A Front-End Electronics Prototype Based on Gigabit Ethernet for the ATLAS Small-Strip Thin Gap Chamber

NASA Astrophysics Data System (ADS)

Hu, Kun; Lu, Houbing; Wang, Xu; Li, Feng; Wang, Xinxin; Geng, Tianru; Yang, Hang; Liu, Shengquan; Han, Liang; Jin, Ge

2017-06-01

A front-end electronics prototype for the ATLAS small-strip Thin Gap Chamber (sTGC) based on gigabit Ethernet has been developed. The prototype is designed to read out signals of pads, wires, and strips of the sTGC detector. The prototype includes two VMM2 chips developed to read out the signals of the sTGC, a Xilinx Kintex-7 field-programmable gate array (FPGA) used for the VMM2 configuration and the events storage, and a gigabit Ethernet transceiver PHY chip for interfacing with a computer. The VMM2 chip is designed for the readout of the Micromegas detector and sTGC detector, which is composed of 64 linear front-end channels. Each channel integrates a charge-sensitive amplifier, a shaper, several analog-to-digital converters, and other digital functions. For a bunch-crossing interval of 25 ns, events are continuously read out by the FPGA and forwarded to the computer. The interface between the computer and the prototype has been measured to reach an error-free rate of 900 Mb/s, therefore making a very effective use of the available bandwidth. Additionally, the computer can control several prototypes of this kind simultaneously via the Ethernet interface. At present, the prototype will be used for the sTGC performance test. The features of the prototype are described in detail.

Induction of Resistance Mediated by an Attenuated Strain of Valsa mali var. mali Using Pathogen-Apple Callus Interaction System

PubMed Central

Zhang, Qingming; Wang, Caixia; Yong, Daojing; Li, Guifang; Dong, Xiangli; Li, Baohua

2014-01-01

To study the induced resistance in apple against Valsa mali var. mali (Vmm), a Vmm–apple callus interaction system was developed to evaluate the induced resistance of an attenuated Vmm strain LXS081501 against further infection by a virulent Vmm strain LXS080601. The infection index was up to 97.32 for apple calli inoculated with LXS080601 alone at 15 days after inoculation whereas it was only 41.84 for calli pretreated with LXS081501 followed by LXS080601 inoculation. In addition, the maximum levels of free proline, soluble sugar, and protein in calli treated with LXS081501 plus LXS080601 were 2.14 to 3.47 times higher than controls and 1.42 to 1.75 times higher than LXS080601 treatment. The activities of defense-related enzymes such as phenylalanine ammonia lyase (PAL), polyphenol oxidase (PPO), peroxidase (POD), and catalase (CAT) as well as β-1,3-glucanase and chitinase in apple calli inoculated with LXS080601 alone or LXS081501 plus LXS080601 increased significantly 24 hai and peaked from 48 to 120 hpi. However, in the latter treatment, the maximum enzyme activities were much higher and the activities always maintained much higher levels than control during the experimental period. These results suggested the roles of osmotic adjustment substances and defense-related enzymes in induced resistance. PMID:25054166

Flux concentration and modulation based magnetoresistive sensor with integrated planar compensation coils

NASA Astrophysics Data System (ADS)

Tian, Wugang; Hu, Jiafei; Pan, Mengchun; Chen, Dixiang; Zhao, Jianqiang

2013-03-01

1/f noise is one of the main noise sources of magnetoresistive (MR) sensors, which can cause intrinsic detection limit at low frequency. To suppress this noise, the solution of flux concentration and vertical motion modulation (VMM) has been proposed. Magnetic hysteresis in MR sensors is another problem, which degrades their response linearity and detection ability. To reduce this impact, the method of pulse magnetization and magnetic compensation field with integrated planar coils has been introduced. A flux concentration and VMM based magnetoresistive prototype sensor with integrated planar coils was fabricated using microelectromechanical-system technology. The response linearity of the prototype sensors is improved from 0.8% to 0.12%. The noise level is reduced near to the thermal noise level, and the low-frequency detection ability of the prototype sensor is enhanced with a factor of more than 80.

AC-electric field dependent electroformation of giant lipid vesicles.

PubMed

Politano, Timothy J; Froude, Victoria E; Jing, Benxin; Zhu, Yingxi

2010-08-01

Giant vesicles of larger than 5 microm, which have been of intense interest for their potential as drug delivery vehicles and as a model system for cell membranes, can be rapidly formed from a spin-coated lipid thin film under an electric field. In this work, we explore the AC-field dependent electroformation of giant lipid vesicles in aqueous media over a wide range of AC-frequency from 1 Hz to 1 MHz and peak-to-peak field strength from 0.212 V/mm to 40 V/mm between two parallel conducting electrode surfaces. By using fluorescence microscopy, we perform in-situ microscopic observations of the structural evolution of giant vesicles formed from spin-coated lipid films under varied uniform AC-electric fields. The real-time observation of bilayer bulging from the lipid film, vesicle growth and fusing further examine the critical role of AC-induced electroosmotic flow of surrounding fluids for giant vesicle formation. A rich AC-frequency and field strength phase diagram is obtained experimentally to predict the AC-electroformation of giant unilamellar vesicles (GUVs) of l-alpha-phosphatidylcholine, where a weak dependence of vesicle size on AC-frequency is observed at low AC-field voltages, showing decreased vesicle size with a narrowed size distribution with increased AC-frequency. Formation of vesicles was shown to be constrained by an upper field strength of 10 V/mm and an upper AC-frequency of 10 kHz. Within these parameters, giant lipid vesicles were formed predominantly unilamellar and prevalent across the entire electrode surfaces. Copyright 2010 Elsevier B.V. All rights reserved.

Purification of CdZnTe by electromigration

NASA Astrophysics Data System (ADS)

Kim, K.; Kim, Sangsu; Hong, Jinki; Lee, Jinseo; Hong, Taekwon; Bolotnikov, A. E.; Camarda, G. S.; James, R. B.

2015-04-01

Electro-migration of ionized/electrically active impurities in CdZnTe (CZT) was successfully demonstrated at elevated temperature with an electric field of 20 V/mm. Copper, which exists in positively charged states, electro-migrated at a speed of 15 μm/h in an electric field of 20 V/mm. A notable variation in impurity concentration along the growth direction with the segregation tendency of the impurities was observed in an electro-migrated CZT boule. Notably, both Ga and Fe, which exist in positively charged states, exhibited the opposite distribution to that of their segregation tendency in Cd(Zn)Te. A CZT detector fabricated from the middle portion of the electro-migrated CZT boule showed an improved mobility-lifetime product of 0.91 × 10-2 cm2/V, compared with that of 1.4 × 10-3 cm2/V, observed in an as-grown (non-electro-migrated) CZT detector. The optimum radiation detector material would have minimum concentration of deep traps required for compensation.

Perspectives on the rapid eye movement sleep switch in rapid eye movement sleep behavior disorder.

PubMed

Ramaligam, Vetrivelan; Chen, Michael C; Saper, Clifford B; Lu, Jun

2013-08-01

Rapid eye movement (REM) sleep in mammals is associated with wakelike cortical and hippocampal activation and concurrent postural muscle atonia. Research during the past 5 decades has revealed the details of the neural circuitry regulating REM sleep and muscle atonia during this state. REM-active glutamatergic neurons in the sublaterodorsal nucleus (SLD) of the dorsal pons are critical for generation for REM sleep atonia. Descending projections from SLD glutamatergic neurons activate inhibitory premotor neurons in the ventromedial medulla (VMM) and in the spinal cord to antagonize the glutamatergic supraspinal inputs on the motor neurons during REM sleep. REM sleep behavior disorder (RBD) consists of simple behaviors (i.e., twitching, jerking) and complex behaviors (i.e., defensive behavior, talking). Animal research has lead to the hypothesis that complex behaviors in RBD are due to SLD pathology, while simple behaviors of RBD may be due to less severe SLD pathology or dysfunction of the VMM, ventral pons, or spinal cord. Copyright © 2013 Elsevier B.V. All rights reserved.

Purification of CdZnTe by Electromigration

DOE PAGES

Kim, K.; Kim, Sangsu; Hong, Jinki; ...

2015-04-14

Electro-migration of ionized/electrically active impurities in CdZnTe (CZT) was successfully demonstrated at elevated temperature with an electric field of 20 V/mm. Copper, which exists in positively charged states, electro-migrated at a speed of 15 lm/h in an electric field of 20 V/mm. A notable variation in impurity concentration along the growth direction with the segregation tendency of the impurities was observed in an electro-migrated CZT boule. Notably, both Ga and Fe, which exist in positively charged states, exhibited the opposite distribution to that of their segregation tendency in Cd(Zn)Te. Furthermore, a CZT detector fabricated from the middle portion of themore » electromigrated CZT boule showed an improved mobility-lifetime product of 0.91 10 -2 cm 2 /V, compared to that of 1.4 10 -3 cm 2 /V, observed in an as-grown (non-electro-migrated) CZT detector. The optimum radiation detector material would have minimum concentration of deep traps required for compensation.« less

Support vector machine in machine condition monitoring and fault diagnosis

NASA Astrophysics Data System (ADS)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

Accuracy of Implant Position Transfer and Surface Detail Reproduction with Different Impression Materials and Techniques

PubMed Central

Alikhasi, Marzieh; Siadat, Hakimeh; Kharazifard, Mohammad Javad

2015-01-01

Objectives: The purpose of this study was to compare the accuracy of implant position transfer and surface detail reproduction using two impression techniques and materials. Materials and Methods: A metal model with two implants and three grooves of 0.25, 0.50 and 0.75 mm in depth on the flat superior surface of a die was fabricated. Ten regular-body polyether (PE) and 10 regular-body polyvinyl siloxane (PVS) impressions with square and conical transfer copings using open tray and closed tray techniques were made for each group. Impressions were poured with type IV stone, and linear and angular displacements of the replica heads were evaluated using a coordinate measuring machine (CMM). Also, accurate reproduction of the grooves was evaluated by a video measuring machine (VMM). These measurements were compared with the measurements calculated on the reference model that served as control, and the data were analyzed with two-way ANOVA and t-test at P= 0.05. Results: There was less linear displacement for PVS and less angular displacement for PE in closed-tray technique, and less linear displacement for PE in open tray technique (P<0.001). Also, the open tray technique showed less angular displacement with the use of PVS impression material. Detail reproduction accuracy was the same in all the groups (P>0.05). Conclusion: The open tray technique was more accurate using PE, and also both closed tray and open tray techniques had acceptable results with the use of PVS. The choice of impression material and technique made no significant difference in surface detail reproduction. PMID:27252761

THE CONSTRUCTION AND VALIDATION OF A MEASURE OF VOCATIONAL MATURITY.

ERIC Educational Resources Information Center

CLARY, JOE R.; WESTBROOK, BERT W.

THIS REPORT DEALS WITH THE ORGANIZATION, RATIONALE, METHODS AND EXPECTED END-PRODUCTS OF A RESEARCH PROJECT (SCHEDULED FOR COMPLETION ON JUNE 23, 1970) FOR THE CONSTRUCTION AND VALIDATION OF A RELIABLE VOCATIONAL MATURITY MEASURE (VMM). THE PROJECT'S EIGHT PHASES AND ACTIVITIES ARE EXPLAINED. THE PROJECT ASSUMES--(1) THE INDIVIDUAL AND SOCIETY AS…

Forty Gb/s hybrid silicon Mach-Zehnder modulator with low chirp.

PubMed

Chen, Hui-Wen; Peters, Jonathan D; Bowers, John E

2011-01-17

We demonstrate a hybrid silicon modulator operating up to 40 Gb/s with 11.4 dB extinction ratio. The modulator has voltage-length product of 2.4 V-mm and chirp of -0.75 over the entire bias range. As a switch, it has a switching time less than 20 ps.

Memristor-Based Analog Computation and Neural Network Classification with a Dot Product Engine.

PubMed

Hu, Miao; Graves, Catherine E; Li, Can; Li, Yunning; Ge, Ning; Montgomery, Eric; Davila, Noraica; Jiang, Hao; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei; Strachan, John Paul

2018-03-01

Using memristor crossbar arrays to accelerate computations is a promising approach to efficiently implement algorithms in deep neural networks. Early demonstrations, however, are limited to simulations or small-scale problems primarily due to materials and device challenges that limit the size of the memristor crossbar arrays that can be reliably programmed to stable and analog values, which is the focus of the current work. High-precision analog tuning and control of memristor cells across a 128 × 64 array is demonstrated, and the resulting vector matrix multiplication (VMM) computing precision is evaluated. Single-layer neural network inference is performed in these arrays, and the performance compared to a digital approach is assessed. Memristor computing system used here reaches a VMM accuracy equivalent of 6 bits, and an 89.9% recognition accuracy is achieved for the 10k MNIST handwritten digit test set. Forecasts show that with integrated (on chip) and scaled memristors, a computational efficiency greater than 100 trillion operations per second per Watt is possible. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Morphology dependent field emission characteristics of ZnS/silicon nanoporous pillar array

NASA Astrophysics Data System (ADS)

Wang, Ling Li; Zhao, Cheng Zhou; Kang, Li Ping; Liu, De Wei; Zhao, Hui Chun; Hao, Shan Peng; Zhang, Yuan Kai; Chen, Zhen Ping; Li, Xin Jian

2016-10-01

Through depositing zinc sulphide (ZnS) nanoparticals on silicon nanoporous pillar array (Si-NPA) and crater-shaped silicon nanoporous pillar array (c-Si-NPA) by chemical bath deposition (CBD) method, ZnS/Si-NPA and c-ZnS/Si-NPA were prepared and the field emission (FE) properties of them were investigated. The turn-on electric fields of were 3.8 V/mm for ZnS/Si-NPA and 5.0 V/mm for c-ZnS/Si-NPA, respectively. The lower turn-on electric fields of ZnS/Si-NPA than that of c-ZnS/Si-NPA were attributed to the different electric distribution of the field emitters causing by the different surface morphology of the two samples, which was further demonstrated via the simulated results by finite element modeling. The FN curves for the ZnS/Si-NPA showed two-slope behavior. All the results indicate that the morphology play an important role in the FE properties and designing an appropriate top morphology for the emitter is a very efficient way to improve the FE performance.

Actualities and Development of Heavy-Duty CNC Machine Tool Thermal Error Monitoring Technology

NASA Astrophysics Data System (ADS)

Zhou, Zu-De; Gui, Lin; Tan, Yue-Gang; Liu, Ming-Yao; Liu, Yi; Li, Rui-Ya

2017-09-01

Thermal error monitoring technology is the key technological support to solve the thermal error problem of heavy-duty CNC (computer numerical control) machine tools. Currently, there are many review literatures introducing the thermal error research of CNC machine tools, but those mainly focus on the thermal issues in small and medium-sized CNC machine tools and seldom introduce thermal error monitoring technologies. This paper gives an overview of the research on the thermal error of CNC machine tools and emphasizes the study of thermal error of the heavy-duty CNC machine tool in three areas. These areas are the causes of thermal error of heavy-duty CNC machine tool and the issues with the temperature monitoring technology and thermal deformation monitoring technology. A new optical measurement technology called the "fiber Bragg grating (FBG) distributed sensing technology" for heavy-duty CNC machine tools is introduced in detail. This technology forms an intelligent sensing and monitoring system for heavy-duty CNC machine tools. This paper fills in the blank of this kind of review articles to guide the development of this industry field and opens up new areas of research on the heavy-duty CNC machine tool thermal error.

[Sequelae of unilateral deep venous thrombosis in plethysmography of the calf].

PubMed

Zicot, M; Depairon, M

1982-01-01

Twenty four patients suffering from unilateral venous disturbances revealed by Doppler and secondary to a deep venous thrombosis were examined. The calf venous haemodynamics was analyzed by use of a strain-jauge plethysmograph. We determined the increase in venous volume due to the inflation of a thigh pneumatic cuff (pressure at 20, 40 and 60 mm Hg; delta V20, delta V40, delta V60). The maximal venous output (Vout) was measured after a quick release of the 60 mm Hg pressure. The maximal venous drainage (VMM) was assessed during a rhythmic exercise (tiptoeing) while standing; delta V20, delta V40 and delta V60 were nearly constantly reduced on the abnormal side (t of Student respectively 3.49; 6.09 and 5.07). Vout dropped proportionaly to delta V60. Some abnormalities due to valvular insufficiency were frequently present in the beginning of the inflation curve at the level of the abnormal limbs. VMM was nearly always largely decreased on the affected side (t = 5.43). The unilateral flow disturbances displayed by the Doppler were regularly going with abnormalities of the capacitive system, well demonstrated by comparison with the non-affected limbs.

Tool path strategy and cutting process monitoring in intelligent machining

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Anisotropic Laminar Piezocomposite Actuator Incorporating Machined PMN-PT Single Crystal Fibers

NASA Technical Reports Server (NTRS)

Wilkie, W. Keats; Inman, Daniel J.; Lloyd, Justin M.; High, James W.

2006-01-01

The design, fabrication, and testing of a flexible, laminar, anisotropic piezoelectric composite actuator utilizing machined PMN-32%PT single crystal fibers is presented. The device consists of a layer of rectangular single crystal piezoelectric fibers in an epoxy matrix, packaged between interdigitated electrode polyimide films. Quasistatic free-strain measurements of the single crystal device are compared with measurements from geometrically identical specimens incorporating polycrystalline PZT-5A and PZT-5H piezoceramic fibers. Free-strain actuation of the single crystal actuator at low bipolar electric fields (+/- 250 V/mm) is approximately 400% greater than that of the baseline PZT-5A piezoceramic device, and 200% greater than that of the PZT-5H device. Free-strain actuation under high unipolar electric fields (0-4kV/mm) is approximately 200% of the PZT-5A baseline device, and 150% of the PZT-5H alternate piezoceramic device. Performance increases at low field are qualitatively consistent with predicted increases based on scaling the low-field d33 piezoelectric constants of the respective piezoelectric materials. High-field increases are much less than scaled d33 estimates, but appear consistent with high-field freestrain measurements reported for similar bulk single-crystal and piezoceramic compositions. Measurements of single crystal actuator capacitance and coupling coefficient are also provided. These properties were poorly predicted using scaled bulk material dielectric and coupling coefficient data. Rules-of-mixtures calculations of the effective elastic properties of the single crystal device and estimated actuation work energy densities are also presented. Results indicate longitudinal stiffnesses significantly lower (50% less) than either piezoceramic device. This suggests that single-crystal piezocomposite actuators will be best suited to low induced-stress, high strain and deflection applications.

Anisotropic Piezocomposite Actuator Incorporating Machined PMN-PT Single Crystal Fibers

NASA Technical Reports Server (NTRS)

Wilkie, W. Keats; Inman, Daniel J.; Lloyd, Justin M.; High, James W.

2004-01-01

The design, fabrication, and testing of a flexible, planar, anisotropic piezoelectric composite actuator utilizing machined PMN-32%PT single crystal fibers is presented. The device consists of a layer of rectangular single crystal piezoelectric fibers in an epoxy matrix, packaged between interdigitated electrode polyimide films. Quasistatic free-strain measurements of the single crystal device are compared with measurements from geometrically identical specimens incorporating polycrystalline PZT-5A and PZT-5H piezoceramic fibers. Free-strain actuation of the single crystal actuator at low bipolar electric fields (+/- 250 V/mm) is approximately 400% greater than that of the baseline PZT-5A piezoceramic device, and 200% greater than that of the PZT-5H device. Free-strain actuation under high unipolar electric fields (0-4kV/mm) is approximately 200% of the PZT-5A baseline device, and 150% of the PZT-5H alternate piezoceramic device. Performance increases at low field are qualitatively consistent with predicted increases based on scaling the low-field d(sub 33) piezoelectric constants of the respective piezoelectric materials. High-field increases are much less than scaled d(sub 33) estimates, but appear consistent with high-field freestrain measurements reported for similar bulk single-crystal and piezoceramic compositions. Measurements of single crystal actuator capacitance and coupling coefficient are also provided. These properties were poorly predicted using scaled bulk material dielectric and coupling coefficient data. Rules-of-mixtures calculations of the effective elastic properties of the single crystal device and estimated actuation work energy densities are also presented. Results indicate longitudinal stiffnesses significantly lower (50% less) than either piezoceramic device. This suggests that single-crystal piezocomposite actuators will be best suited to low induced-stress, high strain and deflection applications.

Exploiting GPUs in Virtual Machine for BioCloud

PubMed Central

Jo, Heeseung; Jeong, Jinkyu; Lee, Myoungho; Choi, Dong Hoon

2013-01-01

Recently, biological applications start to be reimplemented into the applications which exploit many cores of GPUs for better computation performance. Therefore, by providing virtualized GPUs to VMs in cloud computing environment, many biological applications will willingly move into cloud environment to enhance their computation performance and utilize infinite cloud computing resource while reducing expenses for computations. In this paper, we propose a BioCloud system architecture that enables VMs to use GPUs in cloud environment. Because much of the previous research has focused on the sharing mechanism of GPUs among VMs, they cannot achieve enough performance for biological applications of which computation throughput is more crucial rather than sharing. The proposed system exploits the pass-through mode of PCI express (PCI-E) channel. By making each VM be able to access underlying GPUs directly, applications can show almost the same performance as when those are in native environment. In addition, our scheme multiplexes GPUs by using hot plug-in/out device features of PCI-E channel. By adding or removing GPUs in each VM in on-demand manner, VMs in the same physical host can time-share their GPUs. We implemented the proposed system using the Xen VMM and NVIDIA GPUs and showed that our prototype is highly effective for biological GPU applications in cloud environment. PMID:23710465

Exploiting GPUs in virtual machine for BioCloud.

PubMed

Jo, Heeseung; Jeong, Jinkyu; Lee, Myoungho; Choi, Dong Hoon

2013-01-01

Recently, biological applications start to be reimplemented into the applications which exploit many cores of GPUs for better computation performance. Therefore, by providing virtualized GPUs to VMs in cloud computing environment, many biological applications will willingly move into cloud environment to enhance their computation performance and utilize infinite cloud computing resource while reducing expenses for computations. In this paper, we propose a BioCloud system architecture that enables VMs to use GPUs in cloud environment. Because much of the previous research has focused on the sharing mechanism of GPUs among VMs, they cannot achieve enough performance for biological applications of which computation throughput is more crucial rather than sharing. The proposed system exploits the pass-through mode of PCI express (PCI-E) channel. By making each VM be able to access underlying GPUs directly, applications can show almost the same performance as when those are in native environment. In addition, our scheme multiplexes GPUs by using hot plug-in/out device features of PCI-E channel. By adding or removing GPUs in each VM in on-demand manner, VMs in the same physical host can time-share their GPUs. We implemented the proposed system using the Xen VMM and NVIDIA GPUs and showed that our prototype is highly effective for biological GPU applications in cloud environment.

Learning for VMM + WTA Embedded Classifiers

DTIC Science & Technology

2016-03-31

enabling correct classification of each novel acoustic signal (generator, idle car , and idle truck). The classification structure requires, after...measured on our SoC FPAA IC. The test input is composed of signals from urban environment for 3 objects (generator, idle car , and idle truck...classifier results from a rural truck data set, an urban generator set, and urban idle car dataset. Solid lines represent our extracted background

Monitoring Temperature and Fan Speed Using Ganglia and Winbond Chips

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

McCaffrey, Cattie; /SLAC

2006-09-27

Effective monitoring is essential to keep a large group of machines, like the ones at Stanford Linear Accelerator Center (SLAC), up and running. SLAC currently uses Ganglia Monitoring System to observe about 2000 machines, analyzing metrics like CPU usage and I/O rate. However, metrics essential to machine hardware health, such as temperature and fan speed, are not being monitored. Many machines have a Winbond w83782d chip which monitors three temperatures, two of which come from dual CPUs, and returns the information when the sensor command is invoked. Ganglia also provides a feature, gmetric, that allows the users to monitor theirmore » own metrics and incorporate them into the monitoring system. The programming language Perl is chosen to implement a script that invokes the sensors command, extracts the temperature and fan speed information, and calls gmetric with the appropriate arguments. Two machines were used to test the script; the two CPUs on each machine run at about 65 Celsius, which is well within the operating temperature range (The maximum safe temperature range is 77-82 Celsius for the Pentium III processors being used). Installing the script on all machines with a Winbond w83782d chip allows the SLAC Scientific Computing and Computing Services group (SCCS) to better evaluate current cooling methods.« less

Method and apparatus for monitoring machine performance

DOEpatents

Smith, Stephen F.; Castleberry, Kimberly N.

1996-01-01

Machine operating conditions can be monitored by analyzing, in either the time or frequency domain, the spectral components of the motor current. Changes in the electric background noise, induced by mechanical variations in the machine, are correlated to changes in the operating parameters of the machine.

Preliminary Development of Real Time Usage-Phase Monitoring System for CNC Machine Tools with a Case Study on CNC Machine VMC 250

NASA Astrophysics Data System (ADS)

Budi Harja, Herman; Prakosa, Tri; Raharno, Sri; Yuwana Martawirya, Yatna; Nurhadi, Indra; Setyo Nogroho, Alamsyah

2018-03-01

The production characteristic of job-shop industry at which products have wide variety but small amounts causes every machine tool will be shared to conduct production process with dynamic load. Its dynamic condition operation directly affects machine tools component reliability. Hence, determination of maintenance schedule for every component should be calculated based on actual usage of machine tools component. This paper describes study on development of monitoring system to obtaining information about each CNC machine tool component usage in real time approached by component grouping based on its operation phase. A special device has been developed for monitoring machine tool component usage by utilizing usage phase activity data taken from certain electronics components within CNC machine. The components are adaptor, servo driver and spindle driver, as well as some additional components such as microcontroller and relays. The obtained data are utilized for detecting machine utilization phases such as power on state, machine ready state or spindle running state. Experimental result have shown that the developed CNC machine tool monitoring system is capable of obtaining phase information of machine tool usage as well as its duration and displays the information at the user interface application.

Self-assembled software and method of overriding software execution

DOEpatents

Bouchard, Ann M.; Osbourn, Gordon C.

2013-01-08

A computer-implemented software self-assembled system and method for providing an external override and monitoring capability to dynamically self-assembling software containing machines that self-assemble execution sequences and data structures. The method provides an external override machine that can be introduced into a system of self-assembling machines while the machines are executing such that the functionality of the executing software can be changed or paused without stopping the code execution and modifying the existing code. Additionally, a monitoring machine can be introduced without stopping code execution that can monitor specified code execution functions by designated machines and communicate the status to an output device.

Connecting American Manufacturers (CAM) Virtual Manufacturing Marketplace (VMM)

DTIC Science & Technology

2013-11-01

88ABW-2013-5037 1.0 SUMMARY The Connecting American Manufacturing (CAM) initiative was designed to improve the sourcing capability for Department...addition, CAM was designed to increase the number of US companies bidding on DoD business. The team’s overall approach was based on three major...is to designate a North American Industry Classification System (NAICS) code for each opportunity. Every manufacturing opportunity is posted to

Sharpless-76E: astrometry and outflows in a protostellar cluster

NASA Astrophysics Data System (ADS)

Chibueze, James O.; Hamabata, Hideo; Nagayama, Takumi; Omodaka, Toshihiro; Handa, Toshihiro; Sunada, Kazuyoshi; Nakano, Makoto; Ueno, Yuji

2017-04-01

Using VLBI Exploration of Radio Astrometry, we have conducted multi-epoch observations of the H2O masers associated with Sharpless 76E. The measured annual parallax is 0.521 ± 0.024 mas corresponding to the distance of 1.92^{+0.09}_{-0.08} kpc. From the parallax measurement, we obtained the peculiar motion of Sh2-76EMM1 (UMM1, VMM1, WMM1) to be (-9 ± 3, 10 ± 4, 6 ± 4) km s-1and Sh2-76EMM2 (UMM2, VMM2, WMM2) to be (-5 ± 12, 3 ± 14, -21 ± 22) km s-1, where U, V and W are directed towards the Galactic Centre, in the direction of Galactic rotation and towards the Galactic north pole, respectively. The internal motion of the H2O masers trace two separate bipolar outflows, one associated with Sh2-76EMM1 and the other with Sh2-76EMM2. The spectral energy distribution (SED) of Sh2-76EMM1 suggests it to be a class I YSO. We have only limited data points for the SED fit of Sh2-76EMM2, therefore can only speculate it to be probably a class II based on its comparative K-band and H-band magnitudes.

A Differential Monolithically Integrated Inductive Linear Displacement Measurement Microsystem

PubMed Central

Podhraški, Matija; Trontelj, Janez

2016-01-01

An inductive linear displacement measurement microsystem realized as a monolithic Application-Specific Integrated Circuit (ASIC) is presented. The system comprises integrated microtransformers as sensing elements, and analog front-end electronics for signal processing and demodulation, both jointly fabricated in a conventional commercially available four-metal 350-nm CMOS process. The key novelty of the presented system is its full integration, straightforward fabrication, and ease of application, requiring no external light or magnetic field source. Such systems therefore have the possibility of substituting certain conventional position encoder types. The microtransformers are excited by an AC signal in MHz range. The displacement information is modulated into the AC signal by a metal grating scale placed over the microsystem, employing a differential measurement principle. Homodyne mixing is used for the demodulation of the scale displacement information, returned by the ASIC as a DC signal in two quadrature channels allowing the determination of linear position of the target scale. The microsystem design, simulations, and characterization are presented. Various system operating conditions such as frequency, phase, target scale material and distance have been experimentally evaluated. The best results have been achieved at 4 MHz, demonstrating a linear resolution of 20 µm with steel and copper scale, having respective sensitivities of 0.71 V/mm and 0.99 V/mm. PMID:26999146

A Differential Monolithically Integrated Inductive Linear Displacement Measurement Microsystem.

PubMed

Podhraški, Matija; Trontelj, Janez

2016-03-17

An inductive linear displacement measurement microsystem realized as a monolithic Application-Specific Integrated Circuit (ASIC) is presented. The system comprises integrated microtransformers as sensing elements, and analog front-end electronics for signal processing and demodulation, both jointly fabricated in a conventional commercially available four-metal 350-nm CMOS process. The key novelty of the presented system is its full integration, straightforward fabrication, and ease of application, requiring no external light or magnetic field source. Such systems therefore have the possibility of substituting certain conventional position encoder types. The microtransformers are excited by an AC signal in MHz range. The displacement information is modulated into the AC signal by a metal grating scale placed over the microsystem, employing a differential measurement principle. Homodyne mixing is used for the demodulation of the scale displacement information, returned by the ASIC as a DC signal in two quadrature channels allowing the determination of linear position of the target scale. The microsystem design, simulations, and characterization are presented. Various system operating conditions such as frequency, phase, target scale material and distance have been experimentally evaluated. The best results have been achieved at 4 MHz, demonstrating a linear resolution of 20 µm with steel and copper scale, having respective sensitivities of 0.71 V/mm and 0.99 V/mm.

Information integration and diagnosis analysis of equipment status and production quality for machining process

NASA Astrophysics Data System (ADS)

Zan, Tao; Wang, Min; Hu, Jianzhong

2010-12-01

Machining status monitoring technique by multi-sensors can acquire and analyze the machining process information to implement abnormity diagnosis and fault warning. Statistical quality control technique is normally used to distinguish abnormal fluctuations from normal fluctuations through statistical method. In this paper by comparing the advantages and disadvantages of the two methods, the necessity and feasibility of integration and fusion is introduced. Then an approach that integrates multi-sensors status monitoring and statistical process control based on artificial intelligent technique, internet technique and database technique is brought forward. Based on virtual instrument technique the author developed the machining quality assurance system - MoniSysOnline, which has been used to monitoring the grinding machining process. By analyzing the quality data and AE signal information of wheel dressing process the reason of machining quality fluctuation has been obtained. The experiment result indicates that the approach is suitable for the status monitoring and analyzing of machining process.

Process Monitoring Evaluation and Implementation for the Wood Abrasive Machining Process

PubMed Central

Saloni, Daniel E.; Lemaster, Richard L.; Jackson, Steven D.

2010-01-01

Wood processing industries have continuously developed and improved technologies and processes to transform wood to obtain better final product quality and thus increase profits. Abrasive machining is one of the most important of these processes and therefore merits special attention and study. The objective of this work was to evaluate and demonstrate a process monitoring system for use in the abrasive machining of wood and wood based products. The system developed increases the life of the belt by detecting (using process monitoring sensors) and removing (by cleaning) the abrasive loading during the machining process. This study focused on abrasive belt machining processes and included substantial background work, which provided a solid base for understanding the behavior of the abrasive, and the different ways that the abrasive machining process can be monitored. In addition, the background research showed that abrasive belts can effectively be cleaned by the appropriate cleaning technique. The process monitoring system developed included acoustic emission sensors which tended to be sensitive to belt wear, as well as platen vibration, but not loading, and optical sensors which were sensitive to abrasive loading. PMID:22163477

Real-Time Deflection Monitoring for Milling of a Thin-Walled Workpiece by Using PVDF Thin-Film Sensors with a Cantilevered Beam as a Case Study

PubMed Central

Luo, Ming; Liu, Dongsheng; Luo, Huan

2016-01-01

Thin-walled workpieces, such as aero-engine blisks and casings, are usually made of hard-to-cut materials. The wall thickness is very small and it is easy to deflect during milling process under dynamic cutting forces, leading to inaccurate workpiece dimensions and poor surface integrity. To understand the workpiece deflection behavior in a machining process, a new real-time nonintrusive method for deflection monitoring is presented, and a detailed analysis of workpiece deflection for different machining stages of the whole machining process is discussed. The thin-film polyvinylidene fluoride (PVDF) sensor is attached to the non-machining surface of the workpiece to copy the deflection excited by the dynamic cutting force. The relationship between the input deflection and the output voltage of the monitoring system is calibrated by testing. Monitored workpiece deflection results show that the workpiece experiences obvious vibration during the cutter entering the workpiece stage, and vibration during the machining process can be easily tracked by monitoring the deflection of the workpiece. During the cutter exiting the workpiece stage, the workpiece experiences forced vibration firstly, and free vibration exists until the amplitude reduces to zero after the cutter exits the workpiece. Machining results confirmed the suitability of the deflection monitoring system for machining thin-walled workpieces with the application of PVDF sensors. PMID:27626424

Calorie restriction as an anti-invasive therapy for malignant brain cancer in the VM mouse.

PubMed

Shelton, Laura M; Huysentruyt, Leanne C; Mukherjee, Purna; Seyfried, Thomas N

2010-07-23

GBM (glioblastoma multiforme) is the most aggressive and invasive form of primary human brain cancer. We recently developed a novel brain cancer model in the inbred VM mouse strain that shares several characteristics with human GBM. Using bioluminescence imaging, we tested the efficacy of CR (calorie restriction) for its ability to reduce tumour size and invasion. CR targets glycolysis and rapid tumour cell growth in part by lowering circulating glucose levels. The VM-M3 tumour cells were implanted intracerebrally in the syngeneic VM mouse host. Approx. 12-15 days post-implantation, brains were removed and both ipsilateral and contralateral hemispheres were imaged to measure bioluminescence of invading tumour cells. CR significantly reduced the invasion of tumour cells from the implanted ipsilateral hemisphere into the contralateral hemisphere. The total percentage of Ki-67-stained cells within the primary tumour and the total number of blood vessels was also significantly lower in the CR-treated mice than in the mice fed ad libitum, suggesting that CR is anti-proliferative and anti-angiogenic. Our findings indicate that the VM-M3 GBM model is a valuable tool for studying brain tumour cell invasion and for evaluating potential therapeutic approaches for managing invasive brain cancer. In addition, we show that CR can be effective in reducing malignant brain tumour growth and invasion.

Elastic thickness determination based on Vening Meinesz-Moritz and flexural theories of isostasy

NASA Astrophysics Data System (ADS)

Eshagh, Mehdi

2018-06-01

Elastic thickness (Te) is one of mechanical properties of the Earth's lithosphere. The lithosphere is assumed to be a thin elastic shell, which is bended under the topographic, bathymetric and sediment loads on. The flexure of this elastic shell depends on its thickness or Te. Those shells having larger Te flex less. In this paper, a forward computational method is presented based on the Vening Meinesz-Moritz (VMM) and flexural theories of isostasy. Two Moho flexure models are determined using these theories, considering effects of surface and subsurface loads. Different values are selected for Te in the flexural method to see by which one, the closest Moho flexure to that of the VMM is achieved. The effects of topographic/bathymetric, sediments and crustal crystalline masses, and laterally variable upper mantle density, Young's modulus and Poisson's ratio are considered in whole computational process. Our mathematical derivations are based on spherical harmonics, which can be used to estimate Te at any single point, meaning that there is no edge effect in the method. However, the Te map needs to be filtered to remove noise at some points. A median filter with a window size of 5° × 5° and overlap of 4° works well for this purpose. The method is applied to estimate Te over South America using the data of CRUST1.0 and a global gravity model.

