two data sets of protein crystal structures, one containing manually curated structures deposited with anomalous diffraction data and another with automatically filtered, high-resolution structures, were constructed. On the manually curated data set, an SVM classifier was able to distinguish calcium from manganese, zinc, iron and nickel, as well as all five of these ions from water molecules, with a high degree of accuracy. Additionally, SVMs trained on the automatically curated set of high-resolution structures were able to successfully classify most common elemental ions in an independent validation test set. This method is readily extensible to other elemental ions and can also be used in conjunction with previous methods based on a priori expectations of the chemical environment and X-ray scattering.

Automatic Earthquake Detection by Active Learning

Science.gov (United States)

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.

Innovative technologies of outsourcing at the machine-building enterprises of Sverdlovsk area

OpenAIRE

Petr Krylatkov

2010-01-01

The analysis of the basic problems connected with infringements of integrity of the machine-building enterprises of Sverdlovsk area is lead in the article. The specified problems are considered from a position of the complete approach developed by the author. Display of infringements of integrity of some the enterprises are illustrated by data of their inspection. As the effective tool of increase of integrity of the enterprises — expansion use of attitudes of outsourcing is offered. The co...

Tangent: Automatic Differentiation Using Source Code Transformation in Python

OpenAIRE

van Merriënboer, Bart; Wiltschko, Alexander B.; Moldovan, Dan

2017-01-01

Automatic differentiation (AD) is an essential primitive for machine learning programming systems. Tangent is a new library that performs AD using source code transformation (SCT) in Python. It takes numeric functions written in a syntactic subset of Python and NumPy as input, and generates new Python functions which calculate a derivative. This approach to automatic differentiation is different from existing packages popular in machine learning, such as TensorFlow and Autograd. Advantages ar...

Automatic Assignment of Methyl-NMR Spectra of Supramolecular Machines Using Graph Theory.

Science.gov (United States)

Pritišanac, Iva; Degiacomi, Matteo T; Alderson, T Reid; Carneiro, Marta G; Ab, Eiso; Siegal, Gregg; Baldwin, Andrew J

2017-07-19

Methyl groups are powerful probes for the analysis of structure, dynamics and function of supramolecular assemblies, using both solution- and solid-state NMR. Widespread application of the methodology has been limited due to the challenges associated with assigning spectral resonances to specific locations within a biomolecule. Here, we present Methyl Assignment by Graph Matching (MAGMA), for the automatic assignment of methyl resonances. A graph matching protocol examines all possibilities for each resonance in order to determine an exact assignment that includes a complete description of any ambiguity. MAGMA gives 100% accuracy in confident assignments when tested against both synthetic data, and 9 cross-validated examples using both solution- and solid-state NMR data. We show that this remarkable accuracy enables a user to distinguish between alternative protein structures. In a drug discovery application on HSP90, we show the method can rapidly and efficiently distinguish between possible ligand binding modes. By providing an exact and robust solution to methyl resonance assignment, MAGMA can facilitate significantly accelerated studies of supramolecular machines using methyl-based NMR spectroscopy.

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

Directory of Open Access Journals (Sweden)

Jun Yi Wang

Full Text Available Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation to 0.978 (for SegAdapter-corrected segmentation for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large

Hybrid genetic algorithm for minimizing non productive machining ...

African Journals Online (AJOL)

Minimization of non-productive time of tool during machining for 2.5 D milling significantly reduces the machining cost. The tool gets retracted and repositioned several times in multi pocket jobs during rough machining which consumes 15 to 30% of total machining time depending on the complexity of job. The automatic ...

Game-powered machine learning.

Science.gov (United States)

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-04-24

Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the "wisdom of the crowds." Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., "funky jazz with saxophone," "spooky electronica," etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data.

Automatic indexing, compiling and classification

International Nuclear Information System (INIS)

Andreewsky, Alexandre; Fluhr, Christian.

1975-06-01

A review of the principles of automatic indexing, is followed by a comparison and summing-up of work by the authors and by a Soviet staff from the Moscou INFORM-ELECTRO Institute. The mathematical and linguistic problems of the automatic building of thesaurus and automatic classification are examined [fr

Reagent precipitation of copper ions from wastewater of machine-building factories

Science.gov (United States)

Porozhnyuk, L. A.; Lupandina, N. S.; Porozhnyuk, E. V.

2018-03-01

The article presents the results of reagent removal of copper ions from wastewater of machine-building factories. The urgency of the study is conditioned by the widening of the range of effective reagents through the implementation of industrial waste. The investigation covers mineralogical and fractional composition of chalk enrichment waste. In the work, the conditions of thermal activation of chalk enrichment waste used for reagent removal of copper ions from wastewater were elaborated. It was shown that the thermal activation of waste facilitates the increased treatment efficacy up to the set sanitation, hygiene and technological standards.

Quantum Machine Learning

OpenAIRE

Romero García, Cristian

2017-01-01

[EN] In a world in which accessible information grows exponentially, the selection of the appropriate information turns out to be an extremely relevant problem. In this context, the idea of Machine Learning (ML), a subfield of Artificial Intelligence, emerged to face problems in data mining, pattern recognition, automatic prediction, among others. Quantum Machine Learning is an interdisciplinary research area combining quantum mechanics with methods of ML, in which quantum properties allow fo...

Machine Tool Software

Science.gov (United States)

1988-01-01

A NASA-developed software package has played a part in technical education of students who major in Mechanical Engineering Technology at William Rainey Harper College. Professor Hack has been using (APT) Automatically Programmed Tool Software since 1969 in his CAD/CAM Computer Aided Design and Manufacturing curriculum. Professor Hack teaches the use of APT programming languages for control of metal cutting machines. Machine tool instructions are geometry definitions written in APT Language to constitute a "part program." The part program is processed by the machine tool. CAD/CAM students go from writing a program to cutting steel in the course of a semester.

AUTOMATIC LUNG NODULE DETECTION BASED ON STATISTICAL REGION MERGING AND SUPPORT VECTOR MACHINES

Directory of Open Access Journals (Sweden)

Elaheh Aghabalaei Khordehchi

2017-06-01

Full Text Available Lung cancer is one of the most common diseases in the world that can be treated if the lung nodules are detected in their early stages of growth. This study develops a new framework for computer-aided detection of pulmonary nodules thorough a fully-automatic analysis of Computed Tomography (CT images. In the present work, the multi-layer CT data is fed into a pre-processing step that exploits an adaptive diffusion-based smoothing algorithm in which the parameters are automatically tuned using an adaptation technique. After multiple levels of morphological filtering, the Regions of Interest (ROIs are extracted from the smoothed images. The Statistical Region Merging (SRM algorithm is applied to the ROIs in order to segment each layer of the CT data. Extracted segments in consecutive layers are then analyzed in such a way that if they intersect at more than a predefined number of pixels, they are labeled with a similar index. The boundaries of the segments in adjacent layers which have the same indices are then connected together to form three-dimensional objects as the nodule candidates. After extracting four spectral, one morphological, and one textural feature from all candidates, they are finally classified into nodules and non-nodules using the Support Vector Machine (SVM classifier. The proposed framework has been applied to two sets of lung CT images and its performance has been compared to that of nine other competing state-of-the-art methods. The considerable efficiency of the proposed approach has been proved quantitatively and validated by clinical experts as well.

Applying machine-learning techniques to Twitter data for automatic hazard-event classification.

Science.gov (United States)

Filgueira, R.; Bee, E. J.; Diaz-Doce, D.; Poole, J., Sr.; Singh, A.

2017-12-01

The constant flow of information offered by tweets provides valuable information about all sorts of events at a high temporal and spatial resolution. Over the past year we have been analyzing in real-time geological hazards/phenomenon, such as earthquakes, volcanic eruptions, landslides, floods or the aurora, as part of the GeoSocial project, by geo-locating tweets filtered by keywords in a web-map. However, not all the filtered tweets are related with hazard/phenomenon events. This work explores two classification techniques for automatic hazard-event categorization based on tweets about the "Aurora". First, tweets were filtered using aurora-related keywords, removing stop words and selecting the ones written in English. For classifying the remaining between "aurora-event" or "no-aurora-event" categories, we compared two state-of-art techniques: Support Vector Machine (SVM) and Deep Convolutional Neural Networks (CNN) algorithms. Both approaches belong to the family of supervised learning algorithms, which make predictions based on labelled training dataset. Therefore, we created a training dataset by tagging 1200 tweets between both categories. The general form of SVM is used to separate two classes by a function (kernel). We compared the performance of four different kernels (Linear Regression, Logistic Regression, Multinomial Naïve Bayesian and Stochastic Gradient Descent) provided by Scikit-Learn library using our training dataset to build the SVM classifier. The results shown that the Logistic Regression (LR) gets the best accuracy (87%). So, we selected the SVM-LR classifier to categorise a large collection of tweets using the "dispel4py" framework.Later, we developed a CNN classifier, where the first layer embeds words into low-dimensional vectors. The next layer performs convolutions over the embedded word vectors. Results from the convolutional layer are max-pooled into a long feature vector, which is classified using a softmax layer. The CNN's accuracy

Automatic thermographic scanning with the creation of 3D panoramic views of buildings

Science.gov (United States)

Ferrarini, G.; Cadelano, G.; Bortolin, A.

2016-05-01

Infrared thermography is widely applied to the inspection of building, enabling the identification of thermal anomalies due to the presence of hidden structures, air leakages, and moisture. One of the main advantages of this technique is the possibility to acquire rapidly a temperature map of a surface. However, due to the actual low-resolution of thermal camera and the necessity of scanning surfaces with different orientation, during a building survey it is necessary to take multiple images. In this work a device based on quantitative infrared thermography, called aIRview, has been applied during building surveys to automatically acquire thermograms with a camera mounted on a robotized pan tilt unit. The goal is to perform a first rapid survey of the building that could give useful information for the successive quantitative thermal investigations. For each data acquisition, the instrument covers a rotational field of view of 360° around the vertical axis and up to 180° around the horizontal one. The obtained images have been processed in order to create a full equirectangular projection of the ambient. For this reason the images have been integrated into a web visualization tool, working with web panorama viewers such as Google Street View, creating a webpage where it is possible to have a three dimensional virtual visit of the building. The thermographic data are embedded with the visual imaging and with other sensor data, facilitating the understanding of the physical phenomena underlying the temperature distribution.

Annual review in automatic programming

CERN Document Server

Goodman, Richard

2014-01-01

Annual Review in Automatic Programming, Volume 4 is a collection of papers that deals with the GIER ALGOL compiler, a parameterized compiler based on mechanical linguistics, and the JOVIAL language. A couple of papers describes a commercial use of stacks, an IBM system, and what an ideal computer program support system should be. One paper reviews the system of compilation, the development of a more advanced language, programming techniques, machine independence, and program transfer to other machines. Another paper describes the ALGOL 60 system for the GIER machine including running ALGOL pro

Evaluating automatically parallelized versions of the support vector machine

NARCIS (Netherlands)

Codreanu, Valeriu; Droge, Bob; Williams, David; Yasar, Burhan; Yang, Fo; Liu, Baoquan; Dong, Feng; Surinta, Olarik; Schomaker, Lambertus; Roerdink, Jos; Wiering, Marco

2014-01-01

The support vector machine (SVM) is a supervised learning algorithm used for recognizing patterns in data. It is a very popular technique in machine learning and has been successfully used in applications such as image classification, protein classification, and handwriting recognition. However, the

Evaluating automatically parallelized versions of the support vector machine

NARCIS (Netherlands)

Codreanu, V.; Dröge, B.; Williams, D.; Yasar, B.; Yang, P.; Liu, B.; Dong, F.; Surinta, O.; Schomaker, L.R.B.; Roerdink, J.B.T.M.; Wiering, M.A.

2016-01-01

The support vector machine (SVM) is a supervised learning algorithm used for recognizing patterns in data. It is a very popular technique in machine learning and has been successfully used in applications such as image classification, protein classification, and handwriting recognition. However, the

Structural capacity assessment of machine-building enterprises and associations

Directory of Open Access Journals (Sweden)

Prilutskay Maria

2017-01-01

Full Text Available Multidirectional tendencies of machine-building enterprises integration and disintegration resulted in the emergence of the formal and informal associations. These associations consist of the obviously and/or implicitly affiliated legal entities. Thus, a new element appears in the direct enterprise environment, i.e a management company or a head enterprise. The management company influences the participants even in an informal association. New environment restrictions led to the changes in the management structure. The paper considers the enterprise structures interrelation: organizational, financial, production, resource, and others. The authors draw a conclusion that the structures are hierarchy, and there are the coherence structures assessment criteria. The coordinated structures form the structural capacity of the enterprise. The suggested assessment coherence criteria (for example resource and functional structures allow estimating the structural potential and defining the directions of the enterprise efficiency increase.

Numerical simulator of the CANDU fueling machine driving desk

International Nuclear Information System (INIS)

Doca, Cezar

2008-01-01

As a national and European premiere, in the 2003 - 2005 period, at the Institute for Nuclear Research Pitesti two CANDU fueling machine heads, no.4 and no.5, for the Nuclear Power Plant Cernavoda - Unit 2 were successfully tested. To perform the tests of these machines, a special CANDU fueling machine testing rig was built and was (and is) available for this goal. The design of the CANDU fueling machine test rig from the Institute for Nuclear Research Pitesti is a replica of the similar equipment operating in CANDU 6 type nuclear power plants. High technical level of the CANDU fueling machine tests required the using of an efficient data acquisition and processing Computer Control System. The challenging goal was to build a computer system (hardware and software) designed and engineered to control the test and calibration process of these fuel handling machines. The design takes care both of the functionality required to correctly control the CANDU fueling machine and of the additional functionality required to assist the testing process. Both the fueling machine testing rig and staff had successfully assessed by the AECL representatives during two missions. At same the time, at the Institute for Nuclear Research Pitesti was/is developed a numerical simulator for the CANDU fueling machine operators training. The paper presents the numerical simulator - a special PC program (software) which simulates the graphics and the functions and the operations at the main desk of the computer control system. The simulator permits 'to drive' a CANDU fueling machine in two manners: manual or automatic. The numerical simulator is dedicated to the training of operators who operate the CANDU fueling machine in a nuclear power plant with CANDU reactor. (author)

Automatic operation of CSR Drayton coal stockyard. [Australia

Energy Technology Data Exchange (ETDEWEB)

Fauerbach, R

1985-12-01

The automatic remote control of the stackers and reclaimer at the coal stockyard of the CSR Drayton opencast coal mine in Australia is described. Each machine is controlled by an on-board programmable logic controller (PLC) which monitors the machine location in its working section in relation to the other machines and can halt operations should any danger of a collision be imminent.

Two years of experience with an automatic milking system: 1. Time on machine and successful attachments

Directory of Open Access Journals (Sweden)

M. Speroni

2011-03-01

Full Text Available The installation of an automatic milking system (AMS is not simply the replacement of an old traditional milking parlour with a new milking machine, but it requires a deeply modification of herd management (Spahr et al., 1997. Robotic milking is now considered fairly reliable and friendly and more than 1100 commercial farmers have installed at least one milking unit (De Koning et al., 2002. A lot of studies have been carried out on the interactions between milking robot, cows and farmer, but most of them referred to farm in the Northern Europe. On December 2000, an AMS (Voluntary Milking System, VMSTM, De Laval was installed at the Experimental Farm of Istituto Sperimentale per la Zootecnia in Cremona (Italy......

Automatic extraction of Manhattan-World building masses from 3D laser range scans.

Science.gov (United States)

Vanegas, Carlos A; Aliaga, Daniel G; Benes, Bedrich

2012-10-01

We propose a novel approach for the reconstruction of urban structures from 3D point clouds with an assumption of Manhattan World (MW) building geometry; i.e., the predominance of three mutually orthogonal directions in the scene. Our approach works in two steps. First, the input points are classified according to the MW assumption into four local shape types: walls, edges, corners, and edge corners. The classified points are organized into a connected set of clusters from which a volume description is extracted. The MW assumption allows us to robustly identify the fundamental shape types, describe the volumes within the bounding box, and reconstruct visible and occluded parts of the sampled structure. We show results of our reconstruction that has been applied to several synthetic and real-world 3D point data sets of various densities and from multiple viewpoints. Our method automatically reconstructs 3D building models from up to 10 million points in 10 to 60 seconds.

Automatically annotating topics in transcripts of patient-provider interactions via machine learning.

Science.gov (United States)

Wallace, Byron C; Laws, M Barton; Small, Kevin; Wilson, Ira B; Trikalinos, Thomas A

2014-05-01

Annotated patient-provider encounters can provide important insights into clinical communication, ultimately suggesting how it might be improved to effect better health outcomes. But annotating outpatient transcripts with Roter or General Medical Interaction Analysis System (GMIAS) codes is expensive, limiting the scope of such analyses. We propose automatically annotating transcripts of patient-provider interactions with topic codes via machine learning. We use a conditional random field (CRF) to model utterance topic probabilities. The model accounts for the sequential structure of conversations and the words comprising utterances. We assess predictive performance via 10-fold cross-validation over GMIAS-annotated transcripts of 360 outpatient visits (>230,000 utterances). We then use automated in place of manual annotations to reproduce an analysis of 116 additional visits from a randomized trial that used GMIAS to assess the efficacy of an intervention aimed at improving communication around antiretroviral (ARV) adherence. With respect to 6 topic codes, the CRF achieved a mean pairwise kappa compared with human annotators of 0.49 (range: 0.47-0.53) and a mean overall accuracy of 0.64 (range: 0.62-0.66). With respect to the RCT reanalysis, results using automated annotations agreed with those obtained using manual ones. According to the manual annotations, the median number of ARV-related utterances without and with the intervention was 49.5 versus 76, respectively (paired sign test P = 0.07). When automated annotations were used, the respective numbers were 39 versus 55 (P = 0.04). While moderately accurate, the predicted annotations are far from perfect. Conversational topics are intermediate outcomes, and their utility is still being researched. This foray into automated topic inference suggests that machine learning methods can classify utterances comprising patient-provider interactions into clinically relevant topics with reasonable accuracy.

Automatic detection and counting of cattle in UAV imagery based on machine vision technology (Conference Presentation)

Science.gov (United States)

Rahnemoonfar, Maryam; Foster, Jamie; Starek, Michael J.

2017-05-01

Beef production is the main agricultural industry in Texas, and livestock are managed in pasture and rangeland which are usually huge in size, and are not easily accessible by vehicles. The current research method for livestock location identification and counting is visual observation which is very time consuming and costly. For animals on large tracts of land, manned aircraft may be necessary to count animals which is noisy and disturbs the animals, and may introduce a source of error in counts. Such manual approaches are expensive, slow and labor intensive. In this paper we study the combination of small unmanned aerial vehicle (sUAV) and machine vision technology as a valuable solution to manual animal surveying. A fixed-wing UAV fitted with GPS and digital RGB camera for photogrammetry was flown at the Welder Wildlife Foundation in Sinton, TX. Over 600 acres were flown with four UAS flights and individual photographs used to develop orthomosaic imagery. To detect animals in UAV imagery, a fully automatic technique was developed based on spatial and spectral characteristics of objects. This automatic technique can even detect small animals that are partially occluded by bushes. Experimental results in comparison to ground-truth show the effectiveness of our algorithm.

Food Safety by Using Machine Learning for Automatic Classification of Seeds of the South-American Incanut Plant

International Nuclear Information System (INIS)

Lemanzyk, Thomas; Anding, Katharina; Linss, Gerhard; Hernández, Jorge Rodriguez; Theska, René

2015-01-01

The following paper deals with the classification of seeds and seed components of the South-American Incanut plant and the modification of a machine to handle this task. Initially the state of the art is being illustrated. The research was executed in Germany and with a relevant part in Peru and Ecuador. Theoretical considerations for the solution of an automatically analysis of the Incanut seeds were specified. The optimization of the analyzing software and the separation unit of the mechanical hardware are carried out with recognition results. In a final step the practical application of the analysis of the Incanut seeds is held on a trial basis and rated on the bases of statistic values

HAVmS: Highly Available Virtual Machine Computer System Fault Tolerant with Automatic Failback and Close to Zero Downtime

Directory of Open Access Journals (Sweden)

Memmo Federici

2014-12-01

Full Text Available In scientic computing, systems often manage computations that require continuous acquisition of of satellite data and the management of large databases, as well as the execution of analysis software and simulation models (e.g. Monte Carlo or molecular dynamics cell simulations which may require several weeks of continuous run. These systems, consequently, should ensure the continuity of operation even in case of serious faults. HAVmS (High Availability Virtual machine System is a highly available, "fault tolerant" system with zero downtime in case of fault. It is based on the use of Virtual Machines and implemented by two servers with similar characteristics. HAVmS, thanks to the developed software solutions, is unique in its kind since it automatically failbacks once faults have been fixed. The system has been designed to be used both with professional or inexpensive hardware and supports the simultaneous execution of multiple services such as: web, mail, computing and administrative services, uninterrupted computing, data base management. Finally the system is cost effective adopting exclusively open source solutions, is easily manageable and for general use.

A method of numerically controlled machine part programming

Science.gov (United States)

1970-01-01

Computer program is designed for automatically programmed tools. Preprocessor computes desired tool path and postprocessor computes actual commands causing machine tool to follow specific path. It is used on a Cincinnati ATC-430 numerically controlled machine tool.

THE RESTRUCTURING AS STRATEGIC INSTRUMENT IN MANAGEMENT INDUSTRIAL ENTERPRISE RISK (ON EXAMPLE OF THE MACHINE-BUILDING ASSOCIATION «TRUD PLANT»

Directory of Open Access Journals (Sweden)

Vyzhitovich A. M.

2015-06-01

Full Text Available The Article is dedicated to questions of the analysis of the processes restructuring enterprise of machine building. The Presented problems to activity enterprise machine building. Studied the innovative experience of restructuring of one of the leading enterprises - «Machine-building “Trud Plant”» JSC. Restructuring is considered in the study as an important tool in risk management loss of competitiveness and financial stability. Possibility of refining the solution of actual problems of defining approaches to identify the main potential risks and threats to businesses that use restructuring to create a long-term competitive advantage in modern conditions. As the main tools for exploring the internal and external environment of the company proposed a method of SWOT-analysis, a method of grouping projects depending on the location of customers products based on publicly available sources of information. The research led to a number of conclusions: proposed methods help to identify key risks and threats, competitive advantage, the ability of enterprises in the light of the results of past and planned restructuring; study and synthesis of work experience of enterprises with the use of these methods generates suggestions on directions of the State support measures; restructuring strategy of risk management as a tool to retain relevance in the context of import substitution.

Forming the management model in industrial partnerships of the machine-building complex of Ukraine

OpenAIRE

Reshetilova, T.; Kuvaieva, T.

2016-01-01

Stages of development the processes of forming the industrial networks, technological and logistic chains, partnership and their varieties are analyzed. Factors that determine the rate and scale of the process of forming the partnerships in the machine-building complex of Ukraine are established. A group of the factors that lead to forming the vertical partnership based on Partner Relationship Management (PRM) in mining machinery and mining industry are determined and analyzed. It is possible...

An Automatic Building Extraction and Regularisation Technique Using LiDAR Point Cloud Data and Orthoimage

Directory of Open Access Journals (Sweden)

Syed Ali Naqi Gilani

2016-03-01

Full Text Available The development of robust and accurate methods for automatic building detection and regularisation using multisource data continues to be a challenge due to point cloud sparsity, high spectral variability, urban objects differences, surrounding complexity, and data misalignment. To address these challenges, constraints on object’s size, height, area, and orientation are generally benefited which adversely affect the detection performance. Often the buildings either small in size, under shadows or partly occluded are ousted during elimination of superfluous objects. To overcome the limitations, a methodology is developed to extract and regularise the buildings using features from point cloud and orthoimagery. The building delineation process is carried out by identifying the candidate building regions and segmenting them into grids. Vegetation elimination, building detection and extraction of their partially occluded parts are achieved by synthesising the point cloud and image data. Finally, the detected buildings are regularised by exploiting the image lines in the building regularisation process. Detection and regularisation processes have been evaluated using the ISPRS benchmark and four Australian data sets which differ in point density (1 to 29 points/m2, building sizes, shadows, terrain, and vegetation. Results indicate that there is 83% to 93% per-area completeness with the correctness of above 95%, demonstrating the robustness of the approach. The absence of over- and many-to-many segmentation errors in the ISPRS data set indicate that the technique has higher per-object accuracy. While compared with six existing similar methods, the proposed detection and regularisation approach performs significantly better on more complex data sets (Australian in contrast to the ISPRS benchmark, where it does better or equal to the counterparts.

The development of damage identification methods for buildings with image recognition and machine learning techniques utilizing aerial photographs of the 2016 Kumamoto earthquake

Science.gov (United States)

Shohei, N.; Nakamura, H.; Fujiwara, H.; Naoichi, M.; Hiromitsu, T.

2017-12-01

It is important to get schematic information of the damage situation immediately after the earthquake utilizing photographs shot from an airplane in terms of the investigation and the decision-making for authorities. In case of the 2016 Kumamoto earthquake, we have acquired more than 1,800 orthographic projection photographs adjacent to damaged areas. These photos have taken between April 16th and 19th by airplanes, then we have distinguished damages of all buildings with 4 levels, and organized as approximately 296,000 GIS data corresponding to the fundamental Geospatial data published by Geospatial Information Authority of Japan. These data have organized by effort of hundreds of engineers. However, it is not considered practical for more extensive disasters like the Nankai Trough earthquake by only human powers. So, we have been developing the automatic damage identification method utilizing image recognition and machine learning techniques. First, we have extracted training data of more than 10,000 buildings which have equally damage levels divided in 4 grades. With these training data, we have been raster scanning in each scanning ranges of entire images, then clipping patch images which represents damage levels each. By utilizing these patch images, we have been developing discriminant models by two ways. One is a model using the Support Vector Machine (SVM). First, extract a feature quantity of each patch images. Then, with these vector values, calculate the histogram density as a method of Bag of Visual Words (BoVW), then classify borders with each damage grades by SVM. The other one is a model using the multi-layered Neural Network. First, design a multi-layered Neural Network. Second, input patch images and damage levels based on a visual judgement, and then, optimize learning parameters with error backpropagation method. By use of both discriminant models, we are going to discriminate damage levels in each patches, then create the image that shows

Machine technology: a survey

International Nuclear Information System (INIS)

Barbier, M.M.

1981-01-01

An attempt was made to find existing machines that have been upgraded and that could be used for large-scale decontamination operations outdoors. Such machines are in the building industry, the mining industry, and the road construction industry. The road construction industry has yielded the machines in this presentation. A review is given of operations that can be done with the machines available

Design of an ARM-based Automatic Rice-Selling Machine for Cafeterias

Directory of Open Access Journals (Sweden)

Zhiliang Kang

2016-02-01

Full Text Available To address the problems of low selling efficiency, poor sanitation conditions, labor-intensive requirement, and quick rice cooling speed in manual rice selling in cafeterias, especially in colleges and secondary schools, this paper presented an Advanced RISC Machines (ARM microprocessor-based rice-selling machine for cafeterias. The machines consisted of a funnel-shaped rice bin, a thermal insulation box, and a conveying and scattering mechanism. Moreover, this machine exerts fuzzy control over stepper motor rpm, and the motor drives the conveyor belt with a scraper to scatter rice, deliver it, and keep it warm. Apart from an external 4*4 keyboard, a point of sale (POS machine, an ARM process and a pressure sensor, the machine is also equipped with card swiping and weighting mechanisms to achieve functions of card swiping payment and precise measurement, respectively. In addition, detection of the right amount of rice and the alarm function are achieved using an ultrasonic sensor and a beeper, respectively. The presence of the rice container on the rice outlet is detected by an optoelectronic switch. Results show that this rice-selling machine achieves precise measurement, quick card swiping, fast rice selling, stable operation, and good rice heat preservation. Therefore, the mechanical design enables the machine to achieve its goals.

Optics, illumination, and image sensing for machine vision II

International Nuclear Information System (INIS)

Svetkoff, D.J.