Novel tool wear monitoring method in milling difficult-to-machine materials using cutting chip formation

NASA Astrophysics Data System (ADS)

Zhang, P. P.; Guo, Y.; Wang, B.

2017-05-01

The main problems in milling difficult-to-machine materials are the high cutting temperature and rapid tool wear. However it is impossible to investigate tool wear in machining. Tool wear and cutting chip formation are two of the most important representations for machining efficiency and quality. The purpose of this paper is to develop the model of tool wear with cutting chip formation (width of chip and radian of chip) on difficult-to-machine materials. Thereby tool wear is monitored by cutting chip formation. A milling experiment on the machining centre with three sets cutting parameters was performed to obtain chip formation and tool wear. The experimental results show that tool wear increases gradually along with cutting process. In contrast, width of chip and radian of chip decrease. The model is developed by fitting the experimental data and formula transformations. The most of monitored errors of tool wear by the chip formation are less than 10%. The smallest error is 0.2%. Overall errors by the radian of chip are less than the ones by the width of chip. It is new way to monitor and detect tool wear by cutting chip formation in milling difficult-to-machine materials.

A Non-Proteinaceious Toxin from the Venomous Spines of the Lionfish Pterois Volitans (Linnaeus)

DTIC Science & Technology

1985-01-01

Spines of the Lionfish Pterois Volitans (Linnaeus) 6. PERFORMING ORG. REPORT NUMBER 7. AUTHOR(a) S. CONTRACT OR GRANT NUMBER(&) Nair, M.S.R. Cheung, P...and Identify by block number) Tinon Lionfish , Non-Proteinaceous Toxin, Ichthyotoxin 20. A9ST’ACT ? ( mt.e ,vmM N , mid ify by block nusmber) "!;The...COMMUNICATIONS A NON-PROTEINACEOUS TOXIN FROM THE VENOMOUS SPINES OF THE LIONFISH PTEROIS VOLITANS (LINNAEUS) 2 20 M. S. R. NAIR,’ PAUL CHEUNG, INA

Maximizing Computational Capability with Minimal Power

DTIC Science & Technology

2009-03-01

Chip -Scale Energy and Power... and Heat Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of...OpticalBench Mounting Posts Imager Chip LCDinterfaced withthecomputer P o l a r i z e r P o l a r i z e r XYZ Translator Optical Slide VMM Computational Pixel...Signal routing power / memory: ? Power does not include comm off chip (i.e. accessing memory) Power = ½ C Vdd2 f for CMOS Chip to Chip (10pF load min

An Approach to Providing a User Interface for Military Computer-Aided- Instruction in 1980

DTIC Science & Technology

1975-11-01

commercial terminals is the use of a microprocessor unit ( MPU ) LSI chip controller. This technology is flexible and economical •nd can be expected to...various «•gmentt. By using an MPU and developing a software capability, tha vendor can quickly and economically satisfy a large spsctrum of user...the basis for an effective and economical jser interface to military CAI systems. •a. sicumrv CLAMincATioH or THIS P**;:^*— D*. K*fn4) ^vmm m m r

Design Tools for Zero-Net Mass-Flux Separation Control Devices

DTIC Science & Technology

2004-12-01

experimental data. Most of the experimental studies employed either Hot Wire Anemometry (HWA), Particle Image Velocimetry (PIV) or Laser Doppler...To61 View traverse Y Z z to procdspor X * ’ probe I,it, I from laser Sbellows synthetic PMTs extender jet,,, olor i 200 mm 2 ", separator micro...measured using a laser displacement sensor Micro-Epsilon Model ILD2000-10. The sensitivity is 1 V/mm, with a full-scale range of 10 mm and a resolution of

Multi-parameter monitoring of electrical machines using integrated fibre Bragg gratings

NASA Astrophysics Data System (ADS)

Fabian, Matthias; Hind, David; Gerada, Chris; Sun, Tong; Grattan, Kenneth T. V.

2017-04-01

In this paper a sensor system for multi-parameter electrical machine condition monitoring is reported. The proposed FBG-based system allows for the simultaneous monitoring of machine vibration, rotor speed and position, torque, spinning direction, temperature distribution along the stator windings and on the rotor surface as well as the stator wave frequency. This all-optical sensing solution reduces the component count of conventional sensor systems, i.e., all 48 sensing elements are contained within the machine operated by a single sensing interrogation unit. In this work, the sensing system has been successfully integrated into and tested on a permanent magnet motor prototype.

Development of system decision support tools for behavioral trends monitoring of machinery maintenance in a competitive environment

NASA Astrophysics Data System (ADS)

Adeyeri, Michael Kanisuru; Mpofu, Khumbulani

2017-06-01

The article is centred on software system development for manufacturing company that produces polyethylene bags using mostly conventional machines in a competitive world where each business enterprise desires to stand tall. This is meant to assist in gaining market shares, taking maintenance and production decisions by the dynamism and flexibilities embedded in the package as customers' demand varies under the duress of meeting the set goals. The production and machine condition monitoring software (PMCMS) is programmed in C# and designed in such a way to support hardware integration, real-time machine conditions monitoring, which is based on condition maintenance approach, maintenance decision suggestions and suitable production strategies as the demand for products keeps changing in a highly competitive environment. PMCMS works with an embedded device which feeds it with data from the various machines being monitored at the workstation, and the data are read at the base station through transmission via a wireless transceiver and stored in a database. A case study was used in the implementation of the developed system, and the results show that it can monitor the machine's health condition effectively by displaying machines' health status, gives repair suggestions to probable faults, decides strategy for both production methods and maintenance, and, thus, can enhance maintenance performance obviously.

A microcomputer network for control of a continuous mining machine

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

Schiffbauer, W.H.

1993-12-31

This report details a microcomputer-based control and monitoring network that was developed in-house by the U.S. Bureau of Mines and installed on a continuous mining machine. The network consists of microcomputers that are connected together via a single twisted-pair cable. Each microcomputer was developed to provide a particular function in the control process. Machine-mounted microcomputers, in conjunction with the appropriate sensors, provide closed-loop control of the machine, navigation, and environmental monitoring. Off-the-machine microcomputers provide remote control of the machine, sensor status, and a connection to the network so that external computers can access network data and control the continuous miningmore » machine. Because of the network`s generic structure, it can be installed on most mining machines.« less

PCD tool wear and its monitoring in machining tungsten

NASA Astrophysics Data System (ADS)

Wang, Lijiang; Zhang, Zhenlie; Sun, Qi; Liu, Pin

The views of Chinese and foreign researchers are quite different as to whether or not polycrystalline diamond (PCD) tools can machine tungsten that is used in the aerospace and electronic industries. A study is presented that shows the possibility of machining tungsten, and a new method is developed for monitoring the tool wear in production.

A control technology evaluation of state-of-the-art, perchloroethylene dry-cleaning machines.

PubMed

Earnest, G Scott

2002-05-01

NIOSH researchers evaluated the ability of fifth-generation dry-cleaning machines to control occupational exposure to perchloroethylene (PERC). Use of these machines is mandated in some countries; however, less than 1 percent of all U.S. shops have them. A study was conducted at a U.S. dry-cleaning shop where two fifth-generation machines were used. Both machines had a refrigerated condenser as a primary control and a carbon adsorber as a secondary control to recover PERC vapors during the dry cycle. These machines were designed to lower the PERC concentration in the cylinder at the end of the dry cycle to below 290 ppm. A single-beam infrared photometer continuously monitors the PERC concentration in the machine cylinder, and a door interlock prevents opening until the concentration is below 290 ppm. Personal breathing zone air samples were measured for the machine operator and presser. The operator had time-weighted average (TWA) PERC exposures that were less than 2 ppm. Highest exposures occurred during loading and unloading the machine and when performing routine machine maintenance. All presser samples were below the limit of detection. Real-time video exposure monitoring showed that the operator had peak exposures near 160 ppm during loading and unloading the machine (below the OSHA maximum of 300 ppm). This exposure (160 ppm) is an order of magnitude lower than exposures with more traditional machines that are widely used in the United States. The evaluated machines were very effective at reducing TWA PERC exposures as well as peak exposures that occur during machine loading and unloading. State-of-the-art dry-cleaning machines equipped with refrigerated condensers, carbon adsorbers, drum monitors, and door interlocks can provide substantially better protection than more traditional machines that are widely used in the United States.

Microcomputer network for control of a continuous mining machine. Information circular/1993

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

Schiffbauer, W.H.

1993-01-01

The paper details a microcomputer-based control and monitoring network that was developed in-house by the U.S. Bureau of Mines, and installed on a Joy 14 continuous mining machine. The network consists of microcomputers that are connected together via a single twisted pair cable. Each microcomputer was developed to provide a particular function in the control process. Machine-mounted microcomputers in conjunction with the appropriate sensors provide closed-loop control of the machine, navigation, and environmental monitoring. Off-the-machine microcomputers provide remote control of the machine, sensor status, and a connection to the network so that external computers can access network data and controlmore » the continuous mining machine. Although the network was installed on a Joy 14 continuous mining machine, its use extends beyond it. Its generic structure lends itself to installation onto most mining machine types.« less

Using GPS to evaluate productivity and performance of forest machine systems

Treesearch

Steven E. Taylor; Timothy P. McDonald; Matthew W. Veal; Ton E. Grift

2001-01-01

This paper reviews recent research and operational applications of using GPS as a tool to help monitor the locations, travel patterns, performance, and productivity of forest machines. The accuracy of dynamic GPS data collected on forest machines under different levels of forest canopy is reviewed first. Then, the paper focuses on the use of GPS for monitoring forest...

Use of Advanced Machine-Learning Techniques for Non-Invasive Monitoring of Hemorrhage

DTIC Science & Technology

2010-04-01

that state-of-the-art machine learning techniques when integrated with novel non-invasive monitoring technologies could detect subtle, physiological...decompensation. Continuous, non-invasively measured hemodynamic signals (e.g., ECG, blood pressures, stroke volume) were used for the development of machine ... learning algorithms. Accuracy estimates were obtained by building models using 27 subjects and testing on the 28th. This process was repeated 28 times

Development of hardware system using temperature and vibration maintenance models integration concepts for conventional machines monitoring: a case study

NASA Astrophysics Data System (ADS)

Adeyeri, Michael Kanisuru; Mpofu, Khumbulani; Kareem, Buliaminu

2016-03-01

This article describes the integration of temperature and vibration models for maintenance monitoring of conventional machinery parts in which their optimal and best functionalities are affected by abnormal changes in temperature and vibration values thereby resulting in machine failures, machines breakdown, poor quality of products, inability to meeting customers' demand, poor inventory control and just to mention a few. The work entails the use of temperature and vibration sensors as monitoring probes programmed in microcontroller using C language. The developed hardware consists of vibration sensor of ADXL345, temperature sensor of AD594/595 of type K thermocouple, microcontroller, graphic liquid crystal display, real time clock, etc. The hardware is divided into two: one is based at the workstation (majorly meant to monitor machines behaviour) and the other at the base station (meant to receive transmission of machines information sent from the workstation), working cooperatively for effective functionalities. The resulting hardware built was calibrated, tested using model verification and validated through principles pivoted on least square and regression analysis approach using data read from the gear boxes of extruding and cutting machines used for polyethylene bag production. The results got therein confirmed related correlation existing between time, vibration and temperature, which are reflections of effective formulation of the developed concept.

Comparison of 5 reflectance meters for capillary blood glucose determination.

PubMed

Kolopp, M; Louis, J; Pointel, J P; Kohler, F; Drouin, P; Debry, G

1983-03-01

Manufacturing quality, accuracy and users opinion (i.e. medical and nurses staff and patients) were compared among five Destrostix reading reflectance-meters for home-blood-glucose-monitoring. Two machines (dextrometer and glucometer) equipped with microprocessors, integrated circuits and good quality wiring are best made. Reflectance-meter capillary blood glucose measurements were found to be accurate enough for home-blood-glucose-monitoring, compared to a reference method. However, two machines from the same brand were different in blood glucose accuracy. Glucocheck had poorest results. Users prefer small sized, battery powered machines. Glucometer appears to be best suited to home-blood-glucose-monitoring.

A Recommender System in the Cyber Defense Domain

DTIC Science & Technology

2014-03-27

monitoring software is a java based program sending updates to the database on the sensor machine. The host monitoring program gathers information about...3.2.2 Database. A MySQL database located on the sensor machine acts as the storage for the sensors on the network. Snort, Nmap, vulnerability scores, and...machine with the IDS and the recommender is labeled “sensor”. The recommender system code is written in java and compiled using java version 1.6.024

A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method.

PubMed

Li, Wutao; Huang, Zhigang; Lang, Rongling; Qin, Honglei; Zhou, Kai; Cao, Yongbin

2016-03-04

Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM) is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM) algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms) level and the monitoring time is on microsecond (μs) level, which make the proposed approach usable in practical interference monitoring applications.

A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method

PubMed Central

Li, Wutao; Huang, Zhigang; Lang, Rongling; Qin, Honglei; Zhou, Kai; Cao, Yongbin

2016-01-01

Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM) is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM) algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms) level and the monitoring time is on microsecond (μs) level, which make the proposed approach usable in practical interference monitoring applications. PMID:26959020

Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining

PubMed Central

Liang, Qiaokang; Zhang, Dan; Wu, Wanneng; Zou, Kunlin

2016-01-01

Multi-component cutting force sensing systems in manufacturing processes applied to cutting tools are gradually becoming the most significant monitoring indicator. Their signals have been extensively applied to evaluate the machinability of workpiece materials, predict cutter breakage, estimate cutting tool wear, control machine tool chatter, determine stable machining parameters, and improve surface finish. Robust and effective sensing systems with capability of monitoring the cutting force in machine operations in real time are crucial for realizing the full potential of cutting capabilities of computer numerically controlled (CNC) tools. The main objective of this paper is to present a brief review of the existing achievements in the field of multi-component cutting force sensing systems in modern manufacturing. PMID:27854322

Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining.

PubMed

Liang, Qiaokang; Zhang, Dan; Wu, Wanneng; Zou, Kunlin

2016-11-16

Multi-component cutting force sensing systems in manufacturing processes applied to cutting tools are gradually becoming the most significant monitoring indicator. Their signals have been extensively applied to evaluate the machinability of workpiece materials, predict cutter breakage, estimate cutting tool wear, control machine tool chatter, determine stable machining parameters, and improve surface finish. Robust and effective sensing systems with capability of monitoring the cutting force in machine operations in real time are crucial for realizing the full potential of cutting capabilities of computer numerically controlled (CNC) tools. The main objective of this paper is to present a brief review of the existing achievements in the field of multi-component cutting force sensing systems in modern manufacturing.

A Field Programmable Gate Array-Based Reconfigurable Smart-Sensor Network for Wireless Monitoring of New Generation Computer Numerically Controlled Machines

PubMed Central

Moreno-Tapia, Sandra Veronica; Vera-Salas, Luis Alberto; Osornio-Rios, Roque Alfredo; Dominguez-Gonzalez, Aurelio; Stiharu, Ion; de Jesus Romero-Troncoso, Rene

2010-01-01

Computer numerically controlled (CNC) machines have evolved to adapt to increasing technological and industrial requirements. To cover these needs, new generation machines have to perform monitoring strategies by incorporating multiple sensors. Since in most of applications the online Processing of the variables is essential, the use of smart sensors is necessary. The contribution of this work is the development of a wireless network platform of reconfigurable smart sensors for CNC machine applications complying with the measurement requirements of new generation CNC machines. Four different smart sensors are put under test in the network and their corresponding signal processing techniques are implemented in a Field Programmable Gate Array (FPGA)-based sensor node. PMID:22163602

A field programmable gate array-based reconfigurable smart-sensor network for wireless monitoring of new generation computer numerically controlled machines.

PubMed

Moreno-Tapia, Sandra Veronica; Vera-Salas, Luis Alberto; Osornio-Rios, Roque Alfredo; Dominguez-Gonzalez, Aurelio; Stiharu, Ion; Romero-Troncoso, Rene de Jesus

2010-01-01

Computer numerically controlled (CNC) machines have evolved to adapt to increasing technological and industrial requirements. To cover these needs, new generation machines have to perform monitoring strategies by incorporating multiple sensors. Since in most of applications the online Processing of the variables is essential, the use of smart sensors is necessary. The contribution of this work is the development of a wireless network platform of reconfigurable smart sensors for CNC machine applications complying with the measurement requirements of new generation CNC machines. Four different smart sensors are put under test in the network and their corresponding signal processing techniques are implemented in a Field Programmable Gate Array (FPGA)-based sensor node.

Validation of Material Models For Automotive Carbon Fiber Composite Structures Via Physical And Crash Testing (VMM Composites Project)

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

Coppola, Anthony; Faruque, Omar; Truskin, James F

As automotive fuel economy requirements increase, the push for reducing overall vehicle weight will likely include the consideration of materials that have not previously been part of mainstream vehicle design and manufacturing, including carbon fiber composites. Vehicle manufacturers currently rely on computer-aided engineering (CAE) methods as part of the design and development process, so going forward, the ability to accurately and predictably model carbon fiber composites will be necessary. If composites are to be used for structural components, this need applies to both, crash and quasi-static modeling. This final report covers the results of a five-year, $6.89M, 50% cost-shared researchmore » project between Department of Energy (DOE) and the US Advanced Materials Partnership (USAMP) under Cooperative Agreement DE-EE-0005661 known as “Validation of Material Models for Automotive Carbon Fiber Composite Structures Via Physical and Crash Testing (VMM).” The objective of the VMM Composites Project was to validate and assess the ability of physics-based material models to predict crash performance of automotive primary load-carrying carbon fiber composite structures. Simulation material models that were evaluated included micro-mechanics based meso-scale models developed by the University of Michigan (UM) and micro-plane models by Northwestern University (NWU) under previous collaborations with the DOE and Automotive Composites Consortium/USAMP, as well as five commercial crash codes: LS-DYNA, RADIOSS, VPS/PAM-CRASH, Abaqus, and GENOA-MCQ. CAE predictions obtained from seven organizations were compared with experimental results from quasi-static testing and dynamic crash testing of a thermoset carbon fiber composite front-bumper and crush-can (FBCC) system gathered under multiple loading conditions. This FBCC design was developed to demonstrate progressive crush, virtual simulation, tooling, fabrication, assembly, non-destructive evaluation and crash testing advances in order to assess the correlation of the predicted results to the physical tests. The FBCC was developed to meet a goal of 30-35% mass reduction while aiming for equivalent energy absorption as a steel component for which baseline experimental results were obtained from testing in the same crash modes. The project also evaluated crash performance of thermoplastic composite structures fabricated from commercial prepreg materials and low cost carbon fiber sourced from Oak Ridge National Laboratory. The VMM Project determined that no set of predictions from a CAE supplier were found to be universally accurate among all the six crash modes evaluated. In general, crash modes that were most dependent on the properties of the prepreg were more accurate than those that were dependent on the behavior of the joints. The project found that current CAE modeling methods or best practices for carbon fiber composites have not achieved standardization, and accuracy of CAE is highly reliant on the experience of its users. Coupon tests alone are not sufficient to develop an accurate material model, but it is necessary to bridge the gap between the coupon data and performance of the actual structure with a series of subcomponent level tests. Much of the unreliability of the predictions can be attributed to shortcomings in our ability to mathematically link the effects of manufacturing and material variability into the material models. This is a subject of ongoing research in the industry. The final report is organized by key technical tasks to describe how the validation project developed, modeled and compared crash data obtained on the composite FBCC to the multiple sets of CAE predictions. Highlights of the report include a discussion of the quantitative comparison between predictions and experimental data, as well as an in-depth discussion of remaining technological gaps that exist in the industry, which are intended to spur innovations and improvements in CAE technology.« less

[The design and experiment of multi-parameter water quality monitoring microsystem based on MOEMS microspectrometer].

PubMed

Wei, Kang-Lin; Wen, Zhi-Yu; Guo, Jian; Chen, Song-Bo

2012-07-01

Aiming at the monitoring and protecting of water resource environment, a multi-parameter water quality monitoring microsystem based on microspectrometer was put forward in the present paper. The microsystem is mainly composed of MOEMS microspectrometer, flow paths system and embedded measuring & controlling system. It has the functions of self-injecting samples and detection regents, automatic constant temperature, self -stirring, self- cleaning and samples' spectrum detection. The principle prototype machine of the microsystem was developed, and its structure principle was introduced in the paper. Through experiment research, it was proved that the principle prototype machine can rapidly detect quite a few water quality parameters and can meet the demands of on-line water quality monitoring, moreover, the principle prototype machine has strong function expansibility.

Using laser technological unit ALTI "Karavella" for precision components of IEP production

NASA Astrophysics Data System (ADS)

Labin, N. A.; Chursin, A. D.; Paramonov, V. S.; Klimenko, V. I.; Paramonova, G. M.; Kolokolov, I. S.; Vinogradov, K. Y.; Betina, L. L.; Bulychev, N. A.; Dyakov, Yu. A.; Zakharyan, R. A.; Kazaryan, M. A.; Koshelev, K. K.; Kosheleva, O. K.; Grigoryants, A. G.; Shiganov, I. N.; Krasovskii, V. I.; Sachkov, V. I.; Plyaka, P. S.; Feofanov, I. N.; Chen, C.

2015-12-01

The paper revealed the using of industrial production equipment ALTI "Karavella-1", "Karavella-1M", "Karavella-2" and "Karavella-2M" precision components of IEP production [1-4]. The basis for the ALTI using in the IEP have become the positive results of research and development of technologies of foil (0.01-0.2 mm) and thin sheets (0.3-1 mm) materials micromachining by pulsed radiation CVL [5, 6]. To assess the micromachining quality and precision the measuring optical microscope (UHL VMM200), projection microscope (Mitutoyo PV5100) and Carl Zeiss microscope were used.

Opportunities for leveraging OS virtualization in high-end supercomputing.

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

Bridges, Patrick G.; Pedretti, Kevin Thomas Tauke

2010-11-01

This paper examines potential motivations for incorporating virtualization support in the system software stacks of high-end capability supercomputers. We advocate that this will increase the flexibility of these platforms significantly and enable new capabilities that are not possible with current fixed software stacks. Our results indicate that compute, virtual memory, and I/O virtualization overheads are low and can be further mitigated by utilizing well-known techniques such as large paging and VMM bypass. Furthermore, since the addition of virtualization support does not affect the performance of applications using the traditional native environment, there is essentially no disadvantage to its addition.

Ion-implanted WN 0.25{mu}m gate MESFET fabricated using I-line photolithography for application to MMIC and digital IC

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

Oh, E.O.; Yang, J.W.; Park, C.S.

1995-12-31

Straightforward WN 0.25{mu}m gate MESFET process based on direct ion-implantation and i-line photolithography with double exposure process has produced high performance MESFETs. The maximum transconductance of 600mS/mm and the k-factor of 450ms/Vmm were obtained. As high as 65GHz of cut-off frequency has been realized without any deembedding of parasitic effects. The MESFET shows the minimum noise figure of 0.87 dB and the associated gain of 9.97dB at 12GHz.

Machine Learning Techniques in Clinical Vision Sciences.

PubMed

Caixinha, Miguel; Nunes, Sandrina

2017-01-01

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

A deviation based assessment methodology for multiple machine health patterns classification and fault detection

NASA Astrophysics Data System (ADS)

Jia, Xiaodong; Jin, Chao; Buzza, Matt; Di, Yuan; Siegel, David; Lee, Jay

2018-01-01

Successful applications of Diffusion Map (DM) in machine failure detection and diagnosis have been reported in several recent studies. DM provides an efficient way to visualize the high-dimensional, complex and nonlinear machine data, and thus suggests more knowledge about the machine under monitoring. In this paper, a DM based methodology named as DM-EVD is proposed for machine degradation assessment, abnormality detection and diagnosis in an online fashion. Several limitations and challenges of using DM for machine health monitoring have been analyzed and addressed. Based on the proposed DM-EVD, a deviation based methodology is then proposed to include more dimension reduction methods. In this work, the incorporation of Laplacian Eigen-map and Principal Component Analysis (PCA) are explored, and the latter algorithm is named as PCA-Dev and is validated in the case study. To show the successful application of the proposed methodology, case studies from diverse fields are presented and investigated in this work. Improved results are reported by benchmarking with other machine learning algorithms.

A comparison of temporal artery thermometers with internal blood monitors to measure body temperature during hemodialysis.

PubMed

Lunney, Meaghan; Tonelli, Bronwyn; Lewis, Rachel; Wiebe, Natasha; Thomas, Chandra; MacRae, Jennifer; Tonelli, Marcello

2018-06-14

Thermometers that measure core (internal) body temperature are the gold standard for monitoring temperature. Despite that most modern hemodialysis machines are equipped with an internal blood monitor that measures core body temperature, current practice is to use peripheral thermometers. A better understanding of how peripheral thermometers compare with the dialysis machine thermometer may help guide practice. The study followed a prospective cross-sectional design. Hemodialysis patients were recruited from 2 sites in Calgary, Alberta (April - June 2017). Body temperatures were obtained from peripheral (temporal artery) and dialysis machine thermometers concurrently. Paired t-tests, Bland-Altman plots, and quantile-quantile plots were used to compare measurements from the two devices and to explore potential factors affecting temperature in hemodialysis patients. The mean body temperature of 94 hemodialysis patients measured using the temporal artery thermometer (36.7 °C) was significantly different than the dialysis machine thermometer (36.4 °C); p < 0.001. The mean difference (0.27 °C) appeared to be consistent across average temperature (range: 35.8-37.3 °C). Temperature measured by the temporal artery thermometer was statistically and clinically higher than that measured by the dialysis machine thermometer. Using the dialysis machine to monitor body temperature may result in more accurate readings and is likely to reduce the purchasing and maintenance costs associated with manual temperature readings, as well as easing the workload for dialysis staff.

Design of an automatic production monitoring system on job shop manufacturing

NASA Astrophysics Data System (ADS)

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.

Effect of the lithospheric thermal state on the Moho interface: A case study in South America

NASA Astrophysics Data System (ADS)

Bagherbandi, Mohammad; Bai, Yongliang; Sjöberg, Lars E.; Tenzer, Robert; Abrehdary, Majid; Miranda, Silvia; Alcacer Sanchez, Juan M.

2017-07-01

Gravimetric methods applied for Moho recovery in areas with sparse and irregular distribution of seismic data often assume only a constant crustal density. Results of latest studies, however, indicate that corrections for crustal density heterogeneities could improve the gravimetric result, especially in regions with a complex geologic/tectonic structure. Moreover, the isostatic mass balance reflects also the density structure within the lithosphere. The gravimetric methods should therefore incorporate an additional correction for the lithospheric mantle as well as deeper mantle density heterogeneities. Following this principle, we solve the Vening Meinesz-Moritz (VMM) inverse problem of isostasy constrained by seismic data to determine the Moho depth of the South American tectonic plate including surrounding oceans, while taking into consideration the crustal and mantle density heterogeneities. Our numerical result confirms that contribution of sediments significantly modifies the estimation of the Moho geometry especially along the continental margins with large sediment deposits. To account for the mantle density heterogeneities we develop and apply a method in order to correct the Moho geometry for the contribution of the lithospheric thermal state (i.e., the lithospheric thermal-pressure correction). In addition, the misfit between the isostatic and seismic Moho models, attributed mainly to deep mantle density heterogeneities and other geophysical phenomena, is corrected for by applying the non-isostatic correction. The results reveal that the application of the lithospheric thermal-pressure correction improves the RMS fit of the VMM gravimetric Moho solution to the CRUST1.0 (improves ∼ 1.9 km) and GEMMA (∼1.1 km) models and the point-wise seismic data (∼0.7 km) in South America.

Dependences of the geometrical parameters of cell community on stimulation voltage and frequency in chick embryonic cardiomyocytes

NASA Astrophysics Data System (ADS)

Fujii, Koki; Nomura, Fumimasa; Kaneko, Tomoyuki

2018-03-01

To investigate the optimal conditions for electrical stimulation, communities of lined-up chick embryonic cardiomyocytes were evaluated in terms of their threshold voltage for pacing (PVMin) and the half-maximum paced frequency (PF50), with a focus on the following factors: (1) the orientation of the major axis of cell communities to the electric field (EF) direction as the external factor; (2) the number of cells in a cell community, the length of the cell community, and the mean length of cells comprising the community as the internal factors. Firstly, PVMin decreased with increasing length of the cell network oriented parallel to the EF. PVMin was approximately 0.041 ± 0.025 V/mm when the community was sufficiently long. On the other hand, PVMin in the orthogonal orientation was constant at 1.7 ± 0.047 V/mm with no dependence on the length of the cell network. Secondly, we found that PF50 increased with increasing length of the cell network or the number of cells in the network; the PF50 values were 2.03 ± 0.05 and 3.39 ± 0.05 Hz when the respective cell network lengths were 100 µm (n = 43) and more than 300 µm (n = 6) and the cells were oriented parallel to the EF. These findings indicate that it is important to suppress ventricular fibrillation with minimal efficient stimulation by considering the EF direction with respect to the orientation of cardiomyocytes. Furthermore, expanded cells showed the loss of ability to respond to stimulation at higher frequencies. Cardiomyocytes combined with seeded fibroblasts as a cell network at a low density are a possible model of a ventricular remodeling heart.

Agrochemical characterization of vermicomposts produced from residues of Palo Santo (Bursera graveolens) essential oil extraction.

PubMed

Carrión-Paladines, Vinicio; Fries, Andreas; Gómez-Muñoz, Beatriz; García-Ruiz, Roberto

2016-12-01

Fruits of Palo Santo (Bursera graveolens) are used for essential oil extraction. The extraction process is very efficient, because up to 3% of the fresh fruits can be transformed into essential oil; however, a considerable amount of waste is concurrently produced (>97% of the fresh biomass). Recent developments in Ecuadorian policies to foster environmentally friendly agroforestry and industrial practices have led to widespread interest in reusing the waste. This study evaluated the application of four vermicomposts (VMs), which are produced from the waste of the Palo Santo fruit distillation in combination with other raw materials (kitchen leftovers, pig manure, goat manure, and King Grass), for agrochemical use and for carbon (C) and nitrogen (N) decomposition in two soils with different textures. The results showed that the vermicompost mixtures (VMM) were valuable for agricultural utilisation, because total N (min. 2.63%) was relatively high and the C/N ratio (max. 13.3), as well as the lignin (max. 3.8%) and polyphenol (max. 1.6%) contents were low. In addition, N availability increased for both soil types after the application of the VMM. In contrast, N became immobile during decomposition if the VM of the pure waste was added. This likely occurred because of the relatively low total N (1.16%) content and high C/N ratio (35.0). However, the comparatively low C decomposition of this VM type makes its application highly recommendable as a strategy to increase the levels of organic matter and C, as well as for soil reclamation. Overall, these results suggest that the residues of the Palo Santo essential oil extraction are a potential source for vermicompost production and sustainable agriculture. Copyright © 2016 Elsevier Ltd. All rights reserved.

[Extension of cardiac monitoring function by used of ordinary ECG machine].

PubMed

Chen, Zhencheng; Jiang, Yong; Ni, Lili; Wang, Hongyan

2002-06-01

This paper deals with a portable monitor system on liquid crystal display (LCD) based on this available ordinary ECG machine, which is low power and suitable for China's specific condition. Apart from developing the overall scheme of the system, this paper also has completed the design of the hardware and the software. The 80c196 single chip microcomputer is taken as the central microprocessor and real time electrocardiac single is data treated and analyzed in the system. With the performance of ordinary monitor, this machine also possesses the following functions: five types of arrhythmia analysis, alarm, freeze, and record of automatic pappering, convenient in carrying, with alternate-current (AC) or direct-current (DC) powered. The hardware circuit is simplified and the software structure is optimized in this paper. Multiple low power designs and LCD unit design are adopted and completed in it. Popular in usage, low in cost price, the portable monitor system will have a valuable influence on China's monitor system field.

76 FR 37838 - Petitions for Modification of Application of Existing Mandatory Safety Standards

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-28

... may include periodic tests of methane levels and limits on the minimum methane concentrations that may...) Methane monitor(s) will be calibrated on the longwall, continuous mining machine, or cutting machine and... petitioner will test for methane with a hand-held methane detector at least every 10 minutes from the time...

Tool Wear Monitoring Using Time Series Analysis

NASA Astrophysics Data System (ADS)

Song, Dong Yeul; Ohara, Yasuhiro; Tamaki, Haruo; Suga, Masanobu

A tool wear monitoring approach considering the nonlinear behavior of cutting mechanism caused by tool wear and/or localized chipping is proposed, and its effectiveness is verified through the cutting experiment and actual turning machining. Moreover, the variation in the surface roughness of the machined workpiece is also discussed using this approach. In this approach, the residual error between the actually measured vibration signal and the estimated signal obtained from the time series model corresponding to dynamic model of cutting is introduced as the feature of diagnosis. Consequently, it is found that the early tool wear state (i.e. flank wear under 40µm) can be monitored, and also the optimal tool exchange time and the tool wear state for actual turning machining can be judged by this change in the residual error. Moreover, the variation of surface roughness Pz in the range of 3 to 8µm can be estimated by the monitoring of the residual error.

Multi-category micro-milling tool wear monitoring with continuous hidden Markov models

NASA Astrophysics Data System (ADS)

Zhu, Kunpeng; Wong, Yoke San; Hong, Geok Soon

2009-02-01

In-process monitoring of tool conditions is important in micro-machining due to the high precision requirement and high tool wear rate. Tool condition monitoring in micro-machining poses new challenges compared to conventional machining. In this paper, a multi-category classification approach is proposed for tool flank wear state identification in micro-milling. Continuous Hidden Markov models (HMMs) are adapted for modeling of the tool wear process in micro-milling, and estimation of the tool wear state given the cutting force features. For a noise-robust approach, the HMM outputs are connected via a medium filter to minimize the tool state before entry into the next state due to high noise level. A detailed study on the selection of HMM structures for tool condition monitoring (TCM) is presented. Case studies on the tool state estimation in the micro-milling of pure copper and steel demonstrate the effectiveness and potential of these methods.

Detecting Abnormal Machine Characteristics in Cloud Infrastructures

NASA Technical Reports Server (NTRS)

Bhaduri, Kanishka; Das, Kamalika; Matthews, Bryan L.

2011-01-01

In the cloud computing environment resources are accessed as services rather than as a product. Monitoring this system for performance is crucial because of typical pay-peruse packages bought by the users for their jobs. With the huge number of machines currently in the cloud system, it is often extremely difficult for system administrators to keep track of all machines using distributed monitoring programs such as Ganglia1 which lacks system health assessment and summarization capabilities. To overcome this problem, we propose a technique for automated anomaly detection using machine performance data in the cloud. Our algorithm is entirely distributed and runs locally on each computing machine on the cloud in order to rank the machines in order of their anomalous behavior for given jobs. There is no need to centralize any of the performance data for the analysis and at the end of the analysis, our algorithm generates error reports, thereby allowing the system administrators to take corrective actions. Experiments performed on real data sets collected for different jobs validate the fact that our algorithm has a low overhead for tracking anomalous machines in a cloud infrastructure.

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation.

PubMed

Segreto, Tiziana; Caggiano, Alessandra; Karam, Sara; Teti, Roberto

2017-12-12

Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions.

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation

PubMed Central

Segreto, Tiziana; Karam, Sara; Teti, Roberto

2017-01-01

Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions. PMID:29231864

Channel waveguides in glass via silver-sodium field-assisted ion exchange

NASA Technical Reports Server (NTRS)

Forrest, K.; Pagano, S. J.; Viehmann, W.

1986-01-01

Multimode channel waveguides have been formed in sodium aluminosilicate glass by field-assisted diffusion of Ag(+) ions from vacuum-evaporated Ag films. The two-dimensional refractive index profiles of the waveguides were controlled by varying the diffusion time, the diffusion temperature, and the electric field strength. Estimates of the diffusion rate through a strip aperture were obtained, assuming the electric field was strong 120-240 V/mm. The maximum change in refractive index in the sodium aluminosilicate glasses was estimated near 65 percent of the change in soda-lime silicate glass. The physical properties of the glasses are given in a table.

Electrothermal actuation based on carbon nanotube network in silicone elastomer

NASA Astrophysics Data System (ADS)

Chen, L. Z.; Liu, C. H.; Hu, C. H.; Fan, S. S.

2008-06-01

The authors report an electrothermal actuator, which is fabricated by involving carbon nanotube network into the silicone elastomer. The actuators exhibit excellent performances as good as normal dielectric elastomer actuators while working under much lower voltages (e.g., 1.5Vmm-1). They are longitudinal actuators and there is no need for stacking or rolling sheets of materials. In addition, they can satisfy the demand of different voltage applications ranging from dozens of voltages to thousands of voltages by using different carbon nanotube loading composites. Visible maximal strain of 4.4% occurs at an electric power intensity around 0.03Wmm-3.

The Modern Integrated Anaesthesia Workstation

PubMed Central

Patil, Vijaya P; Shetmahajan, Madhavi G; Divatia, Jigeeshu V

2013-01-01

Over the years, the conventional anaesthesia machine has evolved into an advanced carestation. The new machines use advanced electronics, software and technology to offer extensive capabilities for ventilation, monitoring, inhaled agent delivery, low-flow anaesthesia and closed-loop anaesthesia. They offer integrated monitoring and recording facilities and seamless integration with anaesthesia information systems. It is possible to deliver tidal volumes accurately and eliminate several hazards associated with the low pressure system and oxygen flush. Appropriate use can result in enhanced safety and ergonomy of anaesthetic delivery and monitoring. However, these workstations have brought in a new set of limitations and potential drawbacks. There are differences in technology and operational principles amongst the new workstations. Understand the principles of operation of these workstations and have a thorough knowledge of the operating manual of the individual machines. PMID:24249877

Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2017-01-01

This study proposes an adaptive-learning-based method for machine faulty detection and health degradation monitoring. The kernel of the proposed method is an "evolving" model that uses an unsupervised online learning scheme, in which an adaptive hidden Markov model (AHMM) is used for online learning the dynamic health changes of machines in their full life. A statistical index is developed for recognizing the new health states in the machines. Those new health states are then described online by adding of new hidden states in AHMM. Furthermore, the health degradations in machines are quantified online by an AHMM-based health index (HI) that measures the similarity between two density distributions that describe the historic and current health states, respectively. When necessary, the proposed method characterizes the distinct operating modes of the machine and can learn online both abrupt as well as gradual health changes. Our method overcomes some drawbacks of the HIs (e.g., relatively low comprehensibility and applicability) based on fixed monitoring models constructed in the offline phase. Results from its application in a bearing life test reveal that the proposed method is effective in online detection and adaptive assessment of machine health degradation. This study provides a useful guide for developing a condition-based maintenance (CBM) system that uses an online learning method without considerable human intervention.

AE Monitoring of Diamond Turned Rapidly Soldified Aluminium 443

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

Haditha General Hospital Under the Economic Support Fund Program Haditha, Iraq

DTIC Science & Technology

2009-06-23

disease from the use of the restrooms. Photos 10 and 11. Heart monitors and defibrillator machines (left) and standing...350 kilometers west of Baghdad, Haditha is a river-side community with an estimated population of 150,000. The hospital, located in the heart of...medical equipment requiring electricity; specifically, several heart monitors and defibrillator machines (Site Photo 10). This equipment appeared

Identification of Tool Wear when Machining of Austenitic Steels and Titatium by Miniature Machining

NASA Astrophysics Data System (ADS)

Pilc, Jozef; Kameník, Roman; Varga, Daniel; Martinček, Juraj; Sadilek, Marek

2016-12-01

Application of miniature machining is currently rapidly increasing mainly in biomedical industry and machining of hard-to-machine materials. Machinability of materials with increased level of toughness depends on factors that are important in the final state of surface integrity. Because of this, it is necessary to achieve high precision (varying in microns) in miniature machining. If we want to guarantee machining high precision, it is necessary to analyse tool wear intensity in direct interaction with given machined materials. During long-term cutting process, different cutting wedge deformations occur, leading in most cases to a rapid wear and destruction of the cutting wedge. This article deal with experimental monitoring of tool wear intensity during miniature machining.

A real time status monitor for transistor bank driver power limit resistor in boost injection kicker power supply

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

Mi, J.; Tan, Y.; Zhang, W.

2011-03-28

For years suffering of Booster Injection Kicker transistor bank driver regulator troubleshooting, a new real time monitor system has been developed. A simple and floating circuit has been designed and tested. This circuit monitor system can monitor the driver regulator power limit resistor status in real time and warn machine operator if the power limit resistor changes values. This paper will mainly introduce the power supply and the new designed monitoring system. This real time resistor monitor circuit shows a useful method to monitor some critical parts in the booster pulse power supply. After two years accelerator operation, it showsmore » that this monitor works well. Previously, we spent a lot of time in booster machine trouble shooting. We will reinstall all 4 PCB into Euro Card Standard Chassis when the power supply system will be updated.« less

A design of the u-health monitoring system using a Nintendo DS game machine.

PubMed

Lee, Sangjoon; Kim, Jinkwon; Kim, Jungkuk; Lee, Myoungho

2009-01-01

In this paper, we used the hand held type a Nintendo DS Game Machine for consisting of a u-Health Monitoring system. This system is consists of four parts. Biosignal acquire device is the first. The Second is a wireless sensor network device. The third is a wireless base-station for connecting internet network. Displaying units are the last part which were a personal computer and a Nintendo DS game machine. The bio-signal measurement device among the four parts the u-health monitoring system can acquire 7-channels data which have 3-channels ECG(Electrocardiogram), 3-axis accelerometer and tilting sensor data. Acquired data connect up the internet network throughout the wireless sensor network and a base-station. In the experiment, we concurrently display the bio-signals on to a monitor of personal computer and LCD of a Nintendo DS using wireless internet protocol and those monitoring devices placed off to the one side an office building. The result of the experiment, this proposed system effectively can transmit patient's biosignal data as a long time and a long distance. This suggestion of the u-health monitoring system need to operate in the ambulance, general hospitals and geriatric institutions as a u-health monitoring device.

An integrated condition-monitoring method for a milling process using reduced decomposition features

NASA Astrophysics Data System (ADS)

Liu, Jie; Wu, Bo; Wang, Yan; Hu, Youmin

2017-08-01

Complex and non-stationary cutting chatter affects productivity and quality in the milling process. Developing an effective condition-monitoring approach is critical to accurately identify cutting chatter. In this paper, an integrated condition-monitoring method is proposed, where reduced features are used to efficiently recognize and classify machine states in the milling process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition, and Shannon power spectral entropy is calculated to extract features from the decomposed signals. Principal component analysis is adopted to reduce feature size and computational cost. With the extracted feature information, the probabilistic neural network model is used to recognize and classify the machine states, including stable, transition, and chatter states. Experimental studies are conducted, and results show that the proposed method can effectively detect cutting chatter during different milling operation conditions. This monitoring method is also efficient enough to satisfy fast machine state recognition and classification.

Global Positioning System (GPS) and Geographic Information System (GIS) analysis of mobile harvesting equipment and sediment delivery to streams during forest harvest operations on steep terrain: Experimental design

Treesearch

Daniel Bowker; Jeff Stringer; Chris Barton; Songlin Fei

2011-01-01

Sediment mobilized by forest harvest machine traffic contributes substantially to the degradation of headwater stream systems. This study monitored forest harvest machine traffic to analyze how it affects sediment delivery to stream channels. Harvest machines were outfitted with global positioning system (GPS) dataloggers, recording machine movements and working status...

Analysis of NREL Cold-Drink Vending Machines for Energy Savings

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

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

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

Hardware assisted hypervisor introspection.

PubMed

Shi, Jiangyong; Yang, Yuexiang; Tang, Chuan

2016-01-01

In this paper, we introduce hypervisor introspection, an out-of-box way to monitor the execution of hypervisors. Similar to virtual machine introspection which has been proposed to protect virtual machines in an out-of-box way over the past decade, hypervisor introspection can be used to protect hypervisors which are the basis of cloud security. Virtual machine introspection tools are usually deployed either in hypervisor or in privileged virtual machines, which might also be compromised. By utilizing hardware support including nested virtualization, EPT protection and #BP, we are able to monitor all hypercalls belongs to the virtual machines of one hypervisor, include that of privileged virtual machine and even when the hypervisor is compromised. What's more, hypercall injection method is used to simulate hypercall-based attacks and evaluate the performance of our method. Experiment results show that our method can effectively detect hypercall-based attacks with some performance cost. Lastly, we discuss our furture approaches of reducing the performance cost and preventing the compromised hypervisor from detecting the existence of our introspector, in addition with some new scenarios to apply our hypervisor introspection system.

Washing machine usage in remote aboriginal communities.

PubMed

Lloyd, C R

1998-10-01

The use of washing machines was investigated in two remote Aboriginal communities in the Anangu Pitjantjatjara homelands. The aim was to look both at machine reliability and to investigate the health aspect of washing clothes. A total of 39 machines were inspected for wear and component reliability every three months over a one-year period. Of these, 10 machines were monitored in detail for water consumption, hours of use and cycles of operation. The machines monitored were Speed Queen model EA2011 (7 kg washing load) commercial units. The field survey results suggested a high rate of operation of the machines with an average of around 1,100 washing cycles per year (range 150 and 2,300 cycles per year). The results were compared with available figures for the average Australian household. A literature survey, to ascertain the health outcomes relating to washing clothes and bedding, confirmed that washing machines are efficient at removal of bacteria from clothes and bedding but suggested that recontamination of clothing after washing often negated the prior removal. High temperature washing (> 60 degrees C) appeared to be advantageous from a health perspective. With regards to larger organisms, while dust mites and body lice transmission between people would probably be decreased by washing clothes, scabies appeared to be mainly transmitted by body contact and thus transmission would be only marginally decreased by the use of washing machines.

A Self-Aware Machine Platform in Manufacturing Shop Floor Utilizing MTConnect Data

DTIC Science & Technology

2014-10-02

condition monitoring , and equipment time to failure prediction in manufacturing 1 ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2014 589...Component Level Health Monitoring and Prediction One of the characteristics of a self-aware machine is to be able to detect its components...the annual conference of the prognostics and health management society. Filzmoser, P., Garrett, R. G., & Reimann, C . (2005). Mul- tivariate outlier

A low cost implementation of multi-parameter patient monitor using intersection kernel support vector machine classifier

NASA Astrophysics Data System (ADS)

Mohan, Dhanya; Kumar, C. Santhosh

2016-03-01

Predicting the physiological condition (normal/abnormal) of a patient is highly desirable to enhance the quality of health care. Multi-parameter patient monitors (MPMs) using heart rate, arterial blood pressure, respiration rate and oxygen saturation (S pO2) as input parameters were developed to monitor the condition of patients, with minimum human resource utilization. The Support vector machine (SVM), an advanced machine learning approach popularly used for classification and regression is used for the realization of MPMs. For making MPMs cost effective, we experiment on the hardware implementation of the MPM using support vector machine classifier. The training of the system is done using the matlab environment and the detection of the alarm/noalarm condition is implemented in hardware. We used different kernels for SVM classification and note that the best performance was obtained using intersection kernel SVM (IKSVM). The intersection kernel support vector machine classifier MPM has outperformed the best known MPM using radial basis function kernel by an absoute improvement of 2.74% in accuracy, 1.86% in sensitivity and 3.01% in specificity. The hardware model was developed based on the improved performance system using Verilog Hardware Description Language and was implemented on Altera cyclone-II development board.

Condition monitoring of Electric Components

NASA Astrophysics Data System (ADS)

Zaman, Ishtiaque

A universal non-intrusive model of a flexible antenna array is presented in this paper to monitor and identify the failures in electric machines. This adjustable antenna is designed to serve the purpose of condition monitoring of a vast range of electrical components including Induction Motor (IM), Printed Circuit Board (PCB), Synchronous Reluctance Motor (SRM), Permanent Magnet Synchronous Machine (PMSM) etc. by capturing the low frequency magnetic field radiated around these machines. The basic design and specification of the proposed antenna array for low frequency components is portrayed first. The design of the antenna is adjustable to fit for an extensive variety of segments. Subsequent to distinguishing the design and specifications of the antenna, the ideal area of the most delicate stray field has been identified for healthy current streaming around the machineries. Following this, short circuit representing faulty situation has been introduced and compared with the healthy cases. Precision has been found recognizing the faults using this one generic model of Antenna and the results are presented for three different machines i.e. IM, SRM and PMSM. Finite element method has been used to design the antenna and detect the optimum location and faults in the machines. Finally, a 3D Printer is proposed to be employed to build the antenna as per the details tended to in this paper contingent upon the power segments.

Interferometric correction system for a numerically controlled machine

DOEpatents

Burleson, Robert R.

1978-01-01

An interferometric correction system for a numerically controlled machine is provided to improve the positioning accuracy of a machine tool, for example, for a high-precision numerically controlled machine. A laser interferometer feedback system is used to monitor the positioning of the machine tool which is being moved by command pulses to a positioning system to position the tool. The correction system compares the commanded position as indicated by a command pulse train applied to the positioning system with the actual position of the tool as monitored by the laser interferometer. If the tool position lags the commanded position by a preselected error, additional pulses are added to the pulse train applied to the positioning system to advance the tool closer to the commanded position, thereby reducing the lag error. If the actual tool position is leading in comparison to the commanded position, pulses are deleted from the pulse train where the advance error exceeds the preselected error magnitude to correct the position error of the tool relative to the commanded position.

Fluorescence excitation-emission matrix spectroscopy for degradation monitoring of machinery lubricants

NASA Astrophysics Data System (ADS)

Sosnovski, Oleg; Suresh, Pooja; Dudelzak, Alexander E.; Green, Benjamin

2018-02-01

Lubrication oil is a vital component of heavy rotating machinery defining the machine's health, operational safety and effectiveness. Recently, the focus has been on developing sensors that provide real-time/online monitoring of oil condition/lubricity. Industrial practices and standards for assessing oil condition involve various analytical methods. Most these techniques are unsuitable for online applications. The paper presents the results of studying degradation of antioxidant additives in machinery lubricants using Fluorescence Excitation-Emission Matrix (EEM) Spectroscopy and Machine Learning techniques. EEM Spectroscopy is capable of rapid and even standoff sensing; it is potentially applicable to real-time online monitoring.

Performance Monitoring Of A Computer Numerically Controlled (CNC) Lathe Using Pattern Recognition Techniques

NASA Astrophysics Data System (ADS)

Daneshmend, L. K.; Pak, H. A.

1984-02-01

On-line monitoring of the cutting process in CNC lathe is desirable to ensure unattended fault-free operation in an automated environment. The state of the cutting tool is one of the most important parameters which characterises the cutting process. Direct monitoring of the cutting tool or workpiece is not feasible during machining. However several variables related to the state of the tool can be measured on-line. A novel monitoring technique is presented which uses cutting torque as the variable for on-line monitoring. A classifier is designed on the basis of the empirical relationship between cutting torque and flank wear. The empirical model required by the on-line classifier is established during an automated training cycle using machine vision for off-line direct inspection of the tool.

30 CFR 27.21 - Methane-monitoring system.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane-monitoring system. 27.21 Section 27.21... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.21 Methane-monitoring system. (a) A methane-monitoring system shall be so designed that any machine or equipment, which...

30 CFR 27.21 - Methane-monitoring system.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane-monitoring system. 27.21 Section 27.21... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.21 Methane-monitoring system. (a) A methane-monitoring system shall be so designed that any machine or equipment, which...

30 CFR 27.21 - Methane-monitoring system.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 30 Mineral Resources 1 2014-07-01 2014-07-01 false Methane-monitoring system. 27.21 Section 27.21... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.21 Methane-monitoring system. (a) A methane-monitoring system shall be so designed that any machine or equipment, which...

30 CFR 27.21 - Methane-monitoring system.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 30 Mineral Resources 1 2012-07-01 2012-07-01 false Methane-monitoring system. 27.21 Section 27.21... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.21 Methane-monitoring system. (a) A methane-monitoring system shall be so designed that any machine or equipment, which...

30 CFR 27.21 - Methane-monitoring system.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 30 Mineral Resources 1 2013-07-01 2013-07-01 false Methane-monitoring system. 27.21 Section 27.21... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.21 Methane-monitoring system. (a) A methane-monitoring system shall be so designed that any machine or equipment, which...

Network challenges for cyber physical systems with tiny wireless devices: a case study on reliable pipeline condition monitoring.

PubMed

Ali, Salman; Qaisar, Saad Bin; Saeed, Husnain; Khan, Muhammad Farhan; Naeem, Muhammad; Anpalagan, Alagan

2015-03-25

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring

PubMed Central

Ali, Salman; Qaisar, Saad Bin; Saeed, Husnain; Farhan Khan, Muhammad; Naeem, Muhammad; Anpalagan, Alagan

2015-01-01

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed. PMID:25815444

Condition monitoring of turning process using infrared thermography technique - An experimental approach

NASA Astrophysics Data System (ADS)

Prasad, Balla Srinivasa; Prabha, K. Aruna; Kumar, P. V. S. Ganesh

2017-03-01

In metal cutting machining, major factors that affect the cutting tool life are machine tool vibrations, tool tip/chip temperature and surface roughness along with machining parameters like cutting speed, feed rate, depth of cut, tool geometry, etc., so it becomes important for the manufacturing industry to find the suitable levels of process parameters for obtaining maintaining tool life. Heat generation in cutting was always a main topic to be studied in machining. Recent advancement in signal processing and information technology has resulted in the use of multiple sensors for development of the effective monitoring of tool condition monitoring systems with improved accuracy. From a process improvement point of view, it is definitely more advantageous to proactively monitor quality directly in the process instead of the product, so that the consequences of a defective part can be minimized or even eliminated. In the present work, a real time process monitoring method is explored using multiple sensors. It focuses on the development of a test bed for monitoring the tool condition in turning of AISI 316L steel by using both coated and uncoated carbide inserts. Proposed tool condition monitoring (TCM) is evaluated in the high speed turning using multiple sensors such as Laser Doppler vibrometer and infrared thermography technique. The results indicate the feasibility of using the dominant frequency of the vibration signals for the monitoring of high speed turning operations along with temperatures gradient. A possible correlation is identified in both regular and irregular cutting tool wear. While cutting speed and feed rate proved to be influential parameter on the depicted temperatures and depth of cut to be less influential. Generally, it is observed that lower heat and temperatures are generated when coated inserts are employed. It is found that cutting temperatures are gradually increased as edge wear and deformation developed.

Amplifying human ability through autonomics and machine learning in IMPACT

NASA Astrophysics Data System (ADS)

Dzieciuch, Iryna; Reeder, John; Gutzwiller, Robert; Gustafson, Eric; Coronado, Braulio; Martinez, Luis; Croft, Bryan; Lange, Douglas S.

2017-05-01

Amplifying human ability for controlling complex environments featuring autonomous units can be aided by learned models of human and system performance. In developing a command and control system that allows a small number of people to control a large number of autonomous teams, we employ an autonomics framework to manage the networks that represent mission plans and the networks that are composed of human controllers and their autonomous assistants. Machine learning allows us to build models of human and system performance useful for monitoring plans and managing human attention and task loads. Machine learning also aids in the development of tactics that human supervisors can successfully monitor through the command and control system.

Origin of acoustic emission produced during single point machining

NASA Astrophysics Data System (ADS)

Heiple, C. R.; Carpenter, S. H.; Armentrout, D. L.

1991-05-01

Acoustic emission was monitored during single point, continuous machining of 4340 steel and Ti-6Al-4V as a function of heat treatment. Acoustic emission produced during tensile and compressive deformation of these alloys has been previously characterized as a function of heat treatment. Heat treatments which increase the strength of 4340 steel increase the amount of acoustic emission produced during deformation, while heat treatments which increase the strength of Ti-6Al-4V decrease the amount of acoustic emission produced during deformation. If chip deformation were the primary source of acoustic emission during single point machining, then opposite trends in the level of acoustic emission produced during machining as a function of material strength would be expected for these two alloys. Trends in rms acoustic emission level with increasing strength were similar for both alloys, demonstrating that chip deformation is not a major source of acoustic emission in single point machining. Acoustic emission has also been monitored as a function of machining parameters on 6061-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, lead, and teflon. The data suggest that sliding friction between the nose and/or flank of the tool and the newly machined surface is the primary source of acoustic emission. Changes in acoustic emission with tool wear were strongly material dependent.

Machine learning and new vital signs monitoring in civilian en route care: A systematic review of the literature and future implications for the military.

PubMed

Liu, Nehemiah T; Salinas, Jose

2016-11-01

Although air transport medical services are today an integral part of trauma systems in most developed countries, to date, there are no reviews on recent innovations in civilian en route care. The purpose of this systematic review was to identify potential machine learning and new vital signs monitoring technologies in civilian en route care that could help close civilian and military capability gaps in monitoring and the early detection and treatment of various trauma injuries. MEDLINE, the Cochrane Database of Systematic Reviews, and citation review of relevant primary and review articles were searched for studies involving civilian en route care, air medical transport, and technologies from January 2005 to November 2015. Data were abstracted on study design, population, year, sponsors, innovation category, details of technologies, and outcomes. Thirteen observational studies involving civilian medical transport met inclusion criteria. Studies either focused on machine learning and software algorithms (n = 5), new vital signs monitoring (n = 6), or both (n = 2). Innovations involved continuous digital acquisition of physiologic data and parameter extraction. Importantly, all studies (n = 13) demonstrated improved outcomes where applicable and potential use during civilian and military en route care. However, almost all studies required further validation in prospective and/or randomized controlled trials. Potential machine learning technologies and monitoring of novel vital signs such as heart rate variability and complexity in civilian en route care could help enhance en route care for our nation's war fighters. In a complex global environment, they could potentially fill capability gaps such as monitoring and the early detection and treatment of various trauma injuries. However, the impact of these innovations and technologies will require further validation before widespread acceptance and prehospital use. Systematic review, level V.

Safety Features in Anaesthesia Machine

PubMed Central

Subrahmanyam, M; Mohan, S

2013-01-01

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

Quaternion Based Thermal Condition Monitoring System

NASA Astrophysics Data System (ADS)

Wong, Wai Kit; Loo, Chu Kiong; Lim, Way Soong; Tan, Poi Ngee

In this paper, we will propose a new and effective machine condition monitoring system using log-polar mapper, quaternion based thermal image correlator and max-product fuzzy neural network classifier. Two classification characteristics namely: peak to sidelobe ratio (PSR) and real to complex ratio of the discrete quaternion correlation output (p-value) are applied in the proposed machine condition monitoring system. Large PSR and p-value observe in a good match among correlation of the input thermal image with a particular reference image, while small PSR and p-value observe in a bad/not match among correlation of the input thermal image with a particular reference image. In simulation, we also discover that log-polar mapping actually help solving rotation and scaling invariant problems in quaternion based thermal image correlation. Beside that, log-polar mapping can have a two fold of data compression capability. Log-polar mapping can help smoother up the output correlation plane too, hence makes a better measurement way for PSR and p-values. Simulation results also show that the proposed system is an efficient machine condition monitoring system with accuracy more than 98%.

CMM Interim Check (U)

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

Montano, Joshua Daniel

2015-03-23

Coordinate Measuring Machines (CMM) are widely used in industry, throughout the Nuclear Weapons Complex and at Los Alamos National Laboratory (LANL) to verify part conformance to design definition. Calibration cycles for CMMs at LANL are predominantly one year in length. Unfortunately, several nonconformance reports have been generated to document the discovery of a certified machine found out of tolerance during a calibration closeout. In an effort to reduce risk to product quality two solutions were proposed – shorten the calibration cycle which could be costly, or perform an interim check to monitor the machine’s performance between cycles. The CMM interimmore » check discussed makes use of Renishaw’s Machine Checking Gauge. This off-the-shelf product simulates a large sphere within a CMM’s measurement volume and allows for error estimation. Data was gathered, analyzed, and simulated from seven machines in seventeen different configurations to create statistical process control run charts for on-the-floor monitoring.« less

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

NASA Technical Reports Server (NTRS)

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

Holter monitor (24h)

MedlinePlus

... please enable JavaScript. A Holter monitor is a machine that continuously records the heart’s rhythms. The monitor is worn for 24 to 48 hours during normal activity. How the Test is Performed Electrodes (small conducting patches) are stuck ...

Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management.

PubMed

Chang, Ni-Bin; Bai, Kaixu; Chen, Chi-Farn

2017-10-01

Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed "cross-mission data merging and image reconstruction with machine learning" (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

The reflection of evolving bearing faults in the stator current's extended park vector approach for induction machines

NASA Astrophysics Data System (ADS)

Corne, Bram; Vervisch, Bram; Derammelaere, Stijn; Knockaert, Jos; Desmet, Jan

2018-07-01

Stator current analysis has the potential of becoming the most cost-effective condition monitoring technology regarding electric rotating machinery. Since both electrical and mechanical faults are detected by inexpensive and robust current-sensors, measuring current is advantageous on other techniques such as vibration, acoustic or temperature analysis. However, this technology is struggling to breach into the market of condition monitoring as the electrical interpretation of mechanical machine-problems is highly complicated. Recently, the authors built a test-rig which facilitates the emulation of several representative mechanical faults on an 11 kW induction machine with high accuracy and reproducibility. Operating this test-rig, the stator current of the induction machine under test can be analyzed while mechanical faults are emulated. Furthermore, while emulating, the fault-severity can be manipulated adaptively under controllable environmental conditions. This creates the opportunity of examining the relation between the magnitude of the well-known current fault components and the corresponding fault-severity. This paper presents the emulation of evolving bearing faults and their reflection in the Extended Park Vector Approach for the 11 kW induction machine under test. The results confirm the strong relation between the bearing faults and the stator current fault components in both identification and fault-severity. Conclusively, stator current analysis increases reliability in the application as a complete, robust, on-line condition monitoring technology.

Bayesian anomaly detection in monitoring data applying relevance vector machine

NASA Astrophysics Data System (ADS)

Saito, Tomoo

2011-04-01

A method for automatically classifying the monitoring data into two categories, normal and anomaly, is developed in order to remove anomalous data included in the enormous amount of monitoring data, applying the relevance vector machine (RVM) to a probabilistic discriminative model with basis functions and their weight parameters whose posterior PDF (probabilistic density function) conditional on the learning data set is given by Bayes' theorem. The proposed framework is applied to actual monitoring data sets containing some anomalous data collected at two buildings in Tokyo, Japan, which shows that the trained models discriminate anomalous data from normal data very clearly, giving high probabilities of being normal to normal data and low probabilities of being normal to anomalous data.

PROCEDURES FOR ACCURATE PRODUCTION OF COLOR IMAGES FROM SATELLITE OR AIRCRAFT MULTISPECTRAL DIGITAL DATA.

USGS Publications Warehouse

Duval, Joseph S.

1985-01-01

Because the display and interpretation of satellite and aircraft remote-sensing data make extensive use of color film products, accurate reproduction of the color images is important. To achieve accurate color reproduction, the exposure and chemical processing of the film must be monitored and controlled. By using a combination of sensitometry, densitometry, and transfer functions that control film response curves, all of the different steps in the making of film images can be monitored and controlled. Because a sensitometer produces a calibrated exposure, the resulting step wedge can be used to monitor the chemical processing of the film. Step wedges put on film by image recording machines provide a means of monitoring the film exposure and color balance of the machines.

30 CFR 75.342 - Methane monitors.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane monitors. 75.342 Section 75.342 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.342 Methane monitors. (a)(1) MSHA approved methane monitors shall be installed on all face cutting machines, continuous miners, longwall face...

30 CFR 75.342 - Methane monitors.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane monitors. 75.342 Section 75.342 Mineral... SAFETY STANDARDS-UNDERGROUND COAL MINES Ventilation § 75.342 Methane monitors. (a)(1) MSHA approved methane monitors shall be installed on all face cutting machines, continuous miners, longwall face...

30 CFR 57.22308 - Methane monitors (III mines).

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane monitors (III mines). 57.22308 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22308 Methane monitors (III mines). (a) Methane monitors shall be installed on continuous mining machines and longwall mining systems. (b) The...

30 CFR 57.22308 - Methane monitors (III mines).

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane monitors (III mines). 57.22308 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22308 Methane monitors (III mines). (a) Methane monitors shall be installed on continuous mining machines and longwall mining systems. (b) The...

Channel Efficiency with Security Enhancement for Remote Condition Monitoring of Multi Machine System Using Hybrid Huffman Coding

NASA Astrophysics Data System (ADS)

Datta, Jinia; Chowdhuri, Sumana; Bera, Jitendranath

2016-12-01

This paper presents a novel scheme of remote condition monitoring of multi machine system where a secured and coded data of induction machine with different parameters is communicated between a state-of-the-art dedicated hardware Units (DHU) installed at the machine terminal and a centralized PC based machine data management (MDM) software. The DHUs are built for acquisition of different parameters from the respective machines, and hence are placed at their nearby panels in order to acquire different parameters cost effectively during their running condition. The MDM software collects these data through a communication channel where all the DHUs are networked using RS485 protocol. Before transmitting, the parameter's related data is modified with the adoption of differential pulse coded modulation (DPCM) and Huffman coding technique. It is further encrypted with a private key where different keys are used for different DHUs. In this way a data security scheme is adopted during its passage through the communication channel in order to avoid any third party attack into the channel. The hybrid mode of DPCM and Huffman coding is chosen to reduce the data packet length. A MATLAB based simulation and its practical implementation using DHUs at three machine terminals (one healthy three phase, one healthy single phase and one faulty three phase machine) proves its efficacy and usefulness for condition based maintenance of multi machine system. The data at the central control room are decrypted and decoded using MDM software. In this work it is observed that Chanel efficiency with respect to different parameter measurements has been increased very much.

What happened to the TSM

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

Brezovec, D.

1983-11-01

A new coal mining machine that was going to pull some 40 million tons of coal from the Appalachian coalfields by 1986 has had more than its share of start-up problems. The machine, known as the Thin Seam Miner (TSM), is a $2.7-million auger-type mining machine that is designed to bore 220 ft into new or abandoned highwalls (CA 5/82 p. 106). Gamma-ray sensors located near the continuous drum miner-type cutter head monitor for rock and other sensors monitor for methane. The machines are designed to produce about 425 tons per shift from a 36-in.-thick coal seam. The machines weremore » introduced officially to the American coal industry at a luncheon Aug. 19, 1981, in a ballroom at the Lexington, Ky., Hyatt Regency Hotel. At the luncheon, some 200 coal industry executives and others sipped champagne and listened to glowing reports of how 24 of the machines would produce 2.2 million tons of coal by the end of 1981 and 64 of the machines would produce 6.6 million tons by the end of 1982. The machines would be built in Holland by RijnSchelde-Verolme (RSV), a major Dutch shipbuilder, and managed in the United States by Advanced Coal Management (ACM), a company formed for the purpose by James D. Stacy, a colorful, cigar-smoking stock car owner whose experience in the coal business dated from only the mid-1970s.« less

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

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

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

A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring

PubMed Central

Li, Yong; Wang, Xiufeng; Lin, Jing; Shi, Shengyu

2014-01-01

The translational axis is one of the most important subsystems in modern machine tools, as its degradation may result in the loss of the product qualification and lower the control precision. Condition-based maintenance (CBM) has been considered as one of the advanced maintenance schemes to achieve effective, reliable and cost-effective operation of machine systems, however, current vibration-based maintenance schemes cannot be employed directly in the translational axis system, due to its complex structure and the inefficiency of commonly used condition monitoring features. In this paper, a wavelet bicoherence-based quadratic nonlinearity feature is proposed for translational axis condition monitoring by using the torque signature of the drive servomotor. Firstly, the quadratic nonlinearity of the servomotor torque signature is discussed, and then, a biphase randomization wavelet bicoherence is introduced for its quadratic nonlinear detection. On this basis, a quadratic nonlinearity feature is proposed for condition monitoring of the translational axis. The properties of the proposed quadratic nonlinearity feature are investigated by simulations. Subsequently, this feature is applied to the real-world servomotor torque data collected from the X-axis on a high precision vertical machining centre. All the results show that the performance of the proposed feature is much better than that of original condition monitoring features. PMID:24473281

Origin of acoustic emission produced during single point machining

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

Heiple, C.R,.; Carpenter, S.H.; Armentrout, D.L.

1991-01-01

Acoustic emission was monitored during single point, continuous machining of 4340 steel and Ti-6Al-4V as a function of heat treatment. Acoustic emission produced during tensile and compressive deformation of these alloys has been previously characterized as a function of heat treatment. Heat treatments which increase the strength of 4340 steel increase the amount of acoustic emission produced during deformation, while heat treatments which increase the strength of Ti-6Al-4V decrease the amount of acoustic emission produced during deformation. If chip deformation were the primary source of acoustic emission during single point machining, then opposite trends in the level of acoustic emissionmore » produced during machining as a function of material strength would be expected for these two alloys. Trends in rms acoustic emission level with increasing strength were similar for both alloys, demonstrating that chip deformation is not a major source of acoustic emission in single point machining. Acoustic emission has also been monitored as a function of machining parameters on 6061-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, lead, and teflon. The data suggest that sliding friction between the nose and/or flank of the tool and the newly machined surface is the primary source of acoustic emission. Changes in acoustic emission with tool wear were strongly material dependent. 21 refs., 19 figs., 4 tabs.« less

Design of a cardiac monitor in terms of parameters of QRS complex.

PubMed

Chen, Zhen-cheng; Ni, Li-li; Su, Ke-ping; Wang, Hong-yan; Jiang, Da-zong

2002-08-01

Objective. To design a portable cardiac monitor system based on the available ordinary ECG machine and works on the basis of QRS parameters. Method. The 80196 single chip microcomputer was used as the central microprocessor and real time electrocardiac signal was collected and analyzed [correction of analysized] in the system. Result. Apart from the performance of an ordinary monitor, this machine possesses also the following functions: arrhythmia analysis, HRV analysis, alarm, freeze, and record of automatic papering. Convenient in carrying, the system is powered by AC or DC sources. Stability, low power and low cost are emphasized in the hardware design; and modularization method is applied in software design. Conclusion. Popular in usage and low cost made the portable monitor system suitable for use under simple conditions.

Properties and Degradation of Polarization Reversal of Soft BaTiO3 Ceramics for Ferroelectric Thin-Film Devices

NASA Astrophysics Data System (ADS)

Thongrueng, Jirawat; Tsuchiya, Toshio; Masuda, Yoichiro; Fujita, Shigetaka; Nagata, Kunihiro

1999-09-01

Soft BaTiO3 ceramics having a very low coercive field of 65 V/mm were prepared by substituting 9 mol% Hf Zr for the Ti-site of BaTiO3, for applications to ferroelectric thin-film devices. Electrical properties of the soft BaTiO3 ceramics were measured and compared with those of normal BaTiO3 ceramics. By substituting Hf Zr for Ti-site, the phase transition temperatures were controlled, and we could select the preferred crystal structure from the tetragonal, orthorhombic and rhombohedral phases at room temperature. In addition, the preparation and characterization of the soft BaTiO3 thin-films using a sol-gel process were carried out.

Application of TRIZ approach to machine vibration condition monitoring problems

NASA Astrophysics Data System (ADS)

Cempel, Czesław

2013-12-01

Up to now machine condition monitoring has not been seriously approached by TRIZ1TRIZ= Russian acronym for Inventive Problem Solving System, created by G. Altshuller ca 50 years ago. users, and the knowledge of TRIZ methodology has not been applied there intensively. However, there are some introductory papers of present author posted on Diagnostic Congress in Cracow (Cempel, in press [11]), and Diagnostyka Journal as well. But it seems to be further need to make such approach from different sides in order to see, if some new knowledge and technology will emerge. In doing this we need at first to define the ideal final result (IFR) of our innovation problem. As a next we need a set of parameters to describe the problems of system condition monitoring (CM) in terms of TRIZ language and set of inventive principles possible to apply, on the way to IFR. This means we should present the machine CM problem by means of contradiction and contradiction matrix. When specifying the problem parameters and inventive principles, one should use analogy and metaphorical thinking, which by definition is not exact but fuzzy, and leads sometimes to unexpected results and outcomes. The paper undertakes this important problem again and brings some new insight into system and machine CM problems. This may mean for example the minimal dimensionality of TRIZ engineering parameter set for the description of machine CM problems, and the set of most useful inventive principles applied to given engineering parameter and contradictions of TRIZ.