1987-01-01

These proceedings collect papers on the general subject of machine vision. Topics include illumination and viewing systems, x-ray imaging, automatic SMT inspection with x-ray vision, and 3-D sensing for machine vision

Assessment of Machine Learning Algorithms for Automatic Benthic Cover Monitoring and Mapping Using Towed Underwater Video Camera and High-Resolution Satellite Images

Directory of Open Access Journals (Sweden)

Hassan Mohamed

2018-05-01

Full Text Available Benthic habitat monitoring is essential for many applications involving biodiversity, marine resource management, and the estimation of variations over temporal and spatial scales. Nevertheless, both automatic and semi-automatic analytical methods for deriving ecologically significant information from towed camera images are still limited. This study proposes a methodology that enables a high-resolution towed camera with a Global Navigation Satellite System (GNSS to adaptively monitor and map benthic habitats. First, the towed camera finishes a pre-programmed initial survey to collect benthic habitat videos, which can then be converted to geo-located benthic habitat images. Second, an expert labels a number of benthic habitat images to class habitats manually. Third, attributes for categorizing these images are extracted automatically using the Bag of Features (BOF algorithm. Fourth, benthic cover categories are detected automatically using Weighted Majority Voting (WMV ensembles for Support Vector Machines (SVM, K-Nearest Neighbor (K-NN, and Bagging (BAG classifiers. Fifth, WMV-trained ensembles can be used for categorizing more benthic cover images automatically. Finally, correctly categorized geo-located images can provide ground truth samples for benthic cover mapping using high-resolution satellite imagery. The proposed methodology was tested over Shiraho, Ishigaki Island, Japan, a heterogeneous coastal area. The WMV ensemble exhibited 89% overall accuracy for categorizing corals, sediments, seagrass, and algae species. Furthermore, the same WMV ensemble produced a benthic cover map using a Quickbird satellite image with 92.7% overall accuracy.

Automatic Emotional State Detection using Facial Expression Dynamic in Videos

Directory of Open Access Journals (Sweden)

Hongying Meng

2014-11-01

Full Text Available In this paper, an automatic emotion detection system is built for a computer or machine to detect the emotional state from facial expressions in human computer communication. Firstly, dynamic motion features are extracted from facial expression videos and then advanced machine learning methods for classification and regression are used to predict the emotional states. The system is evaluated on two publicly available datasets, i.e. GEMEP_FERA and AVEC2013, and satisfied performances are achieved in comparison with the baseline results provided. With this emotional state detection capability, a machine can read the facial expression of its user automatically. This technique can be integrated into applications such as smart robots, interactive games and smart surveillance systems.

Routine human-competitive machine intelligence by means of genetic programming

Science.gov (United States)

Koza, John R.; Streeter, Matthew J.; Keane, Martin

2004-01-01

Genetic programming is a systematic method for getting computers to automatically solve a problem. Genetic programming starts from a high-level statement of what needs to be done and automatically creates a computer program to solve the problem. The paper demonstrates that genetic programming (1) now routinely delivers high-return human-competitive machine intelligence; (2) is an automated invention machine; (3) can automatically create a general solution to a problem in the form of a parameterized topology; and (4) has delivered a progression of qualitatively more substantial results in synchrony with five approximately order-of-magnitude increases in the expenditure of computer time. Recent results involving the automatic synthesis of the topology and sizing of analog electrical circuits and controllers demonstrate these points.

SETTING OF TASK OF OPTIMIZATION OF THE ACTIVITY OF A MACHINE-BUILDING CLUSTER COMPANY

Directory of Open Access Journals (Sweden)

A. V. Romanenko

2014-01-01

Full Text Available The work is dedicated to the development of methodological approaches to the management of machine-building enterprise on the basis of cost reduction, optimization of the portfolio of orders and capacity utilization in the process of operational management. Evaluation of economic efficiency of such economic entities of the real sector of the economy is determined, including the timing of orders, which depend on the issues of building a production facility, maintenance of fixed assets and maintain them at a given level. Formulated key components of economic-mathematical model of industrial activity and is defined as the optimization criterion. As proposed formula accumulating profits due to production capacity and technology to produce products current direct variable costs, the amount of property tax and expenses appearing as a result of manifestations of variance when performing replacement of production tasks for a single period of time. The main component of the optimization of the production activity of the enterprise on the basis of this criterion is the vector of direct variable costs. It depends on the number of types of products in the current portfolio of orders, production schedules production, the normative time for the release of a particular product available Fund time efficient production positions, the current valuation for certain groups of technological operations and the current priority of operations for the degree of readiness performed internal orders. Modeling of industrial activity based on the proposed provisions would allow the enterprises of machine-building cluster, active innovation, improve the efficient use of available production resources by optimizing current operations at the high uncertainty of the magnitude of the demand planning and carrying out maintenance and routine repairs.

AN OPTIMALIZATION OF NATURAL LIGHTING BY APPLYING AUTOMATIC LIGHTING USING MOTION SENSOR AND LUX SENSOR FOR HISTORICAL OLD BUILDINGS

Directory of Open Access Journals (Sweden)

Saeful Bahri

2016-07-01

Full Text Available ABSTRACT One of the problems that occurs within city centres, particularly within capital cities, is the existence of many historical old buildings. Historical old buildings within city centres, that have abandoned for years because of their condition, suffer from a lack of utilities, infrastructure and facilities [2][3]. These conditions occur because of low levels of maintenance arising as a consequence of a lack of finance of the owner of a building, be they government or private sector. To solve the problem of abandoned historical old buildings, the concept of adaptive reuse can be adopted and applied. This concept of adaptive reuse may continously cover the cost of building maintenance. The adaptive reuse concept usually covers the interior of a building and its utilities, though the need for utilities depends on the function of a building [4]. By adopting a concept of adaptive reuse, new building functions will be designed as the needs and demand of the market dictate, and which is appropriate for feasibility study. One utility element that has to be designed for historical old buildings is the provision of lighting within a building. To minimize the cost of building maintenance, one of the solutions is to optimize natural lighting and to minimize the use of artificial lighting such as lamps. This paper will discuss the extent to which artificial lighting can be minimized by using automatic lighting; the automatic lighting types discussed in this paper are lighting controlled by motion sensor and lux sensor. Keywords: Natural lighting, automatic lighting, motion sensor, lux sensor, historical old buildings ABSTRAK Salah satu permasalahan yang muncul dalam sebuah kota metropolitan, khususnya sebuah ibukota adalah keberadaan dari banyaknya bangunan-bangunan tua bersejarah. Bangunan-bangunan tua bersejarah dalam sebuah kota besar terutama yang diabaikan selama bertahun-tahun biasanya disebabkan karena kondisinya yang menua, minimnya utilitas

Smart machine protection system

International Nuclear Information System (INIS)

Clark, S.; Nelson, D.; Grillo, A.

1992-01-01

A Machine Protection System implemented on the SLC automatically controls the beam repetition rates in the accelerator so that radiation or temperature faults slow the repetition rate to bring the fault within tolerance without shutting down the machine. This process allows the accelerators to aid in the fault diagnostic process, and the protection system automatically restores the beams back to normal rates when the fault is diagnosed and corrected. The user interface includes facilities to monitor the performance of the system, and track rate limits, faults, and recoveries. There is an edit facility to define the devices to be included in the protection system, along with their set points, limits, and trip points. This set point and limit data is downloaded into the CAMAC modules, and the configuration data is complied into a logical decision tree for the 68030 processor. (author)

Smart Machine Protection System

International Nuclear Information System (INIS)

Clark, S.; Nelson, D.; Grillo, A.; Spencer, N.; Hutchinson, D.; Olsen, J.; Millsom, D.; White, G.; Gromme, T.; Allison, S.; Underwood, K.; Zelazny, M.; Kang, H.

1991-11-01

A Machine Protection System implemented on the SLC automatically controls the beam repetition rates in the accelerator so that radiation or temperature faults slow the repetition rate to bring the fault within tolerance without shutting down the machine. This process allows the accelerator to aid in the fault diagnostic process, and the protection system automatically restores the beams back to normal rates when the fault is diagnosed and corrected. The user interface includes facilities to monitor the performance of the system, and track rate limits, faults, and recoveries. There is an edit facility to define the devices to be included in the protection system, along with their set points, limits, and trip points. This set point and limit data is downloaded into the CAMAC modules, and the configuration data is compiled into a logical decision tree for the 68030 processor. 3 figs

Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis.

Science.gov (United States)

Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra

2014-01-01

Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. The aims were to describe how to:(i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and(ii) automatically identify the features that best distinguish the groups. The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo 18 were used,which included 200 healthy Brazilians of both genders. A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods.

Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis

Directory of Open Access Journals (Sweden)

Cíntia Matsuda Toledo

Full Text Available Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario.OBJECTIVE: The aims were to describe how to: (i develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and (ii automatically identify the features that best distinguish the groups.METHODS: The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age. In this study, the descriptions by 144 of the subjects studied in Toledo18 were used, which included 200 healthy Brazilians of both genders.RESULTS AND CONCLUSION:A Support Vector Machine (SVM with a radial basis function (RBF kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS is a strong candidate to replace manual feature selection methods.

General collaboration offer of Johnson Controls regarding the performance of air conditioning automatic control systems and other buildings` automatic control systems

Energy Technology Data Exchange (ETDEWEB)

Gniazdowski, J.

1995-12-31

JOHNSON CONTROLS manufactures measuring and control equipment (800 types) and is as well a {open_quotes}turn-key{close_quotes} supplier of complete automatic controls systems for heating, air conditioning, ventilation and refrigerating engineering branches. The Company also supplies Buildings` Computer-Based Supervision and Monitoring Systems that may be applied in both small and large structures. Since 1990 the company has been performing full-range trade and contracting activities on the Polish market. We have our own well-trained technical staff and we collaborate with a series of designing and contracting enterprises that enable us to have our projects carried out all over Poland. The prices of our supplies and services correspond with the level of the Polish market.

Machine Learning examples on Invenio

CERN Document Server

CERN. Geneva

2017-01-01

This talk will present the different Machine Learning tools that the INSPIRE is developing and integrating in order to automatize as much as possible content selection and curation in a subject based repository.

Design principles of metal-cutting machine tools

CERN Document Server

Koenigsberger, F

1964-01-01

Design Principles of Metal-Cutting Machine Tools discusses the fundamentals aspects of machine tool design. The book covers the design consideration of metal-cutting machine, such as static and dynamic stiffness, operational speeds, gearboxes, manual, and automatic control. The text first details the data calculation and the general requirements of the machine tool. Next, the book discusses the design principles, which include stiffness and rigidity of the separate constructional elements and their combined behavior under load, as well as electrical, mechanical, and hydraulic drives for the op

Automatic microseismic event picking via unsupervised machine learning

Science.gov (United States)

Chen, Yangkang

2018-01-01

Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.

In vivo classification of human skin burns using machine learning and quantitative features captured by optical coherence tomography

Science.gov (United States)

Singla, Neeru; Srivastava, Vishal; Singh Mehta, Dalip

2018-02-01

We report the first fully automated detection of human skin burn injuries in vivo, with the goal of automatic surgical margin assessment based on optical coherence tomography (OCT) images. Our proposed automated procedure entails building a machine-learning-based classifier by extracting quantitative features from normal and burn tissue images recorded by OCT. In this study, 56 samples (28 normal, 28 burned) were imaged by OCT and eight features were extracted. A linear model classifier was trained using 34 samples and 22 samples were used to test the model. Sensitivity of 91.6% and specificity of 90% were obtained. Our results demonstrate the capability of a computer-aided technique for accurately and automatically identifying burn tissue resection margins during surgical treatment.

Remote filter handling machine for Sizewell B

International Nuclear Information System (INIS)

Barker, D.

1993-01-01

Two Filter Handling machines (FHM) have been supplied to Nuclear Electric plc for use at Sizewell B Power Station. These machines have been designed and built following ALARP principles with the functional objective being to remove radioactive filter cartridges from a filter housing and replace them with clean filter cartridges. Operation of the machine is achieved by the prompt of each distinct task via an industrial computer or the prompt of a full cycle using the automatic mode. The design of the machine features many aspects demonstrating ALARP while keeping the machine simple, robust and easy to maintain. (author)

Cart'Eaux: an automatic mapping procedure for wastewater networks using machine learning and data mining

Science.gov (United States)

Bailly, J. S.; Delenne, C.; Chahinian, N.; Bringay, S.; Commandré, B.; Chaumont, M.; Derras, M.; Deruelle, L.; Roche, M.; Rodriguez, F.; Subsol, G.; Teisseire, M.

2017-12-01

In France, local government institutions must establish a detailed description of wastewater networks. The information should be available, but it remains fragmented (different formats held by different stakeholders) and incomplete. In the "Cart'Eaux" project, a multidisciplinary team, including an industrial partner, develops a global methodology using Machine Learning and Data Mining approaches applied to various types of large data to recover information in the aim of mapping urban sewage systems for hydraulic modelling. Deep-learning is first applied using a Convolution Neural Network to localize manhole covers on 5 cm resolution aerial RGB images. The detected manhole covers are then automatically connected using a tree-shaped graph constrained by industry rules. Based on a Delaunay triangulation, connections are chosen to minimize a cost function depending on pipe length, slope and possible intersection with roads or buildings. A stochastic version of this algorithm is currently being developed to account for positional uncertainty and detection errors, and generate sets of probable networks. As more information is required for hydraulic modeling (slopes, diameters, materials, etc.), text data mining is used to extract network characteristics from data posted on the Web or available through governmental or specific databases. Using an appropriate list of keywords, the web is scoured for documents which are saved in text format. The thematic entities are identified and linked to the surrounding spatial and temporal entities. The methodology is developed and tested on two towns in southern France. The primary results are encouraging: 54% of manhole covers are detected with few false detections, enabling the reconstruction of probable networks. The data mining results are still being investigated. It is clear at this stage that getting numerical values on specific pipes will be challenging. Thus, when no information is found, decision rules will be used to

Soft computing in machine learning

CERN Document Server

Park, Jooyoung; Inoue, Atsushi

2014-01-01

As users or consumers are now demanding smarter devices, intelligent systems are revolutionizing by utilizing machine learning. Machine learning as part of intelligent systems is already one of the most critical components in everyday tools ranging from search engines and credit card fraud detection to stock market analysis. You can train machines to perform some things, so that they can automatically detect, diagnose, and solve a variety of problems. The intelligent systems have made rapid progress in developing the state of the art in machine learning based on smart and deep perception. Using machine learning, the intelligent systems make widely applications in automated speech recognition, natural language processing, medical diagnosis, bioinformatics, and robot locomotion. This book aims at introducing how to treat a substantial amount of data, to teach machines and to improve decision making models. And this book specializes in the developments of advanced intelligent systems through machine learning. It...

Automatic Sleep Staging using Multi-dimensional Feature Extraction and Multi-kernel Fuzzy Support Vector Machine

Directory of Open Access Journals (Sweden)

Yanjun Zhang

2014-01-01

Full Text Available This paper employed the clinical Polysomnographic (PSG data, mainly including all-night Electroencephalogram (EEG, Electrooculogram (EOG and Electromyogram (EMG signals of subjects, and adopted the American Academy of Sleep Medicine (AASM clinical staging manual as standards to realize automatic sleep staging. Authors extracted eighteen different features of EEG, EOG and EMG in time domains and frequency domains to construct the vectors according to the existing literatures as well as clinical experience. By adopting sleep samples self-learning, the linear combination of weights and parameters of multiple kernels of the fuzzy support vector machine (FSVM were learned and the multi-kernel FSVM (MK-FSVM was constructed. The overall agreement between the experts' scores and the results presented was 82.53%. Compared with previous results, the accuracy of N1 was improved to some extent while the accuracies of other stages were approximate, which well reflected the sleep structure. The staging algorithm proposed in this paper is transparent, and worth further investigation.

Research on wireless communication technology based on automatic logistics system of welder

Directory of Open Access Journals (Sweden)

Sun Xuan

2018-01-01

Full Text Available In order to meet the requirements of high real-time and high stability of data transmission in automatic welding system, RTU data format and real-time communication mechanism are adopted in this system. In the automatic logistics system through the Ethernet and wireless WIFI technology will palletizer, stacker, AGV car organically together to complete the palletizer automatic crawling the goods, AGV car automatic delivery, stacking machine automatically out of the Dimensional warehouse. .

Research on wireless communication technology based on automatic logistics system of welder

OpenAIRE

Sun Xuan; Wang Zhi-yong; Ma Zhe-dong

2018-01-01

In order to meet the requirements of high real-time and high stability of data transmission in automatic welding system, RTU data format and real-time communication mechanism are adopted in this system. In the automatic logistics system through the Ethernet and wireless WIFI technology will palletizer, stacker, AGV car organically together to complete the palletizer automatic crawling the goods, AGV car automatic delivery, stacking machine automatically out of the Dimensional warehouse. .

Annual review in automatic programming

CERN Document Server

Goodman, Richard

2014-01-01

Annual Review in Automatic Programming focuses on the techniques of automatic programming used with digital computers. Topics covered range from the design of machine-independent programming languages to the use of recursive procedures in ALGOL 60. A multi-pass translation scheme for ALGOL 60 is described, along with some commercial source languages. The structure and use of the syntax-directed compiler is also considered.Comprised of 12 chapters, this volume begins with a discussion on the basic ideas involved in the description of a computing process as a program for a computer, expressed in

GRAPHITIZED STEELS IN MACHINE-BUILDING

Directory of Open Access Journals (Sweden)

I. V. Akimov

2010-01-01

Full Text Available It is shown that graphitized steels in some cases due to its intermediate disposition by structure and characteristics among low-carbon steels and cast irons, can provide the necessary combination of characteristics of construction material and consequently to increase safety and durability of details of metallurgical and machinebuilding industry machines.

Automatic classification of 6-month-old infants at familial risk for language-based learning disorder using a support vector machine.

Science.gov (United States)

Zare, Marzieh; Rezvani, Zahra; Benasich, April A

2016-07-01

This study assesses the ability of a novel, "automatic classification" approach to facilitate identification of infants at highest familial risk for language-learning disorders (LLD) and to provide converging assessments to enable earlier detection of developmental disorders that disrupt language acquisition. Network connectivity measures derived from 62-channel electroencephalogram (EEG) recording were used to identify selected features within two infant groups who differed on LLD risk: infants with a family history of LLD (FH+) and typically-developing infants without such a history (FH-). A support vector machine was deployed; global efficiency and global and local clustering coefficients were computed. A novel minimum spanning tree (MST) approach was also applied. Cross-validation was employed to assess the resultant classification. Infants were classified with about 80% accuracy into FH+ and FH- groups with 89% specificity and precision of 92%. Clustering patterns differed by risk group and MST network analysis suggests that FH+ infants' EEG complexity patterns were significantly different from FH- infants. The automatic classification techniques used here were shown to be both robust and reliable and should provide valuable information when applied to early identification of risk or clinical groups. The ability to identify infants at highest risk for LLD using "automatic classification" strategies is a novel convergent approach that may facilitate earlier diagnosis and remediation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks

International Nuclear Information System (INIS)

Li Qiong; Meng Qinglin; Cai Jiejin; Yoshino, Hiroshi; Mochida, Akashi

2009-01-01

This study presents four modeling techniques for the prediction of hourly cooling load in the building. In addition to the traditional back propagation neural network (BPNN), the radial basis function neural network (RBFNN), general regression neural network (GRNN) and support vector machine (SVM) are considered. All the prediction models have been applied to an office building in Guangzhou, China. Evaluation of the prediction accuracy of the four models is based on the root mean square error (RMSE) and mean relative error (MRE). The simulation results demonstrate that the four discussed models can be effective for building cooling load prediction. The SVM and GRNN methods can achieve better accuracy and generalization than the BPNN and RBFNN methods

Quantum machine learning.

Science.gov (United States)

Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

2017-09-13

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

From Laser Scanning to Finite Element Analysis of Complex Buildings by Using a Semi-Automatic Procedure.

Science.gov (United States)

Castellazzi, Giovanni; D'Altri, Antonio Maria; Bitelli, Gabriele; Selvaggi, Ilenia; Lambertini, Alessandro

2015-07-28

In this paper, a new semi-automatic procedure to transform three-dimensional point clouds of complex objects to three-dimensional finite element models is presented and validated. The procedure conceives of the point cloud as a stacking of point sections. The complexity of the clouds is arbitrary, since the procedure is designed for terrestrial laser scanner surveys applied to buildings with irregular geometry, such as historical buildings. The procedure aims at solving the problems connected to the generation of finite element models of these complex structures by constructing a fine discretized geometry with a reduced amount of time and ready to be used with structural analysis. If the starting clouds represent the inner and outer surfaces of the structure, the resulting finite element model will accurately capture the whole three-dimensional structure, producing a complex solid made by voxel elements. A comparison analysis with a CAD-based model is carried out on a historical building damaged by a seismic event. The results indicate that the proposed procedure is effective and obtains comparable models in a shorter time, with an increased level of automation.

Building machine learning systems with Python

CERN Document Server

Coelho, Luis Pedro

2015-01-01

This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.

Procedural and developmental aspects of a multielement automatic radiochemical machine, applied to neutron irradiated biomedical samples

International Nuclear Information System (INIS)

Iyengar, G.V.

1976-06-01

This report is intended to serve as a practical guide, elaborately describing the working details and some developmental work connected with an automatic multielement radiochemical machine based on thermal neutron activation analysis using ion exchange and partition chromatography. Some of the practical aspects and personal observations after much experience with this versatile multielement method, applied to investigate the elemental composition of different biomedical matrices, are summarized. Standard reference materials are analyzed, and the data are presented with a set of gamma-spectra obtained before and after chemical separation into convenient groups suitable for gamma spectroscopy. The samples analyzed included various human and animal tissues, body fluids, IAEA biological standard reference materials, and samples from the WHO/IAEA project on 'Trace elements in relation to cardiovascular diseases'. Simplified modifications of the radiochemical processing, suitable for fast and routine analysis of clinical samples have also been discussed. (orig.) [de

ROOFN3D: DEEP LEARNING TRAINING DATA FOR 3D BUILDING RECONSTRUCTION

Directory of Open Access Journals (Sweden)

A. Wichmann

2018-05-01

Full Text Available Machine learning methods have gained in importance through the latest development of artificial intelligence and computer hardware. Particularly approaches based on deep learning have shown that they are able to provide state-of-the-art results for various tasks. However, the direct application of deep learning methods to improve the results of 3D building reconstruction is often not possible due, for example, to the lack of suitable training data. To address this issue, we present RoofN3D which provides a new 3D point cloud training dataset that can be used to train machine learning models for different tasks in the context of 3D building reconstruction. It can be used, among others, to train semantic segmentation networks or to learn the structure of buildings and the geometric model construction. Further details about RoofN3D and the developed data preparation framework, which enables the automatic derivation of training data, are described in this paper. Furthermore, we provide an overview of other available 3D point cloud training data and approaches from current literature in which solutions for the application of deep learning to unstructured and not gridded 3D point cloud data are presented.

Evolution of the ATLAS Nightly Build System

International Nuclear Information System (INIS)

Undrus, A

2012-01-01

The ATLAS Nightly Build System is a major component in the ATLAS collaborative software organization, validation, and code approval scheme. For over 10 years of development it has evolved into a factory for automatic release production and grid distribution. The 50 multi-platform branches of ATLAS releases provide vast opportunities for testing new packages, verification of patches to existing software, and migration to new platforms and compilers for ATLAS code that currently contains 2200 packages with 4 million C++ and 1.4 million python scripting lines written by about 1000 developers. Recent development was focused on the integration of ATLAS Nightly Build and Installation systems. The nightly releases are distributed and validated and some are transformed into stable releases used for data processing worldwide. The ATLAS Nightly System is managed by the NICOS control tool on a computing farm with 50 powerful multiprocessor nodes. NICOS provides the fully automated framework for the release builds, testing, and creation of distribution kits. The ATN testing framework of the Nightly System runs unit and integration tests in parallel suites, fully utilizing the resources of multi-core machines, and provides the first results even before compilations complete. The NICOS error detection system is based on several techniques and classifies the compilation and test errors according to their severity. It is periodically tuned to place greater emphasis on certain software defects by highlighting the problems on NICOS web pages and sending automatic e-mail notifications to responsible developers. These and other recent developments will be presented and future plans will be described.

A support vector machine approach to the automatic identification of fluorescence spectra emitted by biological agents

Science.gov (United States)

Gelfusa, M.; Murari, A.; Lungaroni, M.; Malizia, A.; Parracino, S.; Peluso, E.; Cenciarelli, O.; Carestia, M.; Pizzoferrato, R.; Vega, J.; Gaudio, P.

2016-10-01

Two of the major new concerns of modern societies are biosecurity and biosafety. Several biological agents (BAs) such as toxins, bacteria, viruses, fungi and parasites are able to cause damage to living systems either humans, animals or plants. Optical techniques, in particular LIght Detection And Ranging (LIDAR), based on the transmission of laser pulses and analysis of the return signals, can be successfully applied to monitoring the release of biological agents into the atmosphere. It is well known that most of biological agents tend to emit specific fluorescence spectra, which in principle allow their detection and identification, if excited by light of the appropriate wavelength. For these reasons, the detection of the UVLight Induced Fluorescence (UV-LIF) emitted by BAs is particularly promising. On the other hand, the stand-off detection of BAs poses a series of challenging issues; one of the most severe is the automatic discrimination between various agents which emit very similar fluorescence spectra. In this paper, a new data analysis method, based on a combination of advanced filtering techniques and Support Vector Machines, is described. The proposed approach covers all the aspects of the data analysis process, from filtering and denoising to automatic recognition of the agents. A systematic series of numerical tests has been performed to assess the potential and limits of the proposed methodology. The first investigations of experimental data have already given very encouraging results.

Using the TED Talks to Evaluate Spoken Post-editing of Machine Translation

DEFF Research Database (Denmark)

Liyanapathirana, Jeevanthi; Popescu-Belis, Andrei

2016-01-01

This paper presents a solution to evaluate spoken post-editing of imperfect machine translation output by a human translator. We compare two approaches to the combination of machine translation (MT) and automatic speech recognition (ASR): a heuristic algorithm and a machine learning method...

Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns

International Nuclear Information System (INIS)

Chou, Jui-Sheng; Ngo, Ngoc-Tri

2016-01-01

Highlights: • This study develops a novel time-series sliding window forecast system. • The system integrates metaheuristics, machine learning and time-series models. • Site experiment of smart grid infrastructure is installed to retrieve real-time data. • The proposed system accurately predicts energy consumption in residential buildings. • The forecasting system can help users minimize their electricity usage. - Abstract: Smart grids are a promising solution to the rapidly growing power demand because they can considerably increase building energy efficiency. This study developed a novel time-series sliding window metaheuristic optimization-based machine learning system for predicting real-time building energy consumption data collected by a smart grid. The proposed system integrates a seasonal autoregressive integrated moving average (SARIMA) model and metaheuristic firefly algorithm-based least squares support vector regression (MetaFA-LSSVR) model. Specifically, the proposed system fits the SARIMA model to linear data components in the first stage, and the MetaFA-LSSVR model captures nonlinear data components in the second stage. Real-time data retrieved from an experimental smart grid installed in a building were used to evaluate the efficacy and effectiveness of the proposed system. A k-week sliding window approach is proposed for employing historical data as input for the novel time-series forecasting system. The prediction system yielded high and reliable accuracy rates in 1-day-ahead predictions of building energy consumption, with a total error rate of 1.181% and mean absolute error of 0.026 kW h. Notably, the system demonstrates an improved accuracy rate in the range of 36.8–113.2% relative to those of the linear forecasting model (i.e., SARIMA) and nonlinear forecasting models (i.e., LSSVR and MetaFA-LSSVR). Therefore, end users can further apply the forecasted information to enhance efficiency of energy usage in their buildings, especially

Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

Science.gov (United States)

Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

2017-07-01

Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

Design of cylindrical pipe automatic welding control system based on STM32

Science.gov (United States)

Chen, Shuaishuai; Shen, Weicong

2018-04-01

The development of modern economy makes the demand for pipeline construction and construction rapidly increasing, and the pipeline welding has become an important link in pipeline construction. At present, there are still a large number of using of manual welding methods at home and abroad, and field pipe welding especially lacks miniature and portable automatic welding equipment. An automated welding system consists of a control system, which consisting of a lower computer control panel and a host computer operating interface, as well as automatic welding machine mechanisms and welding power systems in coordination with the control system. In this paper, a new control system of automatic pipe welding based on the control panel of the lower computer and the interface of the host computer is proposed, which has many advantages over the traditional automatic welding machine.

As-Built Verification Plan Spent Nuclear Fuel Canister Storage Building MCO Handling Machine

International Nuclear Information System (INIS)

SWENSON, C.E.

2000-01-01

This as-built verification plan outlines the methodology and responsibilities that will be implemented during the as-built field verification activity for the Canister Storage Building (CSB) MCO HANDLING MACHINE (MHM). This as-built verification plan covers THE ELECTRICAL PORTION of the CONSTRUCTION PERFORMED BY POWER CITY UNDER CONTRACT TO MOWAT. The as-built verifications will be performed in accordance Administrative Procedure AP 6-012-00, Spent Nuclear Fuel Project As-Built Verification Plan Development Process, revision I. The results of the verification walkdown will be documented in a verification walkdown completion package, approved by the Design Authority (DA), and maintained in the CSB project files

Virtual NC machine model with integrated knowledge data

International Nuclear Information System (INIS)

Sidorenko, Sofija; Dukovski, Vladimir

2002-01-01

The concept of virtual NC machining was established for providing a virtual product that could be compared with an appropriate designed product, in order to make NC program correctness evaluation, without real experiments. This concept is applied in the intelligent CAD/CAM system named VIRTUAL MANUFACTURE. This paper presents the first intelligent module that enables creation of the virtual models of existed NC machines and virtual creation of new ones, applying modular composition. Creation of a virtual NC machine is carried out via automatic knowledge data saving (features of the created NC machine). (Author)

Machine-vision-based identification of broken inserts in edge profile milling heads

NARCIS (Netherlands)

Fernandez Robles, Laura; Azzopardi, George; Alegre, Enrique; Petkov, Nicolai

This paper presents a reliable machine vision system to automatically detect inserts and determine if they are broken. Unlike the machining operations studied in the literature, we are dealing with edge milling head tools for aggressive machining of thick plates (up to 12 centimetres) in a single

Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain

OpenAIRE

Kurtulmus, A. Besir; Daniel, Kenny

2018-01-01

Using blockchain technology, it is possible to create contracts that offer a reward in exchange for a trained machine learning model for a particular data set. This would allow users to train machine learning models for a reward in a trustless manner. The smart contract will use the blockchain to automatically validate the solution, so there would be no debate about whether the solution was correct or not. Users who submit the solutions won't have counterparty risk that they won't get paid fo...

APPRAISAL OF ECONOMICAL EFFICIENCY OF APPLICATION OF FIBROUS LINING IN THERMAL GASPLASMA FURNACES AND FURNACES OF RESISTANCE OF MACHINE-BUILDING PRODUCTION

Directory of Open Access Journals (Sweden)

V. I. Timoshpolskij

2011-01-01

Full Text Available The carried out calculations showed that partial modernization of thermal furnaces of machine building production by means of replacement of chamotte by fibrous fettling is economically reasonable and has rather short period of payback.

Operating System For Numerically Controlled Milling Machine

Science.gov (United States)

Ray, R. B.

1992-01-01

OPMILL program is operating system for Kearney and Trecker milling machine providing fast easy way to program manufacture of machine parts with IBM-compatible personal computer. Gives machinist "equation plotter" feature, which plots equations that define movements and converts equations to milling-machine-controlling program moving cutter along defined path. System includes tool-manager software handling up to 25 tools and automatically adjusts to account for each tool. Developed on IBM PS/2 computer running DOS 3.3 with 1 MB of random-access memory.

Machine learning systems

Energy Technology Data Exchange (ETDEWEB)

Forsyth, R

1984-05-01

With the dramatic rise of expert systems has come a renewed interest in the fuel that drives them-knowledge. For it is specialist knowledge which gives expert systems their power. But extracting knowledge from human experts in symbolic form has proved arduous and labour-intensive. So the idea of machine learning is enjoying a renaissance. Machine learning is any automatic improvement in the performance of a computer system over time, as a result of experience. Thus a learning algorithm seeks to do one or more of the following: cover a wider range of problems, deliver more accurate solutions, obtain answers more cheaply, and simplify codified knowledge. 6 references.

AUTOMATIC 3D BUILDING MODEL GENERATION FROM LIDAR AND IMAGE DATA USING SEQUENTIAL MINIMUM BOUNDING RECTANGLE

Directory of Open Access Journals (Sweden)

E. Kwak

2012-07-01

Full Text Available Digital Building Model is an important component in many applications such as city modelling, natural disaster planning, and aftermath evaluation. The importance of accurate and up-to-date building models has been discussed by many researchers, and many different approaches for efficient building model generation have been proposed. They can be categorised according to the data source used, the data processing strategy, and the amount of human interaction. In terms of data source, due to the limitations of using single source data, integration of multi-senor data is desired since it preserves the advantages of the involved datasets. Aerial imagery and LiDAR data are among the commonly combined sources to obtain 3D building models with good vertical accuracy from laser scanning and good planimetric accuracy from aerial images. The most used data processing strategies are data-driven and model-driven ones. Theoretically one can model any shape of buildings using data-driven approaches but practically it leaves the question of how to impose constraints and set the rules during the generation process. Due to the complexity of the implementation of the data-driven approaches, model-based approaches draw the attention of the researchers. However, the major drawback of model-based approaches is that the establishment of representative models involves a manual process that requires human intervention. Therefore, the objective of this research work is to automatically generate building models using the Minimum Bounding Rectangle algorithm and sequentially adjusting them to combine the advantages of image and LiDAR datasets.

How to build a time machine: the real science of time travel

CERN Document Server

Clegg, Brian

2013-01-01

A pop science look at time travel technology, from Einstein to Ronald Mallett to present day experiments. Forget fiction: time travel is real.In How to Build a Time Machine, Brian Clegg provides an understanding of what time is and how it can be manipulated. He explores the fascinating world of physics and the remarkable possibilities of real time travel that emerge from quantum entanglement, superluminal speeds, neutron star cylinders and wormholes in space. With the fascinating paradoxes of time travel echoing in our minds will we realize that travel into the future might never be possible? Or will we realize there is no limit on what can be achieved, and take on this ultimate challenge? Only time will tell.

Automatic detection of ischemic stroke based on scaling exponent electroencephalogram using extreme learning machine

Science.gov (United States)

Adhi, H. A.; Wijaya, S. K.; Prawito; Badri, C.; Rezal, M.

2017-03-01

Stroke is one of cerebrovascular diseases caused by the obstruction of blood flow to the brain. Stroke becomes the leading cause of death in Indonesia and the second in the world. Stroke also causes of the disability. Ischemic stroke accounts for most of all stroke cases. Obstruction of blood flow can cause tissue damage which results the electrical changes in the brain that can be observed through the electroencephalogram (EEG). In this study, we presented the results of automatic detection of ischemic stroke and normal subjects based on the scaling exponent EEG obtained through detrended fluctuation analysis (DFA) using extreme learning machine (ELM) as the classifier. The signal processing was performed with 18 channels of EEG in the range of 0-30 Hz. Scaling exponents of the subjects were used as the input for ELM to classify the ischemic stroke. The performance of detection was observed by the value of accuracy, sensitivity and specificity. The result showed, performance of the proposed method to classify the ischemic stroke was 84 % for accuracy, 82 % for sensitivity and 87 % for specificity with 120 hidden neurons and sine as the activation function of ELM.

Some experimental results for an automatic helium liquefier

International Nuclear Information System (INIS)

Watanabe, T.; Kudo, T.; Kuraoka, Y.; Sakura, K.; Tsuruga, H.; Watanabe, T.

1984-01-01

This chapter describes the testing of an automatic cooldown system. The liquefying machine examined is a CTi Model 1400. The automatic helium gas liquefying system is operated by using sequence control with a programmable controller. The automatic mode is carried out by operation of two compressors. The monitoring system consists of 41 remote sensors. Liquid level is measured by a superconducting level meter. The J-T valve and return valve, which require precise control, are operated by pulse motors. The advantages of the automatic cooldown system are reduced operator man power; temperatures and pressures are changed smoothly, so that the flow chart of automation is simple; and the system makes continuous liquefier operation possible

Probabilistic machine learning and artificial intelligence.

Science.gov (United States)

Ghahramani, Zoubin

2015-05-28

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Probabilistic machine learning and artificial intelligence

Science.gov (United States)

Ghahramani, Zoubin

2015-05-01

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

The Engineering Of PCB Processing Machine

International Nuclear Information System (INIS)

Handoyo, Demon; Satmoko, Ari; T, Sapta; Heru, G. B.

2001-01-01

The engineering of PCB processing machine had been done. Purposes of the engineering of PCB processing machine are used to process PCB and to get the data's of characteristic of PCB processing. Further, these data's will be used as setting point when processing of PCB is done with manual and automatic control. The method of processing of PCB are inserting and pulling of the PCB rack to and from Ferro chlorite using electrical motor to corrosive Cu shield parts witch is not used. The experiment have result that the characteristic of operation of PCB processing machine as we hope when designing

The Model of Information Support for Management of Investment Attractiveness of Machine-Building Enterprises

Directory of Open Access Journals (Sweden)

Chernetska Olga V.

2016-11-01

Full Text Available The article discloses the content of the definition of “information support”, identifies basic approaches to the interpretation of this economic category. The main purpose of information support for management of enterprise investment attractiveness is determined. The key components of information support for management of enterprise investment attractiveness are studied. The main types of automated information systems for management of the investment attractiveness of enterprises are identified and characterized. The basic computer programs for assessing the level of investment attractiveness of enterprises are considered. A model of information support for management of investment attractiveness of machine-building enterprises is developed.

LiDAR The Generation of Automatic Mapping for Buildings, Using High Spatial Resolution Digital Vertical Aerial Photography and LiDAR Point Clouds

Directory of Open Access Journals (Sweden)

William Barragán Zaque

2015-06-01

Full Text Available The aim of this paper is to generate photogrammetrie products and to automatically map buildings in the area of interest in vector format. The research was conducted Bogotá using high resolution digital vertical aerial photographs and point clouds obtained using LIDAR technology. Image segmentation was also used, alongside radiometric and geometric digital processes. The process took into account aspects including building height, segmentation algorithms, and spectral band combination. The results had an effectiveness of 97.2 % validated through ground-truthing.

Use of MCAM in creating 3D neutronics model for ITER building

International Nuclear Information System (INIS)

Zeng Qin; Wang Guozhong; Dang Tongqiang; Long Pengcheng; Loughlin, Michael

2012-01-01

Highlights: ► We created a 3D neutronics model of the ITER building. ► The model was produced from the engineering CAD model by MCAM software. ► The neutron flux map in the ITER building was calculated. - Abstract: The three dimensional (3D) neutronics reference model of International Thermonuclear Experimental Reactor (ITER) only defines the tokamak machine and extends to the bio-shield. In order to meet further 3D neutronics analysis needs, it is necessary to create a 3D reference model of the ITER building. Monte Carlo Automatic Modeling Program for Radiation Transport Simulation (MCAM) was developed as a computer aided design (CAD) based bi-directional interface program between general CAD systems and Monte Carlo radiation transport simulation codes. With the help of MCAM version 4.8, the 3D neutronics model of ITER building was created based on the engineering CAD model. The calculation of the neutron flux map in ITER building during operation showed the correctness and usability of the model. This model is the first detailed ITER building 3D neutronics model and it will be made available to all international organization collaborators as a reference model.

Comparison of machine-learning algorithms to build a predictive model for detecting undiagnosed diabetes - ELSA-Brasil: accuracy study.

Science.gov (United States)

Olivera, André Rodrigues; Roesler, Valter; Iochpe, Cirano; Schmidt, Maria Inês; Vigo, Álvaro; Barreto, Sandhi Maria; Duncan, Bruce Bartholow

2017-01-01

Type 2 diabetes is a chronic disease associated with a wide range of serious health complications that have a major impact on overall health. The aims here were to develop and validate predictive models for detecting undiagnosed diabetes using data from the Longitudinal Study of Adult Health (ELSA-Brasil) and to compare the performance of different machine-learning algorithms in this task. Comparison of machine-learning algorithms to develop predictive models using data from ELSA-Brasil. After selecting a subset of 27 candidate variables from the literature, models were built and validated in four sequential steps: (i) parameter tuning with tenfold cross-validation, repeated three times; (ii) automatic variable selection using forward selection, a wrapper strategy with four different machine-learning algorithms and tenfold cross-validation (repeated three times), to evaluate each subset of variables; (iii) error estimation of model parameters with tenfold cross-validation, repeated ten times; and (iv) generalization testing on an independent dataset. The models were created with the following machine-learning algorithms: logistic regression, artificial neural network, naïve Bayes, K-nearest neighbor and random forest. The best models were created using artificial neural networks and logistic regression. -These achieved mean areas under the curve of, respectively, 75.24% and 74.98% in the error estimation step and 74.17% and 74.41% in the generalization testing step. Most of the predictive models produced similar results, and demonstrated the feasibility of identifying individuals with highest probability of having undiagnosed diabetes, through easily-obtained clinical data.

Building the Knowledge Base to Support the Automatic Animation Generation of Chinese Traditional Architecture

Science.gov (United States)

Wei, Gongjin; Bai, Weijing; Yin, Meifang; Zhang, Songmao

We present a practice of applying the Semantic Web technologies in the domain of Chinese traditional architecture. A knowledge base consisting of one ontology and four rule bases is built to support the automatic generation of animations that demonstrate the construction of various Chinese timber structures based on the user's input. Different Semantic Web formalisms are used, e.g., OWL DL, SWRL and Jess, to capture the domain knowledge, including the wooden components needed for a given building, construction sequence, and the 3D size and position of every piece of wood. Our experience in exploiting the current Semantic Web technologies in real-world application systems indicates their prominent advantages (such as the reasoning facilities and modeling tools) as well as the limitations (such as low efficiency).

Automatically quantifying the scientific quality and sensationalism of news records mentioning pandemics: validating a maximum entropy machine-learning model.

Science.gov (United States)

Hoffman, Steven J; Justicz, Victoria

2016-07-01

To develop and validate a method for automatically quantifying the scientific quality and sensationalism of individual news records. After retrieving 163,433 news records mentioning the Severe Acute Respiratory Syndrome (SARS) and H1N1 pandemics, a maximum entropy model for inductive machine learning was used to identify relationships among 500 randomly sampled news records that correlated with systematic human assessments of their scientific quality and sensationalism. These relationships were then computationally applied to automatically classify 10,000 additional randomly sampled news records. The model was validated by randomly sampling 200 records and comparing human assessments of them to the computer assessments. The computer model correctly assessed the relevance of 86% of news records, the quality of 65% of records, and the sensationalism of 73% of records, as compared to human assessments. Overall, the scientific quality of SARS and H1N1 news media coverage had potentially important shortcomings, but coverage was not too sensationalizing. Coverage slightly improved between the two pandemics. Automated methods can evaluate news records faster, cheaper, and possibly better than humans. The specific procedure implemented in this study can at the very least identify subsets of news records that are far more likely to have particular scientific and discursive qualities. Copyright © 2016 Elsevier Inc. All rights reserved.

A Usability Study of the Automatic Ticket Vending Machines for the Middle-aged and Elderly Patrons: The Case of the Taipei Mass Rapid Transit System

Directory of Open Access Journals (Sweden)

Ming-Hsin Lu

2015-12-01

Full Text Available This study explores the usability problems for the middle-aged and elderly users of the automatic ticket vending machines of Taipei Mass Rapid Transit System. Thirty two middle-aged and elderly users (16 men and 16 females were observed in their actual uses of the machines, and 9 of them (6 men and 3 women were interviewed afterwards. The results show that, first, most senior users observed in this study made mistakes at the first step of touching the screen that initiates the ticket buying process. Second, the feedback and voice guidance design need further improvement to facilitate the senior users’ operation of the machines. Based on the findings, this study recommends that: (1 the operation instruction may be improved by enhancing the color contrast and graphics complementing of caption and voice guidance; (2 the transaction interface should be simplified, avoiding using button symbol to show information, and the visual instruction should be supplemented with voice instructions; (3 operation feedback should be strengthened and in line with previous use experience. [Article content in Chinese

RAW MILK IN AUTOMATIC SALE MACHINES: MONITORING PLAN IN PIEDEMONT REGION

Directory of Open Access Journals (Sweden)

S. Gallina

2010-06-01

Full Text Available Raw milk at vending machine is surging in popularity amongst consumers of Northern Italy; indeed in Piedmont Region there are more than 100 vending machines. In June 2008 Piedmont Region set out a specific monitoring plan to check the milk quality. From June to December 2008, 113 raw milk samples were collected at vending machines. Samples were analysed for Listeria monocytogenes, Salmonella spp., coagulase positive staphylococci, Staphylococcus aureus and Campylobacter. Moreover, 100 samples were analysed for the quantification of aflatoxin M1. 26 samples have been resulted Not Conform for the hygienic criteria and 1 exceeded the aflatoxin M1 limit.

Object Recognition System-on-Chip Using the Support Vector Machines

Directory of Open Access Journals (Sweden)

Houzet Dominique

2005-01-01

Full Text Available The first aim of this work is to propose the design of a system-on-chip (SoC platform dedicated to digital image and signal processing, which is tuned to implement efficiently multiply-and-accumulate (MAC vector/matrix operations. The second aim of this work is to implement a recent promising neural network method, namely, the support vector machine (SVM used for real-time object recognition, in order to build a vision machine. With such a reconfigurable and programmable SoC platform, it is possible to implement any SVM function dedicated to any object recognition problem. The final aim is to obtain an automatic reconfiguration of the SoC platform, based on the results of the learning phase on an objects' database, which makes it possible to recognize practically any object without manual programming. Recognition can be of any kind that is from image to signal data. Such a system is a general-purpose automatic classifier. Many applications can be considered as a classification problem, but are usually treated specifically in order to optimize the cost of the implemented solution. The cost of our approach is more important than a dedicated one, but in a near future, hundreds of millions of gates will be common and affordable compared to the design cost. What we are proposing here is a general-purpose classification neural network implemented on a reconfigurable SoC platform. The first version presented here is limited in size and thus in object recognition performances, but can be easily upgraded according to technology improvements.

Reversible machine code and its abstract processor architecture

DEFF Research Database (Denmark)

Axelsen, Holger Bock; Glück, Robert; Yokoyama, Tetsuo

2007-01-01

A reversible abstract machine architecture and its reversible machine code are presented and formalized. For machine code to be reversible, both the underlying control logic and each instruction must be reversible. A general class of machine instruction sets was proven to be reversible, building...

Machine en theater : ontwerpconcepten van winkelgebouwen

OpenAIRE

Kooyman, D.

1999-01-01

Machine and Theater. Design Concepts for Shop Buildings Most retail trade takes place in shops. For retailers a shop is the physical context for their commercial activities. Consumers spend a substantial amount of their time there, making essential purchases and also window shopping. Machine and theatre, design concepts for shop buildings investigates the function and the significance of shop space. The arcade, the department store, the supermarket, the shopping centre, the big-box retail par...

An Automatic Instruction-Level Parallelization of Machine Code

Directory of Open Access Journals (Sweden)

MARINKOVIC, V.

2018-02-01

Full Text Available Prevailing multicores and novel manycores have made a great challenge of modern day - parallelization of embedded software that is still written as sequential. In this paper, automatic code parallelization is considered, focusing on developing a parallelization tool at the binary level as well as on the validation of this approach. The novel instruction-level parallelization algorithm for assembly code which uses the register names after SSA to find independent blocks of code and then to schedule independent blocks using METIS to achieve good load balance is developed. The sequential consistency is verified and the validation is done by measuring the program execution time on the target architecture. Great speedup, taken as the performance measure in the validation process, and optimal load balancing are achieved for multicore RISC processors with 2 to 16 cores (e.g. MIPS, MicroBlaze, etc.. In particular, for 16 cores, the average speedup is 7.92x, while in some cases it reaches 14x. An approach to automatic parallelization provided by this paper is useful to researchers and developers in the area of parallelization as the basis for further optimizations, as the back-end of a compiler, or as the code parallelization tool for an embedded system.

Massively collaborative machine learning

NARCIS (Netherlands)

Rijn, van J.N.

2016-01-01

Many scientists are focussed on building models. We nearly process all information we perceive to a model. There are many techniques that enable computers to build models as well. The field of research that develops such techniques is called Machine Learning. Many research is devoted to develop

The construction of the CMS electromagnetic calorimeter: automatic measurements of the physics parameters of PWO crystals

CERN Multimedia

2005-01-01

Crystal properties (dimensions, optical transmission, light yield) are automatically measured. The pictures show different measurement stations of the automatic machine. Crystals are measured on trays containing five crystals each.

Automatization of welding for nuclear power equipments and facilities

International Nuclear Information System (INIS)

Tamai, Yasumasa; Matsumoto, Teruo; Koyama, Takaichi

1980-01-01

For the requirement of high reliability in the construction of nuclear power plants and the reduction of radiation exposure in the modefying works of existing plants, the automation and remote operation of welding have increased their necessity. In this paper, the present state of the automation of welding for making machines, equipments and pipings for nuclear power plants in Hitachi Ltd. is described, and the aim of developing the automation, the features of the equipments and the state of application to actual plants are introduced, centering around the automation of welding for large structures such as reactor containment vessels and the remote type automatic welding system for pipings. By these automations, the large outcomes were obtained in the improvement of welding quality required for the machines and equipments for atomic energy. Moreover, the conspicuous results were also obtained in case of the peculiar works to nuclear power plants, in which the reduction of the radiation exposure related to human bodies and the welding of high quality are demanded. The present state of the automation of welding for nuclear installations in Hitachi Ltd., the development of automatic welding equipments and the present state of application to actual plants, and the development and application of the automatic pipe working machine for reducing radiation exposure are explained. (Kako, I.)

Machine Learning

CERN Multimedia

CERN. Geneva

2017-01-01

Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

Current Technologies and its Trends of Machine Vision in the Field of Security and Disaster Prevention

Science.gov (United States)

Hashimoto, Manabu; Fujino, Yozo

Image sensing technologies are expected as useful and effective way to suppress damages by criminals and disasters in highly safe and relieved society. In this paper, we describe current important subjects, required functions, technical trends, and a couple of real examples of developed system. As for the video surveillance, recognition of human trajectory and human behavior using image processing techniques are introduced with real examples about the violence detection for elevators. In the field of facility monitoring technologies as civil engineering, useful machine vision applications such as automatic detection of concrete cracks on walls of a building or recognition of crowded people on bridge for effective guidance in emergency are shown.

Drinking water quality concerns and water vending machines

International Nuclear Information System (INIS)

McSwane, D.Z.; Oleckno, W.A.; Eils, L.M.

1994-01-01

Drinking water quality is a vital public health concern to consumers and regulators alike. This article describes some of the current microbiological, chemical, and radiological concerns about drinking water and the evolution of water vending machines. Also addressed are the typical treatment processes used in water vending machines and their effectiveness, as well as a brief examination of a certification program sponsored by the National Automatic Merchandising Association (NAMA), which provides a uniform standard for the design and construction of food and beverage vending machines. For some consumers, the water dispensed from vending machines is an attractive alternative to residential tap water which may be objectionable for aesthetic or other reasons

Automatic Residential/Commercial Classification of Parcels with Solar Panel Detections

Energy Technology Data Exchange (ETDEWEB)

2018-03-25

A computational method to automatically detect solar panels on rooftops to aid policy and financial assessment of solar distributed generation. The code automatically classifies parcels containing solar panels in the U.S. as residential or commercial. The code allows the user to specify an input dataset containing parcels and detected solar panels, and then uses information about the parcels and solar panels to automatically classify the rooftops as residential or commercial using machine learning techniques. The zip file containing the code includes sample input and output datasets for the Boston and DC areas.

a New Approach for the Semi-Automatic Texture Generation of the Buildings Facades, from Terrestrial Laser Scanner Data

Science.gov (United States)

Oniga, E.

2012-07-01

The result of the terrestrial laser scanning is an impressive number of spatial points, each of them being characterized as position by the X, Y and Z co-ordinates, by the value of the laser reflectance and their real color, expressed as RGB (Red, Green, Blue) values. The color code for each LIDAR point is taken from the georeferenced digital images, taken with a high resolution panoramic camera incorporated in the scanner system. In this article I propose a new algorithm for the semiautomatic texture generation, using the color information, the RGB values of every point that has been taken by terrestrial laser scanning technology and the 3D surfaces defining the buildings facades, generated with the Leica Cyclone software. The first step is when the operator defines the limiting value, i.e. the minimum distance between a point and the closest surface. The second step consists in calculating the distances, or the perpendiculars drawn from each point to the closest surface. In the third step we associate the points whose 3D coordinates are known, to every surface, depending on the limiting value. The fourth step consists in computing the Voronoi diagram for the points that belong to a surface. The final step brings automatic association between the RGB value of the color code and the corresponding polygon of the Voronoi diagram. The advantage of using this algorithm is that we can obtain, in a semi-automatic manner, a photorealistic 3D model of the building.

Automatic Recognition Method for Optical Measuring Instruments Based on Machine Vision

Institute of Scientific and Technical Information of China (English)

SONG Le; LIN Yuchi; HAO Liguo

2008-01-01

Based on a comprehensive study of various algorithms, the automatic recognition of traditional ocular optical measuring instruments is realized. Taking a universal tools microscope (UTM) lens view image as an example, a 2-layer automatic recognition model for data reading is established after adopting a series of pre-processing algorithms. This model is an optimal combination of the correlation-based template matching method and a concurrent back propagation (BP) neural network. Multiple complementary feature extraction is used in generating the eigenvectors of the concurrent network. In order to improve fault-tolerance capacity, rotation invariant features based on Zernike moments are extracted from digit characters and a 4-dimensional group of the outline features is also obtained. Moreover, the operating time and reading accuracy can be adjusted dynamically by setting the threshold value. The experimental result indicates that the newly developed algorithm has optimal recognition precision and working speed. The average reading ratio can achieve 97.23%. The recognition method can automatically obtain the results of optical measuring instruments rapidly and stably without modifying their original structure, which meets the application requirements.

Machine learning with R

CERN Document Server

Lantz, Brett

2013-01-01

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or

Development of hole inspection program using touch trigger probe on CNC machine tools

International Nuclear Information System (INIS)

Lee, Chan Ho; Lee, Eung Suk

2012-01-01

According to many customers' requests, optical measurement module (OMM) applications using automatic measuring devices to measure the machined part rapidly on a machine tool have increased steeply. Touch trigger probes are being used for job setup and feature inspection as automatic measuring devices, and this makes quality checking and machining compensation possible. Therefore, in this study, the use of touch trigger probes for accurate measurement of the machined part has been studied and a macro program for a hole measuring cycle has been developed. This hole is the most common feature to be measured, but conventional methods are still not free from measuring error. In addition, the eccentricity change of the least square circle was simulated according to the roundness error in a hole measurement. To evaluate the reliability of this study, the developed hole measuring program was executed to measure the hole plate on the machine and verify the roundness error in the eccentricity simulation result

A survey on Barrett's esophagus analysis using machine learning.

Science.gov (United States)

de Souza, Luis A; Palm, Christoph; Mendel, Robert; Hook, Christian; Ebigbo, Alanna; Probst, Andreas; Messmann, Helmut; Weber, Silke; Papa, João P

2018-05-01

This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

An Automatic Car Counting System Using OverFeat Framework

OpenAIRE

Biswas, Debojit; Su, Hongbo; Wang, Chengyi; Blankenship, Jason; Stevanovic, Aleksandar

2017-01-01

Automatic car counting is an important component in the automated traffic system. Car counting is very important to understand the traffic load and optimize the traffic signals. In this paper, we implemented the Gaussian Background Subtraction Method and OverFeat Framework to count cars. OverFeat Framework is a combination of Convolution Neural Network (CNN) and one machine learning classifier (like Support Vector Machines (SVM) or Logistic Regression). With this study, we showed another poss...

An HTS machine laboratory prototype

DEFF Research Database (Denmark)

Mijatovic, Nenad; Jensen, Bogi Bech; Træholt, Chresten

2012-01-01

This paper describes Superwind HTS machine laboratory setup which is a small scale HTS machine designed and build as a part of the efforts to identify and tackle some of the challenges the HTS machine design may face. One of the challenges of HTS machines is a Torque Transfer Element (TTE) which...... conduction compared to a shaft. The HTS machine was successfully cooled to 77K and tests have been performed. The IV curves of the HTS field winding employing 6 HTS coils indicate that two of the coils had been damaged. The maximal value of the torque during experiments of 78Nm was recorded. Loaded with 33...

A novel framework for diagnosing automatic tool changer and tool life based on cloud computing

Directory of Open Access Journals (Sweden)

Shang-Liang Chen

2016-03-01

Full Text Available Tool change is one among the most frequently performed machining processes, and if there is improper percussion as the tool’s position is changed, the spindle bearing can be damaged. A spindle malfunction can cause problems, such as a knife being dropped or bias in a machined hole. The measures currently taken to avoid such issues, which arose from the available machine tools, only involve determining whether the clapping knife’s state is correct using a spindle and the air adhesion method, which is also used to satisfy the high precision required from mechanical components. Therefore, it cannot be used with any type of machine tool; in addition, improper tapping of the spindle during an automatic tool change cannot be detected. Therefore, this study proposes a new type of diagnostic framework that combines cloud computing and vibration sensors, among of which, tool change is automatically diagnosed using an architecture to identify abnormalities and thereby enhances the reliability and productivity of the machine and equipment.

Facial Expression Recognition Through Machine Learning

Directory of Open Access Journals (Sweden)

Nazia Perveen

2015-08-01

Full Text Available Facial expressions communicate non-verbal cues which play an important role in interpersonal relations. Automatic recognition of facial expressions can be an important element of normal human-machine interfaces it might likewise be utilized as a part of behavioral science and in clinical practice. In spite of the fact that people perceive facial expressions for all intents and purposes immediately solid expression recognition by machine is still a challenge. From the point of view of automatic recognition a facial expression can be considered to comprise of disfigurements of the facial parts and their spatial relations or changes in the faces pigmentation. Research into automatic recognition of the facial expressions addresses the issues encompassing the representation and arrangement of static or dynamic qualities of these distortions or face pigmentation. We get results by utilizing the CVIPtools. We have taken train data set of six facial expressions of three persons and for train data set purpose we have total border mask sample 90 and 30 border mask sample for test data set purpose and we use RST- Invariant features and texture features for feature analysis and then classified them by using k- Nearest Neighbor classification algorithm. The maximum accuracy is 90.