Machine tool locator

DOEpatents

Hanlon, John A.; Gill, Timothy J.

2001-01-01

Machine tools can be accurately measured and positioned on manufacturing machines within very small tolerances by use of an autocollimator on a 3-axis mount on a manufacturing machine and positioned so as to focus on a reference tooling ball or a machine tool, a digital camera connected to the viewing end of the autocollimator, and a marker and measure generator for receiving digital images from the camera, then displaying or measuring distances between the projection reticle and the reference reticle on the monitoring screen, and relating the distances to the actual position of the autocollimator relative to the reference tooling ball. The images and measurements are used to set the position of the machine tool and to measure the size and shape of the machine tool tip, and examine cutting edge wear. patent

30 CFR 57.22306 - Methane monitors (I-A mines).

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane monitors (I-A mines). 57.22306 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22306 Methane monitors (I-A mines). (a) Methane monitors shall be installed on continuous mining machines, longwall mining systems, and on loading...

30 CFR 57.22307 - Methane monitors (II-A mines).

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane monitors (II-A mines). 57.22307 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22307 Methane monitors (II-A mines). (a) Methane monitors shall be installed on continuous mining machines, longwall mining systems, bench and face...

30 CFR 57.22309 - Methane monitors (V-A mines).

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Methane monitors (V-A mines). 57.22309 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22309 Methane monitors (V-A mines). (a) Methane monitors shall be installed on continuous mining machines used in or beyond the last open crosscut...

30 CFR 57.22306 - Methane monitors (I-A mines).

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane monitors (I-A mines). 57.22306 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22306 Methane monitors (I-A mines). (a) Methane monitors shall be installed on continuous mining machines, longwall mining systems, and on loading...

30 CFR 57.22307 - Methane monitors (II-A mines).

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane monitors (II-A mines). 57.22307 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22307 Methane monitors (II-A mines). (a) Methane monitors shall be installed on continuous mining machines, longwall mining systems, bench and face...

30 CFR 57.22309 - Methane monitors (V-A mines).

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Methane monitors (V-A mines). 57.22309 Section... Standards for Methane in Metal and Nonmetal Mines Equipment § 57.22309 Methane monitors (V-A mines). (a) Methane monitors shall be installed on continuous mining machines used in or beyond the last open crosscut...

Implementing Machine Learning in Radiology Practice and Research.

PubMed

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

2017-04-01

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

40 CFR 60.175 - Monitoring of operations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Standards of Performance for Primary Zinc... monitor and record the opacity of gases discharged into the atmosphere from any sintering machine. The... volume. (i) The continuous monitoring system performance evaluation required under § 60.13(c) shall be...

The monitoring of transient regimes on machine tools based on speed, acceleration and active electric power absorbed by motors

NASA Astrophysics Data System (ADS)

Horodinca, M.

2016-08-01

This paper intend to propose some new results related with computer aided monitoring of transient regimes on machine-tools based on the evolution of active electrical power absorbed by the electric motor used to drive the main kinematic chains and the evolution of rotational speed and acceleration of the main shaft. The active power is calculated in numerical format using the evolution of instantaneous voltage and current delivered by electrical power system to the electric motor. The rotational speed and acceleration of the main shaft are calculated based on the signal delivered by a sensor. Three real-time analogic signals are acquired with a very simple computer assisted setup which contains a voltage transformer, a current transformer, an AC generator as rotational speed sensor, a data acquisition system and a personal computer. The data processing and analysis was done using Matlab software. Some different transient regimes were investigated; several important conclusions related with the advantages of this monitoring technique were formulated. Many others features of the experimental setup are also available: to supervise the mechanical loading of machine-tools during cutting processes or for diagnosis of machine-tools condition by active electrical power signal analysis in frequency domain.

A review on prognostic techniques for non-stationary and non-linear rotating systems

NASA Astrophysics Data System (ADS)

Kan, Man Shan; Tan, Andy C. C.; Mathew, Joseph

2015-10-01

The field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under non-linear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under non-stationary and non-linear conditions are also discussed.

Positional reference system for ultraprecision machining

DOEpatents

Arnold, Jones B.; Burleson, Robert R.; Pardue, Robert M.

1982-01-01

A stable positional reference system for use in improving the cutting tool-to-part contour position in numerical controlled-multiaxis metal turning machines is provided. The reference system employs a plurality of interferometers referenced to orthogonally disposed metering bars which are substantially isolated from machine strain induced position errors for monitoring the part and tool positions relative to the metering bars. A microprocessor-based control system is employed in conjunction with the plurality of position interferometers and part contour description data inputs to calculate error components for each axis of movement and output them to corresponding axis drives with appropriate scaling and error compensation. Real-time position control, operating in combination with the reference system, makes possible the positioning of the cutting points of a tool along a part locus with a substantially greater degree of accuracy than has been attained previously in the art by referencing and then monitoring only the tool motion relative to a reference position located on the machine base.

Positional reference system for ultraprecision machining

DOEpatents

Arnold, J.B.; Burleson, R.R.; Pardue, R.M.

1980-09-12

A stable positional reference system for use in improving the cutting tool-to-part contour position in numerical controlled-multiaxis metal turning machines is provided. The reference system employs a plurality of interferometers referenced to orthogonally disposed metering bars which are substantially isolated from machine strain induced position errors for monitoring the part and tool positions relative to the metering bars. A microprocessor-based control system is employed in conjunction with the plurality of positions interferometers and part contour description data input to calculate error components for each axis of movement and output them to corresponding axis driven with appropriate scaling and error compensation. Real-time position control, operating in combination with the reference system, makes possible the positioning of the cutting points of a tool along a part locus with a substantially greater degree of accuracy than has been attained previously in the art by referencing and then monitoring only the tool motion relative to a reference position located on the machine base.

Added value of stress related gene inductions in HepG2 cells as effect measurement in monitoring of air pollution

NASA Astrophysics Data System (ADS)

Nobels, Ingrid; Vanparys, Caroline; Van den Heuvel, Rosette; Vercauteren, Jordy; Blust, Ronny

2012-08-01

In this study we studied the effects of particulate matter samples (PM) through gene expression analysis in a routine air quality monitoring campaign by the Flemish Environment Agency (VMM, Belgium). We selected a human hepatoma (HepG2) multiple endpoint reporter assay for targeted stress related endpoint screening. Organic extracts of air samples (total suspended particles, TSP) were collected during one year in an industrial, urban and background location in Flanders, Belgium. Simultaneously, meteorological conditions (temperature, wind speed and precipitation) and particulate matter size ≤ 10 μM (PM10), organic (OC), elemental (EC) and total (TC) carbon were monitored and air samples were collected for chemical analysis (11 PAHs). Correlations between the induction of the different stress genes and the chemical pollutants were analysed. Exposure of HepG2 cells to daily air equivalents (20 m3) of organic TSP extracts revealed the dominant induction of the xenobiotic response element (Xre) and phase I (Cyp1A1) and phase II (GstYa) biotransformation enzymes. Additional effects were the induction of c-Fos, a proto-oncogen and Gadd45, a marker for cell cycle disturbance and responsive to genotoxic compounds. Inductions of other relevant pathways, such as sequestration of heavy metals, retinoids response, protein misfolding and increased cAMP levels were measured occasionally. A significant correlation was found between the genes Cyp1A1 (a typical marker for presence of PAHs and dioxin like compounds), c-Fos, Gadd45, (responsive to DNA damaging compounds) and the amount of PM10 and elemental carbon (EC) whereas no correlation was found between these genes and total PAHs content. This may suggest that the observed induction of Cyp1A1 and DNA damage related genes was provoked (partially) by other particle bound compounds (e.g. pesticides, PCBs, brominated flame retardants, dioxins, …), than PAHs. The contribution of particle bound compounds, other than PAHs might be important to take into account in risk evaluation of air pollution.

Software framework for prognostic health monitoring of ocean-based power generation

NASA Astrophysics Data System (ADS)

Bowren, Mark

On August 5, 2010 the U.S. Department of Energy (DOE) has designated the Center for Ocean Energy Technology (COET) at Florida Atlantic University (FAU) as a national center for ocean energy research and development of prototypes for open-ocean power generation. Maintenance on ocean-based machinery can be very costly. To avoid unnecessary maintenance it is necessary to monitor the condition of each machine in order to predict problems. This kind of prognostic health monitoring (PHM) requires a condition-based maintenance (CBM) system that supports diagnostic and prognostic analysis of large amounts of data. Research in this field led to the creation of ISO13374 and the development of a standard open-architecture for machine condition monitoring. This thesis explores an implementation of such a system for ocean-based machinery using this framework and current open-standard technologies.

In-situ acoustic signature monitoring in additive manufacturing processes

NASA Astrophysics Data System (ADS)

Koester, Lucas W.; Taheri, Hossein; Bigelow, Timothy A.; Bond, Leonard J.; Faierson, Eric J.

2018-04-01

Additive manufacturing is a rapidly maturing process for the production of complex metallic, ceramic, polymeric, and composite components. The processes used are numerous, and with the complex geometries involved this can make quality control and standardization of the process and inspection difficult. Acoustic emission measurements have been used previously to monitor a number of processes including machining and welding. The authors have identified acoustic signature measurement as a potential means of monitoring metal additive manufacturing processes using process noise characteristics and those discrete acoustic emission events characteristic of defect growth, including cracks and delamination. Results of acoustic monitoring for a metal additive manufacturing process (directed energy deposition) are reported. The work investigated correlations between acoustic emissions and process noise with variations in machine state and deposition parameters, and provided proof of concept data that such correlations do exist.

Three-dimensional dynamic deformation monitoring using a laser-scanning system

NASA Astrophysics Data System (ADS)

Al-Hanbali, Nedal N.; Teskey, William F.

1994-10-01

Non-contact dynamic deformation monitoring (e.g. with a laser scanning system) is very useful in monitoring changes in alignment and changes in size and shape of coupled operating machines. If relative movements between coupled operating machines are large, excessive wear in the machines or unplanned shutdowns due to machinery failure will occur. The purpose of non-contact dynamic deformation monitoring is to identify the causes of large movements and point to remedial action that can be taken to prevent them. The laser scanning system is a laser-based 3D vision system. The system-technique is based on an auto- synchronized triangulation scanning scheme. The system provides accurate, fast, and reliable 3D measurements and can measure objects between 0.5 m to 100 m with a field of view of 40 degree(s) X 50 degree(s). The system is flexible in terms of providing control over the scanned area and depth. The system also provides the user with the intensity image in addition to the depth coded image. This paper reports on the preliminary testing of this system to monitor surface movements and target (point) movements. The monitoring resolution achieved for an operating motorized alignment test rig in the lab was 1 mm for surface movements and 0.50 m for target movements. Raw data manipulation, local calibration, and the method of relating measurements to control points will be discussed. Possibilities for improving the resolution and recommendations for future development will also be presented.

Monitoring Hitting Load in Tennis Using Inertial Sensors and Machine Learning.

PubMed

Whiteside, David; Cant, Olivia; Connolly, Molly; Reid, Machar

2017-10-01

Quantifying external workload is fundamental to training prescription in sport. In tennis, global positioning data are imprecise and fail to capture hitting loads. The current gold standard (manual notation) is time intensive and often not possible given players' heavy travel schedules. To develop an automated stroke-classification system to help quantify hitting load in tennis. Nineteen athletes wore an inertial measurement unit (IMU) on their wrist during 66 video-recorded training sessions. Video footage was manually notated such that known shot type (serve, rally forehand, slice forehand, forehand volley, rally backhand, slice backhand, backhand volley, smash, or false positive) was associated with the corresponding IMU data for 28,582 shots. Six types of machine-learning models were then constructed to classify true shot type from the IMU signals. Across 10-fold cross-validation, a cubic-kernel support vector machine classified binned shots (overhead, forehand, or backhand) with an accuracy of 97.4%. A second cubic-kernel support vector machine achieved 93.2% accuracy when classifying all 9 shot types. With a view to monitoring external load, the combination of miniature inertial sensors and machine learning offers a practical and automated method of quantifying shot counts and discriminating shot types in elite tennis players.

30 CFR 27.24 - Power-shutoff component.

Code of Federal Regulations, 2010 CFR

2010-07-01

... APPROVAL OF MINING PRODUCTS METHANE-MONITORING SYSTEMS Construction and Design Requirements § 27.24 Power... the machine or equipment when actuated by the methane detector at a methane concentration of 2.0... actuated by the methane detector, cause a control circuit to shut down the machine or equipment on which it...

Real Time Network Monitoring and Reporting System

ERIC Educational Resources Information Center

Massengale, Ricky L., Sr.

2009-01-01

With the ability of modern system developers to develop intelligent programs that allows machines to learn, modify and evolve themselves, current trends of reactionary methods to detect and eradicate malicious software code from infected machines is proving to be too costly. Addressing malicious software after an attack is the current methodology…

40 CFR 60.185 - Monitoring of operations.

Code of Federal Regulations, 2011 CFR

2011-07-01

... (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Standards of Performance for Primary Lead... reverberatory furnace, or sintering machine discharge end. The span of this system shall be set at 80 to 100... discharged into the atmosphere from any sintering machine, electric furnace or converter subject to § 60.183...

WISESight : a multispectral smart video-track intrusion monitor.

DOT National Transportation Integrated Search

2015-05-01

International Electronic Machines : Corporation (IEM) developed, tested, and : validated a unique smart video-based : intrusion monitoring system for use at : highway-rail grade crossings. The system : used both thermal infrared (IR) and : visible/ne...

Comparison of portable and conventional ultrasound imaging in spinal curvature measurement

NASA Astrophysics Data System (ADS)

Yan, Christina; Tabanfar, Reza; Kempston, Michael; Borschneck, Daniel; Ungi, Tamas; Fichtinger, Gabor

2016-03-01

PURPOSE: In scoliosis monitoring, tracked ultrasound has been explored as a safer imaging alternative to traditional radiography. The use of ultrasound in spinal curvature measurement requires identification of vertebral landmarks, but bones have reduced visibility in ultrasound imaging and high quality ultrasound machines are often expensive and not portable. In this work, we investigate the image quality and measurement accuracy of a low cost and portable ultrasound machine in comparison to a standard ultrasound machine in scoliosis monitoring. METHODS: Two different kinds of ultrasound machines were tested on three human subjects, using the same position tracker and software. Spinal curves were measured in the same reference coordinate system using both ultrasound machines. Lines were defined by connecting two symmetric landmarks identified on the left and right transverse process of the same vertebrae, and spinal curvature was defined as the transverse process angle between two such lines, projected on the coronal plane. RESULTS: Three healthy volunteers were scanned by both ultrasound configurations. Three experienced observers localized transverse processes as skeletal landmarks and obtained transverse process angles in images obtained from both ultrasounds. The mean difference per transverse process angle measured was 3.00 +/-2.1°. 94% of transverse processes visualized in the Sonix Touch were also visible in the Telemed. Inter-observer error in the Telemed was 4.5° and 4.3° in the Sonix Touch. CONCLUSION: Price, convenience and accessibility suggest the Telemed to be a viable alternative in scoliosis monitoring, however further improvements in measurement protocol and image noise reduction must be completed before implementing the Telemed in the clinical setting.

Principles of control automation of soil compacting machine operating mechanism

NASA Astrophysics Data System (ADS)

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.

Development of a wearable measurement and control unit for personal customizing machine-supported exercise.

PubMed

Wang, Zhihui; Tamura, Naoki; Kiryu, Tohru

2005-01-01

Wearable technology has been used in various health-related fields to develop advanced monitoring solutions. However, the monitoring function alone cannot meet all the requirements of personal customizing machine-supported exercise that have biosignal-based controls. In this paper, we propose a new wearable unit design equipped with measurement and control functions to support the personal customization process. The wearable unit can measure the heart rate and electromyogram signals during exercise and output workload control commands to the exercise machines. We then applied a prototype of the wearable unit to an Internet-based cycle ergometer system. The wearable unit was examined using twelve young people to check its feasibility. The results verified that the unit could successfully adapt to the control of the workload and was effective for continuously supporting gradual changes in physical activities.

Condition Assessment and End-of-Life Prediction System for Electric Machines and Their Loads

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Toliyat, Hamid A.

2005-01-01

An end-of-life prediction system developed for electric machines and their loads could be used in integrated vehicle health monitoring at NASA and in other government agencies. This system will provide on-line, real-time condition assessment and end-of-life prediction of electric machines (e.g., motors, generators) and/or their loads of mechanically coupled machinery (e.g., pumps, fans, compressors, turbines, conveyor belts, magnetic levitation trains, and others). In long-duration space flight, the ability to predict the lifetime of machinery could spell the difference between mission success or failure. Therefore, the system described here may be of inestimable value to the U.S. space program. The system will provide continuous monitoring for on-line condition assessment and end-of-life prediction as opposed to the current off-line diagnoses.

Monitoring of laser material processing using machine integrated low-coherence interferometry

NASA Astrophysics Data System (ADS)

Kunze, Rouwen; König, Niels; Schmitt, Robert

2017-06-01

Laser material processing has become an indispensable tool in modern production. With the availability of high power pico- and femtosecond laser sources, laser material processing is advancing into applications, which demand for highest accuracies such as laser micro milling or laser drilling. In order to enable narrow tolerance windows, a closedloop monitoring of the geometrical properties of the processed work piece is essential for achieving a robust manufacturing process. Low coherence interferometry (LCI) is a high-precision measuring principle well-known from surface metrology. In recent years, we demonstrated successful integrations of LCI into several different laser material processing methods. Within this paper, we give an overview about the different machine integration strategies, that always aim at a complete and ideally telecentric integration of the measurement device into the existing beam path of the processing laser. Thus, highly accurate depth measurements within machine coordinates and a subsequent process control and quality assurance are possible. First products using this principle have already found its way to the market, which underlines the potential of this technology for the monitoring of laser material processing.

An in situ probe for on-line monitoring of cell density and viability on the basis of dark field microscopy in conjunction with image processing and supervised machine learning.

PubMed

Wei, Ning; You, Jia; Friehs, Karl; Flaschel, Erwin; Nattkemper, Tim Wilhelm

2007-08-15

Fermentation industries would benefit from on-line monitoring of important parameters describing cell growth such as cell density and viability during fermentation processes. For this purpose, an in situ probe has been developed, which utilizes a dark field illumination unit to obtain high contrast images with an integrated CCD camera. To test the probe, brewer's yeast Saccharomyces cerevisiae is chosen as the target microorganism. Images of the yeast cells in the bioreactors are captured, processed, and analyzed automatically by means of mechatronics, image processing, and machine learning. Two support vector machine based classifiers are used for separating cells from background, and for distinguishing live from dead cells afterwards. The evaluation of the in situ experiments showed strong correlation between results obtained by the probe and those by widely accepted standard methods. Thus, the in situ probe has been proved to be a feasible device for on-line monitoring of both cell density and viability with high accuracy and stability. (c) 2007 Wiley Periodicals, Inc.

A high sensitivity wear debris sensor using ferrite cores for online oil condition monitoring

NASA Astrophysics Data System (ADS)

Zhu, Xiaoliang; Zhong, Chong; Zhe, Jiang

2017-07-01

Detecting wear debris and measuring the increasing number of wear debris in lubrication oil can indicate abnormal machine wear well ahead of machine failure, and thus are indispensable for online machine health monitoring. A portable wear debris sensor with ferrite cores for online monitoring is presented. The sensor detects wear debris by measuring the inductance change of two planar coils wound around a pair of ferrite cores that make the magnetic flux denser and more uniform in the sensing channel, thereby improving the sensitivity of the sensor. Static testing results showed this wear debris sensor is capable of detecting 11 µm and 50 µm ferrous debris in 1 mm and 7 mm diameter fluidic pipes, respectively; such a high sensitivity has not been achieved before. Furthermore, a synchronized sampling method was also applied to reduce the data size and realize real-time data processing. Dynamic testing results demonstrated that the sensor is capable of detecting wear debris in real time with a high throughput of 750 ml min-1 the measured debris concentration is in good agreement with the actual concentration.

Metallic wear debris sensors: promising developments in failure prevention for wind turbine gearsets and similar components

NASA Astrophysics Data System (ADS)

Poley, Jack; Dines, Michael

2011-04-01

Wind turbines are frequently located in remote, hard-to-reach locations, making it difficult to apply traditional oil analysis sampling of the machine's critical gearset at timely intervals. Metal detection sensors are excellent candidates for sensors designed to monitor machine condition in vivo. Remotely sited components, such as wind turbines, therefore, can be comfortably monitored from a distance. Online sensor technology has come of age with products now capable of identifying onset of wear in time to avoid or mitigate failure. Online oil analysis is now viable, and can be integrated with onsite testing to vet sensor alarms, as well as traditional oil analysis, as furnished by offsite laboratories. Controlled laboratory research data were gathered from tests conducted on a typical wind turbine gearbox, wherein total ferrous particle measurement and metallic particle counting were employed and monitored. The results were then compared with a physical inspection for wear experienced by the gearset. The efficacy of results discussed herein strongly suggests the viability of metallic wear debris sensors in today's wind turbine gearsets, as correlation between sensor data and machine trauma were very good. By extension, similar components and settings would also seem amenable to wear particle sensor monitoring. To our knowledge no experiments such as described herein, have previously been conducted and published.

A Hybrid Generalized Hidden Markov Model-Based Condition Monitoring Approach for Rolling Bearings

PubMed Central

Liu, Jie; Hu, Youmin; Wu, Bo; Wang, Yan; Xie, Fengyun

2017-01-01

The operating condition of rolling bearings affects productivity and quality in the rotating machine process. Developing an effective rolling bearing condition monitoring approach is critical to accurately identify the operating condition. In this paper, a hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings is proposed, where interval valued features are used to efficiently recognize and classify machine states in the machine process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition (VMD). Parameters of the VMD, in the form of generalized intervals, provide a concise representation for aleatory and epistemic uncertainty and improve the robustness of identification. The multi-scale permutation entropy method is applied to extract state features from the decomposed signals in different operating conditions. Traditional principal component analysis is adopted to reduce feature size and computational cost. With the extracted features’ information, the generalized hidden Markov model, based on generalized interval probability, is used to recognize and classify the fault types and fault severity levels. Finally, the experiment results show that the proposed method is effective at recognizing and classifying the fault types and fault severity levels of rolling bearings. This monitoring method is also efficient enough to quantify the two uncertainty components. PMID:28524088

Ultrasensitive and Highly Stable Resistive Pressure Sensors with Biomaterial-Incorporated Interfacial Layers for Wearable Health-Monitoring and Human-Machine Interfaces.

PubMed

Chang, Hochan; Kim, Sungwoong; Jin, Sumin; Lee, Seung-Woo; Yang, Gil-Tae; Lee, Ki-Young; Yi, Hyunjung

2018-01-10

Flexible piezoresistive sensors have huge potential for health monitoring, human-machine interfaces, prosthetic limbs, and intelligent robotics. A variety of nanomaterials and structural schemes have been proposed for realizing ultrasensitive flexible piezoresistive sensors. However, despite the success of recent efforts, high sensitivity within narrower pressure ranges and/or the challenging adhesion and stability issues still potentially limit their broad applications. Herein, we introduce a biomaterial-based scheme for the development of flexible pressure sensors that are ultrasensitive (resistance change by 5 orders) over a broad pressure range of 0.1-100 kPa, promptly responsive (20 ms), and yet highly stable. We show that employing biomaterial-incorporated conductive networks of single-walled carbon nanotubes as interfacial layers of contact-based resistive pressure sensors significantly enhances piezoresistive response via effective modulation of the interlayer resistance and provides stable interfaces for the pressure sensors. The developed flexible sensor is capable of real-time monitoring of wrist pulse waves under external medium pressure levels and providing pressure profiles applied by a thumb and a forefinger during object manipulation at a low voltage (1 V) and power consumption (<12 μW). This work provides a new insight into the material candidates and approaches for the development of wearable health-monitoring and human-machine interfaces.

Ant-Based Cyber Defense (also known as

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

Glenn Fink, PNNL

2015-09-29

ABCD is a four-level hierarchy with human supervisors at the top, a top-level agent called a Sergeant controlling each enclave, Sentinel agents located at each monitored host, and mobile Sensor agents that swarm through the enclaves to detect cyber malice and misconfigurations. The code comprises four parts: (1) the core agent framework, (2) the user interface and visualization, (3) test-range software to create a network of virtual machines including a simulated Internet and user and host activity emulation scripts, and (4) a test harness to allow the safe running of adversarial code within the framework of monitored virtual machines.

Harvesting small trees for bio-energy

Treesearch

John Klepac; Robert Rummer; Jason Thompson

2011-01-01

A conventional whole-tree logging operation consisting of 4-wheeled and 3-wheeled saw-head feller-bunchers, two grapple skidders and a chipper that produces dirty chips was monitored across several stands and machine performance evaluated. Stands were inventoried to determine density, volume, and basal area per acre and will be used to relate machine performance to...

Transmission of hepatitis C virus between hemodialysis patients sharing the same machine.

PubMed

Sartor, Catherine; Brunet, Philippe; Simon, Sophie; Tamalet, Catherine; Berland, Yvon; Drancourt, Michel

2004-07-01

After a patient acquired hepatitis C virus (HCV) infection in our unit, we performed epidemiologic and virologic investigations, including genotyping and phylogenetic analyses. The results provided evidence for HCV transmission between two patients sharing the same machine and suggested possible transmission via accidental contamination of the venous pressure monitoring system.

A Machine Vision Quality Control System for Industrial Acrylic Fibre Production

NASA Astrophysics Data System (ADS)

Heleno, Paulo; Davies, Roger; Correia, Bento A. Brázio; Dinis, João

2002-12-01

This paper describes the implementation of INFIBRA, a machine vision system used in the quality control of acrylic fibre production. The system was developed by INETI under a contract with a leading industrial manufacturer of acrylic fibres. It monitors several parameters of the acrylic production process. This paper presents, after a brief overview of the system, a detailed description of the machine vision algorithms developed to perform the inspection tasks unique to this system. Some of the results of online operation are also presented.

Advances in Machine Technology.

PubMed

Clark, William R; Villa, Gianluca; Neri, Mauro; Ronco, Claudio

2018-01-01

Continuous renal replacement therapy (CRRT) machines have evolved into devices specifically designed for critically ill over the past 40 years. In this chapter, a brief history of this evolution is first provided, with emphasis on the manner in which changes have been made to address the specific needs of the critically ill patient with acute kidney injury. Subsequently, specific examples of technology developments for CRRT machines are discussed, including the user interface, pumps, pressure monitoring, safety features, and anticoagulation capabilities. © 2018 S. Karger AG, Basel.

Comparison of Automated and Manual Recording of Brief Episodes of Intracranial Hypertension and Cerebral Hypoperfusion and Their Association with Outcome After Severe Traumatic Brain Injury

DTIC Science & Technology

2017-03-01

neuro ICP care beyond trauma care. 15. SUBJECT TERMS Advanced machine learning techniques, intracranial pressure, vital signs, monitoring...death and disability in combat casualties [1,2]. Approximately 2 million head injuries occur annually in the United States, resulting in more than...editor. Machine learning and data mining in pattern recognition. Proceedings of the 8th International Workshop on Machine Learning and Data Mining in

29 CFR 1960.26 - Conduct of inspections.

Code of Federal Regulations, 2013 CFR

2013-07-01

... pertinent conditions, structures, machines, apparatus, devices, equipment, and materials therein, and to... environments, the inspector may request employees to wear reasonable and necessary personal monitoring devices... employer shall encourage employees to wear the personal environmental monitoring devices during an...

29 CFR 1960.26 - Conduct of inspections.

Code of Federal Regulations, 2014 CFR

2014-07-01

... pertinent conditions, structures, machines, apparatus, devices, equipment, and materials therein, and to... environments, the inspector may request employees to wear reasonable and necessary personal monitoring devices... employer shall encourage employees to wear the personal environmental monitoring devices during an...

29 CFR 1960.26 - Conduct of inspections.

Code of Federal Regulations, 2012 CFR

2012-07-01

... pertinent conditions, structures, machines, apparatus, devices, equipment, and materials therein, and to... environments, the inspector may request employees to wear reasonable and necessary personal monitoring devices... employer shall encourage employees to wear the personal environmental monitoring devices during an...

29 CFR 1960.26 - Conduct of inspections.

Code of Federal Regulations, 2011 CFR

2011-07-01

... pertinent conditions, structures, machines, apparatus, devices, equipment, and materials therein, and to... environments, the inspector may request employees to wear reasonable and necessary personal monitoring devices... employer shall encourage employees to wear the personal environmental monitoring devices during an...

Design of a real-time tax-data monitoring intelligent card system

NASA Astrophysics Data System (ADS)

Gu, Yajun; Bi, Guotang; Chen, Liwei; Wang, Zhiyuan

2009-07-01

To solve the current problem of low efficiency of domestic Oil Station's information management, Oil Station's realtime tax data monitoring system has been developed to automatically access tax data of Oil pumping machines, realizing Oil-pumping machines' real-time automatic data collection, displaying and saving. The monitoring system uses the noncontact intelligent card or network to directly collect data which can not be artificially modified and so seals the loopholes and improves the tax collection's automatic level. It can perform real-time collection and management of the Oil Station information, and find the problem promptly, achieves the automatic management for the entire process covering Oil sales accounting and reporting. It can also perform remote query to the Oil Station's operation data. This system has broad application future and economic value.

Advanced Monitoring to Improve Combustion Turbine/Combined Cycle Reliability, Availability & Maintainability

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

Leonard Angello

2005-09-30

Power generators are concerned with the maintenance costs associated with the advanced turbines that they are purchasing. Since these machines do not have fully established Operation and Maintenance (O&M) track records, power generators face financial risk due to uncertain future maintenance costs. This risk is of particular concern, as the electricity industry transitions to a competitive business environment in which unexpected O&M costs cannot be passed through to consumers. These concerns have accelerated the need for intelligent software-based diagnostic systems that can monitor the health of a combustion turbine in real time and provide valuable information on the machine's performancemore » to its owner/operators. EPRI, Impact Technologies, Boyce Engineering, and Progress Energy have teamed to develop a suite of intelligent software tools integrated with a diagnostic monitoring platform that, in real time, interpret data to assess the 'total health' of combustion turbines. The 'Combustion Turbine Health Management System' (CTHMS) will consist of a series of 'Dynamic Link Library' (DLL) programs residing on a diagnostic monitoring platform that accepts turbine health data from existing monitoring instrumentation. CTHMS interprets sensor and instrument outputs, correlates them to a machine's condition, provide interpretative analyses, project servicing intervals, and estimate remaining component life. In addition, the CTHMS enables real-time anomaly detection and diagnostics of performance and mechanical faults, enabling power producers to more accurately predict critical component remaining useful life and turbine degradation.« less

Predictive modeling for corrective maintenance of imaging devices from machine logs.

PubMed

Patil, Ravindra B; Patil, Meru A; Ravi, Vidya; Naik, Sarif

2017-07-01

In the cost sensitive healthcare industry, an unplanned downtime of diagnostic and therapy imaging devices can be a burden on the financials of both the hospitals as well as the original equipment manufacturers (OEMs). In the current era of connectivity, it is easier to get these devices connected to a standard monitoring station. Once the system is connected, OEMs can monitor the health of these devices remotely and take corrective actions by providing preventive maintenance thereby avoiding major unplanned downtime. In this article, we present an overall methodology of predicting failure of these devices well before customer experiences it. We use data-driven approach based on machine learning to predict failures in turn resulting in reduced machine downtime, improved customer satisfaction and cost savings for the OEMs. One of the use-case of predicting component failure of PHILIPS iXR system is explained in this article.

Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

PubMed

Chen, Lujie; Dubrawski, Artur; Wang, Donghan; Fiterau, Madalina; Guillame-Bert, Mathieu; Bose, Eliezer; Kaynar, Ata M; Wallace, David J; Guttendorf, Jane; Clermont, Gilles; Pinsky, Michael R; Hravnak, Marilyn

2016-07-01

The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability. Observational cohort study. Twenty-four-bed trauma step-down unit. Two thousand one hundred fifty-three patients. Noninvasive vital sign monitoring data (heart rate, respiratory rate, peripheral oximetry) recorded on all admissions at 1/20 Hz, and noninvasive blood pressure less frequently, and partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were vital sign deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained machine-learning algorithms. The best model was evaluated on test set alerts to enact online alert classification over time. The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve performance of 0.79 (95% CI, 0.67-0.93) for peripheral oximetry at the instant the vital sign first crossed threshold and increased to 0.87 (95% CI, 0.71-0.95) at 3 minutes into the alerting period. Blood pressure area under the curve started at 0.77 (95% CI, 0.64-0.95) and increased to 0.87 (95% CI, 0.71-0.98), whereas respiratory rate area under the curve started at 0.85 (95% CI, 0.77-0.95) and increased to 0.97 (95% CI, 0.94-1.00). Heart rate alerts were too few for model development. Machine-learning models can discern clinically relevant peripheral oximetry, blood pressure, and respiratory rate alerts from artifacts in an online monitoring dataset (area under the curve > 0.87).

Statistical quality control for volumetric modulated arc therapy (VMAT) delivery by using the machine's log data

NASA Astrophysics Data System (ADS)

Cheong, Kwang-Ho; Lee, Me-Yeon; Kang, Sei-Kwon; Yoon, Jai-Woong; Park, Soah; Hwang, Taejin; Kim, Haeyoung; Kim, Kyoung Ju; Han, Tae Jin; Bae, Hoonsik

2015-07-01

The aim of this study is to set up statistical quality control for monitoring the volumetric modulated arc therapy (VMAT) delivery error by using the machine's log data. Eclipse and a Clinac iX linac with the RapidArc system (Varian Medical Systems, Palo Alto, USA) are used for delivery of the VMAT plan. During the delivery of the RapidArc fields, the machine determines the delivered monitor units (MUs) and the gantry angle's position accuracy and the standard deviations of the MU ( σMU: dosimetric error) and the gantry angle ( σGA: geometric error) are displayed on the console monitor after completion of the RapidArc delivery. In the present study, first, the log data were analyzed to confirm its validity and usability; then, statistical process control (SPC) was applied to monitor the σMU and the σGA in a timely manner for all RapidArc fields: a total of 195 arc fields for 99 patients. The MU and the GA were determined twice for all fields, that is, first during the patient-specific plan QA and then again during the first treatment. The sMU and the σGA time series were quite stable irrespective of the treatment site; however, the sGA strongly depended on the gantry's rotation speed. The σGA of the RapidArc delivery for stereotactic body radiation therapy (SBRT) was smaller than that for the typical VMAT. Therefore, SPC was applied for SBRT cases and general cases respectively. Moreover, the accuracy of the potential meter of the gantry rotation is important because the σGA can change dramatically due to its condition. By applying SPC to the σMU and σGA, we could monitor the delivery error efficiently. However, the upper and the lower limits of SPC need to be determined carefully with full knowledge of the machine and log data.

Application of Machine Learning to Rotorcraft Health Monitoring

NASA Technical Reports Server (NTRS)

Cody, Tyler; Dempsey, Paula J.

2017-01-01

Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

Fabric strain sensor integrated with CNPECs for repeated large deformation

NASA Astrophysics Data System (ADS)

Yi, Weijing

Flexible and soft strain sensors that can be used in smart textiles for wearable applications are much desired. They should meet the requirements of low modulus, large working range and good fatigue resistance as well as good sensing performances. However, there were no commercial products available and the objective of the thesis is to investigate fabric strain sensors based on carbon nanoparticle (CNP) filled elastomer composites (CNPECs) for potential wearing applications. Conductive CNPECs were fabricated and investigated. The introduction of silicone oil (SO) significantly decreased modulus of the composites to less than 1 MPa without affecting their deformability and they showed good stability after heat treatment. With increase of CNP concentration, a percolation appeared in electrical resistivity and the composites can be divided into three ranges. I-V curves and impedance spectra together with electro-mechanical studies demonstrated a balance between sensitivity and working range for the composites with CNP concentrations in post percolation range, and were preferred for sensing applications only if the fatigue life was improved. Due to the good elasticity and failure resist property of knitted fabric under repeated extension, it was adopted as substrate to increase the fatigue life of the conductive composites. After optimization of processing parameters, the conductive fabric with CNP concentration of 9.0CNP showed linear I-V curves when voltage is in the range of -1 V/mm and 1 V/mm and negligible capacitive behavior when frequency below 103 Hz even with strain of 60%. It showed higher sensitivity due to the combination of nonlinear resistance-strain behavior of the CNPECs and non-even strain distribution of knitted fabric under extension. The fatigue life of the conductive fabric was greatly improved. Extended on the studies of CNPECs and the coated conductive fabrics, a fabric strain sensor was designed, fabricated and packaged. The Young's modulus of the packaged fabric strain sensor was less than 1 MPa; the strain gauge factor was 4.76 within the strain range of 0-40% and the hysteresis was 5.5%; the resistance relaxation was 5.56% with a constant strain of 40%; the fatigue life of the sensor was more than 100,000 cycles.