Automatic welding of stainless steel tubing

Science.gov (United States)

Clautice, W. E.

1978-01-01

The use of automatic welding for making girth welds in stainless steel tubing was investigated as well as the reduction in fabrication costs resulting from the elimination of radiographic inspection. Test methodology, materials, and techniques are discussed, and data sheets for individual tests are included. Process variables studied include welding amperes, revolutions per minute, and shielding gas flow. Strip chart recordings, as a definitive method of insuring weld quality, are studied. Test results, determined by both radiographic and visual inspection, are presented and indicate that once optimum welding procedures for specific sizes of tubing are established, and the welding machine operations are certified, then the automatic tube welding process produces good quality welds repeatedly, with a high degree of reliability. Revised specifications for welding tubing using the automatic process and weld visual inspection requirements at the Kennedy Space Center are enumerated.

Design and Simulation of Two Robotic Systems for Automatic Artichoke Harvesting

Directory of Open Access Journals (Sweden)

Domenico Longo

2013-12-01

Full Text Available The target of this research project was a feasibility study for the development of a robot for automatic or semi-automatic artichoke harvesting. During this project, different solutions for the mechanical parts of the machine, its control system and the harvesting tools were investigated. Moreover, in cooperation with the department DISPA of University of Catania, different field structures with different kinds of artichoke cultivars were studied and tested. The results of this research could improve artichoke production for preserves industries. As a first step, an investigation on existing machines has been done. From this research, it has been shown that very few machines exist for this purpose. Based also on previous experiences, some proposals for different robotic systems have been done, while the mobile platform itself was developed within another research project. At the current stage, several different configurations of machines and harvesting end-effectors have been designed and simulated using a 3D CAD environment interfaced with Matlab®. Moreover, as support for one of the proposed machines, an artificial vision algorithm has been developed in order to locate the artichokes on the plant, with respect to the robot, using images taken with a standard webcam.

Use of MCAM in creating 3D neutronics model for ITER building

Energy Technology Data Exchange (ETDEWEB)

Zeng Qin [Institute of Nuclear Energy Safety Technology, Chinese Academy of Sciences, Hefei, Anhui 230031 (China); School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027 (China); Wang Guozhong, E-mail: mango33@mail.ustc.edu.cn [School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027 (China); Dang Tongqiang [School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027 (China); Long Pengcheng [Institute of Nuclear Energy Safety Technology, Chinese Academy of Sciences, Hefei, Anhui 230031 (China); School of Nuclear Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027 (China); Loughlin, Michael [ITER Organization, Route de Vinon sur Verdon, 13115 St. Paul-Lz-Durance (France)

2012-08-15

Highlights: Black-Right-Pointing-Pointer We created a 3D neutronics model of the ITER building. Black-Right-Pointing-Pointer The model was produced from the engineering CAD model by MCAM software. Black-Right-Pointing-Pointer The neutron flux map in the ITER building was calculated. - Abstract: The three dimensional (3D) neutronics reference model of International Thermonuclear Experimental Reactor (ITER) only defines the tokamak machine and extends to the bio-shield. In order to meet further 3D neutronics analysis needs, it is necessary to create a 3D reference model of the ITER building. Monte Carlo Automatic Modeling Program for Radiation Transport Simulation (MCAM) was developed as a computer aided design (CAD) based bi-directional interface program between general CAD systems and Monte Carlo radiation transport simulation codes. With the help of MCAM version 4.8, the 3D neutronics model of ITER building was created based on the engineering CAD model. The calculation of the neutron flux map in ITER building during operation showed the correctness and usability of the model. This model is the first detailed ITER building 3D neutronics model and it will be made available to all international organization collaborators as a reference model.

The Scenario Approach to the Development of Strategy of Prevention of Raider Seizure for Machine-Building Enterprise

Directory of Open Access Journals (Sweden)

Momot Tetiana V.

2017-12-01

Full Text Available The article proposes the methodical approach to the choice and substantiation of efficiency of managerial decisions on ensuring economic safety at counteraction of raiding, based on an intellectual instrumental analysis. The ranking of alternatives of managerial decisions on the basis of the received weighted estimates and their fuzzy composition is used. A graphical interpretation of the membership functions of the calculated fuzzy expected utilities of management alternatives for the machine-building enterprises has been constructed and is presented.

Exploring cluster Monte Carlo updates with Boltzmann machines.

Science.gov (United States)

Wang, Lei

2017-11-01

Boltzmann machines are physics informed generative models with broad applications in machine learning. They model the probability distribution of an input data set with latent variables and generate new samples accordingly. Applying the Boltzmann machines back to physics, they are ideal recommender systems to accelerate the Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann machines can even give different cluster Monte Carlo algorithms. The latent representation of the Boltzmann machines can be designed to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four-spin plaquette interactions. In the future, automatic searches in the algorithm space parametrized by Boltzmann machines may discover more innovative Monte Carlo updates.

Exploring cluster Monte Carlo updates with Boltzmann machines

Science.gov (United States)

Wang, Lei

2017-11-01

Boltzmann machines are physics informed generative models with broad applications in machine learning. They model the probability distribution of an input data set with latent variables and generate new samples accordingly. Applying the Boltzmann machines back to physics, they are ideal recommender systems to accelerate the Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann machines can even give different cluster Monte Carlo algorithms. The latent representation of the Boltzmann machines can be designed to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four-spin plaquette interactions. In the future, automatic searches in the algorithm space parametrized by Boltzmann machines may discover more innovative Monte Carlo updates.

vSphere virtual machine management

CERN Document Server

Fitzhugh, Rebecca

2014-01-01

This book follows a step-by-step tutorial approach with some real-world scenarios that vSphere businesses will be required to overcome every day. This book also discusses creating and configuring virtual machines and also covers monitoring virtual machine performance and resource allocation options. This book is for VMware administrators who want to build their knowledge of virtual machine administration and configuration. It's assumed that you have some experience with virtualization administration and vSphere.

Machine en theater : ontwerpconcepten van winkelgebouwen

NARCIS (Netherlands)

Kooyman, D.

1999-01-01

Machine and Theater. Design Concepts for Shop Buildings Most retail trade takes place in shops. For retailers a shop is the physical context for their commercial activities. Consumers spend a substantial amount of their time there, making essential purchases and also window shopping. Machine and

Build and Execute Environment

Energy Technology Data Exchange (ETDEWEB)

2017-04-21

At exascale, the challenge becomes to develop applications that run at scale and use exascale platforms reliably, efficiently, and flexibly. Workflows become much more complex because they must seamlessly integrate simulation and data analytics. They must include down-sampling, post-processing, feature extraction, and visualization. Power and data transfer limitations require these analysis tasks to be run in-situ or in-transit. We expect successful workflows will comprise multiple linked simulations along with tens of analysis routines. Users will have limited development time at scale and, therefore, must have rich tools to develop, debug, test, and deploy applications. At this scale, successful workflows will compose linked computations from an assortment of reliable, well-defined computation elements, ones that can come and go as required, based on the needs of the workflow over time. We propose a novel framework that utilizes both virtual machines (VMs) and software containers to create a workflow system that establishes a uniform build and execution environment (BEE) beyond the capabilities of current systems. In this environment, applications will run reliably and repeatably across heterogeneous hardware and software. Containers, both commercial (Docker and Rocket) and open-source (LXC and LXD), define a runtime that isolates all software dependencies from the machine operating system. Workflows may contain multiple containers that run different operating systems, different software, and even different versions of the same software. We will run containers in open-source virtual machines (KVM) and emulators (QEMU) so that workflows run on any machine entirely in user-space. On this platform of containers and virtual machines, we will deliver workflow software that provides services, including repeatable execution, provenance, checkpointing, and future proofing. We will capture provenance about how containers were launched and how they interact to annotate

Oocytes Polar Body Detection for Automatic Enucleation

Directory of Open Access Journals (Sweden)

Di Chen

2016-02-01

Full Text Available Enucleation is a crucial step in cloning. In order to achieve automatic blind enucleation, we should detect the polar body of the oocyte automatically. The conventional polar body detection approaches have low success rate or low efficiency. We propose a polar body detection method based on machine learning in this paper. On one hand, the improved Histogram of Oriented Gradient (HOG algorithm is employed to extract features of polar body images, which will increase success rate. On the other hand, a position prediction method is put forward to narrow the search range of polar body, which will improve efficiency. Experiment results show that the success rate is 96% for various types of polar bodies. Furthermore, the method is applied to an enucleation experiment and improves the degree of automatic enucleation.

Study of on-machine error identification and compensation methods for micro machine tools

International Nuclear Information System (INIS)

Wang, Shih-Ming; Yu, Han-Jen; Lee, Chun-Yi; Chiu, Hung-Sheng

2016-01-01

Micro machining plays an important role in the manufacturing of miniature products which are made of various materials with complex 3D shapes and tight machining tolerance. To further improve the accuracy of a micro machining process without increasing the manufacturing cost of a micro machine tool, an effective machining error measurement method and a software-based compensation method are essential. To avoid introducing additional errors caused by the re-installment of the workpiece, the measurement and compensation method should be on-machine conducted. In addition, because the contour of a miniature workpiece machined with a micro machining process is very tiny, the measurement method should be non-contact. By integrating the image re-constructive method, camera pixel correction, coordinate transformation, the error identification algorithm, and trajectory auto-correction method, a vision-based error measurement and compensation method that can on-machine inspect the micro machining errors and automatically generate an error-corrected numerical control (NC) program for error compensation was developed in this study. With the use of the Canny edge detection algorithm and camera pixel calibration, the edges of the contour of a machined workpiece were identified and used to re-construct the actual contour of the work piece. The actual contour was then mapped to the theoretical contour to identify the actual cutting points and compute the machining errors. With the use of a moving matching window and calculation of the similarity between the actual and theoretical contour, the errors between the actual cutting points and theoretical cutting points were calculated and used to correct the NC program. With the use of the error-corrected NC program, the accuracy of a micro machining process can be effectively improved. To prove the feasibility and effectiveness of the proposed methods, micro-milling experiments on a micro machine tool were conducted, and the results

Document Classification Using Distributed Machine Learning

OpenAIRE

Aydin, Galip; Hallac, Ibrahim Riza

2018-01-01

In this paper, we investigate the performance and success rates of Na\\"ive Bayes Classification Algorithm for automatic classification of Turkish news into predetermined categories like economy, life, health etc. We use Apache Big Data technologies such as Hadoop, HDFS, Spark and Mahout, and apply these distributed technologies to Machine Learning.

The Three Pillars of Machine Programming

OpenAIRE

Gottschlich, Justin; Solar-Lezama, Armando; Tatbul, Nesime; Carbin, Michael; Rinard, Martin; Barzilay, Regina; Amarasinghe, Saman; Tenenbaum, Joshua B; Mattson, Tim

2018-01-01

In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are: (i) intention, (ii) invention, and(iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in t...

Perspex machine: V. Compilation of C programs

Science.gov (United States)

Spanner, Matthew P.; Anderson, James A. D. W.

2006-01-01

The perspex machine arose from the unification of the Turing machine with projective geometry. The original, constructive proof used four special, perspective transformations to implement the Turing machine in projective geometry. These four transformations are now generalised and applied in a compiler, implemented in Pop11, that converts a subset of the C programming language into perspexes. This is interesting both from a geometrical and a computational point of view. Geometrically, it is interesting that program source can be converted automatically to a sequence of perspective transformations and conditional jumps, though we find that the product of homogeneous transformations with normalisation can be non-associative. Computationally, it is interesting that program source can be compiled for a Reduced Instruction Set Computer (RISC), the perspex machine, that is a Single Instruction, Zero Exception (SIZE) computer.

Theory and practice in machining systems

CERN Document Server

Ito, Yoshimi

2017-01-01

This book describes machining technology from a wider perspective by considering it within the machining space. Machining technology is one of the metal removal activities that occur at the machining point within the machining space. The machining space consists of structural configuration entities, e.g., the main spindle, the turret head and attachments such the chuck and mandrel, and also the form-generating movement of the machine tool itself. The book describes fundamental topics, including the form-generating movement of the machine tool and the important roles of the attachments, before moving on to consider the supply of raw materials into the machining space, and the discharge of swarf from it, and then machining technology itself. Building on the latest research findings “Theory and Practice in Machining System” discusses current challenges in machining. Thus, with the inclusion of introductory and advanced topics, the book can be used as a guide and survey of machining technology for students an...

SRV-automatic handling device

International Nuclear Information System (INIS)

Yamada, Koji

1987-01-01

Automatic handling device for the steam relief valves (SRV's) is developed in order to achieve a decrease in exposure of workers, increase in availability factor, improvement in reliability, improvement in safety of operation, and labor saving. A survey is made during a periodical inspection to examine the actual SVR handling operation. An SRV automatic handling device consists of four components: conveyor, armed conveyor, lifting machine, and control/monitoring system. The conveyor is so designed that the existing I-rail installed in the containment vessel can be used without any modification. This is employed for conveying an SRV along the rail. The armed conveyor, designed for a box rail, is used for an SRV installed away from the rail. By using the lifting machine, an SRV installed away from the I-rail is brought to a spot just below the rail so that the SRV can be transferred by the conveyor. The control/monitoring system consists of a control computer, operation panel, TV monitor and annunciator. The SRV handling device is operated by remote control from a control room. A trial equipment is constructed and performance/function testing is carried out using actual SRV's. As a result, is it shown that the SRV handling device requires only two operators to serve satisfactorily. The required time for removal and replacement of one SRV is about 10 minutes. (Nogami, K.)

The Rationalization of Automatic Units for HPDC Technology

Directory of Open Access Journals (Sweden)

A. Herman

2012-04-01

Full Text Available The paper deals with problem of optimal used automatic workplace for HPDC technology - mainly from aspects of operations sequence, efficiency of work cycle and planning of using and servicing of HPDC casting machine. Presented are possible ways to analyse automatic units for HPDC. The experimental part was focused on the rationalization of the current work cycle time for die casting of aluminium alloy. The working place was described in detail in the project. The measurements were carried out in detail with the help of charts and graphs mapped cycle of casting workplace. Other parameters and settings have been identified.The proposals for improvements were made after the first measurements and these improvements were subsequently verified. The main actions were mainly software modifications of casting center. It is for the reason that today's sophisticated workplaces have the option of a relatively wide range of modifications without any physical harm to machines themselves. It is possible to change settings or unlock some unsatisfactory parameters.

Consequences of heavy machining vis à vis the machine structure – typical applications

International Nuclear Information System (INIS)

Leuch, M

2011-01-01

StarragHeckert has built 5 axis machines since the middle of the 80s for heavy duty milling. The STC-Centres are predominantly utilised in the aerospace industry, especially for milling structural workpieces, casings or Impellers made out of titanium and steel. StarragHeckert has a history of building machines for high performance milling. The machining of these components includes high forces thus spreading the wheat from the chaff. Although FEM calculations and multi-body simulations are carried out in the early stages of development, this paper will illustrate how the real process stability with modal analysis and cutting trials is determined. The experiment observes chatter stability to identify if the machine devices are adequate for the application or if the design has to be improved. Machining parameters of industrial applications are demonstrating the process stability for five axis heavy duties milling of StarragHeckert machine.

Machines and Metaphors

Directory of Open Access Journals (Sweden)

Ángel Martínez García-Posada

2016-10-01

Full Text Available The edition La ley del reloj. Arquitectura, máquinas y cultura moderna (Cátedra, Madrid, 2016 registers the useful paradox of the analogy between architecture and technique. Its author, the architect Eduardo Prieto, also a philosopher, professor and writer, acknowledges the obvious distance from machines to buildings, so great that it can only be solved using strange comparisons, since architecture does not move nor are the machines habitable, however throughout the book, from the origin of the metaphor of the machine, with clarity in his essay and enlightening erudition, he points out with certainty some concomitances of high interest, drawing throughout history a beautiful cartography of the fruitful encounter between organics and mechanics.

Efficiency potential of hot-drink dispensing machines in commercial catering; Effizienzpotenzial bei Heissgetraenkeautomaten in der Betriebsverpflegung

Energy Technology Data Exchange (ETDEWEB)

Grieder, T; Huser, A [Encontrol GmbH, Niederrohrdorf (Switzerland); Schmitz, R [Electrosuisse, Fehraltorf (Switzerland)

2003-07-01

This final report for the Swiss Federal Office of Energy (SFOE) discusses the findings of a project that looked into the energy consumption of automatic hot-drink dispensing machines. The report presents the results of a survey made in Switzerland together with various manufacturers and operators of such machines that are used in the company refreshments sector. The survey provides important information on the current market situation, markets and market shares of individual operating companies as well as on machine technology and energy consumption. Also, obstacles to the improvement of energy efficiency in this area are looked at. Important savings that can be made in the operation of such machines are quoted. The report recommends that the results of a parallel survey of domestic coffee-making machines be taken note of and that effort should rather be concentrated in this area, where energy consumption at the national level is quoted as being around twice as high as for the commercial automatic hot-drink dispensing machines.

Machine learning techniques for persuasion dectection in conversation

OpenAIRE

Ortiz, Pedro.

2010-01-01

Approved for public release; distribution is unlimited We determined that it is possible to automatically detect persuasion in conversations using three traditional machine learning techniques, naive bayes, maximum entropy, and support vector machine. These results are the first of their kind and serve as a baseline for all future work in this field. The three techniques consistently outperformed the baseline F-score, but not at a level that would be useful for real world applications. The...

Ground loops detection system in the RFX machine

International Nuclear Information System (INIS)

Bellina, F.; Pomaro, N.; Trevisan, F.

1996-01-01

RFX is a toroidal machine for the fusion research based on the RFP configuration. During the pulse, in any conductive loop close to the machine very strong currents can be induced, which may damage the diagnostics and the other instrumentation. To avoid loops, the earthing system of the machine is tree-shaped. However, an accidental contact between metallic earthed masses of the machine may give rise to an unwanted loop as well. An automatic system for the detection of ground loops in the earthing system has therefore been developed, which works continuously during shutdown intervals and between pulses. In the paper the design of the detection system is presented, together with the experimental results on prototypes. 4 refs., 3 figs., 1 tab

RCrane: semi-automated RNA model building

International Nuclear Information System (INIS)

Keating, Kevin S.; Pyle, Anna Marie

2012-01-01

RCrane is a new tool for the partially automated building of RNA crystallographic models into electron-density maps of low or intermediate resolution. This tool helps crystallographers to place phosphates and bases into electron density and then automatically predicts and builds the detailed all-atom structure of the traced nucleotides. RNA crystals typically diffract to much lower resolutions than protein crystals. This low-resolution diffraction results in unclear density maps, which cause considerable difficulties during the model-building process. These difficulties are exacerbated by the lack of computational tools for RNA modeling. Here, RCrane, a tool for the partially automated building of RNA into electron-density maps of low or intermediate resolution, is presented. This tool works within Coot, a common program for macromolecular model building. RCrane helps crystallographers to place phosphates and bases into electron density and then automatically predicts and builds the detailed all-atom structure of the traced nucleotides. RCrane then allows the crystallographer to review the newly built structure and select alternative backbone conformations where desired. This tool can also be used to automatically correct the backbone structure of previously built nucleotides. These automated corrections can fix incorrect sugar puckers, steric clashes and other structural problems

MRI in assessing children with learning disability, focal findings, and reduced automaticity.

Science.gov (United States)

Urion, David K; Huff, Hanalise V; Carullo, Maria Paulina

2015-08-18

In children with clinically diagnosed learning disabilities with focal findings on neurologic or neuropsychological evaluations, there is a hypothesized association between disorders in automaticity and focal structural abnormalities observed in brain MRIs. We undertook a retrospective analysis of cases referred to a tertiary-hospital-based learning disabilities program. Individuals were coded as having a focal deficit if either neurologic or neuropsychological evaluation demonstrated focal dysfunction. Those with abnormal MRI findings were categorized based on findings. Children with abnormalities from each of these categories were compared in terms of deficits in automaticity, as measured by the tasks of Rapid Automatized Naming, Rapid Alternating Stimulus Naming, or the timed motor performance battery from the Physical and Neurological Examination for Soft Signs. Data were compared in children with and without disorders of automaticity regarding type of brain structure abnormality. Of the 1,587 children evaluated, 127 had a focal deficit. Eighty-seven had a brain MRI (52 on 1.5-tesla machines and 35 on 3.0-tesla machines). Forty of these images were found to be abnormal. These children were compared with a clinic sample of 150 patients with learning disabilities and no focal findings on examination, who also had undergone MRI. Only 5 of the latter group had abnormalities on MRI. Reduced verbal automaticity was associated with cerebellar abnormalities, whereas reduced automaticity on motor or motor and verbal tasks was associated with white matter abnormalities. Reduced automaticity of retrieval and slow timed motor performance appear to be highly associated with MRI findings. © 2015 American Academy of Neurology.

OptiCentric lathe centering machine

Science.gov (United States)

Buß, C.; Heinisch, J.

2013-09-01

High precision optics depend on precisely aligned lenses. The shift and tilt of individual lenses as well as the air gap between elements require accuracies in the single micron regime. These accuracies are hard to meet with traditional assembly methods. Instead, lathe centering can be used to machine the mount with respect to the optical axis. Using a diamond turning process, all relevant errors of single mounted lenses can be corrected in one post-machining step. Building on the OptiCentric® and OptiSurf® measurement systems, Trioptics has developed their first lathe centering machines. The machine and specific design elements of the setup will be shown. For example, the machine can be used to turn optics for i-line steppers with highest precision.

Expanding Options. A Model to Attract Secondary Students into Nontraditional Vocational Programs. For Emphasis in: Building Trades, Electronics, Health Services, Machine Shop, Welding.

Science.gov (United States)

Good, James D.; DeVore, Mary Ann

This model has been designed for use by Missouri secondary schools in attracting females and males into nontraditional occupational programs. The research-based strategies are intended for implementation in the following areas: attracting females into building trades, electronics, machine shop, and welding; and males into secondary health…

Can we replace the bodily teacher? The Dutch history of teaching machines (1960s)

NARCIS (Netherlands)

Amsing, Hilda T.A.

2016-01-01

At the beginnings of the 1960s Skinner’s teaching machine reached the Netherlands. This machine used programmed instruction to guide children in small steps through the teaching materials. It provided them with carefully chosen questions and automatic feedback, fitting the principles of

Trends of progress in medical technics as far as automatization is concerned

Energy Technology Data Exchange (ETDEWEB)

Agoston, M [Medicor Muevek, Budapest (Hungary)

1978-09-01

Modernization of medical treatment is developing to the direction of establishing big hospitals and policlinics. Highly productive automatic equipments give possibilities for performing the mass examinations with high efficiency. Still the X-ray instruments form the most valuable and indispensable device group. One direction to develop the automatization of these machines is to achieve the best X-ray exposure. The relatively slow but continuous spreading of isotope diagnostic instruments has been connected with a number of results in automatization, too. In the field of sterilization bactericid materials, gas- and ray sterilizing methods, as well as combined systems become used. Automatization has a strong influence on the domain of epidemiology as well.

Machine en Theater. Ontwerpconcepten van winkelgebouwen

NARCIS (Netherlands)

Kooijman, D.C.

1999-01-01

Machine and Theater, Design Concepts for Shop Buildings is a richly illustrated study of the architectural and urban development of retail buildings, focusing on six essential shop types: the passage and the department store in particular in Germany and France in the nineteenth century; supermarkets

Remote machine engineering applications for nuclear facilities decommissioning

International Nuclear Information System (INIS)

Toto, G.; Wyle, H.R.

1983-01-01

Decontamination and decommissioning of a nuclear facility require the application of techniques that protect the worker and the enviroment from radiological contamination and radiation. Remotely operated portable robotic arms, machines, and devices can be applied. The use of advanced systems should enhance the productivity, safety, and cost facets of the efforts; remote automatic tooling and systems may be used on any job where job hazard and other factors justify application. Many problems based on costs, enviromental impact, health, waste generation, and political issues may be mitigated by use of remotely operated machines. The work that man can not do or should not do will have to be done by machines

Automatic fuel charging machine for nuclear power plants

International Nuclear Information System (INIS)

Neda, Toshikazu; Aoki, Shigeo.

1978-01-01

Purpose: To enable smooth, safety and rapid refueling by automatically conducting a series of fuel exchange steps through the combination of calculations for fuel control and refueling works. Constitution: Processing operations are conducted based on various data from the memory unit of a computer that processes and stores various data inputted from a nuclear power plant, calculation systems stored in the memory unit of another computer, peripheral units such as typewriters and process input units. A refueling platform is operated by way of a platform control device and a platform driving device, and fuel exchange is conducted by the operation of a channel mounting and demounting device. (Yoshino, Y.)

Depfix, a Tool for Automatic Rule-based Post-editing of SMT

Directory of Open Access Journals (Sweden)

Rudolf Rosa

2014-09-01

Full Text Available We present Depfix, an open-source system for automatic post-editing of phrase-based machine translation outputs. Depfix employs a range of natural language processing tools to obtain analyses of the input sentences, and uses a set of rules to correct common or serious errors in machine translation outputs. Depfix is currently implemented only for English-to-Czech translation direction, but extending it to other languages is planned.

AUTOMATIC ARCHITECTURAL STYLE RECOGNITION

Directory of Open Access Journals (Sweden)

M. Mathias

2012-09-01

Full Text Available Procedural modeling has proven to be a very valuable tool in the field of architecture. In the last few years, research has soared to automatically create procedural models from images. However, current algorithms for this process of inverse procedural modeling rely on the assumption that the building style is known. So far, the determination of the building style has remained a manual task. In this paper, we propose an algorithm which automates this process through classification of architectural styles from facade images. Our classifier first identifies the images containing buildings, then separates individual facades within an image and determines the building style. This information could then be used to initialize the building reconstruction process. We have trained our classifier to distinguish between several distinct architectural styles, namely Flemish Renaissance, Haussmannian and Neoclassical. Finally, we demonstrate our approach on various street-side images.

Study on ultra-fine w-EDM with on-machine measurement-assisted

International Nuclear Information System (INIS)

Chen Shuntong; Yang Hongye

2011-01-01

The purpose of this study was to develop the on-machine measurement techniques so as to precisely fabricate micro intricate part using ultra-fine w-EDM. The measurement-assisted approach which employs an automatic optical inspection (AOI) is incorporated to ultra-fine w-EDM process to on-machine detect the machining error for next re-machining. The AOI acquires the image through a high resolution CCD device from the contour of the workpiece after roughing in order to further process and recognize the image for determining the residual. This facilitates the on-machine error detection and compensation re-machining. The micro workpiece and electrode are not repositioned during machining. A fabrication for a micro probe of 30-μm diameter is rapidly machined and verified successfully. Based on the proposed technique, on-machine measurement with AOI has been realized satisfactorily.

An enhanced model for automatically extracting topic phrase from ...

African Journals Online (AJOL)

The key benefit foreseen from this automatic document classification is not only related to search engines, but also to many other fields like, document organization, text filtering and semantic index managing. Key words: Keyphrase extraction, machine learning, search engine snippet, document classification, topic tracking ...

Cost-benefit analysis of the ATM automatic deposit service

Directory of Open Access Journals (Sweden)

Ivica Županović

2015-03-01

Full Text Available Bankers and other financial experts have analyzed the value of automated teller machines (ATM in terms of growing consumer demand, rising costs of technology development, decreasing profitability and market share. This paper presents a step-by-step cost-benefit analysis of the ATM automatic deposit service. The first step is to determine user attitudes towards using ATM automatic deposit service by using the Technology Acceptance Model (TAM. The second step is to determine location priorities for ATMs that provide automatic deposit services using the Analytic Hierarchy Process (AHP model. The results of the previous steps enable a highly efficient application of cost-benefit analysis for evaluating costs and benefits of automatic deposit services. To understand fully the proposed procedure outside of theoretical terms, a real-world application of a case study is conducted.

First neutrons from new machine

International Nuclear Information System (INIS)

Gray, D.A.

1985-01-01

Nimrod, the last weak focusing proton machine to be built, provided its first 7 GeV proton beam in 1963 and provided the research fuel for several generations of UK particle physicists. With the decision to build the SNS, the task was to transform the existing facility into a high repetition rate, high intensity machine furnishing the protons to bombard a neutron production target. As well as equipment from Nimrod, the SNS synchrotron also makes use of components from the old NINA electron machine at Daresbury, closed down in 1977. (orig./HSI).