Final Scientific/Technical Report for "Enabling Exascale Hardware and Software Design through Scalable System Virtualization"

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

Dinda, Peter August

2015-03-17

This report describes the activities, findings, and products of the Northwestern University component of the "Enabling Exascale Hardware and Software Design through Scalable System Virtualization" project. The purpose of this project has been to extend the state of the art of systems software for high-end computing (HEC) platforms, and to use systems software to better enable the evaluation of potential future HEC platforms, for example exascale platforms. Such platforms, and their systems software, have the goal of providing scientific computation at new scales, thus enabling new research in the physical sciences and engineering. Over time, the innovations in systems softwaremore » for such platforms also become applicable to more widely used computing clusters, data centers, and clouds. This was a five-institution project, centered on the Palacios virtual machine monitor (VMM) systems software, a project begun at Northwestern, and originally developed in a previous collaboration between Northwestern University and the University of New Mexico. In this project, Northwestern (including via our subcontract to the University of Pittsburgh) contributed to the continued development of Palacios, along with other team members. We took the leadership role in (1) continued extension of support for emerging Intel and AMD hardware, (2) integration and performance enhancement of overlay networking, (3) connectivity with architectural simulation, (4) binary translation, and (5) support for modern Non-Uniform Memory Access (NUMA) hosts and guests. We also took a supporting role in support for specialized hardware for I/O virtualization, profiling, configurability, and integration with configuration tools. The efforts we led (1-5) were largely successful and executed as expected, with code and papers resulting from them. The project demonstrated the feasibility of a virtualization layer for HEC computing, similar to such layers for cloud or datacenter computing. For effort (3), although a prototype connecting Palacios with the GEM5 architectural simulator was demonstrated, our conclusion was that such a platform was less useful for design space exploration than anticipated due to inherent complexity of the connection between the instruction set architecture level and the microarchitectural level. For effort (4), we found that a code injection approach proved to be more fruitful. The results of our efforts are publicly available in the open source Palacios codebase and published papers, all of which are available from the project web site, v3vee.org. Palacios is currently one of the two codebases (the other being Sandia’s Kitten lightweight kernel) that underlies the node operating system for the DOE Hobbes Project, one of two projects tasked with building a systems software prototype for the national exascale computing effort.« less

An In-Process Surface Roughness Recognition System in End Milling Operations

ERIC Educational Resources Information Center

Yang, Lieh-Dai; Chen, Joseph C.

2004-01-01

To develop an in-process quality control system, a sensor technique and a decision-making algorithm need to be applied during machining operations. Several sensor techniques have been used in the in-process prediction of quality characteristics in machining operations. For example, an accelerometer sensor can be used to monitor the vibration of…

Elevating Virtual Machine Introspection for Fine-Grained Process Monitoring: Techniques and Applications

ERIC Educational Resources Information Center

Srinivasan, Deepa

2013-01-01

Recent rapid malware growth has exposed the limitations of traditional in-host malware-defense systems and motivated the development of secure virtualization-based solutions. By running vulnerable systems as virtual machines (VMs) and moving security software from inside VMs to the outside, the out-of-VM solutions securely isolate the anti-malware…

Machine learning techniques for fault isolation and sensor placement

NASA Technical Reports Server (NTRS)

Carnes, James R.; Fisher, Douglas H.

1993-01-01

Fault isolation and sensor placement are vital for monitoring and diagnosis. A sensor conveys information about a system's state that guides troubleshooting if problems arise. We are using machine learning methods to uncover behavioral patterns over snapshots of system simulations that will aid fault isolation and sensor placement, with an eye towards minimality, fault coverage, and noise tolerance.

A Machine-Learning and Filtering Based Data Assimilation Framework for Geologic Carbon Sequestration Monitoring Optimization

NASA Astrophysics Data System (ADS)

Chen, B.; Harp, D. R.; Lin, Y.; Keating, E. H.; Pawar, R.

2017-12-01

Monitoring is a crucial aspect of geologic carbon sequestration (GCS) risk management. It has gained importance as a means to ensure CO2 is safely and permanently stored underground throughout the lifecycle of a GCS project. Three issues are often involved in a monitoring project: (i) where is the optimal location to place the monitoring well(s), (ii) what type of data (pressure, rate and/or CO2 concentration) should be measured, and (iii) What is the optimal frequency to collect the data. In order to address these important issues, a filtering-based data assimilation procedure is developed to perform the monitoring optimization. The optimal monitoring strategy is selected based on the uncertainty reduction of the objective of interest (e.g., cumulative CO2 leak) for all potential monitoring strategies. To reduce the computational cost of the filtering-based data assimilation process, two machine-learning algorithms: Support Vector Regression (SVR) and Multivariate Adaptive Regression Splines (MARS) are used to develop the computationally efficient reduced-order-models (ROMs) from full numerical simulations of CO2 and brine flow. The proposed framework for GCS monitoring optimization is demonstrated with two examples: a simple 3D synthetic case and a real field case named Rock Spring Uplift carbon storage site in Southwestern Wyoming.

How Will I Be Monitored After Heart Surgery?

MedlinePlus

... monitor you are described below. What is an ECG? •An electrocardiogram, or ECG or EKG machine, records your heartbeat. • Tiny wires, ... normally. •A highly trained nurse will watch the ECG at all times. •You’ll be hooked up ...

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

PubMed

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

Mechanical properties of a new mica-based machinable glass ceramic for CAD/CAM restorations.

PubMed

Thompson, J Y; Bayne, S C; Heymann, H O

1996-12-01

Machinable ceramics (Vita Mark II and Dicor MGC) exhibit good short-term clinical performance, but long-term in vivo fracture resistance is still being monitored. The relatively low fracture toughness of currently available machinable ceramics restricts their use to conservative inlays and onlays. A new machinable glass ceramic (MGC-F) has been developed (Corning Inc.) with enhanced fluorescence and machinability. The purpose of this study was to characterize and compare key mechanical properties of MGC-F to Dicor MGC-Light, Dicor MGC-Dark, and Vita Mark II glass ceramics. The mean fracture toughness and indented biaxial flexure strength of MGC-F were each significantly greater (p < or = 0.01) than that of Dicor MGC-Light, Dicor MGC-Dark, and Vita Mark II ceramic materials. The results of this study indicate the potential for better in vivo fracture resistance of MGC-F compared with existing machinable ceramic materials for CAD/CAM restorations.

Towards a generalized energy prediction model for machine tools

PubMed Central

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H.; Dornfeld, David A.; Helu, Moneer; Rachuri, Sudarsan

2017-01-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process. PMID:28652687

Towards a generalized energy prediction model for machine tools.

PubMed

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H; Dornfeld, David A; Helu, Moneer; Rachuri, Sudarsan

2017-04-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

Acoustical Detection Of Leakage In A Combustor

NASA Technical Reports Server (NTRS)

Puster, Richard L.; Petty, Jeffrey L.

1993-01-01

Abnormal combustion excites characteristic standing wave. Acoustical leak-detection system gives early warning of failure, enabling operating personnel to stop combustion process and repair spray bar before leak grows large enough to cause damage. Applicable to engines, gas turbines, furnaces, and other machines in which acoustic emissions at known frequencies signify onset of damage. Bearings in rotating machines monitored for emergence of characteristic frequencies shown in previous tests associated with incipient failure. Also possible to monitor for signs of trouble at multiple frequencies by feeding output of transducer simultaneously to multiple band-pass filters and associated circuitry, including separate trigger circuit set to appropriate level for each frequency.

Flexible architecture of data acquisition firmware based on multi-behaviors finite state machine

NASA Astrophysics Data System (ADS)

Arpaia, Pasquale; Cimmino, Pasquale

2016-11-01

A flexible firmware architecture for different kinds of data acquisition systems, ranging from high-precision bench instruments to low-cost wireless transducers networks, is presented. The key component is a multi-behaviors finite state machine, easily configurable to both low- and high-performance requirements, to diverse operating systems, as well as to on-line and batch measurement algorithms. The proposed solution was validated experimentally on three case studies with data acquisition architectures: (i) concentrated, in a high-precision instrument for magnetic measurements at CERN, (ii) decentralized, for telemedicine remote monitoring of patients at home, and (iii) distributed, for remote monitoring of building's energy loss.

Acoustic emission from single point machining: Part 2, Signal changes with tool wear

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

Heiple, C.R.; Carpenter, S.H.; Armentrout, D.L.

1989-01-01

Changes in acoustic emission signal characteristics with tool wear were monitored during single point machining of 4340 steel and Ti-6Al-4V heat treated to several strength levels, 606l-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, 410 stainless steel, lead, and teflon. No signal characteristic changed in the same way with tool wear for all materials tested. A single change in a particular AE signal characteristic with tool wear valid for all materials probably does not exist. Nevertheless, changes in various signal characteristic with wear for a given material may be sufficient to be used to monitor tool wear.

A testing machine for dental air-turbine handpiece characteristics: free-running speed, stall torque, bearing resistance.

PubMed

Darvell, Brain W; Dyson, J E

2005-01-01

The measurement of performance characteristics of dental air turbine handpieces is of interest with respect to product comparisons, standards specifications and monitoring of bearing longevity in clinical service. Previously, however, bulky and expensive laboratory equipment was required. A portable test machine is described for determining three key characteristics of dental air-turbine handpieces: free-running speed, stall torque and bearing resistance. It relies on a special circuit design for performing a hardware integration of a force signal with respect to rotational position, independent of the rate at which the turbine is allowed to turn during both stall torque and bearing resistance measurements. Free-running speed without the introduction of any imbalance can be readily monitored. From the essential linear relationship between torque and speed, dynamic torque and, hence, power, can then be calculated. In order for these measurements to be performed routinely with the necessary precision of location on the test stage, a detailed procedure for ensuring proper gripping of the handpiece is described. The machine may be used to verify performance claims, standard compliance checks should this be established as appropriate, monitor deterioration with time and usage in the clinical environment and for laboratory investigation of design development.

Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines

NASA Astrophysics Data System (ADS)

Jegadeeshwaran, R.; Sugumaran, V.

2015-02-01

Hydraulic brakes in automobiles are important components for the safety of passengers; therefore, the brakes are a good subject for condition monitoring. The condition of the brake components can be monitored by using the vibration characteristics. On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to such problems. The vibration signals for both good as well as faulty conditions of brakes were acquired from a hydraulic brake test setup with the help of a piezoelectric transducer and a data acquisition system. Descriptive statistical features were extracted from the acquired vibration signals and the feature selection was carried out using the C4.5 decision tree algorithm. There is no specific method to find the right number of features required for classification for a given problem. Hence an extensive study is needed to find the optimum number of features. The effect of the number of features was also studied, by using the decision tree as well as Support Vector Machines (SVM). The selected features were classified using the C-SVM and Nu-SVM with different kernel functions. The results are discussed and the conclusion of the study is presented.

Linear positioning laser calibration setup of CNC machine tools

NASA Astrophysics Data System (ADS)

Sui, Xiulin; Yang, Congjing

2002-10-01

The linear positioning laser calibration setup of CNC machine tools is capable of executing machine tool laser calibraiotn and backlash compensation. Using this setup, hole locations on CNC machien tools will be correct and machien tool geometry will be evaluated and adjusted. Machien tool laser calibration and backlash compensation is a simple and straightforward process. First the setup is to 'find' the stroke limits of the axis. Then the laser head is then brought into correct alignment. Second is to move the machine axis to the other extreme, the laser head is now aligned, using rotation and elevation adjustments. Finally the machine is moved to the start position and final alignment is verified. The stroke of the machine, and the machine compensation interval dictate the amount of data required for each axis. These factors determine the amount of time required for a through compensation of the linear positioning accuracy. The Laser Calibrator System monitors the material temperature and the air density; this takes into consideration machine thermal growth and laser beam frequency. This linear positioning laser calibration setup can be used on CNC machine tools, CNC lathes, horizontal centers and vertical machining centers.

Condition monitoring of distributed systems using two-stage Bayesian inference data fusion

NASA Astrophysics Data System (ADS)

Jaramillo, Víctor H.; Ottewill, James R.; Dudek, Rafał; Lepiarczyk, Dariusz; Pawlik, Paweł

2017-03-01

In industrial practice, condition monitoring is typically applied to critical machinery. A particular piece of machinery may have its own condition monitoring system that allows the health condition of said piece of equipment to be assessed independently of any connected assets. However, industrial machines are typically complex sets of components that continuously interact with one another. In some cases, dynamics resulting from the inception and development of a fault can propagate between individual components. For example, a fault in one component may lead to an increased vibration level in both the faulty component, as well as in connected healthy components. In such cases, a condition monitoring system focusing on a specific element in a connected set of components may either incorrectly indicate a fault, or conversely, a fault might be missed or masked due to the interaction of a piece of equipment with neighboring machines. In such cases, a more holistic condition monitoring approach that can not only account for such interactions, but utilize them to provide a more complete and definitive diagnostic picture of the health of the machinery is highly desirable. In this paper, a Two-Stage Bayesian Inference approach allowing data from separate condition monitoring systems to be combined is presented. Data from distributed condition monitoring systems are combined in two stages, the first data fusion occurring at a local, or component, level, and the second fusion combining data at a global level. Data obtained from an experimental rig consisting of an electric motor, two gearboxes, and a load, operating under a range of different fault conditions is used to illustrate the efficacy of the method at pinpointing the root cause of a problem. The obtained results suggest that the approach is adept at refining the diagnostic information obtained from each of the different machine components monitored, therefore improving the reliability of the health assessment of each individual element, as well as the entire piece of machinery.

Detection of correct and incorrect measurements in real-time continuous glucose monitoring systems by applying a postprocessing support vector machine.

PubMed

Leal, Yenny; Gonzalez-Abril, Luis; Lorencio, Carol; Bondia, Jorge; Vehi, Josep

2013-07-01

Support vector machines (SVMs) are an attractive option for detecting correct and incorrect measurements in real-time continuous glucose monitoring systems (RTCGMSs), because their learning mechanism can introduce a postprocessing strategy for imbalanced datasets. The proposed SVM considers the geometric mean to obtain a more balanced performance between sensitivity and specificity. To test this approach, 23 critically ill patients receiving insulin therapy were monitored over 72 h using an RTCGMS, and a dataset of 537 samples, classified according to International Standards Organization (ISO) criteria (372 correct and 165 incorrect measurements), was obtained. The results obtained were promising for patients with septic shock or with sepsis, for which the proposed system can be considered as reliable. However, this approach cannot be considered suitable for patients without sepsis.

Monitoring and decision making by people in man machine systems

NASA Technical Reports Server (NTRS)

Johannsen, G.

1979-01-01

The analysis of human monitoring and decision making behavior as well as its modeling are described. Classic and optimal control theoretical, monitoring models are surveyed. The relationship between attention allocation and eye movements is discussed. As an example of applications, the evaluation of predictor displays by means of the optimal control model is explained. Fault detection involving continuous signals and decision making behavior of a human operator engaged in fault diagnosis during different operation and maintenance situations are illustrated. Computer aided decision making is considered as a queueing problem. It is shown to what extent computer aids can be based on the state of human activity as measured by psychophysiological quantities. Finally, management information systems for different application areas are mentioned. The possibilities of mathematical modeling of human behavior in complex man machine systems are also critically assessed.

Aspect-Oriented Monitoring of C Programs

NASA Technical Reports Server (NTRS)

Havelund, Klaus; VanWyk, Eric

2008-01-01

The paper presents current work on extending ASPECTC with state machines, resulting in a framework for aspect-oriented monitoring of C programs. Such a framework can be used for testing purposes, or it can be part of a fault protection strategy. The long term goal is to explore the synergy between the fields of runtime verification, focused on program monitoring, and aspect-oriented programming, focused on more general program development issues. The work is inspired by the observation that most work in this direction has been done for JAVA, partly due to the lack of easily accessible extensible compiler frameworks for C. The work is performed using the SILVER extensible attribute grammar compiler framework, in which C has been defined as a host language. Our work consists of extending C with ASPECTC, and subsequently to extend ASPECTC with state machines.

Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes.

PubMed

Ling, Sai Ho; San, Phyo Phyo; Nguyen, Hung T

2016-09-01

Hypoglycemia is a very common in type 1 diabetic persons and can occur at any age. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances. Because of that, an accurate continuing hypoglycemia monitoring system is a very important medical device for diabetic patients. In this paper, we proposed a non-invasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal. To enhance the detection accuracy, extreme learning machine (ELM) is developed to recognize the presence of hypoglycemia. A clinical study of 16 children with T1DM is given to illustrate the good performance of ELM. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Machine for use in monitoring fatigue life for a plurality of elastomeric specimens

NASA Technical Reports Server (NTRS)

Fitzer, G. E. (Inventor)

1977-01-01

An improved machine is described for use in determining the fatigue life for elastomeric specimens. The machine is characterized by a plurality of juxtaposed test stations, specimen support means located at each of the test stations for supporting a plurality of specimens of elastomeric material, and means for subjecting the specimens at each of said stations to sinusoidal strain at a strain rate unique with respect to the strain rate at which the specimens at each of the other stations is subjected to sinusoidal strain.

[Evaluation of Medical Instruments Cleaning Effect of Fluorescence Detection Technique].

PubMed

Sheng, Nan; Shen, Yue; Li, Zhen; Li, Huijuan; Zhou, Chaoqun

2016-01-01

To compare the cleaning effect of automatic cleaning machine and manual cleaning on coupling type surgical instruments. A total of 32 cleaned medical instruments were randomly sampled from medical institutions in Putuo District medical institutions disinfection supply center. Hygiena System SUREII ATP was used to monitor the ATP value, and the cleaning effect was evaluated. The surface ATP values of the medical instrument of manual cleaning were higher than that of the automatic cleaning machine. Coupling type surgical instruments has better cleaning effect of automatic cleaning machine before disinfection, the application is recommended.

Method and system for controlling a permanent magnet machine during fault conditions

DOEpatents

Krefta, Ronald John; Walters, James E.; Gunawan, Fani S.

2004-05-25

Method and system for controlling a permanent magnet machine driven by an inverter is provided. The method allows for monitoring a signal indicative of a fault condition. The method further allows for generating during the fault condition a respective signal configured to maintain a field weakening current even though electrical power from an energy source is absent during said fault condition. The level of the maintained field-weakening current enables the machine to operate in a safe mode so that the inverter is protected from excess voltage.

Environmental noise forecasting based on support vector machine

NASA Astrophysics Data System (ADS)

Fu, Yumei; Zan, Xinwu; Chen, Tianyi; Xiang, Shihan

2018-01-01

As an important pollution source, the noise pollution is always the researcher's focus. Especially in recent years, the noise pollution is seriously harmful to the human beings' environment, so the research about the noise pollution is a very hot spot. Some noise monitoring technologies and monitoring systems are applied in the environmental noise test, measurement and evaluation. But, the research about the environmental noise forecasting is weak. In this paper, a real-time environmental noise monitoring system is introduced briefly. This monitoring system is working in Mianyang City, Sichuan Province. It is monitoring and collecting the environmental noise about more than 20 enterprises in this district. Based on the large amount of noise data, the noise forecasting by the Support Vector Machine (SVM) is studied in detail. Compared with the time series forecasting model and the artificial neural network forecasting model, the SVM forecasting model has some advantages such as the smaller data size, the higher precision and stability. The noise forecasting results based on the SVM can provide the important and accuracy reference to the prevention and control of the environmental noise.

The backend design of an environmental monitoring system upon real-time prediction of groundwater level fluctuation under the hillslope.

PubMed

Lin, Hsueh-Chun; Hong, Yao-Ming; Kan, Yao-Chiang

2012-01-01

The groundwater level represents a critical factor to evaluate hillside landslides. A monitoring system upon the real-time prediction platform with online analytical functions is important to forecast the groundwater level due to instantaneously monitored data when the heavy precipitation raises the groundwater level under the hillslope and causes instability. This study is to design the backend of an environmental monitoring system with efficient algorithms for machine learning and knowledge bank for the groundwater level fluctuation prediction. A Web-based platform upon the model-view controller-based architecture is established with technology of Web services and engineering data warehouse to support online analytical process and feedback risk assessment parameters for real-time prediction. The proposed system incorporates models of hydrological computation, machine learning, Web services, and online prediction to satisfy varieties of risk assessment requirements and approaches of hazard prevention. The rainfall data monitored from the potential landslide area at Lu-Shan, Nantou and Li-Shan, Taichung, in Taiwan, are applied to examine the system design.

Semi-supervised vibration-based classification and condition monitoring of compressors

NASA Astrophysics Data System (ADS)

Potočnik, Primož; Govekar, Edvard

2017-09-01

Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

Portable water quality monitoring system

NASA Astrophysics Data System (ADS)

Nizar, N. B.; Ong, N. R.; Aziz, M. H. A.; Alcain, J. B.; Haimi, W. M. W. N.; Sauli, Z.

2017-09-01

Portable water quality monitoring system was a developed system that tested varied samples of water by using different sensors and provided the specific readings to the user via short message service (SMS) based on the conditions of the water itself. In this water quality monitoring system, the processing part was based on a microcontroller instead of Lead and Copper Rule (LCR) machines to receive the results. By using four main sensors, this system obtained the readings based on the detection of the sensors, respectively. Therefore, users can receive the readings through SMS because there was a connection between Arduino Uno and GSM Module. This system was designed to be portable so that it would be convenient for users to carry it anywhere and everywhere they wanted to since the processor used is smaller in size compared to the LCR machines. It was also developed to ease the user to monitor and control the water quality. However, the ranges of the sensors' detection still a limitation in this study.

The prediction of food additives in the fruit juice based on electronic nose with chemometrics.

PubMed

Qiu, Shanshan; Wang, Jun

2017-09-01

Food additives are added to products to enhance their taste, and preserve flavor or appearance. While their use should be restricted to achieve a technological benefit, the contents of food additives should be also strictly controlled. In this study, E-nose was applied as an alternative to traditional monitoring technologies for determining two food additives, namely benzoic acid and chitosan. For quantitative monitoring, support vector machine (SVM), random forest (RF), extreme learning machine (ELM) and partial least squares regression (PLSR) were applied to establish regression models between E-nose signals and the amount of food additives in fruit juices. The monitoring models based on ELM and RF reached higher correlation coefficients (R 2 s) and lower root mean square errors (RMSEs) than models based on PLSR and SVM. This work indicates that E-nose combined with RF or ELM can be a cost-effective, easy-to-build and rapid detection system for food additive monitoring. Copyright © 2017 Elsevier Ltd. All rights reserved.

Monitoring Business Activity

DTIC Science & Technology

2006-03-01

AFRL-IF-RS-TR-2006-88 Final Technical Report March 2006 MONITORING BUSINESS ACTIVITY New York University...REPORT DATE MARCH 2006 3. REPORT TYPE AND DATES COVERED Final Sep 01 – Oct 05 4. TITLE AND SUBTITLE MONITORING BUSINESS ACTIVITY 6. AUTHOR(S...Accepted to Journal of Machine Learning Research, pending revisions. CeDER Working Paper #CeDER-04-08, Stern School of Business , New York University

Electron Field Emission Properties of Textured Platinum Surfaces

NASA Technical Reports Server (NTRS)

Sovey, James S.

2002-01-01

During ground tests of electric microthrusters and space tests of electrodynamic tethers the electron emitters must successfully operate at environmental pressures possibly as high as 1x10(exp -4) Pa. High partial pressures of oxygen, nitrogen, and water vapor are expected in such environments. A textured platinum surface was used in this work for field emission cathode assessments because platinum does not form oxide films at low temperatures. Although a reproducible cathode conditioning process did not evolve from this work, some short term tests for periods of 1 to 4 hours showed no degradation of emission current at an electric field of 8 V/mm and background pressures of about 1x10(exp -6) Pa. Increases of background pressure by air flow to about 3x10(exp -4) Pa yield a hostile environment for the textured platinum field emission cathode.

Angular approach combined to mechanical model for tool breakage detection by eddy current sensors

NASA Astrophysics Data System (ADS)

Ritou, M.; Garnier, S.; Furet, B.; Hascoet, J. Y.

2014-02-01

The paper presents a new complete approach for Tool Condition Monitoring (TCM) in milling. The aim is the early detection of small damages so that catastrophic tool failures are prevented. A versatile in-process monitoring system is introduced for reliability concerns. The tool condition is determined by estimates of the radial eccentricity of the teeth. An adequate criterion is proposed combining mechanical model of milling and angular approach.Then, a new solution is proposed for the estimate of cutting force using eddy current sensors implemented close to spindle nose. Signals are analysed in the angular domain, notably by synchronous averaging technique. Phase shifts induced by changes of machining direction are compensated. Results are compared with cutting forces measured with a dynamometer table.The proposed method is implemented in an industrial case of pocket machining operation. One of the cutting edges has been slightly damaged during the machining, as shown by a direct measurement of the tool. A control chart is established with the estimates of cutter eccentricity obtained during the machining from the eddy current sensors signals. Efficiency and reliability of the method is demonstrated by a successful detection of the damage.

Beam Loss Monitoring for LHC Machine Protection

NASA Astrophysics Data System (ADS)

Holzer, Eva Barbara; Dehning, Bernd; Effnger, Ewald; Emery, Jonathan; Grishin, Viatcheslav; Hajdu, Csaba; Jackson, Stephen; Kurfuerst, Christoph; Marsili, Aurelien; Misiowiec, Marek; Nagel, Markus; Busto, Eduardo Nebot Del; Nordt, Annika; Roderick, Chris; Sapinski, Mariusz; Zamantzas, Christos

The energy stored in the nominal LHC beams is two times 362 MJ, 100 times the energy of the Tevatron. As little as 1 mJ/cm3 deposited energy quenches a magnet at 7 TeV and 1 J/cm3 causes magnet damage. The beam dumps are the only places to safely dispose of this beam. One of the key systems for machine protection is the beam loss monitoring (BLM) system. About 3600 ionization chambers are installed at likely or critical loss locations around the LHC ring. The losses are integrated in 12 time intervals ranging from 40 μs to 84 s and compared to threshold values defined in 32 energy ranges. A beam abort is requested when potentially dangerous losses are detected or when any of the numerous internal system validation tests fails. In addition, loss data are used for machine set-up and operational verifications. The collimation system for example uses the loss data for set-up and regular performance verification. Commissioning and operational experience of the BLM are presented: The machine protection functionality of the BLM system has been fully reliable; the LHC availability has not been compromised by false beam aborts.

Neural networks with fuzzy Petri nets for modeling a machining process

NASA Astrophysics Data System (ADS)

Hanna, Moheb M.

1998-03-01

The paper presents an intelligent architecture based a feedforward neural network with fuzzy Petri nets for modeling product quality in a CNC machining center. It discusses how the proposed architecture can be used for modeling, monitoring and control a product quality specification such as surface roughness. The surface roughness represents the output quality specification manufactured by a CNC machining center as a result of a milling process. The neural network approach employed the selected input parameters which defined by the machine operator via the CNC code. The fuzzy Petri nets approach utilized the exact input milling parameters, such as spindle speed, feed rate, tool diameter and coolant (off/on), which can be obtained via the machine or sensors system. An aim of the proposed architecture is to model the demanded quality of surface roughness as high, medium or low.

Effects of Selected Task Performance Criteria at Initiating Adaptive Task Real locations

NASA Technical Reports Server (NTRS)

Montgomery, Demaris A.

2001-01-01

In the current report various performance assessment methods used to initiate mode transfers between manual control and automation for adaptive task reallocation were tested. Participants monitored two secondary tasks for critical events while actively controlling a process in a fictional system. One of the secondary monitoring tasks could be automated whenever operators' performance was below acceptable levels. Automation of the secondary task and transfer of the secondary task back to manual control were either human- or machine-initiated. Human-initiated transfers were based on the operator's assessment of the current task demands while machine-initiated transfers were based on the operators' performance. Different performance assessment methods were tested in two separate experiments.

Graph-based structural change detection for rotating machinery monitoring

NASA Astrophysics Data System (ADS)

Lu, Guoliang; Liu, Jie; Yan, Peng

2018-01-01

Detection of structural changes is critically important in operational monitoring of a rotating machine. This paper presents a novel framework for this purpose, where a graph model for data modeling is adopted to represent/capture statistical dynamics in machine operations. Meanwhile we develop a numerical method for computing temporal anomalies in the constructed graphs. The martingale-test method is employed for the change detection when making decisions on possible structural changes, where excellent performance is demonstrated outperforming exciting results such as the autoregressive-integrated-moving average (ARIMA) model. Comprehensive experimental results indicate good potentials of the proposed algorithm in various engineering applications. This work is an extension of a recent result (Lu et al., 2017).

Acoustic emission from single point machining: Part 2, Signal changes with tool wear. Revised

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

Heiple, C.R.; Carpenter, S.H.; Armentrout, D.L.

1989-12-31

Changes in acoustic emission signal characteristics with tool wear were monitored during single point machining of 4340 steel and Ti-6Al-4V heat treated to several strength levels, 606l-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, 410 stainless steel, lead, and teflon. No signal characteristic changed in the same way with tool wear for all materials tested. A single change in a particular AE signal characteristic with tool wear valid for all materials probably does not exist. Nevertheless, changes in various signal characteristic with wear for a given material may be sufficient to be used to monitor tool wear.

Aquatic Toxic Analysis by Monitoring Fish Behavior Using Computer Vision: A Recent Progress

PubMed Central

Fu, Longwen; Liu, Zuoyi

2018-01-01

Video tracking based biological early warning system achieved a great progress with advanced computer vision and machine learning methods. Ability of video tracking of multiple biological organisms has been largely improved in recent years. Video based behavioral monitoring has become a common tool for acquiring quantified behavioral data for aquatic risk assessment. Investigation of behavioral responses under chemical and environmental stress has been boosted by rapidly developed machine learning and artificial intelligence. In this paper, we introduce the fundamental of video tracking and present the pioneer works in precise tracking of a group of individuals in 2D and 3D space. Technical and practical issues suffered in video tracking are explained. Subsequently, the toxic analysis based on fish behavioral data is summarized. Frequently used computational methods and machine learning are explained with their applications in aquatic toxicity detection and abnormal pattern analysis. Finally, advantages of recent developed deep learning approach in toxic prediction are presented. PMID:29849612

Torque shudder protection device and method

DOEpatents

King, Robert D.; De Doncker, Rik W. A. A.; Szczesny, Paul M.

1997-01-01

A torque shudder protection device for an induction machine includes a flux command generator for supplying a steady state flux command and a torque shudder detector for supplying a status including a negative status to indicate a lack of torque shudder and a positive status to indicate a presence of torque shudder. A flux adapter uses the steady state flux command and the status to supply a present flux command identical to the steady state flux command for a negative status and different from the steady state flux command for a positive status. A limiter can receive the present flux command, prevent the present flux command from exceeding a predetermined maximum flux command magnitude, and supply the present flux command to a field oriented controller. After determining a critical electrical excitation frequency at which a torque shudder occurs for the induction machine, a flux adjuster can monitor the electrical excitation frequency of the induction machine and adjust a flux command to prevent the monitored electrical excitation frequency from reaching the critical electrical excitation frequency.

Torque shudder protection device and method

DOEpatents

King, R.D.; Doncker, R.W.A.A. De.; Szczesny, P.M.

1997-03-11

A torque shudder protection device for an induction machine includes a flux command generator for supplying a steady state flux command and a torque shudder detector for supplying a status including a negative status to indicate a lack of torque shudder and a positive status to indicate a presence of torque shudder. A flux adapter uses the steady state flux command and the status to supply a present flux command identical to the steady state flux command for a negative status and different from the steady state flux command for a positive status. A limiter can receive the present flux command, prevent the present flux command from exceeding a predetermined maximum flux command magnitude, and supply the present flux command to a field oriented controller. After determining a critical electrical excitation frequency at which a torque shudder occurs for the induction machine, a flux adjuster can monitor the electrical excitation frequency of the induction machine and adjust a flux command to prevent the monitored electrical excitation frequency from reaching the critical electrical excitation frequency. 5 figs.

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

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

Brunton, Steven

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 robustmore » 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.« less

Analysis of acoustic emission signals and monitoring of machining processes

PubMed

Govekar; Gradisek; Grabec

2000-03-01

Monitoring of a machining process on the basis of sensor signals requires a selection of informative inputs in order to reliably characterize and model the process. In this article, a system for selection of informative characteristics from signals of multiple sensors is presented. For signal analysis, methods of spectral analysis and methods of nonlinear time series analysis are used. With the aim of modeling relationships between signal characteristics and the corresponding process state, an adaptive empirical modeler is applied. The application of the system is demonstrated by characterization of different parameters defining the states of a turning machining process, such as: chip form, tool wear, and onset of chatter vibration. The results show that, in spite of the complexity of the turning process, the state of the process can be well characterized by just a few proper characteristics extracted from a representative sensor signal. The process characterization can be further improved by joining characteristics from multiple sensors and by application of chaotic characteristics.

Effects of retrofit emission controls and work practices on perchloroethylene exposures in small dry-cleaning shops.

PubMed

Ewers, Lynda M; Ruder, Avima M; Petersen, Martin R; Earnest, G Scott; Goldenhar, Linda M

2002-02-01

The effectiveness of commercially available interventions for reducing workers' perchloroethylene exposures in three small dry-cleaning shops was evaluated. Depending upon machine configuration, the intervention consisted of the addition of either a refrigerated condenser or a closed-loop carbon adsorber to the existing dry-cleaning machine. These relatively inexpensive (less than $5000) engineering controls were designed to reduce perchloroethylene emissions when dry-cleaning machine doors were opened for loading or unloading. Effectiveness of the interventions was judged by comparing pre- and postintervention perchloroethylene exposures using three types of measurements in each shop: (1) full-shift, personal breathing zone, air monitoring, (2) next-morning, end-exhaled worker breath concentrations of perchloroethylene, and (3) differences in the end-exhaled breath perchloroethylene concentrations before and after opening the dry-cleaning machine door. In general, measurements supported the hypothesis that machine operators' exposures to perchloroethylene can be reduced. However, work practices, especially maintenance practices, influenced exposures more than was originally anticipated. Only owners of dry-cleaning machines in good repair, with few leaks, should consider retrofitting them, and only after consultation with their machine's manufacturer. If machines are in poor condition, a new machine or alternative technology should be considered. Shop owners and employees should never circumvent safety features on dry-cleaning machines.

An evaluation of retrofit engineering control interventions to reduce perchloroethylene exposures in commercial dry-cleaning shops.

PubMed

Earnest, G Scott; Ewers, Lynda M; Ruder, Avima M; Petersen, Martin R; Kovein, Ronald J

2002-02-01

Real-time monitoring was used to evaluate the ability of engineering control devices retrofitted on two existing dry-cleaning machines to reduce worker exposures to perchloroethylene. In one dry-cleaning shop, a refrigerated condenser was installed on a machine that had a water-cooled condenser to reduce the air temperature, improve vapor recovery, and lower exposures. In a second shop, a carbon adsorber was retrofitted on a machine to adsorb residual perchloroethylene not collected by the existing refrigerated condenser to improve vapor recovery and reduce exposures. Both controls were successful at reducing the perchloroethylene exposures of the dry-cleaning machine operator. Real-time monitoring was performed to evaluate how the engineering controls affected exposures during loading and unloading the dry-cleaning machine, a task generally considered to account for the highest exposures. The real-time monitoring showed that dramatic reductions occurred in exposures during loading and unloading of the dry-cleaning machine due to the engineering controls. Peak operator exposures during loading and unloading were reduced by 60 percent in the shop that had a refrigerated condenser installed on the dry-cleaning machine and 92 percent in the shop that had a carbon adsorber installed. Although loading and unloading exposures were dramatically reduced, drops in full-shift time-weighted average (TWA) exposures were less dramatic. TWA exposures to perchloroethylene, as measured by conventional air sampling, showed smaller reductions in operator exposures of 28 percent or less. Differences between exposure results from real-time and conventional air sampling very likely resulted from other uncontrolled sources of exposure, differences in shop general ventilation before and after the control was installed, relatively small sample sizes, and experimental variability inherent in field research. Although there were some difficulties and complications with installation and maintenance of the engineering controls, this study showed that retrofitting engineering controls may be a feasible option for some dry-cleaning shop owners to reduce worker exposures to perchloroethylene. By installing retrofit controls, a dry-cleaning facility can reduce exposures, in some cases dramatically, and bring operators into compliance with the Occupational Safety and Health Administration (OSHA) peak exposure limit of 300 ppm. Retrofit engineering controls are also likely to enable many dry-cleaning workers to lower their overall personal TWA exposures to perchloroethylene.

A Methodology for Protective Vibration Monitoring of Hydropower Units Based on the Mechanical Properties.

PubMed

Nässelqvist, Mattias; Gustavsson, Rolf; Aidanpää, Jan-Olov

2013-07-01

It is important to monitor the radial loads in hydropower units in order to protect the machine from harmful radial loads. Existing recommendations in the standards regarding the radial movements of the shaft and bearing housing in hydropower units, ISO-7919-5 (International Organization for Standardization, 2005, "ISO 7919-5: Mechanical Vibration-Evaluation of Machine Vibration by Measurements on Rotating Shafts-Part 5: Machine Sets in Hydraulic Power Generating and Pumping Plants," Geneva, Switzerland) and ISO-10816-5 (International Organization for Standardization, 2000, "ISO 10816-5: Mechanical Vibration-Evaluation of Machine Vibration by Measurements on Non-Rotating Parts-Part 5: Machine Sets in Hydraulic Power Generating and Pumping Plants," Geneva, Switzerland), have alarm levels based on statistical data and do not consider the mechanical properties of the machine. The synchronous speed of the unit determines the maximum recommended shaft displacement and housing acceleration, according to these standards. This paper presents a methodology for the alarm and trip levels based on the design criteria of the hydropower unit and the measured radial loads in the machine during operation. When a hydropower unit is designed, one of its design criteria is to withstand certain loads spectra without the occurrence of fatigue in the mechanical components. These calculated limits for fatigue are used to set limits for the maximum radial loads allowed in the machine before it shuts down in order to protect itself from damage due to high radial loads. Radial loads in hydropower units are caused by unbalance, shape deviations, dynamic flow properties in the turbine, etc. Standards exist for balancing and manufacturers (and power plant owners) have recommendations for maximum allowed shape deviations in generators. These standards and recommendations determine which loads, at a maximum, should be allowed before an alarm is sent that the machine needs maintenance. The radial bearing load can be determined using load cells, bearing properties multiplied by shaft displacement, or bearing bracket stiffness multiplied by housing compression or movement. Different load measurement methods should be used depending on the design of the machine and accuracy demands in the load measurement. The methodology presented in the paper is applied to a 40 MW hydropower unit; suggestions are presented for the alarm and trip levels for the machine based on the mechanical properties and radial loads.

Real Time Monitoring System of Pollution Waste on Musi River Using Support Vector Machine (SVM) Method

NASA Astrophysics Data System (ADS)

Fachrurrozi, Muhammad; Saparudin; Erwin

2017-04-01

Real-time Monitoring and early detection system which measures the quality standard of waste in Musi River, Palembang, Indonesia is a system for determining air and water pollution level. This system was designed in order to create an integrated monitoring system and provide real time information that can be read. It is designed to measure acidity and water turbidity polluted by industrial waste, as well as to show and provide conditional data integrated in one system. This system consists of inputting and processing the data, and giving output based on processed data. Turbidity, substances, and pH sensor is used as a detector that produce analog electrical direct current voltage (DC). Early detection system works by determining the value of the ammonia threshold, acidity, and turbidity level of water in Musi River. The results is then presented based on the level group pollution by the Support Vector Machine classification method.

Resolution Studies at Beam Position Monitors at the FLASH Facility at DESY

NASA Astrophysics Data System (ADS)

Baboi, N.; Lund-Nielsen, J.; Noelle, D.; Riesch, W.; Traber, T.; Kruse, J.; Wendt, M.

2006-11-01

More than 60 beam position monitors (BPM) are installed along about 350m of beamline of the Free Electron LASer in Hamburg (FLASH) at DESY. The room-temperature part of the accelerator is equipped mainly with stripline position monitors. In the accelerating cryo-modules there are cavity and re-entrant cavity BPMs, which will not be discussed here. In the undulator part of the machine button BPMs are used. This area requires a single bunch resolution of 10μm. The electronics is based on the AM/PM normalization principle and is externally triggered. Single-bunch position is measured. This paper presents the methods used to determine the resolution of the BPMs. The results based on correlations between different BPMs along the machine are compared to noise measurements in the RF lab. The performance and difficulties with the BPM design and the current electronics as well as its development are discussed.

Machine Learning: Proceedings of the Fifteenth International Conference

DTIC Science & Technology

1998-07-01

Machine Learning Proceedings of the Fifteenth International Conference (ICML 󈨦) Edited by Jude Shavlik MADISON , WISCONSIN JULY 24-27, 1998 fc...W. Dayton Street Madison , WI 53706 PERFORMING ORGANIZATION REPORT NUMBER 144-HD17 9. SPONSORING /MONITORING AGENCY NAMES(S) AND ADDRESS(ES...ANISE Sad 239-18 298-102 University of Wisconsin - Madison Jude W. Shavlik Department of Computer Sciences Professor 1210 West Dayton Street

Energy Harvesting for Soft-Matter Machines and Electronics

DTIC Science & Technology

2016-06-09

AFRL-AFOSR-VA-TR-2016-0353 Energy Harvesting for Soft-Matter Machines and Electronics Carmel Majidi CARNEGIE MELLON UNIVERSITY Final Report 06/09...ES) CARNEGIE MELLON UNIVERSITY 5000 FORBES AVENUE PITTSBURGH, PA 15213-3815 US 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING...livelink.ebs.afrl.af.mil/livelink/llisapi.dll DISTRIBUTION A: Distribution approved for public release. Carnegie Mellon University MECHANICAL ENGINEERING FINAL

Quench monitoring and control system and method of operating same

DOEpatents

Ryan, David Thomas; Laskaris, Evangelos Trifon; Huang, Xianrui

2006-05-30

A rotating machine comprising a superconductive coil and a temperature sensor operable to provide a signal representative of superconductive coil temperature. The rotating machine may comprise a control system communicatively coupled to the temperature sensor. The control system may be operable to reduce electric current in the superconductive coil when a signal representative of a defined superconducting coil temperature is received from the temperature sensor.

Machine Learning: A Crucial Tool for Sensor Design

PubMed Central

Zhao, Weixiang; Bhushan, Abhinav; Santamaria, Anthony D.; Simon, Melinda G.; Davis, Cristina E.

2009-01-01

Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies. PMID:20191110

Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model

PubMed Central

Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

2014-01-01

Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability. PMID:25405514

Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.

PubMed

Tulsyan, Aditya; Garvin, Christopher; Ündey, Cenk

2018-04-06

Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry-wide problem in BPM, referred to as the "Low-N" problem, wherein a product has a limited production history. The current best industrial practice to address the Low-N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in the process that might have an impact on process and product performance. In this article, we propose an approach to solve the Low-N problem by generating an arbitrarily large number of in silico batches through a combination of hardware exploitation and machine-learning methods. To the best of authors' knowledge, this is the first article to provide a solution to the Low-N problem in biopharmaceutical manufacturing using machine-learning methods. Several industrial case studies from bulk drug substance manufacturing are presented to demonstrate the efficacy of the proposed approach for BPM under various Low-N scenarios. © 2018 Wiley Periodicals, Inc.

Force sensor based tool condition monitoring using a heterogeneous ensemble learning model.

PubMed

Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

2014-11-14

Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability.

Ideology of a multiparametric system for estimating the insulation system of electric machines on the basis of absorption testing methods

NASA Astrophysics Data System (ADS)

Kislyakov, M. A.; Chernov, V. A.; Maksimkin, V. L.; Bozhin, Yu. M.

2017-12-01

The article deals with modern methods of monitoring the state and predicting the life of electric machines. In 50% of the cases of failure in the performance of electric machines is associated with insulation damage. As promising, nondestructive methods of control, methods based on the investigation of the processes of polarization occurring in insulating materials are proposed. To improve the accuracy of determining the state of insulation, a multiparametric approach is considered, which is a basis for the development of an expert system for estimating the state of health.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

PubMed Central

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-01-01

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163

LHCb experience with running jobs in virtual machines

NASA Astrophysics Data System (ADS)

McNab, A.; Stagni, F.; Luzzi, C.

2015-12-01

The LHCb experiment has been running production jobs in virtual machines since 2013 as part of its DIRAC-based infrastructure. We describe the architecture of these virtual machines and the steps taken to replicate the WLCG worker node environment expected by user and production jobs. This relies on the uCernVM system for providing root images for virtual machines. We use the CernVM-FS distributed filesystem to supply the root partition files, the LHCb software stack, and the bootstrapping scripts necessary to configure the virtual machines for us. Using this approach, we have been able to minimise the amount of contextualisation which must be provided by the virtual machine managers. We explain the process by which the virtual machine is able to receive payload jobs submitted to DIRAC by users and production managers, and how this differs from payloads executed within conventional DIRAC pilot jobs on batch queue based sites. We describe our operational experiences in running production on VM based sites managed using Vcycle/OpenStack, Vac, and HTCondor Vacuum. Finally we show how our use of these resources is monitored using Ganglia and DIRAC.

Cyclostationarity approach for monitoring chatter and tool wear in high speed milling

NASA Astrophysics Data System (ADS)

Lamraoui, M.; Thomas, M.; El Badaoui, M.

2014-02-01

Detection of chatter and tool wear is crucial in the machining process and their monitoring is a key issue, for: (1) insuring better surface quality, (2) increasing productivity and (3) protecting both machines and safe workpiece. This paper presents an investigation of chatter and tool wear using the cyclostationary method to process the vibrations signals acquired from high speed milling. Experimental cutting tests were achieved on slot milling operation of aluminum alloy. The experimental set-up is designed for acquisition of accelerometer signals and encoding information picked up from an encoder. The encoder signal is used for re-sampling accelerometers signals in angular domain using a specific algorithm that was developed in LASPI laboratory. The use of cyclostationary on accelerometer signals has been applied for monitoring chatter and tool wear in high speed milling. The cyclostationarity appears on average properties (first order) of signals, on the energetic properties (second order) and it generates spectral lines at cyclic frequencies in spectral correlation. Angular power and kurtosis are used to analyze chatter phenomena. The formation of chatter is characterized by unstable, chaotic motion of the tool and strong anomalous fluctuations of cutting forces. Results show that stable machining generates only very few cyclostationary components of second order while chatter is strongly correlated to cyclostationary components of second order. By machining in the unstable region, chatter results in flat angular kurtosis and flat angular power, such as a pseudo (white) random signal with flat spectrum. Results reveal that spectral correlation and Wigner Ville spectrum or integrated Wigner Ville issued from second-order cyclostationary are an efficient parameter for the early diagnosis of faults in high speed machining, such as chatter, tool wear and bearings, compared to traditional stationary methods. Wigner Ville representation of the residual signal shows that the energy corresponding to the tooth passing decreases when chatter phenomenon occurs. The effect of the tool wear and the number of broken teeth on the excitation of structure resonances appears in Wigner Ville presentation.

A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine

NASA Astrophysics Data System (ADS)

Peng, Chong; Wang, Lun; Liao, T. Warren

2015-10-01

Currently, chatter has become the critical factor in hindering machining quality and productivity in machining processes. To avoid cutting chatter, a new method based on dynamic cutting force simulation model and support vector machine (SVM) is presented for the prediction of chatter stability lobes. The cutting force is selected as the monitoring signal, and the wavelet energy entropy theory is used to extract the feature vectors. A support vector machine is constructed using the MATLAB LIBSVM toolbox for pattern classification based on the feature vectors derived from the experimental cutting data. Then combining with the dynamic cutting force simulation model, the stability lobes diagram (SLD) can be estimated. Finally, the predicted results are compared with existing methods such as zero-order analytical (ZOA) and semi-discretization (SD) method as well as actual cutting experimental results to confirm the validity of this new method.

Comparison of four machine learning methods for object-oriented change detection in high-resolution satellite imagery

NASA Astrophysics Data System (ADS)

Bai, Ting; Sun, Kaimin; Deng, Shiquan; Chen, Yan

2018-03-01

High resolution image change detection is one of the key technologies of remote sensing application, which is of great significance for resource survey, environmental monitoring, fine agriculture, military mapping and battlefield environment detection. In this paper, for high-resolution satellite imagery, Random Forest (RF), Support Vector Machine (SVM), Deep belief network (DBN), and Adaboost models were established to verify the possibility of different machine learning applications in change detection. In order to compare detection accuracy of four machine learning Method, we applied these four machine learning methods for two high-resolution images. The results shows that SVM has higher overall accuracy at small samples compared to RF, Adaboost, and DBN for binary and from-to change detection. With the increase in the number of samples, RF has higher overall accuracy compared to Adaboost, SVM and DBN.

Configuration Management and Infrastructure Monitoring Using CFEngine and Icinga for Real-time Heterogeneous Data Taking Environment

NASA Astrophysics Data System (ADS)

Poat, M. D.; Lauret, J.; Betts, W.

2015-12-01

The STAR online computing environment is an intensive ever-growing system used for real-time data collection and analysis. Composed of heterogeneous and sometimes groups of custom-tuned machines, the computing infrastructure was previously managed by manual configurations and inconsistently monitored by a combination of tools. This situation led to configuration inconsistency and an overload of repetitive tasks along with lackluster communication between personnel and machines. Globally securing this heterogeneous cyberinfrastructure was tedious at best and an agile, policy-driven system ensuring consistency, was pursued. Three configuration management tools, Chef, Puppet, and CFEngine have been compared in reliability, versatility and performance along with a comparison of infrastructure monitoring tools Nagios and Icinga. STAR has selected the CFEngine configuration management tool and the Icinga infrastructure monitoring system leading to a versatile and sustainable solution. By leveraging these two tools STAR can now swiftly upgrade and modify the environment to its needs with ease as well as promptly react to cyber-security requests. By creating a sustainable long term monitoring solution, the detection of failures was reduced from days to minutes, allowing rapid actions before the issues become dire problems, potentially causing loss of precious experimental data or uptime.

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults

PubMed Central

Verschueren, Sabine M. P.; Degens, Hans; Morse, Christopher I.; Onambélé, Gladys L.

2017-01-01

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual’s physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry. PMID:29155839

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults.

PubMed

Wullems, Jorgen A; Verschueren, Sabine M P; Degens, Hans; Morse, Christopher I; Onambélé, Gladys L

2017-01-01

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual's physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry.

Research on intelligent monitoring technology of machining process

NASA Astrophysics Data System (ADS)

Wang, Taiyong; Meng, Changhong; Zhao, Guoli

1995-08-01

Based upon research on sound and vibration characteristics of tool condition, we explore the multigrade monitoring system which takes single-chip microcomputers as the core hardware. By using the specially designed pickup true signal devices, we can more effectively do the intelligent multigrade monitoring and forecasting, and furthermore, we can build the tool condition models adaptively. This is the key problem in FMS, CIMS, and even the IMS.

Pulsed ultrasonic instruments for monitoring the strength of materials

NASA Astrophysics Data System (ADS)

Korolev, Mikhail Viktorovich; Starikov, Boris Pavlovich; Karpel'Son, Arkadii Efimovich

The book is concerned with various aspects of the design of portable instruments for the ultrasonic monitoring of the strength and ductile characteristics of structural materials, including metals, alloys, and ceramics. Particular attention is given to methods for increasing the accuracy of the instruments while reducing their size, which is particularly important in the design of miniature instruments for the real-time monitoring of machines, mechanisms, and structures during operation.

Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines

PubMed Central

He, Kun; Yang, Zhijun; Bai, Yun; Long, Jianyu; Li, Chuan

2018-01-01

Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers. PMID:29690641

Wireless and Powerless Sensing Node System Developed for Monitoring Motors.

PubMed

Lee, Dasheng

2008-08-27

Reliability and maintainability of tooling systems can be improved through condition monitoring of motors. However, it is difficult to deploy sensor nodes due to the harsh environment of industrial plants. Sensor cables are easily damaged, which renders the monitoring system deployed to assure the machine's reliability itself unreliable. A wireless and powerless sensing node integrated with a MEMS (Micro Electro-Mechanical System) sensor, a signal processor, a communication module, and a self-powered generator was developed in this study for implementation of an easily mounted network sensor for monitoring motors. A specially designed communication module transmits a sequence of electromagnetic (EM) pulses in response to the sensor signals. The EM pulses can penetrate through the machine's metal case and delivers signals from the sensor inside the motor to the external data acquisition center. By using induction power, which is generated by the motor's shaft rotation, the sensor node is self-sustaining; therefore, no power line is required. A monitoring system, equipped with novel sensing nodes, was constructed to test its performance. The test results illustrate that, the novel sensing node developed in this study can effectively enhance the reliability of the motor monitoring system and it is expected to be a valuable technology, which will be available to the plant for implementation in a reliable motor management program.

Wireless and Powerless Sensing Node System Developed for Monitoring Motors

PubMed Central

Lee, Dasheng

2008-01-01

Reliability and maintainability of tooling systems can be improved through condition monitoring of motors. However, it is difficult to deploy sensor nodes due to the harsh environment of industrial plants. Sensor cables are easily damaged, which renders the monitoring system deployed to assure the machine's reliability itself unreliable. A wireless and powerless sensing node integrated with a MEMS (Micro Electro-Mechanical System) sensor, a signal processor, a communication module, and a self-powered generator was developed in this study for implementation of an easily mounted network sensor for monitoring motors. A specially designed communication module transmits a sequence of electromagnetic (EM) pulses in response to the sensor signals. The EM pulses can penetrate through the machine's metal case and delivers signals from the sensor inside the motor to the external data acquisition center. By using induction power, which is generated by the motor's shaft rotation, the sensor node is self-sustaining; therefore, no power line is required. A monitoring system, equipped with novel sensing nodes, was constructed to test its performance. The test results illustrate that, the novel sensing node developed in this study can effectively enhance the reliability of the motor monitoring system and it is expected to be a valuable technology, which will be available to the plant for implementation in a reliable motor management program. PMID:27873798

Intelligent Fault Diagnosis of Delta 3D Printers Using Attitude Sensors Based on Support Vector Machines.

PubMed

He, Kun; Yang, Zhijun; Bai, Yun; Long, Jianyu; Li, Chuan

2018-04-23

Health condition is a vital factor affecting printing quality for a 3D printer. In this work, an attitude monitoring approach is proposed to diagnose the fault of the delta 3D printer using support vector machines (SVM). An attitude sensor was mounted on the moving platform of the printer to monitor its 3-axial attitude angle, angular velocity, vibratory acceleration and magnetic field intensity. The attitude data of the working printer were collected under different conditions involving 12 fault types and a normal condition. The collected data were analyzed for diagnosing the health condition. To this end, the combination of binary classification, one-against-one with least-square SVM, was adopted for fault diagnosis modelling by using all channels of attitude monitoring data in the experiment. For comparison, each one channel of the attitude monitoring data was employed for model training and testing. On the other hand, a back propagation neural network (BPNN) was also applied to diagnose fault using the same data. The best fault diagnosis accuracy (94.44%) was obtained when all channels of the attitude monitoring data were used with SVM modelling. The results indicate that the attitude monitoring with SVM is an effective method for the fault diagnosis of delta 3D printers.

Manufacturing process applications team (MATEAM). [technology transfer in the areas of machine tools and robots

NASA Technical Reports Server (NTRS)

1979-01-01

The transfer of NASA technology to the industrial sector is reported. Presentations to the machine tool and robot industries and direct technology transfers of the Adams Manipulator arm, a-c motor control, and the bolt tension monitor are discussed. A listing of proposed RTOP programs with strong potential is included. A detailed description of the rotor technology available to industry is given.

Explosion Monitoring with Machine Learning: A LSTM Approach to Seismic Event Discrimination

NASA Astrophysics Data System (ADS)

Magana-Zook, S. A.; Ruppert, S. D.

2017-12-01

The streams of seismic data that analysts look at to discriminate natural from man- made events will soon grow from gigabytes of data per day to exponentially larger rates. This is an interesting problem as the requirement for real-time answers to questions of non-proliferation will remain the same, and the analyst pool cannot grow as fast as the data volume and velocity will. Machine learning is a tool that can solve the problem of seismic explosion monitoring at scale. Using machine learning, and Long Short-term Memory (LSTM) models in particular, analysts can become more efficient by focusing their attention on signals of interest. From a global dataset of earthquake and explosion events, a model was trained to recognize the different classes of events, given their spectrograms. Optimal recurrent node count and training iterations were found, and cross validation was performed to evaluate model performance. A 10-fold mean accuracy of 96.92% was achieved on a balanced dataset of 30,002 instances. Given that the model is 446.52 MB it can be used to simultaneously characterize all incoming signals by researchers looking at events in isolation on desktop machines, as well as at scale on all of the nodes of a real-time streaming platform. LLNL-ABS-735911

A hybrid approach for nondestructive assessment and design optimisation and testing of in-service machinery

NASA Astrophysics Data System (ADS)

Rahman, Abdul Ghaffar Abdul; Noroozi, Siamak; Dupac, Mihai; Mahathir Syed Mohd Al-Attas, Syed; Vinney, John E.

2013-03-01

Complex rotating machinery requires regular condition monitoring inspections to assess their running conditions and their structural integrity to prevent catastrophic failures. Machine failures can be divided into two categories. First is the wear and tear during operation, they range from bearing defects, gear damage, misalignment, imbalance or mechanical looseness, for which simple condition-based maintenance techniques can easily detect the root cause and trigger remedial action process. The second factor in machine failure is caused by the inherent design faults that usually happened due to many reasons such as improper installation, poor servicing, bad workmanship and structural dynamics design deficiency. In fact, individual machines components are generally dynamically well designed and rigorously tested. However, when these machines are assembled on sight and linked together, their dynamic characteristics will change causing unexpected behaviour of the system. Since nondestructive evaluation provides an excellent alternative to the classical monitoring and proved attractive due to the possibility of performing reliable assessments of all types of machinery, the novel dynamic design verification procedure - based on the combination of in-service operation deflection shape measurement, experimental modal analysis and iterative inverse finite element analysis - proposed here allows quick identification of structural weakness, and helps to provide and verify the solutions.

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition.

PubMed

Caggiano, Alessandra

2018-03-09

Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features ( k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear ( VB max ) was achieved, with predicted values very close to the measured tool wear values.

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

PubMed Central

2018-01-01

Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features (k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax) was achieved, with predicted values very close to the measured tool wear values. PMID:29522443

A High Performance Torque Sensor for Milling Based on a Piezoresistive MEMS Strain Gauge

PubMed Central

Qin, Yafei; Zhao, Yulong; Li, Yingxue; Zhao, You; Wang, Peng

2016-01-01

In high speed and high precision machining applications, it is important to monitor the machining process in order to ensure high product quality. For this purpose, it is essential to develop a dynamometer with high sensitivity and high natural frequency which is suited to these conditions. This paper describes the design, calibration and performance of a milling torque sensor based on piezoresistive MEMS strain. A detailed design study is carried out to optimize the two mutually-contradictory indicators sensitivity and natural frequency. The developed torque sensor principally consists of a thin-walled cylinder, and a piezoresistive MEMS strain gauge bonded on the surface of the sensing element where the shear strain is maximum. The strain gauge includes eight piezoresistances and four are connected in a full Wheatstone circuit bridge, which is used to measure the applied torque force during machining procedures. Experimental static calibration results show that the sensitivity of torque sensor has been improved to 0.13 mv/Nm. A modal impact test indicates that the natural frequency of torque sensor reaches 1216 Hz, which is suitable for high speed machining processes. The dynamic test results indicate that the developed torque sensor is stable and practical for monitoring the milling process. PMID:27070620

Infection Risk From Conducted Electrical Weapon Probes: What Do We Know?

PubMed

Kroll, Mark W; Ritter, Mollie B; Guilbault, Richard A; Panescu, Dorin

2016-11-01

Concern has been raised over the infection risk of the TASER electrical weapon since the probes penetrate the skin. The manufacturing process produces unsterilized probes with a 5% rate of Staphylococcus aureus contamination. Voluntary recipients (n = 208) of probe exposures were surveyed and there were no self-observations of infection. With over 3.3 million probe landings, there have been 10 case reports of penetrations of sensitive tissue with no reported infections. The electrical field was modeled and found that the electrical pulses generate a field of over 1200 V/mm on the dart portion. This is sufficient to sterilize the dart via electroporation. Electrical weapon probes appear to have a very low (possibly zero) rate of infection. The factors leading to this low infection rate appear to be a manufacturing process producing a low rate of bacterial contamination and the pulses sterilizing the dart via electroporation. © 2016 American Academy of Forensic Sciences.

Refractive Effects in Remote Sensing of the Atmosphere with Infrared Transmission Spectroscopy

DTIC Science & Technology

1975-06-01

J(0O^-H)D HNm*ji^ fta )(j𔃺,ff,ffooooo^HH-i^fyN<Mi\\fv(flmrt^flj;*^^^ ss 2» — < (M O* (T1 0» ff. O’OOO^O’lMM^^NHN^iniOiMHCOhHOOh- O W B ’C ^ O H...8217*O«DN^ *-* rg m ^ tr> fMD ^tr(?,croo’)0:i,H^-<H^’V|M^|M’M^’n^w^ •*• •* *t *t >r ii^ m IT 63 ooooooooooOO(^(7*0’-«mu>«>^4^0t-*(7’f*-^mo^’^ gf f- tr...JOOOUOUOOOU4«NOmiM^ff(DNNHI -« in oooooouuoooagoiv)Loiiiri.irniwKi<«o«iN’«rtOo ooooooooooooo~o-r-rrta.tartoOrt3f^O’»---o»i7’«f-, fta -<*o

Surface Preparation Methods to Enhance Dynamic Surface Property Measurements of Shocked Metal Surfaces

NASA Astrophysics Data System (ADS)

Zellner, Michael; McNeil, Wendy; Gray, George, III; Huerta, David; King, Nicholas; Neal, George; Payton, Jeremy; Rubin, Jim; Stevens, Gerald; Turley, William; Buttler, William

2008-03-01

This effort investigates surface-preparation methods to enhance dynamic surface-property measurements of shocked metal surfaces. To assess the ability of making reliable and consistent dynamic surface-property measurements, the amount of material ejected from the free-surface upon shock release to vacuum (ejecta) was monitored for shocked Al-1100 and Sn targets. Four surface preparation methods were considered: fly-cut machined finish, diamond-turned machine finish, polished finish, and ball-rolled. The samples were shock loaded by in-contact detonation of HE PBX-9501 on the front-side of the metal coupons. Ejecta production at the back-side or free-side of the metal coupons was monitored using piezoelectric pins, optical shadowgraphy, and x-ray attenuation radiography.

Surface preparation methods to enhance dynamic surface property measurements of shocked metal surfaces

NASA Astrophysics Data System (ADS)

Zellner, M. B.; Vogan McNeil, W.; Gray, G. T.; Huerta, D. C.; King, N. S. P.; Neal, G. E.; Valentine, S. J.; Payton, J. R.; Rubin, J.; Stevens, G. D.; Turley, W. D.; Buttler, W. T.

2008-04-01

This effort investigates surface-preparation methods to enhance dynamic surface-property measurements of shocked metal surfaces. To assess the ability of making reliable and consistent dynamic surface-property measurements, the amount of material ejected from the free surface upon shock release to vacuum (ejecta) was monitored for shocked Al-1100 and Sn targets. Four surface-preparation methods were considered: Fly-cut machine finish, diamond-turned machine finish, polished finish, and ball rolled. The samples were shock loaded by in-contact detonation of HE PBX-9501 on the front side of the metal coupons. Ejecta production at the back side or free side of the metal coupons was monitored using piezoelectric pins, optical shadowgraphy, and x-ray attenuation radiography.

Urban land use monitoring from computer-implemented processing of airborne multispectral data

NASA Technical Reports Server (NTRS)

Todd, W. J.; Mausel, P. W.; Baumgardner, M. F.

1976-01-01

Machine processing techniques were applied to multispectral data obtained from airborne scanners at an elevation of 600 meters over central Indianapolis in August, 1972. Computer analysis of these spectral data indicate that roads (two types), roof tops (three types), dense grass (two types), sparse grass (two types), trees, bare soil, and water (two types) can be accurately identified. Using computers, it is possible to determine land uses from analysis of type, size, shape, and spatial associations of earth surface images identified from multispectral data. Land use data developed through machine processing techniques can be programmed to monitor land use changes, simulate land use conditions, and provide impact statistics that are required to analyze stresses placed on spatial systems.

Mass-spectrometric monitoring of the intravenous anesthetic concentration in the breathing circuit of an anesthesia machine

NASA Astrophysics Data System (ADS)

Elizarov, A. Yu.; Levshankov, A. I.

2011-04-01

Interaction between inhalational anesthetic sevoflurane and an absorber of CO2 (soda lime) in the breathing circuit of an anesthesia machine during low-flow anesthesia (0.5 l of a fresh gaseous mixture per minute) is studied with the mass-spectrometric method. Monitoring data for the concentration of sevoflurane and three toxic products of sevoflurane decompositions (substances A, B, and C) during anesthesia in the inspiration-expiration regime are presented. The highest concentration of substance A is found to be 65 ppm. The biochemical blood analysis before and after anesthesia shows that nephropathy is related to the function of liver toxicity. It is found that inhalational anesthetic sevoflurane influences the concentration of intravenous hypnotic propofol in blood.

Present and Future of M2M

NASA Astrophysics Data System (ADS)

Ono, Satoru; Watanabe, Takashi

In recent years, the rapid progress in the development of hardware and software technologies enables tiny and low cost information devices hereinafter referred to as Machine to be widely available. M2M (Machine to Machine) has been of much attention where many tiny machines are connected to each other through networks with minimal human intervention to provide smooth and intelligent management. M2M is a promising core technology providing timely, flexible, efficient and comprehensive service at low cost. M2M has wide variety of applications including energy management system, environmental monitoring system, intelligent transport system, industrial automation system and other applications. M2M consists of terminals and networks that connect them. In this paper, we mainly focus on M2M networking and mention the future direction of the technology.

Parameter monitoring compensation system and method

DOEpatents

Barkman, William E.; Babelay, Edwin F.; DeMint, Paul D.; Hebble, Thomas L.; Igou, Richard E.; Williams, Richard R.; Klages, Edward J.; Rasnick, William H.

1995-01-01

A compensation system for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along preprogrammed path during a machining operation utilizes sensors for gathering information at a preselected stage of a machining operation relating to an actual condition. The controller compares the actual condition to a condition which the program presumes to exist at the preselected stage and alters the program in accordance with detected variations between the actual condition and the assumed condition. Such conditions may be related to process parameters, such as a position, dimension or shape of the cutting tool or workpiece or an environmental temperature associated with the machining operation, and such sensors may be a contact or a non-contact type of sensor or a temperature transducer.

The ASSERT Virtual Machine Kernel: Support for Preservation of Temporal Properties

NASA Astrophysics Data System (ADS)

Zamorano, J.; de la Puente, J. A.; Pulido, J. A.; Urueña

2008-08-01

A new approach to building embedded real-time software has been developed in the ASSERT project. One of its key elements is the concept of a virtual machine preserving the non-functional properties of the system, and especially real-time properties, all the way down from high- level design models down to executable code. The paper describes one instance of the virtual machine concept that provides support for the preservation of temporal properties both at the source code level —by accept- ing only "legal" entities, i.e. software components with statically analysable real-tim behaviour— and at run-time —by monitoring the temporal behaviour of the system. The virtual machine has been validated on several pilot projects carried out by aerospace companies in the framework of the ASSERT project.

On-line Monitoring for Cutting Tool Wear Condition Based on the Parameters

NASA Astrophysics Data System (ADS)

Han, Fenghua; Xie, Feng

2017-07-01

In the process of cutting tools, it is very important to monitor the working state of the tools. On the basis of acceleration signal acquisition under the constant speed, time domain and frequency domain analysis of relevant indicators monitor the online of tool wear condition. The analysis results show that the method can effectively judge the tool wear condition in the process of machining. It has certain application value.

Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2015-12-01

Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

Human Factors and Robotics: Current Status and Future Prospects.

DTIC Science & Technology

1981-10-01

relatively simple "pick and place" machines which have mechanical arms and hands for transferring workpieces, and may be reprogrammable . Japan’s...tasks can consist of self-monitoring of activity or the control of other machines. Spot welding in the manufacture of automobiles represents probably...application alone. On an automobile production line, the robot must be able to remember several different body styles (e.g., 2-door versus 4-door) with

Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment.

PubMed

Eskofier, Bjoern M; Lee, Sunghoon I; Daneault, Jean-Francois; Golabchi, Fatemeh N; Ferreira-Carvalho, Gabriela; Vergara-Diaz, Gloria; Sapienza, Stefano; Costante, Gianluca; Klucken, Jochen; Kautz, Thomas; Bonato, Paolo

2016-08-01

The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.

Applying data fusion techniques for benthic habitat mapping and monitoring in a coral reef ecosystem

NASA Astrophysics Data System (ADS)

Zhang, Caiyun

2015-06-01

Accurate mapping and effective monitoring of benthic habitat in the Florida Keys are critical in developing management strategies for this valuable coral reef ecosystem. For this study, a framework was designed for automated benthic habitat mapping by combining multiple data sources (hyperspectral, aerial photography, and bathymetry data) and four contemporary imagery processing techniques (data fusion, Object-based Image Analysis (OBIA), machine learning, and ensemble analysis). In the framework, 1-m digital aerial photograph was first merged with 17-m hyperspectral imagery and 10-m bathymetry data using a pixel/feature-level fusion strategy. The fused dataset was then preclassified by three machine learning algorithms (Random Forest, Support Vector Machines, and k-Nearest Neighbor). Final object-based habitat maps were produced through ensemble analysis of outcomes from three classifiers. The framework was tested for classifying a group-level (3-class) and code-level (9-class) habitats in a portion of the Florida Keys. Informative and accurate habitat maps were achieved with an overall accuracy of 88.5% and 83.5% for the group-level and code-level classifications, respectively.

An illustration of new methods in machine condition monitoring, Part I: stochastic resonance

NASA Astrophysics Data System (ADS)

Worden, K.; Antoniadou, I.; Marchesiello, S.; Mba, C.; Garibaldi, L.

2017-05-01

There have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damage-sensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-of-the-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The first paper in the pair will deal with feature extraction. Although some papers have appeared in the recent past considering stochastic resonance as a means of amplifying damage information in signals, they have largely relied on ad hoc specifications of the resonator used. In contrast, the current paper will adopt a principled optimisation-based approach to the resonator design. The paper will also show that a discrete dynamical system can provide all the benefits of a continuous system, but also provide a considerable speed-up in terms of simulation time in order to facilitate the optimisation approach.

Needs of ergonomic design at control units in production industries.

PubMed

Levchuk, I; Schäfer, A; Lang, K-H; Gebhardt, Hj; Klussmann, A

2012-01-01

During the last decades, an increasing use of innovative technologies in manufacturing areas was monitored. A huge amount of physical workload was replaced by the change from conventional machine tools to computer-controlled units. CNC systems spread in current production processes. Because of this, machine operators today mostly have an observational function. This caused increasing of static work (e.g., standing, sitting) and cognitive demands (e.g., process observation). Machine operators have a high responsibility, because mistakes may lead to human injuries as well as to product losses - and in consequence may lead to high monetary losses (for the company) as well. Being usable often means for a CNC machine being efficient. An intuitive usability and an ergonomic organization of CNC workplaces can be an essential basis to reduce the risk of failures in operation as well as physical complaints (e.g. pain or diseases because of bad body posture during work). In contrast to conventional machines, CNC machines are equipped both with hardware and software. An intuitive and clear-sighted operating of CNC systems is a requirement for quick learning of new systems. Within this study, a survey was carried out among trainees learning the operation of CNC machines.