On The Subject of Thinking Machines

OpenAIRE

Olafenwa , John ,; Olafenwa , Moses

2018-01-01

An investigation of the concepts of thoughts, imagination and consciousness in learning machines.; 68 years ago, Alan Turing proposed the question "Can Machines Think" in his seminal paper [1] titled "Computing Machinery and Intelligence" and he formulated the "Imitation Game" also known as the Turing test as a way to answer this question without referring to a rather ambiguous dictionary definition of the word "Think" We have come a long way to building intelligent machines, in fact, the rat...

Combined machine-readable and visually authenticated optical devices

Science.gov (United States)

Souparis, Hugues

1996-03-01

Optical variable devices are now widely used on documents or values. The most recent optical visual features with high definition, animation, brightness, special color tune, provide excellent first and second levels of authentication. Human eye is the only instrument required to check the authenticity. This is a major advantage of OVDs in many circumstances, such as currency exchange, ID street control . . . But, under other circumstances, such as automatic payments with banknotes, volume ID controls at boarders, ID controls in shops . . . an automatic authentication will be necessary or more reliable. When both a visual and automated authentication are required, the combination, on the same security component, of a variable image and a machine readable optical element is a very secure and cost effective solution for the protection of documents. Several techniques are now available an can be selected depending upon the respective roles of the machine readability and visual control.

Models of evaluating efficiency and risks on integration of cloud-base IT-services of the machine-building enterprise: a system approach

Science.gov (United States)

Razumnikov, S.; Kurmanbay, A.

2016-04-01

The present paper suggests a system approach to evaluation of the effectiveness and risks resulted from the integration of cloud-based services in a machine-building enterprise. This approach makes it possible to estimate a set of enterprise IT applications and choose the applications to be migrated to the cloud with regard to specific business requirements, a technological strategy and willingness to risk.

Tendency of the 18-8 type corrosion-resistant steel to cracking in automatic building-up of copper and copper base alloys in argon

International Nuclear Information System (INIS)

Abramovich, V.R.; Andronik, V.A.

1978-01-01

Studied was the tendency of the 18-8 type corrosion-resistant steel to cracking during automatic building-up of copper and bronze in argon. The investigation was carried out on the 0kh18n10t steel in argon. It had been established, that the degree of copper penetration into the steel inceases with the increase in the time of the 0Kh18n10t steel contact with liquid copper. Liquid copper and copper base alloys have a detrimental effect on mechanical properties of the steel under external tensile load during intercontant. It is shown that in building-up of copper base alloys on the steel-0Kh18n10t, tendency of the steel to cracking decreases with increase in stiffness of a surfaced weld metal plate and with decrease in building-up energy per unit length. The causes of macrocracking in steel at building-up non-ferrous metals are explained. The technological procedures to avoid cracking are suggested

HUMAN MACHINE COOPERATIVE TELEROBOTICS

International Nuclear Information System (INIS)

William R. Hamel; Spivey Douglass; Sewoong Kim; Pamela Murray; Yang Shou; Sriram Sridharan; Ge Zhang; Scott Thayer; Rajiv V. Dubey

2003-01-01

The remediation and deactivation and decommissioning (D and D) of nuclear waste storage tanks using telerobotics is one of the most challenging tasks faced in environmental cleanup. Since a number of tanks have reached the end of their design life and some of them have leaks, the unstructured, uncertain and radioactive environment makes the work inefficient and expensive. However, the execution time of teleoperation consumes ten to hundred times that of direct contact with an associated loss in quality. Thus, a considerable effort has been expended to improve the quality and efficiency of telerobotics by incorporating into teleoperation and robotic control functions such as planning, trajectory generation, vision, and 3-D modeling. One example is the Robot Task Space Analyzer (RTSA), which has been developed at the Robotics and Electromechanical Systems Laboratory (REMSL) at the University of Tennessee in support of the D and D robotic work at the Oak Ridge National Laboratory and the National Energy Technology Laboratory. This system builds 3-D models of the area of interest in task space through automatic image processing and/or human interactive manual modeling. The RTSA generates a task plan file, which describes the execution of a task including manipulator and tooling motions. The high level controller of the manipulator interprets the task plan file and executes the task automatically. Thus, if the environment is not highly unstructured, a tooling task, which interacts with environment, will be executed in the autonomous mode. Therefore, the RTSA not only increases the system efficiency, but also improves the system reliability because the operator will act as backstop for safe operation after the 3-D models and task plan files are generated. However, unstructured conditions of environment and tasks necessitate that the telerobot operates in the teleoperation mode for successful execution of task. The inefficiency in the teleoperation mode led to the

HUMAN MACHINE COOPERATIVE TELEROBOTICS

Energy Technology Data Exchange (ETDEWEB)

William R. Hamel; Spivey Douglass; Sewoong Kim; Pamela Murray; Yang Shou; Sriram Sridharan; Ge Zhang; Scott Thayer; Rajiv V. Dubey

2003-06-30

The remediation and deactivation and decommissioning (D&D) of nuclear waste storage tanks using telerobotics is one of the most challenging tasks faced in environmental cleanup. Since a number of tanks have reached the end of their design life and some of them have leaks, the unstructured, uncertain and radioactive environment makes the work inefficient and expensive. However, the execution time of teleoperation consumes ten to hundred times that of direct contact with an associated loss in quality. Thus, a considerable effort has been expended to improve the quality and efficiency of telerobotics by incorporating into teleoperation and robotic control functions such as planning, trajectory generation, vision, and 3-D modeling. One example is the Robot Task Space Analyzer (RTSA), which has been developed at the Robotics and Electromechanical Systems Laboratory (REMSL) at the University of Tennessee in support of the D&D robotic work at the Oak Ridge National Laboratory and the National Energy Technology Laboratory. This system builds 3-D models of the area of interest in task space through automatic image processing and/or human interactive manual modeling. The RTSA generates a task plan file, which describes the execution of a task including manipulator and tooling motions. The high level controller of the manipulator interprets the task plan file and executes the task automatically. Thus, if the environment is not highly unstructured, a tooling task, which interacts with environment, will be executed in the autonomous mode. Therefore, the RTSA not only increases the system efficiency, but also improves the system reliability because the operator will act as backstop for safe operation after the 3-D models and task plan files are generated. However, unstructured conditions of environment and tasks necessitate that the telerobot operates in the teleoperation mode for successful execution of task. The inefficiency in the teleoperation mode led to the research

The microbiological quality of pasteurized milk sold by automatic vending machines.

Science.gov (United States)

Angelidis, A S; Tsiota, S; Pexara, A; Govaris, A

2016-06-01

The microbiological quality of pasteurized milk samples (n = 39) collected during 13 weekly intervals from three automatic vending machines (AVM) in Greece was investigated. Microbiological counts (total aerobic (TAC), total psychrotrophic (TPC), Enterobacteriaceae (EC), and psychrotrophic aerobic bacterial spore counts (PABSC)) were obtained at the time of sampling and at the end of shelf-life (3 days) after storage of the samples at 4 or 8°C. TAC were found to be below the 10(7 ) CFU ml(-1) limit of pasteurized milk spoilage both during sampling as well as when milk samples were stored at either storage temperature for 3 days. Enterobacteriaceae populations were below 1 CFU ml(-1) in 69·2% of the samples tested at the time of sampling, whereas the remaining samples contained low numbers, typically less than 10 CFU ml(-1) . All samples tested negative for the presence of Listeria monocytogenes. Analogous microbiological data were also obtained by sampling and testing prepackaged, retail samples of pasteurized milk from two dairy companies in Greece (n = 26). From a microbiological standpoint, the data indicate that the AVM milk samples meet the quality standards of pasteurized milk. However, the prepackaged, retail milk samples yielded better results in terms of TAC, TPC and EC, compared to the AVM samples at the end of shelf-life. Recently, Greek dairy farmers organized in cooperatives launched the sale of pasteurized milk via AVM and this study reports on the microbiological quality of this product. The data show that AVM milk is sold at proper refrigeration temperatures and meets the quality standards of pasteurized milk throughout the manufacturer's specified shelf-life. However, based on the microbiological indicators tested, the keeping quality of the tested prepackaged, retail samples of pasteurized milk at the end of shelf-life upon storage under suboptimal refrigeration temperature (8°C) was better. © 2016 The Society for Applied

SPARCHS: Symbiotic, Polymorphic, Automatic, Resilient, Clean-Slate, Host Security

Science.gov (United States)

2016-03-01

SPARCHS: SYMBIOTIC , POLYMORPHIC, AUTOMATIC, RESILIENT, CLEAN-SLATE, HOST SECURITY COLUMBIA UNIVERSITY MARCH 2016 FINAL... SYMBIOTIC , POLYMORPHIC, AUTOTOMIC, RESILIENT, CLEAN-SLATE, HOST SECURITY 5a. CONTRACT NUMBER N/A 5b. GRANT NUMBER FA8750-10-2-0253 5c. PROGRAM...17 4.2.3 SYMBIOTIC EMBEDDED MACHINES

Automatic extraction of drug indications from FDA drug labels.

Science.gov (United States)

Khare, Ritu; Wei, Chih-Hsuan; Lu, Zhiyong

2014-01-01

Extracting computable indications, i.e. drug-disease treatment relationships, from narrative drug resources is the key for building a gold standard drug indication repository. The two steps to the extraction problem are disease named-entity recognition (NER) to identify disease mentions from a free-text description and disease classification to distinguish indications from other disease mentions in the description. While there exist many tools for disease NER, disease classification is mostly achieved through human annotations. For example, we recently resorted to human annotations to prepare a corpus, LabeledIn, capturing structured indications from the drug labels submitted to FDA by pharmaceutical companies. In this study, we present an automatic end-to-end framework to extract structured and normalized indications from FDA drug labels. In addition to automatic disease NER, a key component of our framework is a machine learning method that is trained on the LabeledIn corpus to classify the NER-computed disease mentions as "indication vs. non-indication." Through experiments with 500 drug labels, our end-to-end system delivered 86.3% F1-measure in drug indication extraction, with 17% improvement over baseline. Further analysis shows that the indication classifier delivers a performance comparable to human experts and that the remaining errors are mostly due to disease NER (more than 50%). Given its performance, we conclude that our end-to-end approach has the potential to significantly reduce human annotation costs.

Model-based machine learning.

Science.gov (United States)

Bishop, Christopher M

2013-02-13

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

Scheduling algorithms for automatic control systems for technological processes

Science.gov (United States)

Chernigovskiy, A. S.; Tsarev, R. Yu; Kapulin, D. V.

2017-01-01

Wide use of automatic process control systems and the usage of high-performance systems containing a number of computers (processors) give opportunities for creation of high-quality and fast production that increases competitiveness of an enterprise. Exact and fast calculations, control computation, and processing of the big data arrays - all of this requires the high level of productivity and, at the same time, minimum time of data handling and result receiving. In order to reach the best time, it is necessary not only to use computing resources optimally, but also to design and develop the software so that time gain will be maximal. For this purpose task (jobs or operations), scheduling techniques for the multi-machine/multiprocessor systems are applied. Some of basic task scheduling methods for the multi-machine process control systems are considered in this paper, their advantages and disadvantages come to light, and also some usage considerations, in case of the software for automatic process control systems developing, are made.

Improvement of human operator vibroprotection system in the utility machine

Science.gov (United States)

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.

Present status and prospects for vending machines; Jido hanbaiki no genjo to tenbo

Energy Technology Data Exchange (ETDEWEB)

Hirano, Y. [Fuji Denki Reiki Co. Ltd., Tokyo (Japan); Ota, T.; Iwamoto, S. [Fuji Electric Co. Ltd., Tokyo (Japan)

1999-08-10

The number of automatic vending and service machines installed in Japan at the end of 1998 is about 5.5 million. The number of units per head exceeds that of USA, a pioneer in vending machines. They are now playing an indispensable role in daily living. Recent needs of vending machines have increased in social requests or problems, such as (1) strengthening of crime prevention, (2) consideration of global ecology and (3) prevention of minors from using alcoholic drinks vending machines. This paper describes the current state of marketing and future prospects for vending machines. (author)

Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera

Science.gov (United States)

Kachach, Redouane; Cañas, José María

2016-05-01

Using video in traffic monitoring is one of the most active research domains in the computer vision community. TrafficMonitor, a system that employs a hybrid approach for automatic vehicle tracking and classification on highways using a simple stationary calibrated camera, is presented. The proposed system consists of three modules: vehicle detection, vehicle tracking, and vehicle classification. Moving vehicles are detected by an enhanced Gaussian mixture model background estimation algorithm. The design includes a technique to resolve the occlusion problem by using a combination of two-dimensional proximity tracking algorithm and the Kanade-Lucas-Tomasi feature tracking algorithm. The last module classifies the shapes identified into five vehicle categories: motorcycle, car, van, bus, and truck by using three-dimensional templates and an algorithm based on histogram of oriented gradients and the support vector machine classifier. Several experiments have been performed using both real and simulated traffic in order to validate the system. The experiments were conducted on GRAM-RTM dataset and a proper real video dataset which is made publicly available as part of this work.

Learning as a Machine: Crossovers between Humans and Machines

Science.gov (United States)

Hildebrandt, Mireille

2017-01-01

This article is a revised version of the keynote presented at LAK '16 in Edinburgh. The article investigates some of the assumptions of learning analytics, notably those related to behaviourism. Building on the work of Ivan Pavlov, Herbert Simon, and James Gibson as ways of "learning as a machine," the article then develops two levels of…

Development of Semi-Automatic Lathe by using Intelligent Soft Computing Technique

Science.gov (United States)

Sakthi, S.; Niresh, J.; Vignesh, K.; Anand Raj, G.

2018-03-01

This paper discusses the enhancement of conventional lathe machine to semi-automated lathe machine by implementing a soft computing method. In the present scenario, lathe machine plays a vital role in the engineering division of manufacturing industry. While the manual lathe machines are economical, the accuracy and efficiency are not up to the mark. On the other hand, CNC machine provide the desired accuracy and efficiency, but requires a huge capital. In order to over come this situation, a semi-automated approach towards the conventional lathe machine is developed by employing stepper motors to the horizontal and vertical drive, that can be controlled by Arduino UNO -microcontroller. Based on the input parameters of the lathe operation the arduino coding is been generated and transferred to the UNO board. Thus upgrading from manual to semi-automatic lathe machines can significantly increase the accuracy and efficiency while, at the same time, keeping a check on investment cost and consequently provide a much needed escalation to the manufacturing industry.

Dynamic analysis of the BPX machine structure

International Nuclear Information System (INIS)

Dahlgen, F.; Citrolo, J.; Knutson, D.; Kalish, M.

1992-01-01

A preliminary analysis of the response of the BPX machine structure to a seismic input was performed. MSC/NASTRAN 5 , a general purpose XXX element computer code, has been used. The purpose of this paper is to assess the probable range of seismically induced stresses and deflections in the machine substructure which connects the machine to the test cell floor, with particular emphasis on the shear pins which will be used to attach the TF coil modules to the machine substructure (for a more detailed description of the shear pins and structure see ref. 4 in these proceedings). The model was developed with sufficient detail to be used subsequently to investigate the transient response to various dynamic loading conditions imposed on the structure by the PF, TF, and Vacuum Vessel, during normal and off-normal operations. The model does not include the mass and stiffness of the building or the building-soil interaction and as such can only be considered an interim assessment of the dynamic response of the machine to the S.S.E.(this is the Safe Shutdown Earthquake which is also the Design XXX Earthquake for all major structural components)

Grammar-based Automatic 3D Model Reconstruction from Terrestrial Laser Scanning Data

Science.gov (United States)

Yu, Q.; Helmholz, P.; Belton, D.; West, G.

2014-04-01

The automatic reconstruction of 3D buildings has been an important research topic during the last years. In this paper, a novel method is proposed to automatically reconstruct the 3D building models from segmented data based on pre-defined formal grammar and rules. Such segmented data can be extracted e.g. from terrestrial or mobile laser scanning devices. Two steps are considered in detail. The first step is to transform the segmented data into 3D shapes, for instance using the DXF (Drawing Exchange Format) format which is a CAD data file format used for data interchange between AutoCAD and other program. Second, we develop a formal grammar to describe the building model structure and integrate the pre-defined grammars into the reconstruction process. Depending on the different segmented data, the selected grammar and rules are applied to drive the reconstruction process in an automatic manner. Compared with other existing approaches, our proposed method allows the model reconstruction directly from 3D shapes and takes the whole building into account.

Automatic Energy Control And Monitoring System For Building

Directory of Open Access Journals (Sweden)

Hnin Nu Thaung

2015-08-01

Full Text Available The use of smart home technology in the home or building offers significant potential for energy savings. In this paper an energy management system based on wireless sensor networks. The proposed system is composed of two main components a wireless sensor network and monitoring terminal. Wireless sensors are used for sensing and transmitting electricity data and remote monitoring and control of appliances are provided to users through computer. The system enables users to save energy by monitoring and controlling appliances through terminal. This paper gives an overview of sensor technology and wireless networks in the development of an intelligent energy management system for buildings. This technology has ample potential to change the way live and work. ZigBee is used as a communication medium in building intelligent energy management system in this paper. From the prototype setup it is shown that ZigBee is a suitable technology to be adopted as the communication infrastructure in energy management system for buildings .The proposed system can be installed and maintained in residential environments with ease.

Enhancing Three-dimensional Movement Control System for Assemblies of Machine-Building Facilities

Science.gov (United States)

Kuzyakov, O. N.; Andreeva, M. A.

2018-01-01

Aspects of enhancing three-dimensional movement control system are given in the paper. Such system is to be used while controlling assemblies of machine-building facilities, which is a relevant issue. The base of the system known is three-dimensional movement control device with optical principle of action. The device consists of multi point light emitter and light receiver matrix. The processing of signals is enhanced to increase accuracy of measurements by switching from discrete to analog signals. Light receiver matrix is divided into four areas, and the output value of each light emitter in each matrix area is proportional to its luminance level. Thus, determing output electric signal value of each light emitter in corresponding area leads to determing position of multipoint light emitter and position of object tracked. This is done by using Case-based reasoning method, the precedent in which is described as integral signal value of each matrix area, coordinates of light receivers, which luminance level is high, and decision to be made in this situation.

Status Checking System of Home Appliances using machine learning

Directory of Open Access Journals (Sweden)

Yoon Chi-Yurl

2017-01-01

Full Text Available This paper describes status checking system of home appliances based on machine learning, which can be applied to existing household appliances without networking function. Designed status checking system consists of sensor modules, a wireless communication module, cloud server, android application and a machine learning algorithm. The developed system applied to washing machine analyses and judges the four-kinds of appliance’s status such as staying, washing, rinsing and spin-drying. The measurements of sensor and transmission of sensing data are operated on an Arduino board and the data are transmitted to cloud server in real time. The collected data are parsed by an Android application and injected into the machine learning algorithm for learning the status of the appliances. The machine learning algorithm compares the stored learning data with collected real-time data from the appliances. Our results are expected to contribute as a base technology to design an automatic control system based on machine learning technology for household appliances in real-time.

Machine learning techniques to examine large patient databases.

Science.gov (United States)

Meyfroidt, Geert; Güiza, Fabian; Ramon, Jan; Bruynooghe, Maurice

2009-03-01

Computerization in healthcare in general, and in the operating room (OR) and intensive care unit (ICU) in particular, is on the rise. This leads to large patient databases, with specific properties. Machine learning techniques are able to examine and to extract knowledge from large databases in an automatic way. Although the number of potential applications for these techniques in medicine is large, few medical doctors are familiar with their methodology, advantages and pitfalls. A general overview of machine learning techniques, with a more detailed discussion of some of these algorithms, is presented in this review.

Bootstrapped neural nets versus regression kriging in the digital mapping of pedological attributes: the automatic and time-consuming perspectives

Science.gov (United States)

Langella, Giuliano; Basile, Angelo; Bonfante, Antonello; Manna, Piero; Terribile, Fabio

2013-04-01

Digital soil mapping procedures are widespread used to build two-dimensional continuous maps about several pedological attributes. Our work addressed a regression kriging (RK) technique and a bootstrapped artificial neural network approach in order to evaluate and compare (i) the accuracy of prediction, (ii) the susceptibility of being included in automatic engines (e.g. to constitute web processing services), and (iii) the time cost needed for calibrating models and for making predictions. Regression kriging is maybe the most widely used geostatistical technique in the digital soil mapping literature. Here we tried to apply the EBLUP regression kriging as it is deemed to be the most statistically sound RK flavor by pedometricians. An unusual multi-parametric and nonlinear machine learning approach was accomplished, called BAGAP (Bootstrap aggregating Artificial neural networks with Genetic Algorithms and Principal component regression). BAGAP combines a selected set of weighted neural nets having specified characteristics to yield an ensemble response. The purpose of applying these two particular models is to ascertain whether and how much a more cumbersome machine learning method could be much promising in making more accurate/precise predictions. Being aware of the difficulty to handle objects based on EBLUP-RK as well as BAGAP when they are embedded in environmental applications, we explore the susceptibility of them in being wrapped within Web Processing Services. Two further kinds of aspects are faced for an exhaustive evaluation and comparison: automaticity and time of calculation with/without high performance computing leverage.

Learning scikit-learn machine learning in Python

CERN Document Server

Garreta, Raúl

2013-01-01

The book adopts a tutorial-based approach to introduce the user to Scikit-learn.If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.

Design And Construction Of Controller System And Data Acquisition Of Creep Test Machine

International Nuclear Information System (INIS)

Farokhi; Arhatari, B.D.; DT. SonyTj.. Histori; Sudarno; Haryanto, Mudi; Triyadi, Ari

2001-01-01

Design and construction of creep test machine have been done to get a higher performance of controller system and data acquisition of that machine. The Design and construction were made by adding an automatic power control circuit, an interface and computer program on PC. The interface circuit is made in a form of a card which applicable on the compatible ISA-IBM PC. The computer program is written in turbo C++. With that modification, the test results show reduction in measurement error from 80μm to 90μm. The modification gives also benefit semi-automatic of the creep test machine. It means decreasing on the operator dependence. Another advantages are to make easier on the result data reading, to show the result data on the real time or on file, to make easier on appearing of a test result curve and on the result data analysis

PREZICEREA PERFORMANŢELOR STUDENŢILOR FOLOSIND ÎNVĂŢAREA AUTOMATĂ (Machine Learning

Directory of Open Access Journals (Sweden)

Maria CRISTEI

2017-08-01

Full Text Available În prezent, învăţarea automată (machine learning ocupă un loc important în inteligenţa artificială, preocupându-se de dezvoltarea algoritmilor ce permit unui sistem informatic să înveţe date, reguli şi algoritmi. Învăţarea automată pre­supune în primul rând identificarea şi implementarea unei modalităţi cât mai eficiente de reprezentare a informaţiilor, în sensul facilitării căutării, reorganizării şi modificării acestora. În acest sens, în prezentul articol se descrie utilitatea şi aplicabilitatea tehnicilor de învăţare automată supervizată la problemele de predicţie şi implementarea acestora în dez­vol­tarea aplicaţiilor informatice. Aplicaţia elaborată este unică prin felul ei de executare a modelului machine learning de predicţie. Metodologia folosită în aplicaţia elaborată este mixtă, cuprinzând tehnologii complexe de ultimă oră: mediul de dezvoltare Jupyter Notebook, limbajul de programare Python împreună cu cele mai populare librării ale acestuia utilizate în machine learning, instrumente de dezvoltare a aplicaţiei web Flask.PREDICTING STUDENT PERFORMANCE USING MACHINE LEARNINGAt present, machine learning occupies an important place in artificial intelligence, and is concerned with the development of algorithms that allow an information system to learn data, rules, and algorithms. Automatic learning involves first and foremost the identification and implementation of a more efficient way of representing information in order to facilitate search, reorganization and change. In this respect, this article describes the utility and applicability of supervised auto­mated learning techniques to prediction problems and their implementation in the development of computer applications. The elaborate application is unique in its way of executing the Machine learning prediction model. The methodology used in the developed application is mixed, including state-of-the-art complex

Pattern-Driven Automatic Parallelization

Directory of Open Access Journals (Sweden)

Christoph W. Kessler

1996-01-01

Full Text Available This article describes a knowledge-based system for automatic parallelization of a wide class of sequential numerical codes operating on vectors and dense matrices, and for execution on distributed memory message-passing multiprocessors. Its main feature is a fast and powerful pattern recognition tool that locally identifies frequently occurring computations and programming concepts in the source code. This tool also works for dusty deck codes that have been "encrypted" by former machine-specific code transformations. Successful pattern recognition guides sophisticated code transformations including local algorithm replacement such that the parallelized code need not emerge from the sequential program structure by just parallelizing the loops. It allows access to an expert's knowledge on useful parallel algorithms, available machine-specific library routines, and powerful program transformations. The partially restored program semantics also supports local array alignment, distribution, and redistribution, and allows for faster and more exact prediction of the performance of the parallelized target code than is usually possible.

Sensitivity Analysis Based SVM Application on Automatic Incident Detection of Rural Road in China

Directory of Open Access Journals (Sweden)

Xingliang Liu

2018-01-01

Full Text Available Traditional automatic incident detection methods such as artificial neural networks, backpropagation neural network, and Markov chains are not suitable for addressing the incident detection problem of rural roads in China which have a relatively high accident rate and a low reaction speed caused by the character of small traffic volume. This study applies the support vector machine (SVM and parameter sensitivity analysis methods to build an accident detection algorithm in a rural road condition, based on real-time data collected in a field experiment. The sensitivity of four parameters (speed, front distance, vehicle group time interval, and free driving ratio is analyzed, and the data sets of two parameters with a significant sensitivity are chosen to form the traffic state feature vector. The SVM and k-fold cross validation (K-CV methods are used to build the accident detection algorithm, which shows an excellent performance in detection accuracy (98.15% of the training data set and 87.5% of the testing data set. Therefore, the problem of low incident reaction speed of rural roads in China could be solved to some extent.

Machine Translation from Text

Science.gov (United States)

Habash, Nizar; Olive, Joseph; Christianson, Caitlin; McCary, John

Machine translation (MT) from text, the topic of this chapter, is perhaps the heart of the GALE project. Beyond being a well defined application that stands on its own, MT from text is the link between the automatic speech recognition component and the distillation component. The focus of MT in GALE is on translating from Arabic or Chinese to English. The three languages represent a wide range of linguistic diversity and make the GALE MT task rather challenging and exciting.

Automatic tuning of free electron lasers

Energy Technology Data Exchange (ETDEWEB)

Agapov, Ilya; Zagorodnov, Igor [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Geloni, Gianluca [European XFEL, Schenefeld (Germany); Tomin, Sergey [European XFEL, Schenefeld (Germany); NRC Kurchatov Institute, Moscow (Russian Federation)

2017-04-07

Existing FEL facilities often suffer from stability issues: so electron orbit, transverse electron optics, electron bunch compression and other parameters have to be readjusted often to account for drifts in performance of various components. The tuning procedures typically employed in operation are often manual and lengthy. We have been developing a combination of model-free and model-based automatic tuning methods to meet the needs of present and upcoming XFEL facilities. Our approach has been implemented at FLASH to achieve automatic SASE tuning using empirical control of orbit, electron optics and bunch compression. In this paper we describe our approach to empirical tuning, the software which implements it, and the results of using it at FLASH.We also discuss the potential of using machine learning and model-based techniques in tuning methods.

Automatic tuning of free electron lasers

International Nuclear Information System (INIS)

Agapov, Ilya; Zagorodnov, Igor; Geloni, Gianluca; Tomin, Sergey

2017-01-01

Existing FEL facilities often suffer from stability issues: so electron orbit, transverse electron optics, electron bunch compression and other parameters have to be readjusted often to account for drifts in performance of various components. The tuning procedures typically employed in operation are often manual and lengthy. We have been developing a combination of model-free and model-based automatic tuning methods to meet the needs of present and upcoming XFEL facilities. Our approach has been implemented at FLASH to achieve automatic SASE tuning using empirical control of orbit, electron optics and bunch compression. In this paper we describe our approach to empirical tuning, the software which implements it, and the results of using it at FLASH.We also discuss the potential of using machine learning and model-based techniques in tuning methods.

The Employment Effects of High-Technology: A Case Study of Machine Vision. Research Report No. 86-19.

Science.gov (United States)

Chen, Kan; Stafford, Frank P.

A case study of machine vision was conducted to identify and analyze the employment effects of high technology in general. (Machine vision is the automatic acquisition and analysis of an image to obtain desired information for use in controlling an industrial activity, such as the visual sensor system that gives eyes to a robot.) Machine vision as…

Discrete data processing from a scintillation sensor for exposure automatic machine in medical radiography

International Nuclear Information System (INIS)

Karadimov, D.; Petukhov, N.; Dimitrova, S.; Vasilev, M.