Correlation between use time of machine and decline curve for emerging enterprise information systems

NASA Astrophysics Data System (ADS)

Chang, Yao-Chung; Lai, Chin-Feng; Chuang, Chi-Cheng; Hou, Cheng-Yu

2018-04-01

With the progress of science and technology, more and more machines are adpot to help human life better and more convenient. When the machines have been used for a longer period of time so that the machine components are getting old, the amount of power comsumption will increase and easily cause the machine to overheat. This also causes a waste of invisible resources. If the Internet of Everything (IoE) technologies are able to be applied into the enterprise information systems for monitoring the machines use time, it can not only make energy can be effectively used, but aslo create a safer living environment. To solve the above problem, the correlation predict model is established to collect the data of power consumption converted into power eigenvalues. This study takes the power eigenvalue as the independent variable and use time as the dependent variable in order to establish the decline curve. Ultimately, the scoring and estimation modules are employed to seek the best power eigenvalue as the independent variable. To predict use time, the correlation is discussed between the use time and the decline curve to improve the entire behavioural analysis of the facilitate recognition of the use time of machines.

Characteristics of the Arcing Plasma Formation Effect in Spark-Assisted Chemical Engraving of Glass, Based on Machine Vision

PubMed Central

Wu, Dung-Sheng

2018-01-01

Spark-assisted chemical engraving (SACE) is a non-traditional machining technology that is used to machine electrically non-conducting materials including glass, ceramics, and quartz. The processing accuracy, machining efficiency, and reproducibility are the key factors in the SACE process. In the present study, a machine vision method is applied to monitor and estimate the status of a SACE-drilled hole in quartz glass. During the machining of quartz glass, the spring-fed tool electrode was pre-pressured on the quartz glass surface to feed the electrode that was in contact with the machining surface of the quartz glass. In situ image acquisition and analysis of the SACE drilling processes were used to analyze the captured image of the state of the spark discharge at the tip and sidewall of the electrode. The results indicated an association between the accumulative size of the SACE-induced spark area and deepness of the hole. The results indicated that the evaluated depths of the SACE-machined holes were a proportional function of the accumulative spark size with a high degree of correlation. The study proposes an innovative computer vision-based method to estimate the deepness and status of SACE-drilled holes in real time. PMID:29565303

Characteristics of the Arcing Plasma Formation Effect in Spark-Assisted Chemical Engraving of Glass, Based on Machine Vision.

PubMed

Ho, Chao-Ching; Wu, Dung-Sheng

2018-03-22

Spark-assisted chemical engraving (SACE) is a non-traditional machining technology that is used to machine electrically non-conducting materials including glass, ceramics, and quartz. The processing accuracy, machining efficiency, and reproducibility are the key factors in the SACE process. In the present study, a machine vision method is applied to monitor and estimate the status of a SACE-drilled hole in quartz glass. During the machining of quartz glass, the spring-fed tool electrode was pre-pressured on the quartz glass surface to feed the electrode that was in contact with the machining surface of the quartz glass. In situ image acquisition and analysis of the SACE drilling processes were used to analyze the captured image of the state of the spark discharge at the tip and sidewall of the electrode. The results indicated an association between the accumulative size of the SACE-induced spark area and deepness of the hole. The results indicated that the evaluated depths of the SACE-machined holes were a proportional function of the accumulative spark size with a high degree of correlation. The study proposes an innovative computer vision-based method to estimate the deepness and status of SACE-drilled holes in real time.

Tapping and listening: a new approach to bolt looseness monitoring

NASA Astrophysics Data System (ADS)

Kong, Qingzhao; Zhu, Junxiao; Ho, Siu Chun Michael; Song, Gangbing

2018-07-01

Bolted joints are among the most common building blocks used across different types of structures, and are often the key components that sew all other structural parts together. Monitoring and assessment of looseness in bolted structures is one of the most attractive topics in mechanical, aerospace, and civil engineering. This paper presents a new percussion-based non-destructive approach to determine the health condition of bolted joints with the help of machine learning. The proposed method is very similar to the percussive diagnostic techniques used in clinical examinations to diagnose the health of patients. Due to the different interfacial properties among the bolts, nuts and the host structure, bolted joints can generate unique sounds when it is excited by impacts, such as from tapping. Power spectrum density, as a signal feature, was used to recognize and classify recorded tapping data. A machine learning model using the decision tree method was employed to identify the bolt looseness level. Experiments demonstrated that the newly proposed method for bolt looseness detection is very easy to implement by ‘listening to tapping’ and the monitoring accuracy is very high. With the rapid in robotics, the proposed approach has great potential to be implemented with intimately weaving robotics and machine learning to produce a cyber-physical system that can automatically inspect and determine the health of a structure.

Online Vibration Monitoring of a Water Pump Machine to Detect Its Malfunction Components Based on Artificial Neural Network

NASA Astrophysics Data System (ADS)

Rahmawati, P.; Prajitno, P.

2018-04-01

Vibration monitoring is a measurement instrument used to identify, predict, and prevent failures in machine instruments[6]. This is very needed in the industrial applications, cause any problem with the equipment or plant translates into economical loss and they are mostly monitored component off-line[2]. In this research, a system has been developed to detect the malfunction of the components of Shimizu PS-128BT water pump machine, such as capacitor, bearing and impeller by online measurements. The malfunction components are detected by taking vibration data using a Micro-Electro-Mechanical System(MEMS)-based accelerometer that are acquired by using Raspberry Pi microcomputer and then the data are converted into the form of Relative Power Ratio(RPR). In this form the signal acquired from different components conditions have different patterns. The collected RPR used as the base of classification process for recognizing the damage components of the water pump that are conducted by Artificial Neural Network(ANN). Finally, the damage test result will be sent via text message using GSM module that are connected to Raspberry Pi microcomputer. The results, with several measurement readings, with each reading in 10 minutes duration for each different component conditions, all cases yield 100% of accuracies while in the case of defective capacitor yields 90% of accuracy.

An Effective Mechanism for Virtual Machine Placement using Aco in IAAS Cloud

NASA Astrophysics Data System (ADS)

Shenbaga Moorthy, Rajalakshmi; Fareentaj, U.; Divya, T. K.

2017-08-01

Cloud computing provides an effective way to dynamically provide numerous resources to meet customer demands. A major challenging problem for cloud providers is designing efficient mechanisms for optimal virtual machine Placement (OVMP). Such mechanisms enable the cloud providers to effectively utilize their available resources and obtain higher profits. In order to provide appropriate resources to the clients an optimal virtual machine placement algorithm is proposed. Virtual machine placement is NP-Hard problem. Such NP-Hard problem can be solved using heuristic algorithm. In this paper, Ant Colony Optimization based virtual machine placement is proposed. Our proposed system focuses on minimizing the cost spending in each plan for hosting virtual machines in a multiple cloud provider environment and the response time of each cloud provider is monitored periodically, in such a way to minimize delay in providing the resources to the users. The performance of the proposed algorithm is compared with greedy mechanism. The proposed algorithm is simulated in Eclipse IDE. The results clearly show that the proposed algorithm minimizes the cost, response time and also number of migrations.

Vibration monitoring via nano-composite piezoelectric foam bushings

NASA Astrophysics Data System (ADS)

Bird, Evan T.; Merrell, A. Jake; Anderson, Brady K.; Newton, Cory N.; Rosquist, Parker G.; Fullwood, David T.; Bowden, Anton E.; Seeley, Matthew K.

2016-11-01

Most mechanical systems produce vibrations as an inherent side effect of operation. Though some vibrations are acceptable in operation, others can cause damage or signal a machine’s imminent failure. These vibrations would optimally be monitored in real-time, without human supervision to prevent failure and excessive wear in machinery. This paper explores a new alternative to currently-used machine-monitoring equipment, namely a piezoelectric foam sensor system. These sensors are made of a silicone-based foam embedded with nano- and micro-scale conductive particles. Upon impact, they emit an electric response that is directly correlated with impact energy, with no electrical power input. In the present work, we investigated their utility as self-sensing bushings on machinery. These sensors were found to accurately detect both the amplitude and frequency of typical machine vibrations. The bushings could potentially save time and money over other vibration sensing mechanisms, while simultaneously providing a potential control input that could be utilized for correcting vibrational imbalance.

[A field study on the work load and muscle fatigue at neck-shoulder in female sewing machine operators by using surface electromyography].

PubMed

Zhang, Fei-ruo; Wang, Sheng; He, Li-hua; Zhang, Ying; Wu, Shan-shan; Li, Jing-yun; Hu, Guang-yi; Ye, Kang-ping

2011-03-01

To study neck and shoulder work-related muscle fatigue of female sewing machine operators. 18 health female sewing machine operators without musculoskeletal disorders work in Beijing garment industry factory as volunteers in participate of this study. The maximal voluntary contraction (MVC) and 20% MVC of bilateral upper trapezium and cervical erectors spinae was tested before sewing operations, then the whole 20 time windows (1 time window = 10 min) sewing machine operations was monitored and the surface electromyography (sEMG) signals simultaneously was recorded after monitoring the 20%MVC was tested. Use amplitude analysis method to reduction recorded EMG signals. During work, the median load for the left cervical erector spinae (LCES), right cervical erector spinae (RCES), left upper trapezium (LUT) and right upper trapezium (RUT) respectively was 6.78 ± 1.05, 6.94 ± 1.12, 5.68 ± 2.56 and 6.47 ± 3.22, work load of right is higher than the left; static load analysis indicated the value of RMS(20%MVC) before work was higher than that value after work, the increase of right CES and UT RMS(20%MVC) was more; the largest 20%MVE of bilateral CES occurred at 20th time window, and that of bilateral UT happened at 16th. The work load of female sewing machine operators is sustained "static" load, and work load of right neck-shoulder is higher than left, right neck-shoulder muscle is more fatigable and much serious once fatigued.

State of the Art and Challenges of Radio Spectrum Monitoring in China

NASA Astrophysics Data System (ADS)

Lu, Q. N.; Yang, J. J.; Jin, Z. Y.; Chen, D. Z.; Huang, M.

2017-10-01

This paper provides an overview of radio spectrum monitoring in China. First, research background, the motivation is described and then train of thought, the prototype system, and the accomplishments are presented. Current radio spectrum monitoring systems are man-machine communication systems, which are unable to detect and process the radio interference automatically. In order to realize intelligent radio monitoring and spectrum management, we proposed an Internet of Things-based spectrum sensing approach using information system architecture and implemented a pilot program; then some very interesting results were obtained.

Parameter monitoring compensation system and method

DOEpatents

Barkman, W.E.; Babelay, E.F.; DeMint, P.D.; Hebble, T.L.; Igou, R.E.; Williams, R.R.; Klages, E.J.; Rasnick, W.H.

1995-02-07

A compensation system is described for a computer-controlled machining apparatus having a controller and including a cutting tool and a workpiece holder which are movable relative to one another along a preprogrammed path during a machining operation. It utilizes sensors for gathering information at a preselected stage of a machining operation relating to an actual condition. The controller compares the actual condition to a condition which the program presumes to exist at the preselected stage and alters the program in accordance with detected variations between the actual condition and the assumed condition. Such conditions may be related to process parameters, such as a position, dimension or shape of the cutting tool or workpiece or an environmental temperature associated with the machining operation, and such sensors may be a contact or a non-contact type of sensor or a temperature transducer. 7 figs.

Secure Autonomous Automated Scheduling (SAAS). Rev. 1.1

NASA Technical Reports Server (NTRS)

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

2010-01-01

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

The Altar Machine in the Church Mother of Gangi (Palermo, Italy). Interpretation of the past uses, scientific investigation and preservation challenge

PubMed Central

2012-01-01

Background The aim of this work was to study the Altar Machine in the Church Mother of Gangi, a little town near Palermo (Italy) regarding the history, the technical manufacture, the constitutive materials and the state of preservation. The Altar Machine was dated back to the second half of the 18th century; it is constituted by carved and painted wood, a complex system of winch and pulleys allows move various statues and parts of the Machine in accordance with the baroque scenography machineries. Results The observation and survey of the mechanisms allowed formulate hypothesis on a more ancient mode of operation of the Altar Machine. Laboratory analysis revealed the presence of many superimposed layers constituted by several different materials (protein binders, siccative oils, natural terpene resins, shellac, calcium carbonate, gypsum, lead white, brass, zinc white, iron oxides) and different wood species employed for the original and restoration elements of the Machine. This is due to a continuous usage of the object that has got a demo-ethno-anthropological significance. Microclimate monitoring (relative humidity RH and temperature T) put in evidence that most of the data fall outside the tolerance intervals, i.e. the RH and T limits defined by the international standards. In particular, T values were generally high (out of the tolerance range) but they appeared to be quite constant; on the other hand RH values fell almost always inside the tolerance area but they often exhibited dangerous variations. Conclusions The characterization of the constitutive materials provided useful information both to support the dating of the Machine proposed by the inscription and to obtain a base of data for a possible conservation work. The microclimate monitoring put in evidence that the temperature and relative humidity values are not always suitable to correctly preserve the artefact. The careful in situ investigation confirmed an on-going climate induced damage to the Altar Machine that, associated to the deterioration caused by its usage, may have dramatic consequences on this unique and peculiar work of art. The results of this work will have potential implications in the near future regarding a probable conservation project on the Machine. PMID:22616574

Virtual reality hardware and graphic display options for brain-machine interfaces

PubMed Central

Marathe, Amar R.; Carey, Holle L.; Taylor, Dawn M.

2009-01-01

Virtual reality hardware and graphic displays are reviewed here as a development environment for brain-machine interfaces (BMIs). Two desktop stereoscopic monitors and one 2D monitor were compared in a visual depth discrimination task and in a 3D target-matching task where able-bodied individuals used actual hand movements to match a virtual hand to different target hands. Three graphic representations of the hand were compared: a plain sphere, a sphere attached to the fingertip of a realistic hand and arm, and a stylized pacman-like hand. Several subjects had great difficulty using either stereo monitor for depth perception when perspective size cues were removed. A mismatch in stereo and size cues generated inappropriate depth illusions. This phenomenon has implications for choosing target and virtual hand sizes in BMI experiments. Target matching accuracy was about as good with the 2D monitor as with either 3D monitor. However, users achieved this accuracy by exploring the boundaries of the hand in the target with carefully controlled movements. This method of determining relative depth may not be possible in BMI experiments if movement control is more limited. Intuitive depth cues, such as including a virtual arm, can significantly improve depth perception accuracy with or without stereo viewing. PMID:18006069

Heart Rate Monitors

NASA Technical Reports Server (NTRS)

1990-01-01

Under a NASA grant, Dr. Robert M. Davis and Dr. William M. Portnoy came up with a new type of electrocardiographic electrode that would enable long term use on astronauts. Their invention was an insulated capacitive electrode constructed of a thin dielectric film. NASA subsequently licensed the electrode technology to Richard Charnitski, inventor of the VersaClimber, who founded Heart Rate, Inc., to further develop and manufacture personal heart monitors and to produce exercise machines using the technology for the physical fitness, medical and home markets. Same technology is on both the Home and Institutional Model VersaClimbers. On the Home Model an infrared heart beat transmitter is worn under exercise clothing. Transmitted heart rate is used to control the work intensity on the VersaClimber using the heart rate as the speedometer of the exercise. This offers advantages to a full range of users from the cardiac rehab patient to the high level physical conditioning of elite athletes. The company manufactures and markets five models of the 1*2*3 HEART RATE monitors that are used wherever people exercise to accurately monitor their heart rate. Company is developing a talking heart rate monitor that works with portable headset radios. A version of the heart beat transmitter will be available to the manufacturers of other aerobic exercise machines.

Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control.

PubMed

Adeleke, Jude Adekunle; Moodley, Deshendran; Rens, Gavin; Adewumi, Aderemi Oluyinka

2017-04-09

Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM 2 . 5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM 2 . 5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web.

Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control

PubMed Central

Adeleke, Jude Adekunle; Moodley, Deshendran; Rens, Gavin; Adewumi, Aderemi Oluyinka

2017-01-01

Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM2.5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM2.5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web. PMID:28397776

Hydrocarbon pneumonia

MedlinePlus

... pneumonia is caused by drinking or breathing in gasoline , kerosene , furniture polish , paint thinner, or other oily ... Arterial blood gas monitoring Breathing support, including oxygen, inhalation treatment, breathing tube and ventilator (machine), in severe ...

Lymphangiogram

MedlinePlus

... type of x-ray machine, called a fluoroscope, projects the images on a TV monitor. The provider ... commercial use must be authorized in writing by ADAM Health Solutions. About MedlinePlus Site Map FAQs Customer ...

Machine assisted histogram classification

NASA Astrophysics Data System (ADS)

Benyó, B.; Gaspar, C.; Somogyi, P.

2010-04-01

LHCb is one of the four major experiments under completion at the Large Hadron Collider (LHC). Monitoring the quality of the acquired data is important, because it allows the verification of the detector performance. Anomalies, such as missing values or unexpected distributions can be indicators of a malfunctioning detector, resulting in poor data quality. Spotting faulty or ageing components can be either done visually using instruments, such as the LHCb Histogram Presenter, or with the help of automated tools. In order to assist detector experts in handling the vast monitoring information resulting from the sheer size of the detector, we propose a graph based clustering tool combined with machine learning algorithm and demonstrate its use by processing histograms representing 2D hitmaps events. We prove the concept by detecting ion feedback events in the LHCb experiment's RICH subdetector.

Overview of recent trends and developments for BPM systems

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

Wendt, M.; /Fermilab

2011-08-01

Beam position monitoring (BPM) systems are the workhorse of beam diagnostics for almost any kind of charged particle accelerator: linear, circular or transport-lines, operating with leptons, hadrons or heavy ions. BPMs are essential for beam commissioning, accelerator fault analysis and trouble shooting, machine optics, as well as lattice measurements, and finally, for accelerator optimization, in order to achieve the ultimate beam quality. This presentation summarizes the efforts of the beam instrumentation community on recent developments and advances on BPM technologies, i.e. BPM pickup monitors and front-end electronics (analog and digital). Principles, examples, and state-of-the-art status on various BPM techniques, servingmore » hadron and heavy ion machines, sync light synchrotron's, as well as electron linacs for FEL or HEP applications are outlined.« less

Fault diagnosis of helical gearbox using acoustic signal and wavelets

NASA Astrophysics Data System (ADS)

Pranesh, SK; Abraham, Siju; Sugumaran, V.; Amarnath, M.

2017-05-01

The efficient transmission of power in machines is needed and gears are an appropriate choice. Faults in gears result in loss of energy and money. The monitoring and fault diagnosis are done by analysis of the acoustic and vibrational signals which are generally considered to be unwanted by products. This study proposes the usage of machine learning algorithm for condition monitoring of a helical gearbox by using the sound signals produced by the gearbox. Artificial faults were created and subsequently signals were captured by a microphone. An extensive study using different wavelet transformations for feature extraction from the acoustic signals was done, followed by waveletselection and feature selection using J48 decision tree and feature classification was performed using K star algorithm. Classification accuracy of 100% was obtained in the study

AUTOMATING ASSET KNOWLEDGE WITH MTCONNECT.

PubMed

Venkatesh, Sid; Ly, Sidney; Manning, Martin; Michaloski, John; Proctor, Fred

2016-01-01

In order to maximize assets, manufacturers should use real-time knowledge garnered from ongoing and continuous collection and evaluation of factory-floor machine status data. In discrete parts manufacturing, factory machine monitoring has been difficult, due primarily to closed, proprietary automation equipment that make integration difficult. Recently, there has been a push in applying the data acquisition concepts of MTConnect to the real-time acquisition of machine status data. MTConnect is an open, free specification aimed at overcoming the "Islands of Automation" dilemma on the shop floor. With automated asset analysis, manufacturers can improve production to become lean, efficient, and effective. The focus of this paper will be on the deployment of MTConnect to collect real-time machine status to automate asset management. In addition, we will leverage the ISO 22400 standard, which defines an asset and quantifies asset performance metrics. In conjunction with these goals, the deployment of MTConnect in a large aerospace manufacturing facility will be studied with emphasis on asset management and understanding the impact of machine Overall Equipment Effectiveness (OEE) on manufacturing.

The coffee-machine bacteriome: biodiversity and colonisation of the wasted coffee tray leach

PubMed Central

Vilanova, Cristina; Iglesias, Alba; Porcar, Manuel

2015-01-01

Microbial communities are ubiquitous in both natural and artificial environments. However, microbial diversity is usually reduced under strong selection pressures, such as those present in habitats rich in recalcitrant or toxic compounds displaying antimicrobial properties. Caffeine is a natural alkaloid present in coffee, tea and soft drinks with well-known antibacterial properties. Here we present the first systematic analysis of coffee machine-associated bacteria. We sampled the coffee waste reservoir of ten different Nespresso machines and conducted a dynamic monitoring of the colonization process in a new machine. Our results reveal the existence of a varied bacterial community in all the machines sampled, and a rapid colonisation process of the coffee leach. The community developed from a pioneering pool of enterobacteria and other opportunistic taxa to a mature but still highly variable microbiome rich in coffee-adapted bacteria. The bacterial communities described here, for the first time, are potential drivers of biotechnologically relevant processes including decaffeination and bioremediation. PMID:26592442

The coffee-machine bacteriome: biodiversity and colonisation of the wasted coffee tray leach.

PubMed

Vilanova, Cristina; Iglesias, Alba; Porcar, Manuel

2015-11-23

Microbial communities are ubiquitous in both natural and artificial environments. However, microbial diversity is usually reduced under strong selection pressures, such as those present in habitats rich in recalcitrant or toxic compounds displaying antimicrobial properties. Caffeine is a natural alkaloid present in coffee, tea and soft drinks with well-known antibacterial properties. Here we present the first systematic analysis of coffee machine-associated bacteria. We sampled the coffee waste reservoir of ten different Nespresso machines and conducted a dynamic monitoring of the colonization process in a new machine. Our results reveal the existence of a varied bacterial community in all the machines sampled, and a rapid colonisation process of the coffee leach. The community developed from a pioneering pool of enterobacteria and other opportunistic taxa to a mature but still highly variable microbiome rich in coffee-adapted bacteria. The bacterial communities described here, for the first time, are potential drivers of biotechnologically relevant processes including decaffeination and bioremediation.

Ultimate computing. Biomolecular consciousness and nano Technology

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

Hameroff, S.R.

1987-01-01

The book advances the premise that the cytoskeleton is the cell's nervous system, the biological controller/computer. If indeed cytoskeletal dynamics in the nanoscale (billionth meter, billionth second) are the texture of intracellular information processing, emerging ''NanoTechnologies'' (scanning tunneling microscopy, Feynman machines, von Neumann replicators, etc.) should enable direct monitoring, decoding and interfacing between biological and technological information devices. This in turn could result in important biomedical applications and perhaps a merger of mind and machine: Ultimate Computing.

Aerospace Mechanisms Symposium (22nd) Held at Hampton, Virginia on 4-6 May 1988.

DTIC Science & Technology

1988-05-06

monitoring is accomplished by a pressure transducer located near the hole drilled through the vessel wall between seals. A lip is machined on the...are presented and a design example involving a machine tool metrology bench is given. Design goals included thousandfold strain attenuation in the...systems such as a metrology bench, etc. These bodies must be supported. Six degrees of freedom must be fixed, but if the base upon which they are

Detection of low level benzene exposure in supermarket wrappers by urinary muconic acid.

PubMed

E S Johnson S Halabi G Netto G Lucier W Bechtold R Henderson

1999-01-01

Women who use the 'hot wire' and 'cool rod' machines to wrap meat in supermarkets are potentially exposed to low levels of benzene and polycyclic aromatic hydrocarbons present in fumes emitted during the thermal decomposition of the plastic used to wrap meat. In order to evaluate whether the benzene metabolite trans, trans-muconic acid (MA) can be used to monitor these low levels, we collected urine samples from supermarket workers, and assayed the urine for MA. Geometric mean after-shift MA levels were highest for subjects who used the 'hot wire' machine, i.e. > 300 ng mg-1 creatinine (Cr). The corresponding levels for subjects who used the 'cool rod' machine were similar to those for subjects who did not use either type of machine, and were much lower. These results indicate that urinary muconic acid has some potential for use in monitoring benzene exposures of less than 1 part per million (ppm). The study detected very high background MA levels (exceeding 2000 ng mg-1 Cr) in some subjects, suggesting that individuals in the general population without occupational exposure to benzene may have urinary MA levels equivalent to exposure to up to 2 ppm benzene in ambient air. However, since non-benzene sources of the metabolite cannot be completely ruled out as partially responsible for these high levels, the public health significance of this finding is not known at the moment.

An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/Breakage-Condition Monitoring.

PubMed

Chung, Tien-Kan; Yeh, Po-Chen; Lee, Hao; Lin, Cheng-Mao; Tseng, Chia-Yung; Lo, Wen-Tuan; Wang, Chieh-Min; Wang, Wen-Chin; Tu, Chi-Jen; Tasi, Pei-Yuan; Chang, Jui-Wen

2016-02-23

An attachable electromagnetic-energy-harvester driven wireless vibration-sensing system for monitoring milling-processes and cutter-wear/breakage-conditions is demonstrated. The system includes an electromagnetic energy harvester, three single-axis Micro Electro-Mechanical Systems (MEMS) accelerometers, a wireless chip module, and corresponding circuits. The harvester consisting of magnets with a coil uses electromagnetic induction to harness mechanical energy produced by the rotating spindle in milling processes and consequently convert the harnessed energy to electrical output. The electrical output is rectified by the rectification circuit to power the accelerometers and wireless chip module. The harvester, circuits, accelerometer, and wireless chip are integrated as an energy-harvester driven wireless vibration-sensing system. Therefore, this completes a self-powered wireless vibration sensing system. For system testing, a numerical-controlled machining tool with various milling processes is used. According to the test results, the system is fully self-powered and able to successfully sense vibration in the milling processes. Furthermore, by analyzing the vibration signals (i.e., through analyzing the electrical outputs of the accelerometers), criteria are successfully established for the system for real-time accurate simulations of the milling-processes and cutter-conditions (such as cutter-wear conditions and cutter-breaking occurrence). Due to these results, our approach can be applied to most milling and other machining machines in factories to realize more smart machining technologies.

An Attachable Electromagnetic Energy Harvester Driven Wireless Sensing System Demonstrating Milling-Processes and Cutter-Wear/Breakage-Condition Monitoring

PubMed Central

Chung, Tien-Kan; Yeh, Po-Chen; Lee, Hao; Lin, Cheng-Mao; Tseng, Chia-Yung; Lo, Wen-Tuan; Wang, Chieh-Min; Wang, Wen-Chin; Tu, Chi-Jen; Tasi, Pei-Yuan; Chang, Jui-Wen

2016-01-01

An attachable electromagnetic-energy-harvester driven wireless vibration-sensing system for monitoring milling-processes and cutter-wear/breakage-conditions is demonstrated. The system includes an electromagnetic energy harvester, three single-axis Micro Electro-Mechanical Systems (MEMS) accelerometers, a wireless chip module, and corresponding circuits. The harvester consisting of magnets with a coil uses electromagnetic induction to harness mechanical energy produced by the rotating spindle in milling processes and consequently convert the harnessed energy to electrical output. The electrical output is rectified by the rectification circuit to power the accelerometers and wireless chip module. The harvester, circuits, accelerometer, and wireless chip are integrated as an energy-harvester driven wireless vibration-sensing system. Therefore, this completes a self-powered wireless vibration sensing system. For system testing, a numerical-controlled machining tool with various milling processes is used. According to the test results, the system is fully self-powered and able to successfully sense vibration in the milling processes. Furthermore, by analyzing the vibration signals (i.e., through analyzing the electrical outputs of the accelerometers), criteria are successfully established for the system for real-time accurate simulations of the milling-processes and cutter-conditions (such as cutter-wear conditions and cutter-breaking occurrence). Due to these results, our approach can be applied to most milling and other machining machines in factories to realize more smart machining technologies. PMID:26907297

The technique of entropy optimization in motor current signature analysis and its application in the fault diagnosis of gear transmission

NASA Astrophysics Data System (ADS)

Chen, Xiaoguang; Liang, Lin; Liu, Fei; Xu, Guanghua; Luo, Ailing; Zhang, Sicong

2012-05-01

Nowadays, Motor Current Signature Analysis (MCSA) is widely used in the fault diagnosis and condition monitoring of machine tools. However, although the current signal has lower SNR (Signal Noise Ratio), it is difficult to identify the feature frequencies of machine tools from complex current spectrum that the feature frequencies are often dense and overlapping by traditional signal processing method such as FFT transformation. With the study in the Motor Current Signature Analysis (MCSA), it is found that the entropy is of importance for frequency identification, which is associated with the probability distribution of any random variable. Therefore, it plays an important role in the signal processing. In order to solve the problem that the feature frequencies are difficult to be identified, an entropy optimization technique based on motor current signal is presented in this paper for extracting the typical feature frequencies of machine tools which can effectively suppress the disturbances. Some simulated current signals were made by MATLAB, and a current signal was obtained from a complex gearbox of an iron works made in Luxembourg. In diagnosis the MCSA is combined with entropy optimization. Both simulated and experimental results show that this technique is efficient, accurate and reliable enough to extract the feature frequencies of current signal, which provides a new strategy for the fault diagnosis and the condition monitoring of machine tools.

Survey of Machine Learning Methods for Database Security

NASA Astrophysics Data System (ADS)

Kamra, Ashish; Ber, Elisa

Application of machine learning techniques to database security is an emerging area of research. In this chapter, we present a survey of various approaches that use machine learning/data mining techniques to enhance the traditional security mechanisms of databases. There are two key database security areas in which these techniques have found applications, namely, detection of SQL Injection attacks and anomaly detection for defending against insider threats. Apart from the research prototypes and tools, various third-party commercial products are also available that provide database activity monitoring solutions by profiling database users and applications. We present a survey of such products. We end the chapter with a primer on mechanisms for responding to database anomalies.

An Intelligent Sensor Array Distributed System for Vibration Analysis and Acoustic Noise Characterization of a Linear Switched Reluctance Actuator

PubMed Central

Salvado, José; Espírito-Santo, António; Calado, Maria

2012-01-01

This paper proposes a distributed system for analysis and monitoring (DSAM) of vibrations and acoustic noise, which consists of an array of intelligent modules, sensor modules, communication bus and a host PC acting as data center. The main advantages of the DSAM are its modularity, scalability, and flexibility for use of different type of sensors/transducers, with analog or digital outputs, and for signals of different nature. Its final cost is also significantly lower than other available commercial solutions. The system is reconfigurable, can operate either with synchronous or asynchronous modes, with programmable sampling frequencies, 8-bit or 12-bit resolution and a memory buffer of 15 kbyte. It allows real-time data-acquisition for signals of different nature, in applications that require a large number of sensors, thus it is suited for monitoring of vibrations in Linear Switched Reluctance Actuators (LSRAs). The acquired data allows the full characterization of the LSRA in terms of its response to vibrations of structural origins, and the vibrations and acoustic noise emitted under normal operation. The DSAM can also be used for electrical machine condition monitoring, machine fault diagnosis, structural characterization and monitoring, among other applications. PMID:22969364

The Fresenius Medical Care home hemodialysis system.

PubMed

Schlaeper, Christian; Diaz-Buxo, Jose A

2004-01-01

The Fresenius Medical Care home dialysis system consists of a newly designed machine, a central monitoring system, a state-of-the-art reverse osmosis module, ultrapure water, and all the services associated with a successful implementation. The 2008K@home hemodialysis machine has the flexibility to accommodate the changing needs of the home hemodialysis patient and is well suited to deliver short daily or prolonged nocturnal dialysis using a broad range of dialysate flows and concentrates. The intuitive design, large graphic illustrations, and step-by-step tutorial make this equipment very user friendly. Patient safety is assured by the use of hydraulic systems with a long history of reliability, smart alarm algorithms, and advanced electronic monitoring. To further patient comfort with their safety at home, the 2008K@home is enabled to communicate with the newly designed iCare remote monitoring system. The Aquaboss Smart reverse osmosis (RO) system is compact, quiet, highly efficient, and offers an improved hygienic design. The RO module reduces water consumption by monitoring the water flow of the dialysis system and adjusting water production accordingly. The Diasafe Plus filter provides ultrapure water, known for its long-term benefits. This comprehensive approach includes planning, installation, technical and clinical support, and customer service.

Human Machine Interface Programming and Testing

NASA Technical Reports Server (NTRS)

Foster, Thomas Garrison

2013-01-01

Human Machine Interface (HMI) Programming and Testing is about creating graphical displays to mimic mission critical ground control systems in order to provide NASA engineers with the ability to monitor the health management of these systems in real time. The Health Management System (HMS) is an online interactive human machine interface system that monitors all Kennedy Ground Control Subsystem (KGCS) hardware in the field. The Health Management System is essential to NASA engineers because it allows remote control and monitoring of the health management systems of all the Programmable Logic Controllers (PLC) and associated field devices. KGCS will have equipment installed at the launch pad, Vehicle Assembly Building, Mobile Launcher, as well as the Multi-Purpose Processing Facility. I am designing graphical displays to monitor and control new modules that will be integrated into the HMS. The design of the display screen will closely mimic the appearance and functionality of the actual modules. There are many different field devices used to monitor health management and each device has its own unique set of health management related data, therefore each display must also have its own unique way to display this data. Once the displays are created, the RSLogix5000 application is used to write software that maps all the required data read from the hardware to the graphical display. Once this data is mapped to its corresponding display item, the graphical display and hardware device will be connected through the same network in order to test all possible scenarios and types of data the graphical display was designed to receive. Test Procedures will be written to thoroughly test out the displays and ensure that they are working correctly before being deployed to the field. Additionally, the Kennedy Ground Controls Subsystem's user manual will be updated to explain to the NASA engineers how to use the new module displays.

Multispectral Image Processing for Plants

NASA Technical Reports Server (NTRS)

Miles, Gaines E.

1991-01-01

The development of a machine vision system to monitor plant growth and health is one of three essential steps towards establishing an intelligent system capable of accurately assessing the state of a controlled ecological life support system for long-term space travel. Besides a network of sensors, simulators are needed to predict plant features, and artificial intelligence algorithms are needed to determine the state of a plant based life support system. Multispectral machine vision and image processing can be used to sense plant features, including health and nutritional status.

CAM: A high-performance cellular-automaton machine

NASA Astrophysics Data System (ADS)

Toffoli, Tommaso

1984-01-01

CAM is a high-performance machine dedicated to the simulation of cellular automata and other distributed dynamical systems. Its speed is about one-thousand times greater than that of a general-purpose computer programmed to do the same task; in practical terms, this means that CAM can show the evolution of cellular automata on a color monitor with an update rate, dynamic range, and spatial resolution comparable to those of a Super-8 movie, thus permitting intensive interactive experimentation. Machines of this kind can open up novel fields of research, and in this context it is important that results be easy to obtain, reproduce, and transmit. For these reasons, in designing CAM it was important to achieve functional simplicity, high flexibility, and moderate production cost. We expect that many research groups will be able to own their own copy of the machine to do research with.

Telepresence and telerobotics

NASA Technical Reports Server (NTRS)

Garin, John; Matteo, Joseph; Jennings, Von Ayre

1988-01-01

The capability for a single operator to simultaneously control complex remote multi degree of freedom robotic arms and associated dextrous end effectors is being developed. An optimal solution within the realm of current technology, can be achieved by recognizing that: (1) machines/computer systems are more effective than humans when the task is routine and specified, and (2) humans process complex data sets and deal with the unpredictable better than machines. These observations lead naturally to a philosophy in which the human's role becomes a higher level function associated with planning, teaching, initiating, monitoring, and intervening when the machine gets into trouble, while the machine performs the codifiable tasks with deliberate efficiency. This concept forms the basis for the integration of man and telerobotics, i.e., robotics with the operator in the control loop. The concept of integration of the human in the loop and maximizing the feed-forward and feed-back data flow is referred to as telepresence.

What is consciousness, and could machines have it?

PubMed

Dehaene, Stanislas; Lau, Hakwan; Kouider, Sid

2017-10-27

The controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it: the human brain. We suggest that the word "consciousness" conflates two different types of information-processing computations in the brain: the selection of information for global broadcasting, thus making it flexibly available for computation and report (C1, consciousness in the first sense), and the self-monitoring of those computations, leading to a subjective sense of certainty or error (C2, consciousness in the second sense). We argue that despite their recent successes, current machines are still mostly implementing computations that reflect unconscious processing (C0) in the human brain. We review the psychological and neural science of unconscious (C0) and conscious computations (C1 and C2) and outline how they may inspire novel machine architectures. Copyright © 2017, American Association for the Advancement of Science.

Miniaturisation of Pressure-Sensitive Paint Measurement Systems Using Low-Cost, Miniaturised Machine Vision Cameras.