1983-01-01

An exposure automatic machine with a thin plastic scintillator plate collimated by light guide with a photoconverter is described. Due to the physical processes nature in the scintillator accounted by the photomultiplier there is a possibility of a radiation analysis as well as of a digital processing of the results. Two alternative devices are shown. In the first of them the photomultiplier output signal is fed to a pulse-height selector at the output of which standard logical level pulses are obtained. These pulses are fed to a data register and then to a digital comparator where they are compared with a preset quantity selected depending on X-ray film sensitivity and foil combinations as well as on the desired film darkening. The ionizing radiation interruption is controlled by a switch unit. The detector spectral sensitivity correction is accomplished changing the photomultiplier supply voltage. In the second alternative a noise and ionizing radiation discriminator is used where a pulse-height selection according to the radiant energy is carried out. A digital comparator and a switching circuit control the ionizing radiation. By a second switching circuit the spectral distributed pulses from the discriminator are fed to a spectral analyser controlling dinamically the digital comparator compensating for the ionizing radiation spectral response influence. The second alternative advantage is that it allows for the radiation parameters control both in radiograph mode and X-ray examination mode. Due to the system fast-acting the device can be used to measure very short exposures as well as in serial examinations

A Review of Related Work on Machine Learning in Semiconductor Manufacturing and Assembly Lines

OpenAIRE

Stanisavljevic, Darko; Spitzer, Michael

2017-01-01

This paper deals with applications of machine learning algorithms in manufacturing. Machine learning can be defined as a field of computer science that gives computers the ability to learn without explicitly developing the needed algorithms. Manufacturing is the production of merchandise by manual labour, machines and tools. The focus of this paper is on automatic production lines. The areas of interest of this paper are semiconductor manufacturing and production on assembly lines. The purpos...

Automatic discovery of the communication network topology for building a supercomputer model

Science.gov (United States)

Sobolev, Sergey; Stefanov, Konstantin; Voevodin, Vadim

2016-10-01

The Research Computing Center of Lomonosov Moscow State University is developing the Octotron software suite for automatic monitoring and mitigation of emergency situations in supercomputers so as to maximize hardware reliability. The suite is based on a software model of the supercomputer. The model uses a graph to describe the computing system components and their interconnections. One of the most complex components of a supercomputer that needs to be included in the model is its communication network. This work describes the proposed approach for automatically discovering the Ethernet communication network topology in a supercomputer and its description in terms of the Octotron model. This suite automatically detects computing nodes and switches, collects information about them and identifies their interconnections. The application of this approach is demonstrated on the "Lomonosov" and "Lomonosov-2" supercomputers.

Future Smart Cooking Machine System Design

Directory of Open Access Journals (Sweden)

Dewi Agushinta R.

2013-11-01

Full Text Available There are many tools make human task get easier. Cooking has become a basic necessity for human beings, since food is one of basic human needs. Until now, the cooking equipment being used is still a hand tool. However everyone has slightly high activity. The presence of cooking tools that can do the cooking work by itself is now necessary. Future Smart Cooking Machine is an artificial intelligence machine that can do cooking work automatically. With this system design, the time is minimized and the ease of work is expected to be achieved. The development of this system is carried out with System Development Life Cycle (SDLC methods. Prototyping method used in this system is a throw-away prototyping approach. At the end of this research there will be produced a cooking machine system design including physical design engine and interface design.

Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies

OpenAIRE

Dadvar, M.; Trieschnigg, Rudolf Berend; de Jong, Franciska M.G.

2014-01-01

Cyberbullying is becoming a major concern in online environments with troubling consequences. However, most of the technical studies have focused on the detection of cyberbullying through identifying harassing comments rather than preventing the incidents by detecting the bullies. In this work we study the automatic detection of bully users on YouTube. We compare three types of automatic detection: an expert system, supervised machine learning models, and a hybrid type combining the two. All ...

Deep convolutional neural networks for building extraction from orthoimages and dense image matching point clouds

Science.gov (United States)

Maltezos, Evangelos; Doulamis, Nikolaos; Doulamis, Anastasios; Ioannidis, Charalabos

2017-10-01

Automatic extraction of buildings from remote sensing data is an attractive research topic, useful for several applications, such as cadastre and urban planning. This is mainly due to the inherent artifacts of the used data and the differences in viewpoint, surrounding environment, and complex shape and size of the buildings. This paper introduces an efficient deep learning framework based on convolutional neural networks (CNNs) toward building extraction from orthoimages. In contrast to conventional deep approaches in which the raw image data are fed as input to the deep neural network, in this paper the height information is exploited as an additional feature being derived from the application of a dense image matching algorithm. As test sites, several complex urban regions of various types of buildings, pixel resolutions and types of data are used, located in Vaihingen in Germany and in Perissa in Greece. Our method is evaluated using the rates of completeness, correctness, and quality and compared with conventional and other "shallow" learning paradigms such as support vector machines. Experimental results indicate that a combination of raw image data with height information, feeding as input to a deep CNN model, provides potentials in building detection in terms of robustness, flexibility, and efficiency.

Collaborative Systems – Finite State Machines

Directory of Open Access Journals (Sweden)

Ion IVAN

2011-01-01

Full Text Available In this paper the finite state machines are defined and formalized. There are presented the collaborative banking systems and their correspondence is done with finite state machines. It highlights the role of finite state machines in the complexity analysis and performs operations on very large virtual databases as finite state machines. It builds the state diagram and presents the commands and documents transition between the collaborative systems states. The paper analyzes the data sets from Collaborative Multicash Servicedesk application and performs a combined analysis in order to determine certain statistics. Indicators are obtained, such as the number of requests by category and the load degree of an agent in the collaborative system.

Machine throughput improvement achieved using innovative control technique

International Nuclear Information System (INIS)

Sharma, V.; Acharya, S.; Mittal, K.C.

2012-01-01

In any type of fully or semi automatic machine the control systems plays an important role. The control system on the one hand has to consider the human psychology, intelligence requirement for an operator, and attention needed from him. On the other hand the complexity of the control has also to be understood well before designing a control system that can be handled comfortably and safely by the operator. As far as the user experience/comfort is concerned the design of control system GUI is vital. Considering these two aspects related to the user of the machine it is evident that the control system design is very important because it is has to accommodate the human behaviour and skill sets required/available as well as the capability of the machine under the control of the control system. An intelligently designed control system can enhance the productivity of the machine. (author)

Spike Pattern Recognition for Automatic Collimation Alignment

CERN Document Server

Azzopardi, Gabriella; Salvachua Ferrando, Belen Maria; Mereghetti, Alessio; Redaelli, Stefano; CERN. Geneva. ATS Department

2017-01-01

The LHC makes use of a collimation system to protect its sensitive equipment by intercepting potentially dangerous beam halo particles. The appropriate collimator settings to protect the machine against beam losses relies on a very precise alignment of all the collimators with respect to the beam. The beam center at each collimator is then found by touching the beam halo using an alignment procedure. Until now, in order to determine whether a collimator is aligned with the beam or not, a user is required to follow the collimator’s BLM loss data and detect spikes. A machine learning (ML) model was trained in order to automatically recognize spikes when a collimator is aligned. The model was loosely integrated with the alignment implementation to determine the classification performance and reliability, without effecting the alignment process itself. The model was tested on a number of collimators during this MD and the machine learning was able to output the classifications in real-time.

Machine learning in updating predictive models of planning and scheduling transportation projects

Science.gov (United States)

1997-01-01

A method combining machine learning and regression analysis to automatically and intelligently update predictive models used in the Kansas Department of Transportations (KDOTs) internal management system is presented. The predictive models used...

A multi-label learning based kernel automatic recommendation method for support vector machine.

Science.gov (United States)

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

Fully automatic time-window selection using machine learning for global adjoint tomography

Science.gov (United States)

Chen, Y.; Hill, J.; Lei, W.; Lefebvre, M. P.; Bozdag, E.; Komatitsch, D.; Tromp, J.

2017-12-01

Selecting time windows from seismograms such that the synthetic measurements (from simulations) and measured observations are sufficiently close is indispensable in a global adjoint tomography framework. The increasing amount of seismic data collected everyday around the world demands "intelligent" algorithms for seismic window selection. While the traditional FLEXWIN algorithm can be "automatic" to some extent, it still requires both human input and human knowledge or experience, and thus is not deemed to be fully automatic. The goal of intelligent window selection is to automatically select windows based on a learnt engine that is built upon a huge number of existing windows generated through the adjoint tomography project. We have formulated the automatic window selection problem as a classification problem. All possible misfit calculation windows are classified as either usable or unusable. Given a large number of windows with a known selection mode (select or not select), we train a neural network to predict the selection mode of an arbitrary input window. Currently, the five features we extract from the windows are its cross-correlation value, cross-correlation time lag, amplitude ratio between observed and synthetic data, window length, and minimum STA/LTA value. More features can be included in the future. We use these features to characterize each window for training a multilayer perceptron neural network (MPNN). Training the MPNN is equivalent to solve a non-linear optimization problem. We use backward propagation to derive the gradient of the loss function with respect to the weighting matrices and bias vectors and use the mini-batch stochastic gradient method to iteratively optimize the MPNN. Numerical tests show that with a careful selection of the training data and a sufficient amount of training data, we are able to train a robust neural network that is capable of detecting the waveforms in an arbitrary earthquake data with negligible detection error

Automatic incrementalization of Prolog based static analyses

DEFF Research Database (Denmark)

Eichberg, Michael; Kahl, Matthias; Saha, Diptikalyan

2007-01-01

Modem development environments integrate various static analyses into the build process. Analyses that analyze the whole project whenever the project changes are impractical in this context. We present an approach to automatic incrementalization of analyses that are specified as tabled logic...... programs and evaluated using incremental tabled evaluation, a technique for efficiently updating memo tables in response to changes in facts and rules. The approach has been implemented and integrated into the Eclipse IDE. Our measurements show that this technique is effective for automatically...

Expansion mechanisms for indigenously developed horizontal honing machines (Paper No. 06)

International Nuclear Information System (INIS)

Murthy, G.S.K.; Devarajan, N.

1987-02-01

Coolant channel components for nuclear reactors require scratch free and smooth interior surfaces in addition to control on size. This calls for finish machining by honing process. At the time when these were required to be made, there were no manufacturers in India who were making honing machines especially of horizontal type. In order to meet this requirement, Central Workshops of Bhabha Atomic Research Centre developed and manufactured two horizontal honing machines which can handle tubes upto three metres in length. One of the machines has been so made to accommodate jobs upto six metres in length. Stone expansion mechanisms used in these machines were of automatic hydraulic type combined with a mechanical expansion device. Details of these mechanisms have been discussed in this paper. (author). 3 figs

Lynx: Automatic Elderly Behavior Prediction in Home Telecare

Directory of Open Access Journals (Sweden)

Jose Manuel Lopez-Guede

2015-01-01

Full Text Available This paper introduces Lynx, an intelligent system for personal safety at home environments, oriented to elderly people living independently, which encompasses a decision support machine for automatic home risk prevention, tested in real-life environments to respond to real time situations. The automatic system described in this paper prevents such risks by an advanced analytic methods supported by an expert knowledge system. It is minimally intrusive, using plug-and-play sensors and machine learning algorithms to learn the elder’s daily activity taking into account even his health records. If the system detects that something unusual happens (in a wide sense or if something is wrong relative to the user’s health habits or medical recommendations, it sends at real-time alarm to the family, care center, or medical agents, without human intervention. The system feeds on information from sensors deployed in the home and knowledge of subject physical activities, which can be collected by mobile applications and enriched by personalized health information from clinical reports encoded in the system. The system usability and reliability have been tested in real-life conditions, with an accuracy larger than 81%.

Lynx: Automatic Elderly Behavior Prediction in Home Telecare

Science.gov (United States)

Lopez-Guede, Jose Manuel; Moreno-Fernandez-de-Leceta, Aitor; Martinez-Garcia, Alexeiw; Graña, Manuel

2015-01-01

This paper introduces Lynx, an intelligent system for personal safety at home environments, oriented to elderly people living independently, which encompasses a decision support machine for automatic home risk prevention, tested in real-life environments to respond to real time situations. The automatic system described in this paper prevents such risks by an advanced analytic methods supported by an expert knowledge system. It is minimally intrusive, using plug-and-play sensors and machine learning algorithms to learn the elder's daily activity taking into account even his health records. If the system detects that something unusual happens (in a wide sense) or if something is wrong relative to the user's health habits or medical recommendations, it sends at real-time alarm to the family, care center, or medical agents, without human intervention. The system feeds on information from sensors deployed in the home and knowledge of subject physical activities, which can be collected by mobile applications and enriched by personalized health information from clinical reports encoded in the system. The system usability and reliability have been tested in real-life conditions, with an accuracy larger than 81%. PMID:26783514

RCrane: semi-automated RNA model building.

Science.gov (United States)

Keating, Kevin S; Pyle, Anna Marie

2012-08-01

RNA crystals typically diffract to much lower resolutions than protein crystals. This low-resolution diffraction results in unclear density maps, which cause considerable difficulties during the model-building process. These difficulties are exacerbated by the lack of computational tools for RNA modeling. Here, RCrane, a tool for the partially automated building of RNA into electron-density maps of low or intermediate resolution, is presented. This tool works within Coot, a common program for macromolecular model building. RCrane helps crystallographers to place phosphates and bases into electron density and then automatically predicts and builds the detailed all-atom structure of the traced nucleotides. RCrane then allows the crystallographer to review the newly built structure and select alternative backbone conformations where desired. This tool can also be used to automatically correct the backbone structure of previously built nucleotides. These automated corrections can fix incorrect sugar puckers, steric clashes and other structural problems.

SILOS, Reused Machine-Buildings: A Proposal for Its Transformation

Science.gov (United States)

Garrido-Cifuentes, María; Santiago-Zaragoza, Juan Manuel; Lafuente-Bolívar, Francisco Javier

2017-10-01

Second half of the 20th century: The Spanish rural landscape changes with the imposing profile of Silos. In the urban edge, next to the railroad, they are risen competing in height with church steeples. Today they are inseparable elements of the image of many peoples of cereal geography of any region of Spain. They are consequence of the autarkic economy of that time (storage of grain and state control over distribution and price) Silos were the answer given by the engineering efficiency and economy of means: A type of store operated by screw-conveyor moved with electricity, transport grain to fill high slender square plant cells. Hydraulic push offset between cells that form a matrix-walled plates constructed of thin sheets of brick or concrete block, armed only horizontally. And huge vertical loads carried by thick foundation reinforced concrete pillars. The political regime change and the energy crisis of the late seventies caused these magnificent building-machines stopped being used. Its radical specialization led them to death. After years of neglect and transfer of ownership between administrations, a consciousness has emerged in Spain (National Plan of Industrial Heritage, 2000) that has prevented its demolition, and recognize the values they bring to the landscape and structural-construction of its own, as beautiful works of Engineering which are worth cataloguing / protection. Hence this proposal tries to intervene these giants through new uses, transforming, allowing their conservation. This process investigates various structural types and implements strict standards of construction resolved with efficient construction solutions. The result is returned to society by publishing their work, while stressing heritage values, demonstrates the new strength of these local systems.

New methods for the voltage drop desensitization of frequency converters for asynchronous machines; De nouvelles methodes de desensibilisation aux creux de tension des convertisseurs de frequence pour machines asynchrones

Energy Technology Data Exchange (ETDEWEB)

David, A.

1995-12-31

In order to reduce the vulnerability of electronic speed variators to load transients and outages, and more especially to short ones (inferior to one second), which represent 95 pc of voltage drop and failures, Electricite de France (EDF) has developed (and patented) several insensitivity techniques: voltage drop compensation, free-wheel machine speed identification, automatic adjustment and synchronization of converter and machine during the drop. These techniques have been validated through experiments

Machine learning for evolution strategies

CERN Document Server

Kramer, Oliver

2016-01-01

This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.

Automatic incrementalization of Prolog based static analyses

DEFF Research Database (Denmark)

Eichberg, Michael; Kahl, Matthias; Saha, Diptikalyan

2007-01-01

Modem development environments integrate various static analyses into the build process. Analyses that analyze the whole project whenever the project changes are impractical in this context. We present an approach to automatic incrementalization of analyses that are specified as tabled logic prog...

Design of an automatic production monitoring system on job shop manufacturing

Science.gov (United States)

Prasetyo, Hoedi; Sugiarto, Yohanes; Rosyidi, Cucuk Nur

2018-02-01

Every production process requires monitoring system, so the desired efficiency and productivity can be monitored at any time. This system is also needed in the job shop type of manufacturing which is mainly influenced by the manufacturing lead time. Processing time is one of the factors that affect the manufacturing lead time. In a conventional company, the recording of processing time is done manually by the operator on a sheet of paper. This method is prone to errors. This paper aims to overcome this problem by creating a system which is able to record and monitor the processing time automatically. The solution is realized by utilizing electric current sensor, barcode, RFID, wireless network and windows-based application. An automatic monitoring device is attached to the production machine. It is equipped with a touch screen-LCD so that the operator can use it easily. Operator identity is recorded through RFID which is embedded in his ID card. The workpiece data are collected from the database by scanning the barcode listed on its monitoring sheet. A sensor is mounted on the machine to measure the actual machining time. The system's outputs are actual processing time and machine's capacity information. This system is connected wirelessly to a workshop planning application belongs to the firm. Test results indicated that all functions of the system can run properly. This system successfully enables supervisors, PPIC or higher level management staffs to monitor the processing time quickly with a better accuracy.

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing

OpenAIRE

Li, Chunhua; Zhao, Pengpeng; Sheng, Victor S.; Xian, Xuefeng; Wu, Jian; Cui, Zhiming

2017-01-01

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality i...

Towards automatic lithological classification from remote sensing data using support vector machines

Science.gov (United States)

Yu, Le; Porwal, Alok; Holden, Eun-Jung; Dentith, Michael

2010-05-01

Remote sensing data can be effectively used as a mean to build geological knowledge for poorly mapped terrains. Spectral remote sensing data from space- and air-borne sensors have been widely used to geological mapping, especially in areas of high outcrop density in arid regions. However, spectral remote sensing information by itself cannot be efficiently used for a comprehensive lithological classification of an area due to (1) diagnostic spectral response of a rock within an image pixel is conditioned by several factors including the atmospheric effects, spectral and spatial resolution of the image, sub-pixel level heterogeneity in chemical and mineralogical composition of the rock, presence of soil and vegetation cover; (2) only surface information and is therefore highly sensitive to the noise due to weathering, soil cover, and vegetation. Consequently, for efficient lithological classification, spectral remote sensing data needs to be supplemented with other remote sensing datasets that provide geomorphological and subsurface geological information, such as digital topographic model (DEM) and aeromagnetic data. Each of the datasets contain significant information about geology that, in conjunction, can potentially be used for automated lithological classification using supervised machine learning algorithms. In this study, support vector machine (SVM), which is a kernel-based supervised learning method, was applied to automated lithological classification of a study area in northwestern India using remote sensing data, namely, ASTER, DEM and aeromagnetic data. Several digital image processing techniques were used to produce derivative datasets that contained enhanced information relevant to lithological discrimination. A series of SVMs (trained using k-folder cross-validation with grid search) were tested using various combinations of input datasets selected from among 50 datasets including the original 14 ASTER bands and 36 derivative datasets (including 14

Safety Aspects of EPS-3000 Electron Beam Machine

International Nuclear Information System (INIS)

Siti Aiasah Hashim; Shari Jahar; Ayub Muhamad; Sarada Idris

2011-01-01

The EPS-3000 electron beam machine was installed and commission in 1991 at the Alurtron Electron Beam Irradiation Centre. It is utilized as a tool to enhance finished products through electron beam irradiation. The machine and its auxiliary systems were built with highest safety in mind due to the possible dangers that it can cause during the irradiation activities. Automatic stops may be activated via various interlocks to protect the integrity of the machine. This type of interlocks are controlled by the set upper and lower limits, mostly related to the machine high voltage (and beam) generation and cooling systems. Radiation safety is also taken care of by provision of shielding and area monitoring. Other potential hazards include ozone poisoning and electromagnetic field (EMF) could be generated by the high voltage. This paper describes the safety and security systems installed within the facility as measures to protect the workers and general public from radiation and other physical threats. (author)

Automatic Encoding and Language Detection in the GSDL

Directory of Open Access Journals (Sweden)

Otakar Pinkas

2014-10-01

Full Text Available Automatic detection of encoding and language of the text is part of the Greenstone Digital Library Software (GSDL for building and distributing digital collections. It is developed by the University of Waikato (New Zealand in cooperation with UNESCO. The automatic encoding and language detection in Slavic languages is difficult and it sometimes fails. The aim is to detect cases of failure. The automatic detection in the GSDL is based on n-grams method. The most frequent n-grams for Czech are presented. The whole process of automatic detection in the GSDL is described. The input documents to test collections are plain texts encoded in ISO-8859-1, ISO-8859-2 and Windows-1250. We manually evaluated the quality of automatic detection. To the causes of errors belong the improper language model predominance and the incorrect switch to Windows-1250. We carried out further tests on documents that were more complex.

MO-G-BRE-04: Automatic Verification of Daily Treatment Deliveries and Generation of Daily Treatment Reports for a MR Image-Guided Treatment Machine

International Nuclear Information System (INIS)

Yang, D; Li, X; Li, H; Wooten, H; Green, O; Rodriguez, V; Mutic, S

2014-01-01

Purpose: Two aims of this work were to develop a method to automatically verify treatment delivery accuracy immediately after patient treatment and to develop a comprehensive daily treatment report to provide all required information for daily MR-IGRT review. Methods: After systematically analyzing the requirements for treatment delivery verification and understanding the available information from a novel MR-IGRT treatment machine, we designed a method to use 1) treatment plan files, 2) delivery log files, and 3) dosimetric calibration information to verify the accuracy and completeness of daily treatment deliveries. The method verifies the correctness of delivered treatment plans and beams, beam segments, and for each segment, the beam-on time and MLC leaf positions. Composite primary fluence maps are calculated from the MLC leaf positions and the beam-on time. Error statistics are calculated on the fluence difference maps between the plan and the delivery. We also designed the daily treatment delivery report by including all required information for MR-IGRT and physics weekly review - the plan and treatment fraction information, dose verification information, daily patient setup screen captures, and the treatment delivery verification results. Results: The parameters in the log files (e.g. MLC positions) were independently verified and deemed accurate and trustable. A computer program was developed to implement the automatic delivery verification and daily report generation. The program was tested and clinically commissioned with sufficient IMRT and 3D treatment delivery data. The final version has been integrated into a commercial MR-IGRT treatment delivery system. Conclusion: A method was developed to automatically verify MR-IGRT treatment deliveries and generate daily treatment reports. Already in clinical use since December 2013, the system is able to facilitate delivery error detection, and expedite physician daily IGRT review and physicist weekly chart

Behind the machines

CERN Multimedia

Laëtitia Pedroso

2010-01-01

One of the first things we think about when someone mentions physics is the machines. But behind the machines, there are the men and women who design, build and operate them. In an exhibition at the Thinktank planetarium’s art gallery in Birmingham (UK), Claudia Marcelloni and her husband Neal Hartman—she is a photographer and Outreach Officer for ATLAS, while he is an engineer working on the ATLAS pixel detector—explore the human side of scientists.   The exhibition at the Thinktank Planetarium art gallery, Birmingham (UK). It all began two years ago with the publication of Exploring the mystery of matter, a book about ATLAS. “A Norwegian physicist friend, Heidi Sandaker, saw my photographs and suggested that I display them in a museum. I thought this was an interesting idea, except that the photos consisted entirely of depictions of machinery, with human beings completely absent. For me, showing the people who are behind the machines and the fascination ...

The Bearingless Electrical Machine

Science.gov (United States)

Bichsel, J.

1992-01-01

Electromagnetic bearings allow the suspension of solids. For rotary applications, the most important physical effect is the force of a magnetic circuit to a high permeable armature, called the MAXWELL force. Contrary to the commonly used MAXWELL bearings, the bearingless electrical machine will take advantage of the reaction force of a conductor carrying a current in a magnetic field. This kind of force, called Lorentz force, generates the torque in direct current, asynchronous and synchronous machines. The magnetic field, which already exists in electrical machines and helps to build up the torque, can also be used for the suspension of the rotor. Besides the normal winding of the stator, a special winding was added, which generates forces for levitation. So a radial bearing, which is integrated directly in the active part of the machine, and the motor use the laminated core simultaneously. The winding was constructed for the levitating forces in a special way so that commercially available standard ac inverters for drives can be used. Besides wholly magnetic suspended machines, there is a wide range of applications for normal drives with ball bearings. Resonances of the rotor, especially critical speeds, can be damped actively.

Automatic Motion Generation for Robotic Milling Optimizing Stiffness with Sample-Based Planning

Directory of Open Access Journals (Sweden)

Julian Ricardo Diaz Posada

2017-01-01

Full Text Available Optimal and intuitive robotic machining is still a challenge. One of the main reasons for this is the lack of robot stiffness, which is also dependent on the robot positioning in the Cartesian space. To make up for this deficiency and with the aim of increasing robot machining accuracy, this contribution describes a solution approach for optimizing the stiffness over a desired milling path using the free degree of freedom of the machining process. The optimal motion is computed based on the semantic and mathematical interpretation of the manufacturing process modeled on its components: product, process and resource; and by configuring automatically a sample-based motion problem and the transition-based rapid-random tree algorithm for computing an optimal motion. The approach is simulated on a CAM software for a machining path revealing its functionality and outlining future potentials for the optimal motion generation for robotic machining processes.

Generalized synthetic aperture radar automatic target recognition by convolutional neural network with joint use of two-dimensional principal component analysis and support vector machine

Science.gov (United States)

Zheng, Ce; Jiang, Xue; Liu, Xingzhao

2017-10-01

Convolutional neural network (CNN), as a vital part of the deep learning research field, has shown powerful potential for automatic target recognition (ATR) of synthetic aperture radar (SAR). However, the high complexity caused by the deep structure of CNN makes it difficult to generalize. An improved form of CNN with higher generalization capability and less probability of overfitting, which further improves the efficiency and robustness of the SAR ATR system, is proposed. The convolution layers of CNN are combined with a two-dimensional principal component analysis algorithm. Correspondingly, the kernel support vector machine is utilized as the classifier layer instead of the multilayer perceptron. The verification experiments are implemented using the moving and stationary target acquisition and recognition database, and the results validate the efficiency of the proposed method.

Virtual Things for Machine Learning Applications

OpenAIRE

Bovet , Gérôme; Ridi , Antonio; Hennebert , Jean

2014-01-01

International audience; Internet-of-Things (IoT) devices, especially sensors are pro-ducing large quantities of data that can be used for gather-ing knowledge. In this field, machine learning technologies are increasingly used to build versatile data-driven models. In this paper, we present a novel architecture able to ex-ecute machine learning algorithms within the sensor net-work, presenting advantages in terms of privacy and data transfer efficiency. We first argument that some classes of ...

Semi-automatic fluoroscope

International Nuclear Information System (INIS)

Tarpley, M.W.

1976-10-01

Extruded aluminum-clad uranium-aluminum alloy fuel tubes must pass many quality control tests before irradiation in Savannah River Plant nuclear reactors. Nondestructive test equipment has been built to automatically detect high and low density areas in the fuel tubes using x-ray absorption techniques with a video analysis system. The equipment detects areas as small as 0.060-in. dia with 2 percent penetrameter sensitivity. These areas are graded as to size and density by an operator using electronic gages. Video image enhancement techniques permit inspection of ribbed cylindrical tubes and make possible the testing of areas under the ribs. Operation of the testing machine, the special low light level television camera, and analysis and enhancement techniques are discussed

Machinability of IPS Empress 2 framework ceramic.

Science.gov (United States)

Schmidt, C; Weigl, P

2000-01-01

Using ceramic materials for an automatic production of ceramic dentures by CAD/CAM is a challenge, because many technological, medical, and optical demands must be considered. The IPS Empress 2 framework ceramic meets most of them. This study shows the possibilities for machining this ceramic with economical parameters. The long life-time requirement for ceramic dentures requires a ductile machined surface to avoid the well-known subsurface damages of brittle materials caused by machining. Slow and rapid damage propagation begins at break outs and cracks, and limits life-time significantly. Therefore, ductile machined surfaces are an important demand for machine dental ceramics. The machining tests were performed with various parameters such as tool grain size and feed speed. Denture ceramics were machined by jig grinding on a 5-axis CNC milling machine (Maho HGF 500) with a high-speed spindle up to 120,000 rpm. The results of the wear test indicate low tool wear. With one tool, you can machine eight occlusal surfaces including roughing and finishing. One occlusal surface takes about 60 min machining time. Recommended parameters for roughing are middle diamond grain size (D107), cutting speed v(c) = 4.7 m/s, feed speed v(ft) = 1000 mm/min, depth of cut a(e) = 0.06 mm, width of contact a(p) = 0.8 mm, and for finishing ultra fine diamond grain size (D46), cutting speed v(c) = 4.7 m/s, feed speed v(ft) = 100 mm/min, depth of cut a(e) = 0.02 mm, width of contact a(p) = 0.8 mm. The results of the machining tests give a reference for using IPS Empress(R) 2 framework ceramic in CAD/CAM systems. Copyright 2000 John Wiley & Sons, Inc.

Automatic Quality Inspection of Percussion Cap Mass Production by Means of 3D Machine Vision and Machine Learning Techniques

Science.gov (United States)

Tellaeche, A.; Arana, R.; Ibarguren, A.; Martínez-Otzeta, J. M.

The exhaustive quality control is becoming very important in the world's globalized market. One of these examples where quality control becomes critical is the percussion cap mass production. These elements must achieve a minimum tolerance deviation in their fabrication. This paper outlines a machine vision development using a 3D camera for the inspection of the whole production of percussion caps. This system presents multiple problems, such as metallic reflections in the percussion caps, high speed movement of the system and mechanical errors and irregularities in percussion cap placement. Due to these problems, it is impossible to solve the problem by traditional image processing methods, and hence, machine learning algorithms have been tested to provide a feasible classification of the possible errors present in the percussion caps.

Development and evaluation of new semi-automatic TLD reader software

International Nuclear Information System (INIS)

Pathan, M.S.; Pradhan, S.M.; Palani Selvam, T.; Datta, D.

2018-01-01

Nowadays, all technology advancement is primarily focused on creating the user-friendly environment while operating any machine, also minimizing the human errors by automation of procedures. In the present study development and evaluation of new software for semi-automatic TLD badge reader (TLDBR-7B) is presented. The software provides an interactive interface and is compatible with latest windows OS as well as USB mode of data communication. Important new features of the software are automatic glow curve analysis for identifying any abnormality, event log register, user defined limits on TL count and time of temperature stabilization for readout interruption and auto reading resumption options

A Fully Automatic Fresh Apple Juicer: Peeling, Coring, Slicing and Juicing

Directory of Open Access Journals (Sweden)

Hu Fuwen

2017-01-01

Full Text Available With the fresh apple juice as an example, a fully automatic and intelligent juicer prototype was built via the integrated application of servo positioning modules, human-machine interface, image vision sensor system and 3D printing. All steps including peeling, coring, slicing and juicing were achieved automatically. The challenging technical problems about the identification and orientation of apple core, and adaptive peeling were settled creatively. The trial operation results illustrated that the fresh apple juice can be produced without manual intervention and the system has potential application in the crowded sites, such as mall, school, restaurant and hospital.

Evaluation of cleaning and disinfection performance of automatic washer disinfectors machines in programs presenting different cycle times and temperatures.

Science.gov (United States)

Bergo, Maria do Carmo Noronha Cominato

2006-01-01

Thermal washer-disinfectors represent a technology that brought about great advantages such as, establishment of protocols, standard operating procedures, reduction in occupational risk of a biological and environmental nature. The efficacy of the cleaning and disinfection obtained by automatic washer disinfectors machines in running programs with different times and temperatures determined by the different official agencies was validated according to recommendations from ISO Standards 15883-1/1999 and HTM2030 (NHS Estates, 1997) for the determining of the Minimum Lethality and DAL both theoretically and through the use with thermocouples. In order to determine the cleaning efficacy, the Soil Test, Biotrace Pro-tect and the Protein Test Kit were used. The procedure to verify the CFU count of viable microorganisms was performed before and after the thermal disinfection. This article shows that the results are in compliance with the ISO and HTM Standards. The validation steps confirmed the high efficacy level of the Medical Washer-Disinfectors. This protocol enabled the evaluation of the procedure based on evidence supported by scientific research, aiming at the support of the Supply Center multi-professional personnel with information and the possibility of developing further research.

Towards Automatic Semantic Labelling of 3D City Models

Science.gov (United States)

Rook, M.; Biljecki, F.; Diakité, A. A.

2016-10-01

The lack of semantic information in many 3D city models is a considerable limiting factor in their use, as a lot of applications rely on semantics. Such information is not always available, since it is not collected at all times, it might be lost due to data transformation, or its lack may be caused by non-interoperability in data integration from other sources. This research is a first step in creating an automatic workflow that semantically labels plain 3D city model represented by a soup of polygons, with semantic and thematic information, as defined in the CityGML standard. The first step involves the reconstruction of the topology, which is used in a region growing algorithm that clusters upward facing adjacent triangles. Heuristic rules, embedded in a decision tree, are used to compute a likeliness score for these regions that either represent the ground (terrain) or a RoofSurface. Regions with a high likeliness score, to one of the two classes, are used to create a decision space, which is used in a support vector machine (SVM). Next, topological relations are utilised to select seeds that function as a start in a region growing algorithm, to create regions of triangles of other semantic classes. The topological relationships of the regions are used in the aggregation of the thematic building features. Finally, the level of detail is detected to generate the correct output in CityGML. The results show an accuracy between 85 % and 99 % in the automatic semantic labelling on four different test datasets. The paper is concluded by indicating problems and difficulties implying the next steps in the research.

Automatized welding equipment for manufacturing steel cells for special buildings

International Nuclear Information System (INIS)

Weikert, F.; Winter, K.P.

1986-01-01

In GDR's nuclear power plant construction, reinforced concrete wall cells are used to construct pressure and full pressure containments for WWER-440 and WWER-1000 reactors, respectively. Welding processes for the prefabrication of steel cells as reinforcement have been automatized in order to increase both labor productivity and quality assurance. 11 figs

Genesis machines

CERN Document Server

Amos, Martyn

2014-01-01

Silicon chips are out. Today's scientists are using real, wet, squishy, living biology to build the next generation of computers. Cells, gels and DNA strands are the 'wetware' of the twenty-first century. Much smaller and more intelligent, these organic computers open up revolutionary possibilities. Tracing the history of computing and revealing a brave new world to come, Genesis Machines describes how this new technology will change the way we think not just about computers - but about life itself.

Issues of human resources management in the enterprises of the machine-building complex of the Tyumen region

Directory of Open Access Journals (Sweden)

Lez’er Victoria

2018-01-01

Full Text Available This article, the authors have considered theoretical-methodological and practical aspects of human resource management in the process of restructuring enterprises of the machine-building complex of the Tyumen region. Based on the study of the evolution of theories concerning the human factor in the economy, the systematization and generalization of the provisions contained therein, the system of categories in the field of human resource management is substantiated. The basic principles of such management have been identified and supplemented, the tasks facing the enterprise management have been clarified, new tools for human resource management have been proposed, to ensure the completeness, continuity and validity of the decisions made in the field of human resources management in the restructuring of industrial enterprises.

Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data.

Science.gov (United States)

Pan, Leyun; Cheng, Caixia; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia

2017-05-07

A variety of compartment models are used for the quantitative analysis of dynamic positron emission tomography (PET) data. Traditionally, these models use an iterative fitting (IF) method to find the least squares between the measured and calculated values over time, which may encounter some problems such as the overfitting of model parameters and a lack of reproducibility, especially when handling noisy data or error data. In this paper, a machine learning (ML) based kinetic modeling method is introduced, which can fully utilize a historical reference database to build a moderate kinetic model directly dealing with noisy data but not trying to smooth the noise in the image. Also, due to the database, the presented method is capable of automatically adjusting the models using a multi-thread grid parameter searching technique. Furthermore, a candidate competition concept is proposed to combine the advantages of the ML and IF modeling methods, which could find a balance between fitting to historical data and to the unseen target curve. The machine learning based method provides a robust and reproducible solution that is user-independent for VOI-based and pixel-wise quantitative analysis of dynamic PET data.

Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data

Science.gov (United States)

Pan, Leyun; Cheng, Caixia; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia

2017-05-01

A variety of compartment models are used for the quantitative analysis of dynamic positron emission tomography (PET) data. Traditionally, these models use an iterative fitting (IF) method to find the least squares between the measured and calculated values over time, which may encounter some problems such as the overfitting of model parameters and a lack of reproducibility, especially when handling noisy data or error data. In this paper, a machine learning (ML) based kinetic modeling method is introduced, which can fully utilize a historical reference database to build a moderate kinetic model directly dealing with noisy data but not trying to smooth the noise in the image. Also, due to the database, the presented method is capable of automatically adjusting the models using a multi-thread grid parameter searching technique. Furthermore, a candidate competition concept is proposed to combine the advantages of the ML and IF modeling methods, which could find a balance between fitting to historical data and to the unseen target curve. The machine learning based method provides a robust and reproducible solution that is user-independent for VOI-based and pixel-wise quantitative analysis of dynamic PET data.

Fusion of Linear and Mel Frequency Cepstral Coefficients for Automatic Classification of Reptiles

Directory of Open Access Journals (Sweden)

Juan J. Noda

2017-02-01

Full Text Available Bioacoustic research of reptile calls and vocalizations has been limited due to the general consideration that they are voiceless. However, several species of geckos, turtles, and crocodiles are abletoproducesimpleandevencomplexvocalizationswhicharespecies-specific.Thisworkpresents a novel approach for the automatic taxonomic identification of reptiles through their bioacoustics by applying pattern recognition techniques. The sound signals are automatically segmented, extracting each call from the background noise. Then, their calls are parametrized using Linear and Mel Frequency Cepstral Coefficients (LFCC and MFCC to serve as features in the classification stage. In this study, 27 reptile species have been successfully identified using two machine learning algorithms: K-Nearest Neighbors (kNN and Support Vector Machine (SVM. Experimental results show an average classification accuracy of 97.78% and 98.51%, respectively.

EXPORT ACTIVITY OF MASHINE-BUILDING ENTERPRISES WITHIN TRANSFORMATION OF UKRAINIAN FOREIGN TRADE

OpenAIRE

Yarosh-Dmytrenko, Liudmila Oleksiivna

2017-01-01

Urgency of the research. Transformational processes in Ukrainian economy and its integration to European and world economic areas set new challenges to domestic machine building. Target setting. In modern conditions, national machine building suffers from huge economic crisis and loses its competitive fight in domestic and, what is sufficiently important for the economy of Ukraine, in foreign markets. Actual scientific researches and issues analysis. The operation of Ukrainian machine buildin...

Methods of control the machining process

Directory of Open Access Journals (Sweden)

Yu.V. Petrakov

2017-12-01

Full Text Available Presents control methods, differentiated by the time of receipt of information used: a priori, a posteriori and current. When used a priori information to determine the mode of cutting is carried out by simulation the process of cutting allowance, where the shape of the workpiece and the details are presented in the form of wireframes. The office for current information provides for a system of adaptive control and modernization of CNC machine, where in the input of the unit shall be computed by using established optimization software. For the control by a posteriori information of the proposed method of correction of shape-generating trajectory in the second pass measurement surface of the workpiece formed by the first pass. Developed programs that automatically design the adjusted file for machining.

Automated Deployment of Advanced Controls and Analytics in Buildings

Science.gov (United States)

Pritoni, Marco

Buildings use 40% of primary energy in the US. Recent studies show that developing energy analytics and enhancing control strategies can significantly improve their energy performance. However, the deployment of advanced control software applications has been mostly limited to academic studies. Larger-scale implementations are prevented by the significant engineering time and customization required, due to significant differences among buildings. This study demonstrates how physics-inspired data-driven models can be used to develop portable analytics and control applications for buildings. Specifically, I demonstrate application of these models in all phases of the deployment of advanced controls and analytics in buildings: in the first phase, "Site Preparation and Interface with Legacy Systems" I used models to discover or map relationships among building components, automatically gathering metadata (information about data points) necessary to run the applications. During the second phase: "Application Deployment and Commissioning", models automatically learn system parameters, used for advanced controls and analytics. In the third phase: "Continuous Monitoring and Verification" I utilized models to automatically measure the energy performance of a building that has implemented advanced control strategies. In the conclusions, I discuss future challenges and suggest potential strategies for these innovative control systems to be widely deployed in the market. This dissertation provides useful new tools in terms of procedures, algorithms, and models to facilitate the automation of deployment of advanced controls and analytics and accelerate their wide adoption in buildings.

Commercial effectiveness assessment of implementation the energy efficiency raising of the building project due to introduction of automatic heat consumption control

Directory of Open Access Journals (Sweden)

Zvonareva Yu.N.

2017-01-01

Full Text Available Introduction of the automated metering stations and regulation (AUU located directly in the heated building besides creation of comfortable conditions indoors leads to decrease in consumption of thermal energy. The annual expected effect of realization of the offered actions (installation of metering stations and automatic control can make up to 22% consumed and that isn–t less important, the paid thermal energy. In general, efficiency of implementation of the project on introduction of AUU can be characterized by considerable decrease in heat consumption of the building and, respectively, reduction of a payment for the consumed energy resources. In this paper we evaluated the effectiveness of implementation of increase of energy efficiency of the building investment project (hereinafter SP. We calculated the ratio of expenses and the results considered actions for inhabitants of an apartment house located in Kazan after installation of a weather-dependent regulation. As a result of calculation of the imitating model created on the basis of basic data and the investment project plan the main results of determination of economic efficiency of the Project have been received. For the analysis and increase of reliability of a settlement assessment of efficiency of the investment project calculations at different options of a set of basic data are executed.

Computer vision for automatic inspection of agricultural produce

Science.gov (United States)

Molto, Enrique; Blasco, Jose; Benlloch, Jose V.

1999-01-01

Fruit and vegetables suffer different manipulations from the field to the final consumer. These are basically oriented towards the cleaning and selection of the product in homogeneous categories. For this reason, several research projects, aimed at fast, adequate produce sorting and quality control are currently under development around the world. Moreover, it is possible to find manual and semi- automatic commercial system capable of reasonably performing these tasks.However, in many cases, their accuracy is incompatible with current European market demands, which are constantly increasing. IVIA, the Valencian Research Institute of Agriculture, located in Spain, has been involved in several European projects related with machine vision for real-time inspection of various agricultural produces. This paper will focus on the work related with two products that have different requirements: fruit and olives. In the case of fruit, the Institute has developed a vision system capable of providing assessment of the external quality of single fruit to a robot that also receives information from other senors. The system use four different views of each fruit and has been tested on peaches, apples and citrus. Processing time of each image is under 500 ms using a conventional PC. The system provides information about primary and secondary color, blemishes and their extension, and stem presence and position, which allows further automatic orientation of the fruit in the final box using a robotic manipulator. Work carried out in olives was devoted to fast sorting of olives for consumption at table. A prototype has been developed to demonstrate the feasibility of a machine vision system capable of automatically sorting 2500 kg/h olives using low-cost conventional hardware.

Lamb wave based automatic damage detection using matching pursuit and machine learning

International Nuclear Information System (INIS)

Agarwal, Sushant; Mitra, Mira

2014-01-01

In this study, matching pursuit (MP) has been tested with machine learning algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) to automate the process of damage detection in metallic plates. Here, damage detection is done using the Lamb wave response in a thin aluminium plate simulated using a finite element (FE) method. To reduce the complexity of the Lamb wave response, only the A 0 mode is excited and sensed. The procedure adopted for damage detection consists of three major steps, involving signal processing and machine learning (ML). In the first step, MP is used for de-noising and enhancing the sparsity of the database. In the existing literature, MP is used to decompose any signal into a linear combination of waveforms that are selected from a redundant dictionary. In this work, MP is deployed in two stages to make the database sparse as well as to de-noise it. After using MP on the database, it is then passed as input data for ML classifiers. ANN and SVM are used to detect the location of the potential damage from the reduced data. The study demonstrates that the SVM is a robust classifier in the presence of noise and is more efficient than the ANN. Out-of-sample data are used for the validation of the trained and tested classifier. Trained classifiers are found to be successful in the detection of damage with a detection rate of more than 95%. (paper)

Using machine learning to classify image features from canine pelvic radiographs

DEFF Research Database (Denmark)

McEvoy, Fintan; Amigo Rubio, Jose Manuel

2013-01-01

As the number of images per study increases in the field of veterinary radiology, there is a growing need for computer-assisted diagnosis techniques. The purpose of this study was to evaluate two machine learning statistical models for automatically identifying image regions that contain the canine...

Machine learning a probabilistic perspective

CERN Document Server

Murphy, Kevin P

2012-01-01

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic method...

A hybrid particle swarm optimization-SVM classification for automatic cardiac auscultation

Directory of Open Access Journals (Sweden)

Prasertsak Charoen

2017-04-01

Full Text Available Cardiac auscultation is a method for a doctor to listen to heart sounds, using a stethoscope, for examining the condition of the heart. Automatic cardiac auscultation with machine learning is a promising technique to classify heart conditions without need of doctors or expertise. In this paper, we develop a classification model based on support vector machine (SVM and particle swarm optimization (PSO for an automatic cardiac auscultation system. The model consists of two parts: heart sound signal processing part and a proposed PSO for weighted SVM (WSVM classifier part. In this method, the PSO takes into account the degree of importance for each feature extracted from wavelet packet (WP decomposition. Then, by using principle component analysis (PCA, the features can be selected. The PSO technique is used to assign diverse weights to different features for the WSVM classifier. Experimental results show that both continuous and binary PSO-WSVM models achieve better classification accuracy on the heart sound samples, by reducing system false negatives (FNs, compared to traditional SVM and genetic algorithm (GA based SVM.

A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

Science.gov (United States)

S K, Somasundaram; P, Alli

2017-11-09

The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automatic detection of DR diseases is performed only by using the computational technique is the great solution. An automatic method is more reliable to determine the presence of an abnormality in Fundus images (FI) but, the classification process is poorly performed. Recently, few research works have been designed for analyzing texture discrimination capacity in FI to distinguish the healthy images. However, the feature extraction (FE) process was not performed well, due to the high dimensionality. Therefore, to identify retinal features for DR disease diagnosis and early detection using Machine Learning and Ensemble Classification method, called, Machine Learning Bagging Ensemble Classifier (ML-BEC) is designed. The ML-BEC method comprises of two stages. The first stage in ML-BEC method comprises extraction of the candidate objects from Retinal Images (RI). The candidate objects or the features for DR disease diagnosis include blood vessels, optic nerve, neural tissue, neuroretinal rim, optic disc size, thickness and variance. These features are initially extracted by applying Machine Learning technique called, t-distributed Stochastic Neighbor Embedding (t-SNE). Besides, t-SNE generates a probability distribution across high-dimensional images where the images are separated into similar and dissimilar pairs. Then, t-SNE describes a similar probability distribution across the points in the low-dimensional map. This lessens the Kullback-Leibler divergence among two distributions regarding the locations of the points on the map. The second stage comprises of application of ensemble classifiers to the extracted features for providing accurate analysis of digital FI using machine learning. In this stage, an automatic detection

Superconducting Coil Winding Machine Control System

Energy Technology Data Exchange (ETDEWEB)

Nogiec, J. M. [Fermilab; Kotelnikov, S. [Fermilab; Makulski, A. [Fermilab; Walbridge, D. [Fermilab; Trombly-Freytag, K. [Fermilab

2016-10-05

The Spirex coil winding machine is used at Fermilab to build coils for superconducting magnets. Recently this ma-chine was equipped with a new control system, which al-lows operation from both a computer and a portable remote control unit. This control system is distributed between three layers, implemented on a PC, real-time target, and FPGA, providing respectively HMI, operational logic and direct controls. The system controls motion of all mechan-ical components and regulates the cable tension. Safety is ensured by a failsafe, redundant system.

Risk assessment of atmospheric emissions using machine learning

OpenAIRE

Cervone, G.; Franzese, P.; Ezber, Y.; Boybeyi, Z.

2008-01-01

Supervised and unsupervised machine learning algorithms are used to perform statistical and logical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions.

First, a clustering algorithm is used to automatically group the results of different transport and dispersion simulations according to specific cloud characteristics. Then, a symbolic classification algorithm is employed to find compl...

Advanced methods in NDE using machine learning approaches

Science.gov (United States)

Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

2018-04-01

Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability

Strategies for energy saving in buildings by means of automatic control; Estrategias de ahorro de energia en inmuebles mediante el control automatico

Energy Technology Data Exchange (ETDEWEB)

Garcia Trujillo, Oscar [Johnson Controls de Mexico S. A. de C. V., Mexico, D. F. (Mexico)

1998-12-31

In this paper some of the measures and strategies for energy saving that can be applied in different types of buildings, mainly in hotels and office buildings, are presented. The proposed actions are accomplished with the aid of the automatic control equipment and the operation and/or maintenance personnel that supervises and establishes the control parameters of each one of the strategies than are focused to the appropriate utilization of the electric energy. [Espanol] En este documento se presentan algunas de las medidas y estrategias de ahorro de energia que pueden ser aplicadas en diferentes tipos de inmuebles, principalmente en hoteles y en edificios de oficinas. Las acciones propuestas son realizadas con la ayuda del equipo de control automatico y del personal de operacion y/o mantenimiento quien supervisa y establece los parametros de control de cada una de las estrategias que son destinadas a la buena utilizacion de la energia electrica.

Strategies for energy saving in buildings by means of automatic control; Estrategias de ahorro de energia en inmuebles mediante el control automatico

Energy Technology Data Exchange (ETDEWEB)

Garcia Trujillo, Oscar [Johnson Controls de Mexico S. A. de C. V., Mexico, D. F. (Mexico)

1999-12-31

In this paper some of the measures and strategies for energy saving that can be applied in different types of buildings, mainly in hotels and office buildings, are presented. The proposed actions are accomplished with the aid of the automatic control equipment and the operation and/or maintenance personnel that supervises and establishes the control parameters of each one of the strategies than are focused to the appropriate utilization of the electric energy. [Espanol] En este documento se presentan algunas de las medidas y estrategias de ahorro de energia que pueden ser aplicadas en diferentes tipos de inmuebles, principalmente en hoteles y en edificios de oficinas. Las acciones propuestas son realizadas con la ayuda del equipo de control automatico y del personal de operacion y/o mantenimiento quien supervisa y establece los parametros de control de cada una de las estrategias que son destinadas a la buena utilizacion de la energia electrica.

Distributed dynamic simulations of networked control and building performance applications

NARCIS (Netherlands)

Yahiaoui, Azzedine

2018-01-01

The use of computer-based automation and control systems for smart sustainable buildings, often so-called Automated Buildings (ABs), has become an effective way to automatically control, optimize, and supervise a wide range of building performance applications over a network while achieving the

Timer-based data acquisitioning of creep testing machines

International Nuclear Information System (INIS)

Rana, M.A.; Farooq, M.A.; Ali, L.

1998-01-01

Duration of a creep test may be short or long term extending over several years. Continuous operation of a computer for automatic data acquisition of creep testing machines is useless. Timer based data acquisitioning of the machines already interface with IBM-Pc/AT and compatibles has been streamlined for economical use of the computer. A locally designed and fabricated timer has been introduced in the system in this regard to meet the requirements of the system. The timer switches on the computer according to pre scheduled interval of time of capture creep data in Real time. The periodically captured data is logged on the hard disk for analysis and report generation. (author)

Shape understanding system machine understanding and human understanding

CERN Document Server

Les, Zbigniew

2015-01-01

This is the third book presenting selected results of research on the further development of the shape understanding system (SUS) carried out by authors in the newly founded Queen Jadwiga Research Institute of Understanding. In this book the new term Machine Understanding is introduced referring to a new area of research aiming to investigate the possibility of building machines with the ability to understand. It is presented that SUS needs to some extent mimic human understanding and for this reason machines are evaluated according to the rules applied for the evaluation of human understanding. The book shows how to formulate problems and how it can be tested if the machine is able to solve these problems.    

A study of the utility of heat collectors in reducing the response time of automatic fire sprinklers located in production modules of Building 707

International Nuclear Information System (INIS)

Shanley, J.H. Jr.; Budnick, E.K. Jr.

1990-01-01

Several of the ten production Modules in Building 707 at the Department of Energy Rocky Flats Plant recently underwent an alteration which can adversely affect the performance of the installed automatic fire sprinkler systems. The Modules have an approximate floor to ceiling height of 17.5 ft. The alterations involved removing the drop ceilings in the Modules which had been at a height of 12 ft above the floor. The sprinkler systems were originally installed with the sprinkler heads located below the drop ceiling in accordance with the nationally recognized NFPA 13, Standard for the Installation of Automatic Sprinkler Systems. The ceiling removal affects the sprinkler's response time and also violates NFPA 13. The scope of this study included evaluation of the feasibility of utilizing heat collectors to reduce the delays in sprinkler response created by the removal of the drop ceilings. The study also includes evaluation of substituting quick response sprinklers for the standard sprinklers currently in place, in combination with a heat collector

Crawl and crowd to bring machine translation to under-resourced languages

NARCIS (Netherlands)

Toral Ruiz, Antonio

2017-01-01

We present a widely applicable methodology to bring machine translation (MT) to under-resourced languages in a cost-effective and rapid manner. Our proposal relies on web crawling to automatically acquire parallel data to train statistical MT systems if any such data can be found for the language

Gall mite inspection on dormant black currant buds using machine vision

DEFF Research Database (Denmark)

Nielsen, M. R.; Stigaard Laursen, Morten; Jonassen, M. S.

2013-01-01

This paper presents a novel machine vision-based approach detecting and mapping gall mite infection in dormant buds on black currant bushes. A vehicle was fitted with four cameras and RTK-GPS. Results compared automatic detection to human decisions based on the images, and by mapping the results...

Machine en Theater. Ontwerpconcepten van winkelgebouwen

OpenAIRE

Kooijman, D.C.

1999-01-01

Machine and Theater, Design Concepts for Shop Buildings is a richly illustrated study of the architectural and urban development of retail buildings, focusing on six essential shop types: the passage and the department store in particular in Germany and France in the nineteenth century; supermarkets and malls and their relation to the suburbanisation and the emerging car use; and the peripheral retail park and location-free virtual store as the most recent developments. On the basis of a larg...

Building Automation Systems.

Science.gov (United States)

Honeywell, Inc., Minneapolis, Minn.

A number of different automation systems for use in monitoring and controlling building equipment are described in this brochure. The system functions include--(1) collection of information, (2) processing and display of data at a central panel, and (3) taking corrective action by sounding alarms, making adjustments, or automatically starting and…

Design and development of semi-automatic radiation test and calibration facility

International Nuclear Information System (INIS)

Yadav, Ashok Kumar; Chouhan, V.K.; Narayan, Pradeep

2008-01-01

Semi-automatic gamma radiation test and calibration facility have been designed, developed and commissioned at Defence Laboratory Jodhpur (DLJ). The facility comprises of medium and high dose rate range setup using 30 Ci Cobalt-60 source, in a portable remotely operated Techops camera and a 15000 Ci 60 Co source in a Tele-therapy machine. The radiation instruments can be positioned at any desired position using a computer controlled positioner having three translational and one rotational motion. User friendly software helps in positioning the Device Under Test (DUT) at any desired dose rate or distance and acquire the data automatically. The servo and stepper motor controlled positioner helps in achieving the required precision and accuracy for the radiation calibration of the instruments. This paper describes the semi-automatic radiation test and calibration facility commissioned at DLJ. (author)

Automatic inspection Pads second generation; Inspeccion automatica de pastillas de segunda generacion

Energy Technology Data Exchange (ETDEWEB)

Gallardo-Lancho gonzalez, J. F.

2010-07-01

In recent years, development has addressed Enusa a second generation robot for automatic inspection of tablets incorporating the following advances: more advanced systems that improve vision quality inspection equipment, conducting the inspection in line with the grinding operation, increased productivity of the inspection process to be unnecessary pills buildup in trays and lay-out of the most rational equipment allowing cleaning it easier and faster. This second generation machine is already part of the automatic inspection equipment developed by Enusa and is an example of the ongoing commitment to the development Enusa and innovation in nuclear technology.

Flocking small smart machines: An experiment in cooperative, multi-machine control

International Nuclear Information System (INIS)

Klarer, P.R.

1998-03-01

The intent and purpose of this work was to investigate and demonstrate cooperative behavior among a group of mobile robot machines. The specific goal of this work was to build a small swarm of identical machines and control them in such a way as to show a coordinated movement of the group in a flocking manner, similar to that observed in nature. Control of the swarm's individual members and its overall configuration is available to the human user via a graphic man-machine interface running on a base station control computer. Any robot may be designated as the nominal leader through the interface tool, which then may be commanded to proceed to a particular geographic destination. The remainder of the flock follows the leader by maintaining their relative positions in formation, as specified by the human controller through the interface. The formation's configuration can be altered manually through an interactive graphic-based tool. An alternative mode of control allows for teleoperation of one robot, with the flock following along as described above

RESEARCH OF THE LEVEL OF THE INNOVATIVE DEVELOPMENT OF THE MACHINE BUILDING ENTERPRISES OF UKRAINE IN KHARKOV REGION

Directory of Open Access Journals (Sweden)

Kateryna Koliedina

2015-11-01

Full Text Available In today’s world of the science and new technologies development all the countries including Ukraine are entrusted with a task of refocusing on the innovative way of evolution. The level of innovativeness of the country depends on the development of enterprises in this regard since exactly the industrial enterprises are able to induce the innovative process and be a multiplier of other economic sectors. Thus it is expedient to re-equip the industry of the state and encourage exactly the high-tech branches thereof. In this regard the goal of this paper is to explore and assess the influence of the criteria of innovative activity of the industrial enterprises on the level of the innovative development thereof. To reach the mentioned goal the criteria of innovative activity of the leading machine building enterprises of Ukraine in Kharkov region are chosen as the object of research. The studied enterprises are innovatively active in Kharkov region according to the criteria of the work thereof. The following criteria of innovative activity of the enterprises are chosen for analysis: the volume of the sold innovative products; the charges for innovation; the research intensity of the innovative products; the share of the studied enterprises in the total volume of the products sold in this region; the quantity of the new technological processes; the quantity of the new innovative kinds of products. The chosen criteria are important for enterprise in relation to the innovative activity and in case of changes have a substantial influence on its development and stability. Methodology. To research the level of innovative development of the machine building enterprises of Ukraine in Kharkov region the author prop: 1 Formation of the matrix of the basic data; 2 Standardization of the basic data; 3 Differentiation of the characteristics on inciters and disincentives; 4 Construction of the etalon; 5 Determination of the distance between the objects and

Automatic welding detection by an intelligent tool pipe inspection

Science.gov (United States)

Arizmendi, C. J.; Garcia, W. L.; Quintero, M. A.

2015-07-01

This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called “smart pig” in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.

Exploration of Web Users' Search Interests through Automatic Subject Categorization of Query Terms.

Science.gov (United States)

Pu, Hsiao-tieh; Yang, Chyan; Chuang, Shui-Lung

2001-01-01

Proposes a mechanism that carefully integrates human and machine efforts to explore Web users' search interests. The approach consists of a four-step process: extraction of core terms; construction of subject taxonomy; automatic subject categorization of query terms; and observation of users' search interests. Research findings are proved valuable…

Machines géantes pour sonder l'univers de l'atome

CERN Multimedia

Wilde, M, S

1966-01-01

To always more deeply explore the infinitely small world of the atom, Science is paradoxically brought to build buildings and machines increasingly larger - Giant accelerators producing high energy particle beams that can dissociate the structures of the atomic nucleus

Remote maintenance in the building of the reactor of power plants

International Nuclear Information System (INIS)

Bonin, R.

1984-01-01

Examples describing the different operations requiring remote control for reactor maintenance are given. These operations include: refueling machines (for closure stud, vessel flange cleaning, screwing plug for channel head, swimming pool decontamination) in-service inspection machines (MIS, spider for eddy current testing of steam generator, television) and routine or accidental maintenance (leak detection in water boxes, maintenance spider, opening or closing primary manways, decontamination manipulators and various automatic control devices) [fr

Building Customer Churn Prediction Models in Fitness Industry with Machine Learning Methods

OpenAIRE

Shan, Min

2017-01-01

With the rapid growth of digital systems, churn management has become a major focus within customer relationship management in many industries. Ample research has been conducted for churn prediction in different industries with various machine learning methods. This thesis aims to combine feature selection and supervised machine learning methods for defining models of churn prediction and apply them on fitness industry. Forward selection is chosen as feature selection methods. Support Vector ...

Machine learning approach for automatic quality criteria detection of health web pages.

Science.gov (United States)

Gaudinat, Arnaud; Grabar, Natalia; Boyer, Célia

2007-01-01

The number of medical websites is constantly growing [1]. Owing to the open nature of the Web, the reliability of information available on the Web is uneven. Internet users are overwhelmed by the quantity of information available on the Web. The situation is even more critical in the medical area, as the content proposed by health websites can have a direct impact on the users' well being. One way to control the reliability of health websites is to assess their quality and to make this assessment available to users. The HON Foundation has defined a set of eight ethical principles. HON's experts are working in order to manually define whether a given website complies with s the required principles. As the number of medical websites is constantly growing, manual expertise becomes insufficient and automatic systems should be used in order to help medical experts. In this paper we present the design and the evaluation of an automatic system conceived for the categorisation of medical and health documents according to he HONcode ethical principles. A first evaluation shows promising results. Currently the system shows 0.78 micro precision and 0.73 F-measure, with 0.06 errors.

Transforming Biology Assessment with Machine Learning: Automated Scoring of Written Evolutionary Explanations

Science.gov (United States)

Nehm, Ross H.; Ha, Minsu; Mayfield, Elijah

2012-01-01

This study explored the use of machine learning to automatically evaluate the accuracy of students' written explanations of evolutionary change. Performance of the Summarization Integrated Development Environment (SIDE) program was compared to human expert scoring using a corpus of 2,260 evolutionary explanations written by 565 undergraduate…

Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA

OpenAIRE

Deng, Li; Wang, Guohua; Yu, Suihuai

2016-01-01

In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm) were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive cha...

An experimental result of estimating an application volume by machine learning techniques.

Science.gov (United States)

Hasegawa, Tatsuhito; Koshino, Makoto; Kimura, Haruhiko

2015-01-01

In this study, we improved the usability of smartphones by automating a user's operations. We developed an intelligent system using machine learning techniques that periodically detects a user's context on a smartphone. We selected the Android operating system because it has the largest market share and highest flexibility of its development environment. In this paper, we describe an application that automatically adjusts application volume. Adjusting the volume can be easily forgotten because users need to push the volume buttons to alter the volume depending on the given situation. Therefore, we developed an application that automatically adjusts the volume based on learned user settings. Application volume can be set differently from ringtone volume on Android devices, and these volume settings are associated with each specific application including games. Our application records a user's location, the volume setting, the foreground application name and other such attributes as learning data, thereby estimating whether the volume should be adjusted using machine learning techniques via Weka.

Effect of Extended State Observer and Automatic Voltage Regulator on Synchronous Machine Connected to Infinite Bus Power System

Science.gov (United States)

Angu, Rittu; Mehta, R. K.

2018-04-01

This paper presents a robust controller known as Extended State Observer (ESO) in order to improve the stability and voltage regulation of a synchronous machine connected to an infinite bus power system through a transmission line. The ESO-based control scheme is implemented with an automatic voltage regulator in conjunction with an excitation system to enhance the damping of low frequency power system oscillations, as the Power System Stabilizer (PSS) does. The implementation of PSS excitation control techniques however requires reliable information about the entire states, though they are not always directly measureable. To address this issue, the proposed ESO provides the estimate of system states as well as disturbance state together in order to improve not only the damping but also compensates system efficiently in presence of parameter uncertainties and external disturbances. The Closed-Loop Poles (CLPs) of the system have been assigned by the symmetric root locus technique, with the desired level of system damping provided by the dominant CLPs. The performance of the system is analyzed through simulating at different operating conditions. The control method is not only capable of providing zero estimation error in steady-state, but also shows robustness in tracking the reference command under parametric variations and external disturbances. Illustrative examples have been provided to demonstrate the effectiveness of the developed methodology.

Building Automatic Grading Tools for Basic of Programming Lab in an Academic Institution

Science.gov (United States)

Harimurti, Rina; Iwan Nurhidayat, Andi; Asmunin

2018-04-01

The skills of computer programming is a core competency that must be mastered by students majoring in computer sciences. The best way to improve this skill is through the practice of writing many programs to solve various problems from simple to complex. It takes hard work and a long time to check and evaluate the results of student labs one by one, especially if the number of students a lot. Based on these constrain, web proposes Automatic Grading Tools (AGT), the application that can evaluate and deeply check the source code in C, C++. The application architecture consists of students, web-based applications, compilers, and operating systems. Automatic Grading Tools (AGT) is implemented MVC Architecture and using open source software, such as laravel framework version 5.4, PostgreSQL 9.6, Bootstrap 3.3.7, and jquery library. Automatic Grading Tools has also been tested for real problems by submitting source code in C/C++ language and then compiling. The test results show that the AGT application has been running well.

Fully automatized renal parenchyma volumetry using a support vector machine based recognition system for subject-specific probability map generation in native MR volume data

Science.gov (United States)

Gloger, Oliver; Tönnies, Klaus; Mensel, Birger; Völzke, Henry

2015-11-01

In epidemiological studies as well as in clinical practice the amount of produced medical image data strongly increased in the last decade. In this context organ segmentation in MR volume data gained increasing attention for medical applications. Especially in large-scale population-based studies organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time-consuming and prone to reader variability, large-scale studies need automatized methods to perform organ segmentation. Fully automatic organ segmentation in native MR image data has proven to be a very challenging task. Imaging artifacts as well as inter- and intrasubject MR-intensity differences complicate the application of supervised learning strategies. Thus, we propose a modularized framework of a two-stepped probabilistic approach that generates subject-specific probability maps for renal parenchyma tissue, which are refined subsequently by using several, extended segmentation strategies. We present a three class-based support vector machine recognition system that incorporates Fourier descriptors as shape features to recognize and segment characteristic parenchyma parts. Probabilistic methods use the segmented characteristic parenchyma parts to generate high quality subject-specific parenchyma probability maps. Several refinement strategies including a final shape-based 3D level set segmentation technique are used in subsequent processing modules to segment renal parenchyma. Furthermore, our framework recognizes and excludes renal cysts from parenchymal volume, which is important to analyze renal functions. Volume errors and Dice coefficients show that our presented framework outperforms existing approaches.

Fully automatized renal parenchyma volumetry using a support vector machine based recognition system for subject-specific probability map generation in native MR volume data

International Nuclear Information System (INIS)

Gloger, Oliver; Völzke, Henry; Tönnies, Klaus; Mensel, Birger

2015-01-01

In epidemiological studies as well as in clinical practice the amount of produced medical image data strongly increased in the last decade. In this context organ segmentation in MR volume data gained increasing attention for medical applications. Especially in large-scale population-based studies organ volumetry is highly relevant requiring exact organ segmentation. Since manual segmentation is time-consuming and prone to reader variability, large-scale studies need automatized methods to perform organ segmentation. Fully automatic organ segmentation in native MR image data has proven to be a very challenging task. Imaging artifacts as well as inter- and intrasubject MR-intensity differences complicate the application of supervised learning strategies. Thus, we propose a modularized framework of a two-stepped probabilistic approach that generates subject-specific probability maps for renal parenchyma tissue, which are refined subsequently by using several, extended segmentation strategies. We present a three class-based support vector machine recognition system that incorporates Fourier descriptors as shape features to recognize and segment characteristic parenchyma parts. Probabilistic methods use the segmented characteristic parenchyma parts to generate high quality subject-specific parenchyma probability maps. Several refinement strategies including a final shape-based 3D level set segmentation technique are used in subsequent processing modules to segment renal parenchyma. Furthermore, our framework recognizes and excludes renal cysts from parenchymal volume, which is important to analyze renal functions. Volume errors and Dice coefficients show that our presented framework outperforms existing approaches. (paper)

Wireless Device-to-Device (D2D) Links for Machine-to-Machine (M2M) Communication

DEFF Research Database (Denmark)

Pratas, Nuno; Popovski, Petar

2017-01-01

Device-to-Device (D2D) communications will play an important role in the fifth generation (5G) cellular networks, by increasing the spatial reuse of spectrum resources and enabling communication links with low latency. D2D is composed of two fundamental building blocks: proximity discovery...... and direct communication between nearby users. Another emerging trend in wireless cellular systems is Machine-to-Machine (M2M) communications, often characterized by fixed, low transmission rates. In this chapter we motivate the synergy between D2D and M2M, and present technologies that enable M2M-via-D2D...

The epidural needle guidance with an intelligent and automatic identification system for epidural anesthesia

Science.gov (United States)

Kao, Meng-Chun; Ting, Chien-Kun; Kuo, Wen-Chuan

2018-02-01

Incorrect placement of the needle causes medical complications in the epidural block, such as dural puncture or spinal cord injury. This study proposes a system which combines an optical coherence tomography (OCT) imaging probe with an automatic identification (AI) system to objectively identify the position of the epidural needle tip. The automatic identification system uses three features as image parameters to distinguish the different tissue by three classifiers. Finally, we found that the support vector machine (SVM) classifier has highest accuracy, specificity, and sensitivity, which reached to 95%, 98%, and 92%, respectively.

Automatic visual grading of grain products by machine vision

Science.gov (United States)

Dubosclard, Pierre; Larnier, Stanislas; Konik, Hubert; Herbulot, Ariane; Devy, Michel

2015-11-01

This paper presents two automatic methods for visual grading, deterministic and probabilistic, designed to solve the industrial problem of evaluation of seed lots from the characterization of a representative sample. The sample is thrown in bulk onto a tray placed in a chamber for acquiring color image in a controlled and reproducible manner. Two image-processing methods have been developed to separate and then characterize each seed present in the image. A shape learning is performed on isolated seeds. Collected information is used for the segmentation. The first approach adopted for the segmentation step is based on simple criteria such as regions, edges, and normals to the boundary. Marked point processes are used in the second approach, leading to tackling of the problem by a technique of energy minimization. In both approaches, an active contour with prior shape is performed to improve the results. A classification is done on shape or color descriptors to evaluate the quality of the sample.

Stability of the CAE of a digital mammography machine; Estabilidad del CAE de un mamografo digital

Energy Technology Data Exchange (ETDEWEB)

Fernandez Lara, A. A.; Ruiz Morales, C.; Buades Forner, M. J.; Tobarra, B. M.

2013-07-01

We analyzed the long-term reproducibility automatic exposure control (CAE) of a digital mammography machine for possible drifts and the factors that have led to changes in its baseline are discussed. (Author)

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

Directory of Open Access Journals (Sweden)

Ruben Leal Mubarqui

Full Text Available 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.

Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm

Science.gov (United States)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Hollingsworth, Alan B.; Danala, Gopichandh; Mirniaharikandehei, Seyedehnafiseh; Qiu, Yuchen; Liu, Hong; Zheng, Bin

2018-02-01

In order to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, this study aims to investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. A dataset involving negative mammograms acquired from 500 women was assembled. This dataset was divided into two age-matched classes of 250 high risk cases in which cancer was detected in the next subsequent mammography screening and 250 low risk cases, which remained negative. First, a computer-aided image processing scheme was applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, a multi-feature fusion based machine learning classifier was built to predict the risk of cancer detection in the next mammography screening. A leave-one-case-out (LOCO) cross-validation method was applied to train and test the machine learning classifier embedded with a LLP algorithm, which generated a new operational vector with 4 features using a maximal variance approach in each LOCO process. Results showed a 9.7% increase in risk prediction accuracy when using this LPP-embedded machine learning approach. An increased trend of adjusted odds ratios was also detected in which odds ratios increased from 1.0 to 11.2. This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.

Child vocalization composition as discriminant information for automatic autism detection.

Science.gov (United States)

Xu, Dongxin; Gilkerson, Jill; Richards, Jeffrey; Yapanel, Umit; Gray, Sharmi

2009-01-01

Early identification is crucial for young children with autism to access early intervention. The existing screens require either a parent-report questionnaire and/or direct observation by a trained practitioner. Although an automatic tool would benefit parents, clinicians and children, there is no automatic screening tool in clinical use. This study reports a fully automatic mechanism for autism detection/screening for young children. This is a direct extension of the LENA (Language ENvironment Analysis) system, which utilizes speech signal processing technology to analyze and monitor a child's natural language environment and the vocalizations/speech of the child. It is discovered that child vocalization composition contains rich discriminant information for autism detection. By applying pattern recognition and machine learning approaches to child vocalization composition data, accuracy rates of 85% to 90% in cross-validation tests for autism detection have been achieved at the equal-error-rate (EER) point on a data set with 34 children with autism, 30 language delayed children and 76 typically developing children. Due to its easy and automatic procedure, it is believed that this new tool can serve a significant role in childhood autism screening, especially in regards to population-based or universal screening.

Machine Learning Techniques in Clinical Vision Sciences.

Science.gov (United States)

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

Analysis and application of two recursive parametric estimation algorithms for an asynchronous machine

International Nuclear Information System (INIS)

Damek, Nawel; Kamoun, Samira

2011-01-01

In this communication, two recursive parametric estimation algorithms are analyzed and applied to an squirrelcage asynchronous machine located at the research ''Unit of Automatic Control'' (UCA) at ENIS. The first algorithm which, use the transfer matrix mathematical model, is based on the gradient principle. The second algorithm, which use the state-space mathematical model, is based on the minimization of the estimation error. These algorithms are applied as a key technique to estimate asynchronous machine with unknown, but constant or timevarying parameters. Stator voltage and current are used as measured data. The proposed recursive parametric estimation algorithms are validated on the experimental data of an asynchronous machine under normal operating condition as full load. The results show that these algorithms can estimate effectively the machine parameters with reliability.

Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning

DEFF Research Database (Denmark)

Mølgaard, Lasse Lohilahti; Buus, Ole Thomsen; Larsen, Jan

2017-01-01

We present a data-driven machine learning approach to detect drug- and explosives-precursors using colorimetric sensor technology for air-sampling. The sensing technology has been developed in the context of the CRIM-TRACK project. At present a fully- integrated portable prototype for air sampling...... of the highly multi-variate data produced from the colorimetric chip a number of machine learning techniques are employed to provide reliable classification of target analytes from confounders found in the air streams. We demonstrate that a data-driven machine learning method using dimensionality reduction...... in combination with a probabilistic classifier makes it possible to produce informative features and a high detection rate of analytes. Furthermore, the probabilistic machine learning approach provides a means of automatically identifying unreliable measurements that could produce false predictions...

Hybrid polylingual object model: an efficient and seamless integration of Java and native components on the Dalvik virtual machine.

Science.gov (United States)

Huang, Yukun; Chen, Rong; Wei, Jingbo; Pei, Xilong; Cao, Jing; Prakash Jayaraman, Prem; Ranjan, Rajiv

2014-01-01

JNI in the Android platform is often observed with low efficiency and high coding complexity. Although many researchers have investigated the JNI mechanism, few of them solve the efficiency and the complexity problems of JNI in the Android platform simultaneously. In this paper, a hybrid polylingual object (HPO) model is proposed to allow a CAR object being accessed as a Java object and as vice in the Dalvik virtual machine. It is an acceptable substitute for JNI to reuse the CAR-compliant components in Android applications in a seamless and efficient way. The metadata injection mechanism is designed to support the automatic mapping and reflection between CAR objects and Java objects. A prototype virtual machine, called HPO-Dalvik, is implemented by extending the Dalvik virtual machine to support the HPO model. Lifespan management, garbage collection, and data type transformation of HPO objects are also handled in the HPO-Dalvik virtual machine automatically. The experimental result shows that the HPO model outweighs the standard JNI in lower overhead on native side, better executing performance with no JNI bridging code being demanded.

Machine vision applications for physical security, quality assurance and personnel dosimetry

International Nuclear Information System (INIS)

Kar, S.; Shrikhande, S.V.; Suresh Babu, R.M.

2016-01-01

Machine vision is the technology used to provide imaging-based solutions to variety of applications, relevant to nuclear facilities and other industries. It uses computerized image analysis for automatic inspection, process control, object sorting, parts assembly, human identity authentication, and so on. In this article we discuss the in-house developed machine vision systems at EISD, BARC for three specific areas: Biometric recognition for physical security, visual inspection for QA of fuel pellets, and fast neutron personnel dosimetry. The advantages in using these systems include objective decision making, reduced man-rem, operational consistency, and capability of statistical quantitative analysis. (author)

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing.

Science.gov (United States)

Li, Chunhua; Zhao, Pengpeng; Sheng, Victor S; Xian, Xuefeng; Wu, Jian; Cui, Zhiming

2017-01-01

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing

Directory of Open Access Journals (Sweden)

Chunhua Li

2017-01-01

Full Text Available Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.

Monitoring large rotating machines at EDF

International Nuclear Information System (INIS)

Chevalier, R.; Bourgeois, P.; Le Reverend, D.

1992-09-01

At Electricite de France (EDF), since 1978, the operating instruments which ensure the DETECTION function, have been completed on turbogenerators by a specialized ''off-line'' vibration monitoring system, which allows a posteriori DIAGNOSIS analysis. However because of a need of a real time and more elaborated DETECTION function, the concept of the Monitoring and Diagnosis Aid Station (Poste de Surveillance et d'Aide au Diagnostic: PSAD) has been developed. It federates the processing of monitoring, organized into several functions, and includes the monitoring of turbogenerators (TGS) and reactor coolant pumps (RCP). The purpose of this paper is to present, on the one hand, the monitoring functions of TGS and RCP and on the other, the first experimental results on the behaviour of three RCP, obtained through a SAMT (Surveillance Automatisee des Machines Tournantes - Automatic monitoring of rotating machines) prototype. (authors). 2 figs., 4 tabs., 4 refs

A translator and simulator for the Burroughs D machine

Science.gov (United States)

Roberts, J.

1972-01-01

The D Machine is described as a small user microprogrammable computer designed to be a versatile building block for such diverse functions as: disk file controllers, I/O controllers, and emulators. TRANSLANG is an ALGOL-like language, which allows D Machine users to write microprograms in an English-like format as opposed to creating binary bit pattern maps. The TRANSLANG translator parses TRANSLANG programs into D Machine microinstruction bit patterns which can be executed on the D Machine simulator. In addition to simulation and translation, the two programs also offer several debugging tools, such as: a full set of diagnostic error messages, register dumps, simulated memory dumps, traces on instructions and groups of instructions, and breakpoints.

Fully Automatic Spot Welding System for Application in Automotive Industry

Directory of Open Access Journals (Sweden)

Peter Puschner

2015-12-01

Full Text Available Abstract A Virtual Machine has led to a fully automatic spot welding system. All necessary parameters are created by measuring systems and algorithms running in the Virtual Machine. A hybrid operating circuit allows the Virtual Machine to read the exact process voltage between the tips of the electrodes every 50 µs. Actual welding voltage and current allow for the first time reading process impedance, electric power and total energy being transferred to the spot weld. Necessary energy input is calculated by a calorimetric model after measuring the total thickness of the materials to be welded as soon as the welding gun is positioned at the workpiece. A precision potentiometer implemented in the gun delivers the total material thickness within the 0.1 mm range during the pre-pressure phases. The internal databank of the Virtual Machine controls all essential parameters to guide the total welding process. Special generator characteristics of the welding power unit are created by the Virtual Machine just during the upslope and the welding phases. So the process will be initialized in differentiating the kind of material, mild steel or high strengthen steel. This will affect the kind of energy input and current decrease during the upslope and downslope phases.

Development of Automated Procedures to Generate Reference Building Models for ASHRAE Standard 90.1 and India’s Building Energy Code and Implementation in OpenStudio

Energy Technology Data Exchange (ETDEWEB)

Parker, Andrew [National Renewable Energy Lab. (NREL), Golden, CO (United States); Haves, Philip [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Jegi, Subhash [International Institute of Information Technology, Hyderabad (India); Garg, Vishal [International Institute of Information Technology, Hyderabad (India); Ravache, Baptiste [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2017-09-14

This paper describes a software system for automatically generating a reference (baseline) building energy model from the proposed (as-designed) building energy model. This system is built using the OpenStudio Software Development Kit (SDK) and is designed to operate on building energy models in the OpenStudio file format.

Automatic ice-cream characterization by impedance measurements for optimal machine setting

OpenAIRE

Grossi , Marco; Lanzoni , Massimo; Lazzarini , Roberto; Riccò , Bruno

2012-01-01

International audience; Electrical characterization of products is gaining increasing interest in the food industry for quality monitoring and control. In particular, this is the case in the ice-cream industry, where machines dedicated to store ice-cream mixes are programmed ''ad hoc'' for different groups of products. To this purpose, the present work shows that essential product classification (discrimination between milk based and fruit based ice-cream mixes) can be done by means of a tech...

Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming

Czech Academy of Sciences Publication Activity Database

Křen, T.; Pilát, M.; Neruda, Roman

2017-01-01

Roč. 26, č. 5 (2017), č. článku 1760020. ISSN 0218-2130 R&D Projects: GA ČR GA15-19877S Grant - others:GA MŠk(CZ) LM2015042 Institutional support: RVO:67985807 Keywords : genetic programming * machine learning workflows * asynchronous evolutionary algorithm Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 0.778, year: 2016

Experimental improvement of the technology of cutting of high-pressure hoses with metal braid on hand cutting machine

OpenAIRE

Karpenko, Mykola; Bogdevicius, Marijonas; Prentkovskis, Olegas

2016-01-01

In the article the review of the problem of improvement of technology of high pressure hoses cutting on the hand cutting machines is analyzed. Different methods of cutting of high pressure hoses into the billets are overviewed and the quality of edge cuts of hoses is analyzed. The comparison of treatment on automatic cutting machines and on hand cutting machines is carried out. Different experimental techniques of improvement of the quality of edges cutting of high pressure hoses are prese...

SU-E-T-373: A Motorized Stage for Fast and Accurate QA of Machine Isocenter

International Nuclear Information System (INIS)

Moore, J; Velarde, E; Wong, J

2014-01-01

Purpose: Precision delivery of radiation dose relies on accurate knowledge of the machine isocenter under a variety of machine motions. This is typically determined by performing a Winston-Lutz test consisting of imaging a known object at multiple gantry/collimator/table angles and ensuring that the maximum offset is within specified tolerance. The first step in the Winston-Lutz test is careful placement of a ball bearing at the machine isocenter as determined by repeated imaging and shifting until accurate placement has been determined. Conventionally this is performed by adjusting a stage manually using vernier scales which carry the limitation that each adjustment must be done inside the treatment room with the risks of inaccurate adjustment of the scale and physical bumping of the table. It is proposed to use a motorized system controlled outside of the room to improve the required time and accuracy of these tests. Methods: The three dimensional vernier scales are replaced by three motors with accuracy of 1 micron and a range of 25.4mm connected via USB to a computer in the control room. Software is designed which automatically detects the motors and assigns them to proper axes and allows for small shifts to be entered and performed. Input values match calculated offsets in magnitude and sign to reduce conversion errors. Speed of setup, number of iterations to setup, and accuracy of final placement are assessed. Results: Automatic BB placement required 2.25 iterations and 13 minutes on average while manual placement required 3.76 iterations and 37.5 minutes. The average final XYZ offsets is 0.02cm, 0.01cm, 0.04cm for automatic setup and 0.04cm, 0.02cm, 0.04cm for manual setup. Conclusion: Automatic placement decreased time and repeat iterations for setup while improving placement accuracy. Automatic placement greatly reduces the time required to perform QA

Automatic 3D modeling of the urban landscape

NARCIS (Netherlands)

Esteban, I.; Dijk, J.; Groen, F.

2010-01-01

In this paper we present a fully automatic system for building 3D models of urban areas at the street level. We propose a novel approach for the accurate estimation of the scale consistent camera pose given two previous images. We employ a new method for global optimization and use a novel sampling

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

Science.gov (United States)

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

Monitoring of large rotating machines at EDF

International Nuclear Information System (INIS)

Chevalier, R.; Oswald, G.P.; Morel, J.

1993-09-01

The purpose of equipment surveillance is the prevention of major risks, the early detection of abnormal conditions and post-incident analysis to correct faults observed. At EDF, overall vibration monitoring in the control room was supplemented by a special vibration monitoring system. However, in order to satisfy more elaborate, real time detection requirements and benefit from the new possibilities offered by computer-based systems, EDF has developed the PSAD concept (Surveillance and Diagnosis-aid Station) which groups surveillance processing, organized on surveillance functions including turbogenerator and reactor coolant pump surveillance. The purpose of the present paper is to describe the turbogenerator and reactor coolant pump surveillance functions and present the first examples of reactor coolant pump behaviour feedback using a PSAD mockup (Automated Surveillance of Rotating Machines). In the first place, surveillance implies determining exactly what has to be monitored. This entails considering incidents liable to affect machine behaviour and, of course, specifying both the vibration quantities and those defining the operating condition of the machine considered which are necessary to be able to interpret the vibrations. Data processing requirements concern detection of faults and diagnosis aids. Faults detection must be automatic, but not the diagnosis function. Data can be processed to evidence one or several faults, using the most appropriate data display system. Interpretation is then entrusted to experts. To satisfy the above requirements, the PSAD system integrates two new concepts: distributed surveillance, involving depth distribution (different layers of software organized for increasingly sophisticated and gradually narrowing data processing) and space distribution (the work is performed in the most appropriate place, whether this be the plant, with automatic real time processing, or elsewhere if the complexity of the diagnosis so requires

Using supervised machine learning to code policy issues: Can classifiers generalize across contexts?

NARCIS (Netherlands)

Burscher, B.; Vliegenthart, R.; de Vreese, C.H.

2015-01-01

Content analysis of political communication usually covers large amounts of material and makes the study of dynamics in issue salience a costly enterprise. In this article, we present a supervised machine learning approach for the automatic coding of policy issues, which we apply to news articles

Machine Learning for ATLAS DDM Network Metrics

CERN Document Server

Lassnig, Mario; The ATLAS collaboration; Vamosi, Ralf

2016-01-01

The increasing volume of physics data is posing a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from our ongoing automation efforts. First, we describe our framework for distributed data management and network metrics, automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for network-aware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.