PubMed

Quinn, Mark Kenneth; Spinosa, Emanuele; Roberts, David A

2017-07-25

Measurements of pressure-sensitive paint (PSP) have been performed using new or non-scientific imaging technology based on machine vision tools. Machine vision camera systems are typically used for automated inspection or process monitoring. Such devices offer the benefits of lower cost and reduced size compared with typically scientific-grade cameras; however, their optical qualities and suitability have yet to be determined. This research intends to show relevant imaging characteristics and also show the applicability of such imaging technology for PSP. Details of camera performance are benchmarked and compared to standard scientific imaging equipment and subsequent PSP tests are conducted using a static calibration chamber. The findings demonstrate that machine vision technology can be used for PSP measurements, opening up the possibility of performing measurements on-board small-scale model such as those used for wind tunnel testing or measurements in confined spaces with limited optical access.

Miniaturisation of Pressure-Sensitive Paint Measurement Systems Using Low-Cost, Miniaturised Machine Vision Cameras

PubMed Central

Spinosa, Emanuele; Roberts, David A.

2017-01-01

Measurements of pressure-sensitive paint (PSP) have been performed using new or non-scientific imaging technology based on machine vision tools. Machine vision camera systems are typically used for automated inspection or process monitoring. Such devices offer the benefits of lower cost and reduced size compared with typically scientific-grade cameras; however, their optical qualities and suitability have yet to be determined. This research intends to show relevant imaging characteristics and also show the applicability of such imaging technology for PSP. Details of camera performance are benchmarked and compared to standard scientific imaging equipment and subsequent PSP tests are conducted using a static calibration chamber. The findings demonstrate that machine vision technology can be used for PSP measurements, opening up the possibility of performing measurements on-board small-scale model such as those used for wind tunnel testing or measurements in confined spaces with limited optical access. PMID:28757553

Process capability improvement through DMAIC for aluminum alloy wheel machining

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

Residual Error Based Anomaly Detection Using Auto-Encoder in SMD Machine Sound.

PubMed

Oh, Dong Yul; Yun, Il Dong

2018-04-24

Detecting an anomaly or an abnormal situation from given noise is highly useful in an environment where constantly verifying and monitoring a machine is required. As deep learning algorithms are further developed, current studies have focused on this problem. However, there are too many variables to define anomalies, and the human annotation for a large collection of abnormal data labeled at the class-level is very labor-intensive. In this paper, we propose to detect abnormal operation sounds or outliers in a very complex machine along with reducing the data-driven annotation cost. The architecture of the proposed model is based on an auto-encoder, and it uses the residual error, which stands for its reconstruction quality, to identify the anomaly. We assess our model using Surface-Mounted Device (SMD) machine sound, which is very complex, as experimental data, and state-of-the-art performance is successfully achieved for anomaly detection.

SU-E-T-473: A Patient-Specific QC Paradigm Based On Trajectory Log Files and DICOM Plan Files

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

DeMarco, J; McCloskey, S; Low, D

Purpose: To evaluate a remote QC tool for monitoring treatment machine parameters and treatment workflow. Methods: The Varian TrueBeamTM linear accelerator is a digital machine that records machine axis parameters and MLC leaf positions as a function of delivered monitor unit or control point. This information is saved to a binary trajectory log file for every treatment or imaging field in the patient treatment session. A MATLAB analysis routine was developed to parse the trajectory log files for a given patient, compare the expected versus actual machine and MLC positions as well as perform a cross-comparison with the DICOM-RT planmore » file exported from the treatment planning system. The parsing routine sorts the trajectory log files based on the time and date stamp and generates a sequential report file listing treatment parameters and provides a match relative to the DICOM-RT plan file. Results: The trajectory log parsing-routine was compared against a standard record and verify listing for patients undergoing initial IMRT dosimetry verification and weekly and final chart QC. The complete treatment course was independently verified for 10 patients of varying treatment site and a total of 1267 treatment fields were evaluated including pre-treatment imaging fields where applicable. In the context of IMRT plan verification, eight prostate SBRT plans with 4-arcs per plan were evaluated based on expected versus actual machine axis parameters. The average value for the maximum RMS MLC error was 0.067±0.001mm and 0.066±0.002mm for leaf bank A and B respectively. Conclusion: A real-time QC analysis program was tested using trajectory log files and DICOM-RT plan files. The parsing routine is efficient and able to evaluate all relevant machine axis parameters during a patient treatment course including MLC leaf positions and table positions at time of image acquisition and during treatment.« less

Development and Application of On-line Monitor for the ZLW-1 Axis Cracks

NASA Astrophysics Data System (ADS)

Shi-jun, Yang; Qian-hui, Yang; Jian-guo, Jin

2018-03-01

This article mainly introduces a method that uses acoustic emission techniques to achieve on-line monitor for the shaft cracks and crack growth. According to this method, axis crack monitor is produced by acoustic emission techniques. This instrument can apply to all the pressure vessels, pipelines and rotor machines that can bear buckling load. It has the online real-time monitoring, automatic recording, printing, sound and light alarm, collecting crack information function. After a series of tests in both laboratory and field, it shows that this instrument is very versatile and possesses broad prospects of development and application.

Tips for Improving Seed Planting Efficiency

Treesearch

R. Kasten Dumroese; David L. Wenny; Susan J. Morrison

2002-01-01

The efficiency of a precision seeder was improved by adding a mirror so employees could monitor seed levels and by marking seeds with brightly colored talc to quickly verify the accuracy of the machine.

A Biological Signal-Based Stress Monitoring Framework for Children Using Wearable Devices.

PubMed

Choi, Yerim; Jeon, Yu-Mi; Wang, Lin; Kim, Kwanho

2017-08-23

The safety of children has always been an important issue, and several studies have been conducted to determine the stress state of a child to ensure the safety. Audio signals and biological signals including heart rate are known to be effective for stress state detection. However, collecting those data requires specialized equipment, which is not appropriate for the constant monitoring of children, and advanced data analysis is required for accurate detection. In this regard, we propose a stress state detection framework which utilizes both audio signal and heart rate collected from wearable devices, and adopted machine learning methods for the detection. Experiments using real-world data were conducted to compare detection performances across various machine learning methods and noise levels of audio signal. Adopting the proposed framework in the real-world will contribute to the enhancement of child safety.

Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis

PubMed Central

Lee, Jonguk; Choi, Heesu; Park, Daihee; Chung, Yongwha; Kim, Hee-Young; Yoon, Sukhan

2016-01-01

Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs) from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs) for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and diagnosis of faults using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods. PMID:27092509

Assessing the depth of hypnosis of xenon anaesthesia with the EEG.

PubMed

Stuttmann, Ralph; Schultz, Arthur; Kneif, Thomas; Krauss, Terence; Schultz, Barbara

2010-04-01

Xenon was approved as an inhaled anaesthetic in Germany in 2005 and in other countries of the European Union in 2007. Owing to its low blood/gas partition coefficient, xenons effects on the central nervous system show a fast onset and offset and, even after long xenon anaesthetics, the wake-up times are very short. The aim of this study was to examine which electroencephalogram (EEG) stages are reached during xenon application and whether these stages can be identified by an automatic EEG classification. Therefore, EEG recordings were performed during xenon anaesthetics (EEG monitor: Narcotrend®). A total of 300 EEG epochs were assessed visually with regard to the EEG stages. These epochs were also classified automatically by the EEG monitor Narcotrend® using multivariate algorithms. There was a high correlation between visual and automatic classification (Spearman's rank correlation coefficient r=0.957, prediction probability Pk=0.949). Furthermore, it was observed that very deep stages of hypnosis were reached which are characterised by EEG activity in the low frequency range (delta waves). The burst suppression pattern was not seen. In deep hypnosis, in contrast to the xenon EEG, the propofol EEG was characterised by a marked superimposed higher frequency activity. To ensure an optimised dosage for the single patient, anaesthetic machines for xenon should be combined with EEG monitoring. To date, only a few anaesthetic machines for xenon are available. Because of the high price of xenon, new and further developments of machines focus on optimizing xenon consumption.

A microprocessor-based system for continuous monitoring of radiation levels around the CERN PS and PSB accelerators

NASA Astrophysics Data System (ADS)

Agoritsas, V.; Beck, F.; Benincasa, G. P.; Bovigny, J. P.

1986-06-01

This paper describes a new beam loss monitor system which has been installed in the PS and PSB machines, replacing an earlier system. The new system is controlled by a microprocessor which can operate independently of the accelerator control system, though setting up and central display are usually done remotely, using the standard control system facilities.

Heart Rate Monitor

NASA Technical Reports Server (NTRS)

1984-01-01

In the mid 70's, NASA saw a need for a long term electrocardiographic electrode suitable for use on astronauts. Heart Rate Inc.'s insulated capacitive electrode is constructed of thin dielectric film applied to stainless steel surface, originally developed under a grant by Texas Technical University. HRI, Inc. was awarded NASA license and continued development of heart rate monitor for use on exercise machines for physical fitness and medical markets.

Stochastic Models of Polymer Systems

DTIC Science & Technology

2016-01-01

SECURITY CLASSIFICATION OF: The stochastic gradient decent algorithm is the now the "algorithm of choice" for very large machine learning problems...information about the behavior of the algorithm. At the same time, we were also able to formulate various acceleration techniques in precise math terms... gradient decent, REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 10. SPONSOR/MONITOR’S ACRONYM(S) ARO 8. PERFORMING

Machine Learning and Data Mining for Comprehensive Test Ban Treaty Monitoring

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

Russell, S; Vaidya, S

2009-07-30

The Comprehensive Test Ban Treaty (CTBT) is gaining renewed attention in light of growing worldwide interest in mitigating risks of nuclear weapons proliferation and testing. Since the International Monitoring System (IMS) installed the first suite of sensors in the late 1990's, the IMS network has steadily progressed, providing valuable support for event diagnostics. This progress was highlighted at the recent International Scientific Studies (ISS) Conference in Vienna in June 2009, where scientists and domain experts met with policy makers to assess the current status of the CTBT Verification System. A strategic theme within the ISS Conference centered on exploring opportunitiesmore » for further enhancing the detection and localization accuracy of low magnitude events by drawing upon modern tools and techniques for machine learning and large-scale data analysis. Several promising approaches for data exploitation were presented at the Conference. These are summarized in a companion report. In this paper, we introduce essential concepts in machine learning and assess techniques which could provide both incremental and comprehensive value for event discrimination by increasing the accuracy of the final data product, refining On-Site-Inspection (OSI) conclusions, and potentially reducing the cost of future network operations.« less

A hybrid prognostic model for multistep ahead prediction of machine condition

NASA Astrophysics Data System (ADS)

Roulias, D.; Loutas, T. H.; Kostopoulos, V.

2012-05-01

Prognostics are the future trend in condition based maintenance. In the current framework a data driven prognostic model is developed. The typical procedure of developing such a model comprises a) the selection of features which correlate well with the gradual degradation of the machine and b) the training of a mathematical tool. In this work the data are taken from a laboratory scale single stage gearbox under multi-sensor monitoring. Tests monitoring the condition of the gear pair from healthy state until total brake down following several days of continuous operation were conducted. After basic pre-processing of the derived data, an indicator that correlated well with the gearbox condition was obtained. Consecutively the time series is split in few distinguishable time regions via an intelligent data clustering scheme. Each operating region is modelled with a feed-forward artificial neural network (FFANN) scheme. The performance of the proposed model is tested by applying the system to predict the machine degradation level on unseen data. The results show the plausibility and effectiveness of the model in following the trend of the timeseries even in the case that a sudden change occurs. Moreover the model shows ability to generalise for application in similar mechanical assets.

Development and Implementation of a Simplified Tool Measuring System

NASA Astrophysics Data System (ADS)

Chen, Jenn-Yih; Lee, Bean-Yin; Lee, Kuang-Chyi; Chen, Zhao-Kai

2010-01-01

This paper presents a simplified system for measuring geometric profiles of end mills. Firstly, a CCD camera was used to capture images of cutting tools. Then, an image acquisition card with the encoding function was adopted to convert the source of image into an USB port of a PC, and the image could be shown on a monitor. In addition, two linear scales were mounted on the X-Y table for positioning and measuring purposes. The signals of the linear scales were transmitted into a 4-axis quadrature encoder with 4-channel counter card for position monitoring. The C++ Builder was utilized for designing the user friendly human machine interface of the measuring system of tools. There is a cross line on the image of the interface to show a coordinate for the position measurement. Finally, a well-known tool measuring and inspection machine was employed for the measuring standard. This study compares the difference of the measuring results by using the machine and the proposed system. Experimental results show that the percentage of measuring error is acceptable for some geometric parameters of the square or ball nose end mills. Therefore, the results demonstrate the effectiveness of the presented approach.

Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning

PubMed Central

Roh, Jongryun; Park, Hyeong-jun; Lee, Kwang Jin; Hyeong, Joonho; Kim, Sayup

2018-01-01

Sitting posture monitoring systems (SPMSs) help assess the posture of a seated person in real-time and improve sitting posture. To date, SPMS studies reported have required many sensors mounted on the backrest plate and seat plate of a chair. The present study, therefore, developed a system that measures a total of six sitting postures including the posture that applied a load to the backrest plate, with four load cells mounted only on the seat plate. Various machine learning algorithms were applied to the body weight ratio measured by the developed SPMS to identify the method that most accurately classified the actual sitting posture of the seated person. After classifying the sitting postures using several classifiers, average and maximum classification rates of 97.20% and 97.94%, respectively, were obtained from nine subjects with a support vector machine using the radial basis function kernel; the results obtained by this classifier showed a statistically significant difference from the results of multiple classifications using other classifiers. The proposed SPMS was able to classify six sitting postures including the posture with loading on the backrest and showed the possibility of classifying the sitting posture even though the number of sensors is reduced. PMID:29329261

Monitoring the tobacco use epidemic II. The Agent: Current and Emerging Tobacco Products

PubMed Central

Stellman, Steven D.; Djordjevic, Mirjana V.

2009-01-01

Objective This Agent paper summarizes the findings and recommendations of the Agent (product) Working Group of the November, 2002, National Tobacco Monitoring, Research and Evaluation Workshop. Methods The Agent Working Group evaluated the need to develop new surveillance systems for quantifying ingredients and emissions of tobacco and tobacco smoke and to improve methods to assess uptake and metabolism of these constituents taking into account variability in human smoking behavior. Results The toxic properties of numerous tobacco and tobacco smoke constituents are well known, yet systematic monitoring of tobacco products has historically been limited to tar, nicotine, and CO in mainstream cigarette smoke using a machine-smoking protocol that does not reflect human smoking behavior. Toxicity of smokeless tobacco products has not been regularly monitored. Tobacco products are constantly changing and untested products are introduced into the marketplace with great frequency, including potential reduced-exposure products (PREPs). The public health impact of new or modified tobacco products is unknown. Conclusions Systematic surveillance is recommended for mainstream smoke constituents such as polycyclic aromatic hydrocarbons (PAH), tobacco-specific nitrosamines (TSNA), total and free-base nicotine, volatile organic compounds, aromatic amines, and metals; and design attributes including tobacco blend, additives, and filter ventilation. Research on smoking topography is recommended to help define machine-smoking protocols for monitoring emissions reflective of human smoking behavior. Recommendations are made for marketplace product sampling and for population monitoring of smoking topography, emissions of toxic constituents, biomarkers of exposure and, eventually, risk of tobacco-related diseases. PMID:18848577

A data-driven approach of load monitoring on laminated composite plates using support vector machine

NASA Astrophysics Data System (ADS)

Gwon, Y. S.; Fekrmandi, H.

2018-03-01

In this study, the surface response to excitation method (SuRE) is investigated using a data-driven method for load monitoring on a laminated composite plate structure. The SuRE method is an emerging approach in ultrasonic wavebased structural health monitoring (SHM) field. In this method, a range of high-frequency, surface-guided waves are excited on the structure using piezoceramic elements. The waves propagate on the structure and interact with internal or surface damages. Initially, a baseline data of the intact structure is created by measuring the frequency transfer function between the excitation and sensing point. The integrity of structure is evaluated by monitoring changes in the frequency spectrums. The SuRE method has effectively been used for a variety of SHM applications including the detection of loose bolts, delamination in composite structures, internal corrosion in pipelines, and load and impact monitoring. Data obtained using the SuRE method was used for identifying the location of the applied load on a laminated composite plate using Support Vector Machine (SVM). A set of two piezoelectric elements were attached on the surface of the plate. A sweep excitation (150-250 kHz) generated surface-guided waves, and the transmitted waves were monitored at the sensory positions. The reference data set was measured simultaneously from the sensors. The plate was subjected to static loads while health monitoring data was being captured using the SuRE method. The confusion matrix indicated that the model classified correctly with up to 99.8% accuracy.

Using Amazon Web Services (AWS) to enable real-time, remote sensing of biophysical and anthropogenic conditions in green infrastructure systems in Philadelphia, an ultra-urban application of the Internet of Things (IoT)

NASA Astrophysics Data System (ADS)

Montalto, F. A.; Yu, Z.; Soldner, K.; Israel, A.; Fritch, M.; Kim, Y.; White, S.

2017-12-01

Urban stormwater utilities are increasingly using decentralized "green" infrastructure (GI) systems to capture stormwater and achieve compliance with regulations. Because environmental conditions, and design varies by GSI facility, monitoring of GSI systems under a range of conditions is essential. Conventional monitoring efforts can be costly because in-field data logging requires intense data transmission rates. The Internet of Things (IoT) can be used to more cost-effectively collect, store, and publish GSI monitoring data. Using 3G mobile networks, a cloud-based database was built on an Amazon Web Services (AWS) EC2 virtual machine to store and publish data collected with environmental sensors deployed in the field. This database can store multi-dimensional time series data, as well as photos and other observations logged by citizen scientists through a public engagement mobile app through a new Application Programming Interface (API). Also on the AWS EC2 virtual machine, a real-time QAQC flagging algorithm was developed to validate the sensor data streams.

Wireless Infrastructure M2M Network For Distributed Power Grid Monitoring

PubMed Central

Gharavi, Hamid; Hu, Bin

2018-01-01

With the massive integration of distributed renewable energy sources (RESs) into the power system, the demand for timely and reliable network quality monitoring, control, and fault analysis is rapidly growing. Following the successful deployment of Phasor Measurement Units (PMUs) in transmission systems for power monitoring, a new opportunity to utilize PMU measurement data for power quality assessment in distribution grid systems is emerging. The main problem however, is that a distribution grid system does not normally have the support of an infrastructure network. Therefore, the main objective in this paper is to develop a Machine-to-Machine (M2M) communication network that can support wide ranging sensory data, including high rate synchrophasor data for real-time communication. In particular, we evaluate the suitability of the emerging IEEE 802.11ah standard by exploiting its important features, such as classifying the power grid sensory data into different categories according to their traffic characteristics. For performance evaluation we use our hardware in the loop grid communication network testbed to access the performance of the network. PMID:29503505

Monitoring Method of Cutting Force by Using Additional Spindle Sensors

NASA Astrophysics Data System (ADS)

Sarhan, Ahmed Aly Diaa; Matsubara, Atsushi; Sugihara, Motoyuki; Saraie, Hidenori; Ibaraki, Soichi; Kakino, Yoshiaki

This paper describes a monitoring method of cutting forces for end milling process by using displacement sensors. Four eddy-current displacement sensors are installed on the spindle housing of a machining center so that they can detect the radial motion of the rotating spindle. Thermocouples are also attached to the spindle structure in order to examine the thermal effect in the displacement sensing. The change in the spindle stiffness due to the spindle temperature and the speed is investigated as well. Finally, the estimation performance of cutting forces using the spindle displacement sensors is experimentally investigated by machining tests on carbon steel in end milling operations under different cutting conditions. It is found that the monitoring errors are attributable to the thermal displacement of the spindle, the time lag of the sensing system, and the modeling error of the spindle stiffness. It is also shown that the root mean square errors between estimated and measured amplitudes of cutting forces are reduced to be less than 20N with proper selection of the linear stiffness.

Wireless Infrastructure M2M Network For Distributed Power Grid Monitoring.

PubMed

Gharavi, Hamid; Hu, Bin

2017-01-01

With the massive integration of distributed renewable energy sources (RESs) into the power system, the demand for timely and reliable network quality monitoring, control, and fault analysis is rapidly growing. Following the successful deployment of Phasor Measurement Units (PMUs) in transmission systems for power monitoring, a new opportunity to utilize PMU measurement data for power quality assessment in distribution grid systems is emerging. The main problem however, is that a distribution grid system does not normally have the support of an infrastructure network. Therefore, the main objective in this paper is to develop a Machine-to-Machine (M2M) communication network that can support wide ranging sensory data, including high rate synchrophasor data for real-time communication. In particular, we evaluate the suitability of the emerging IEEE 802.11ah standard by exploiting its important features, such as classifying the power grid sensory data into different categories according to their traffic characteristics. For performance evaluation we use our hardware in the loop grid communication network testbed to access the performance of the network.

Large space structures fabrication experiment. [on-orbit fabrication of graphite/thermoplastic beams

NASA Technical Reports Server (NTRS)

1978-01-01

The fabrication machine used for the rolltrusion and on-orbit forming of graphite thermoplastic (CTP) strip material into structural sections is described. The basic process was analytically developed parallel with, and integrated into the conceptual design of, a flight experiment machine for producing a continuous triangular cross section truss. The machine and its associated ancillary equipment are mounted on a Space Lab pallet. Power, thermal control, and instrumentation connections are made during ground installation. Observation, monitoring, caution and warning, and control panels and displays are installed at the payload specialist station in the orbiter. The machine is primed before flight by initiation of beam forming, to include attachment of the first set of cross members and anchoring of the diagonal cords. Control of the experiment will be from the orbiter mission specialist station. Normal operation is by automatic processing control software. Machine operating data are displayed and recorded on the ground. Data is processed and formatted to show progress of the major experiment parameters including stable operation, physical symmetry, joint integrity, and structural properties.

Simulation-driven machine learning: Bearing fault classification

NASA Astrophysics Data System (ADS)

Sobie, Cameron; Freitas, Carina; Nicolai, Mike

2018-01-01

Increasing the accuracy of mechanical fault detection has the potential to improve system safety and economic performance by minimizing scheduled maintenance and the probability of unexpected system failure. Advances in computational performance have enabled the application of machine learning algorithms across numerous applications including condition monitoring and failure detection. Past applications of machine learning to physical failure have relied explicitly on historical data, which limits the feasibility of this approach to in-service components with extended service histories. Furthermore, recorded failure data is often only valid for the specific circumstances and components for which it was collected. This work directly addresses these challenges for roller bearings with race faults by generating training data using information gained from high resolution simulations of roller bearing dynamics, which is used to train machine learning algorithms that are then validated against four experimental datasets. Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping (DTW) to bearing fault classification is proposed as a robust, parameter free method for race fault detection.

Aggregation of Electric Current Consumption Features to Extract Maintenance KPIs

NASA Astrophysics Data System (ADS)

Simon, Victor; Johansson, Carl-Anders; Galar, Diego

2017-09-01

All electric powered machines offer the possibility of extracting information and calculating Key Performance Indicators (KPIs) from the electric current signal. Depending on the time window, sampling frequency and type of analysis, different indicators from the micro to macro level can be calculated for such aspects as maintenance, production, energy consumption etc. On the micro-level, the indicators are generally used for condition monitoring and diagnostics and are normally based on a short time window and a high sampling frequency. The macro indicators are normally based on a longer time window with a slower sampling frequency and are used as indicators for overall performance, cost or consumption. The indicators can be calculated directly from the current signal but can also be based on a combination of information from the current signal and operational data like rpm, position etc. One or several of those indicators can be used for prediction and prognostics of a machine's future behavior. This paper uses this technique to calculate indicators for maintenance and energy optimization in electric powered machines and fleets of machines, especially machine tools.

AUTOMATING ASSET KNOWLEDGE WITH MTCONNECT

PubMed Central

Venkatesh, Sid; Ly, Sidney; Manning, Martin; Michaloski, John; Proctor, Fred

2017-01-01

In order to maximize assets, manufacturers should use real-time knowledge garnered from ongoing and continuous collection and evaluation of factory-floor machine status data. In discrete parts manufacturing, factory machine monitoring has been difficult, due primarily to closed, proprietary automation equipment that make integration difficult. Recently, there has been a push in applying the data acquisition concepts of MTConnect to the real-time acquisition of machine status data. MTConnect is an open, free specification aimed at overcoming the “Islands of Automation” dilemma on the shop floor. With automated asset analysis, manufacturers can improve production to become lean, efficient, and effective. The focus of this paper will be on the deployment of MTConnect to collect real-time machine status to automate asset management. In addition, we will leverage the ISO 22400 standard, which defines an asset and quantifies asset performance metrics. In conjunction with these goals, the deployment of MTConnect in a large aerospace manufacturing facility will be studied with emphasis on asset management and understanding the impact of machine Overall Equipment Effectiveness (OEE) on manufacturing. PMID:28691121

Method and apparatus for monitoring the thickness of a coal rib during rib formation

DOEpatents

Mowrey, Gary L.; Ganoe, Carl W.; Monaghan, William D.

1996-01-01

Apparatus for monitoring the position of a mining machine cutting a new entry in a coal seam relative to an adjacent, previously cut entry to determine the distance between a near face of the adjacent previously cut entry and a new face adjacent thereto of a new entry being cut by the mining machine which together define the thickness of a coal rib being formed between the new entry and the adjacent previously cut entry during the new entry-cutting operation. The monitoring apparatus; includes a transmit antenna mounted on the mining machine and spaced inwardly from the new face of the coal rib for transmitting radio energy towards the coal rib so that one portion of the radio energy is reflected by the new face which is defined at an air-coal interface between the new entry and the coal rib and another portion of the radio energy is reflected by the near face of the coal rib which is defined at an air-coal interface between the coal rib and the adjacent previously cut entry. A receive antenna mounted on the mining machine and spaced inwardly of the new face of the coal rib receives the one portion of the radio energy reflected by the new face and also receives the another portion of the radio energy reflected by the near face. A processor determines a first elapsed time period equal to the time required for the one portion of the radio energy reflected by the new face to travel between the transmit antenna and the receive antenna and also determines a second elapsed time period equal to the time required for the another portion of the radio energy reflected by the near face to travel between the transmit antenna and the receive antenna and thereafter calculates the thickness of the coal rib being formed as a function of the difference between the first and second elapsed time periods.

Development of Polythiophene/Acrylonitrile-Butadiene Rubbers for Artificial Muscle

NASA Astrophysics Data System (ADS)

Thipdech, Pacharavalee; Sirivat, Anuvat

2007-03-01

Electroactive polymers (EAPs) can respond to the applied electrical field by an extension or a retraction. In this work, we are interested in using an elastomeric blend for electroactive applications, acrylonitirle-butadiene rubber (NBR) containing a conductive polymer (Poly(3-thiopheneacetic acid, PTAA); the latter can be synthesized via oxidative polymerization. FT-IR, Thermogravimetric analysis (TGA), ^1H-NMR, UV-visible spectroscopy, and SEM are used to characterize the conductive polymer. Electrorheological properties are measured and investigated in terms of acrylonitrile content, blending ratio, doping level, and temperature. Experiments are carried out under oscillatory shear mode and with applied electric field strength varying from 0 to 2 kV/mm. Dielectric properties, conductivities are measured and correlated with the storage modulus responses. The storage modulus sensitivity, δG'G'0of the pure rubbers increases with increasing electric field strength. They attain the maximum values of about 30% and become constant at electric strength at and above 1000 V/mm.

A Direct Ultrasound Irradiation Method Synthesis of Monodisperse ZnO Microspheres for Varistors Ceramics Application

NASA Astrophysics Data System (ADS)

Chen, Tao; Wang, Mao-Hua; Zhang, Han-Ping; Liu, Jin-Ran; Yao, Da-Chuan

2016-08-01

Monodisperse and uniform ZnO microspheres were synthesized via an ultrasound irradiation method. The microstructure and morphology of the as-prepared sample were characterized by x-ray powder diffraction, Fourier transformation infrared spectra and scanning electron microscopy. The results indicate that the size of ZnO microspheres was strongly affected by the Zn(NO3)2·6H2O. As the amount of the precursor increased, the diameters of the ZnO microspheres can be turned from ˜500 nm to ˜2 μm. The electrical properties of the varistors ceramics prepared from the as-obtained ZnO powders were investigated. The results show that the varistors ceramics made from ZnO with a size of ˜500 nm and sintered in air at 1150°C for 2 h possess a density of 5.50 g/cm3 corresponding to 95.1% of the theoretical density, with breakdown voltage of 280.9 V/mm and nonlinear coefficient of ˜61.3.

Magnetic sensor for high temperature using a laminate composite of magnetostrictive material and piezoelectric material

NASA Astrophysics Data System (ADS)

Ueno, Toshiyuki; Higuchi, Toshiro

2005-05-01

A high sensitive and heat-resistive magnetic sensor using a magnetostrictive/piezoelectric laminate composite is investigated. The sensing principle is based on the magnetostrictive- and piezoelectric effect, whereby a detected yoke displacement is transduced into a voltage on the piezoelectric materials. The sensor is intended to detect the displacement of a ferromagnetic object in a high temperature environment, where conventional magnetic sensors are not useful. Such applications include sensors in engine of automobile and machinery used in material processing. The sensor features combination of a laminate composite of magnetostrictive/piezoelectric materials with high Curie temperatures and an appropriate magnetic circuit to convert mechanical displacement to sensor voltages and suppress temperature fluctuation. This paper describes the sensing principle and shows experimental results using a composite of Terfenol-D and Lithium Niobate to assure high sensitivity of 50V/mm at bias gap of 0.1mm and a temperature operating range over 200 °C.

A programmable point-of-care device for external CSF drainage and monitoring.

PubMed

Simkins, Jeffrey R; Subbian, Vignesh; Beyette, Fred R

2014-01-01

This paper presents a prototype of a programmable cerebrospinal fluid (CSF) external drainage system that can accurately measure the dispensed fluid volume. It is based on using a miniature spectrophotometer to collect color data to inform drain rate and pressure monitoring. The prototype was machined with 1 μm dimensional accuracy. The current device can reliably monitor the total accumulated fluid volume, the drain rate, the programmed pressure, and the pressure read from the sensor. Device requirements, fabrication processes, and preliminary results with an experimental set-up are also presented.

Updating the Synchrotron Radiation Monitor at TLS

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

Kuo, C. H.; Hsu, S. Y.; Wang, C. J.

2007-01-19

The synchrotron radiation monitor provides useful information to support routine operation and physics experiments using the beam. Precisely knowing the profile of the beam helps to improve machine performance. The synchrotron radiation monitor at the Taiwan Light Source (TLS) was recently upgraded. The optics and modeling were improved to increase the accuracy of measurement in the small beam size. A high-performance IEEE-1394 digital CCD camera was used to improve the quality of images and extend the dynamic range of measurement. The image analysis is also improved. This report summarizes status and results.

Feasibility of vibration monitoring of small rotating machines for the environmental control and life support systems (ECLSS) of the NASA advanced space craft

NASA Technical Reports Server (NTRS)

Milner, G. Martin; Black, Mike; Hovenga, Mike; Mcclure, Paul; Miller, Patrice

1988-01-01

The application of vibration monitoring to the rotating machinery typical of ECLSS components in advanced NASA spacecraft was studied. It is found that the weighted summation of the accelerometer power spectrum is the most successful detection scheme for a majority of problem types. Other detection schemes studied included high-frequency demodulation, cepstrum, clustering, and amplitude processing.

Composite Failure Analysis Handbook. Volume 2. Technical Handbook/ Part 2. Atlas of Fractographs

DTIC Science & Technology

1992-02-01

ADDRESS( ES ) 8. PERFORMING ORGANIZATION Northrop Corporation REPORT NUMBER Aircraft Division One Northrop Avenue Hawthorne, California 90250-3277 9...SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS( ES ) 10. SPONSORING/ MONITORING Wright Laboratory (WL/MLSA) AGENCY REPORT NUMBER Materials Directorate...specimens of 0/90 laminates were tested in a Satec 25,000-lb capacity Universal test machine with the crosshead speed set at .001 in/min. Deflection

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

PubMed

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

2014-05-01

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

Application of Classification Algorithm of Machine Learning and Buffer Analysis in Torism Regional Planning

NASA Astrophysics Data System (ADS)

Zhang, T. H.; Ji, H. W.; Hu, Y.; Ye, Q.; Lin, Y.

2018-04-01

Remote Sensing (RS) and Geography Information System (GIS) technologies are widely used in ecological analysis and regional planning. With the advantages of large scale monitoring, combination of point and area, multiple time-phases and repeated observation, they are suitable for monitoring and analysis of environmental information in a large range. In this study, support vector machine (SVM) classification algorithm is used to monitor the land use and land cover change (LUCC), and then to perform the ecological evaluation for Chaohu lake tourism area quantitatively. The automatic classification and the quantitative spatial-temporal analysis for the Chaohu Lake basin are realized by the analysis of multi-temporal and multispectral satellite images, DEM data and slope information data. Furthermore, the ecological buffer zone analysis is also studied to set up the buffer width for each catchment area surrounding Chaohu Lake. The results of LUCC monitoring from 1992 to 2015 has shown obvious affections by human activities. Since the construction of the Chaohu Lake basin is in the crucial stage of the rapid development of urbanization, the application of RS and GIS technique can effectively provide scientific basis for land use planning, ecological management, environmental protection and tourism resources development in the Chaohu Lake Basin.

A machine learning approach to the accurate prediction of monitor units for a compact proton machine.

PubMed

Sun, Baozhou; Lam, Dao; Yang, Deshan; Grantham, Kevin; Zhang, Tiezhi; Mutic, Sasa; Zhao, Tianyu

2018-05-01

Clinical treatment planning systems for proton therapy currently do not calculate monitor units (MUs) in passive scatter proton therapy due to the complexity of the beam delivery systems. Physical phantom measurements are commonly employed to determine the field-specific output factors (OFs) but are often subject to limited machine time, measurement uncertainties and intensive labor. In this study, a machine learning-based approach was developed to predict output (cGy/MU) and derive MUs, incorporating the dependencies on gantry angle and field size for a single-room proton therapy system. The goal of this study was to develop a secondary check tool for OF measurements and eventually eliminate patient-specific OF measurements. The OFs of 1754 fields previously measured in a water phantom with calibrated ionization chambers and electrometers for patient-specific fields with various range and modulation width combinations for 23 options were included in this study. The training data sets for machine learning models in three different methods (Random Forest, XGBoost and Cubist) included 1431 (~81%) OFs. Ten-fold cross-validation was used to prevent "overfitting" and to validate each model. The remaining 323 (~19%) OFs were used to test the trained models. The difference between the measured and predicted values from machine learning models was analyzed. Model prediction accuracy was also compared with that of the semi-empirical model developed by Kooy (Phys. Med. Biol. 50, 2005). Additionally, gantry angle dependence of OFs was measured for three groups of options categorized on the selection of the second scatters. Field size dependence of OFs was investigated for the measurements with and without patient-specific apertures. All three machine learning methods showed higher accuracy than the semi-empirical model which shows considerably large discrepancy of up to 7.7% for the treatment fields with full range and full modulation width. The Cubist-based solution outperformed all other models (P < 0.001) with the mean absolute discrepancy of 0.62% and maximum discrepancy of 3.17% between the measured and predicted OFs. The OFs showed a small dependence on gantry angle for small and deep options while they were constant for large options. The OF decreased by 3%-4% as the field radius was reduced to 2.5 cm. Machine learning methods can be used to predict OF for double-scatter proton machines with greater prediction accuracy than the most popular semi-empirical prediction model. By incorporating the gantry angle dependence and field size dependence, the machine learning-based methods can be used for a sanity check of OF measurements and bears the potential to eliminate the time-consuming patient-specific OF measurements. © 2018 American Association of Physicists in Medicine.

Bridge Health Monitoring Using a Machine Learning Strategy

DOT National Transportation Integrated Search

2017-01-01

The goal of this project was to cast the SHM problem within a statistical pattern recognition framework. Techniques borrowed from speaker recognition, particularly speaker verification, were used as this discipline deals with problems very similar to...

A wearable computing platform for developing cloud-based machine learning models for health monitoring applications.

PubMed

Patel, Shyamal; McGinnis, Ryan S; Silva, Ikaro; DiCristofaro, Steve; Mahadevan, Nikhil; Jortberg, Elise; Franco, Jaime; Martin, Albert; Lust, Joseph; Raj, Milan; McGrane, Bryan; DePetrillo, Paolo; Aranyosi, A J; Ceruolo, Melissa; Pindado, Jesus; Ghaffari, Roozbeh

2016-08-01

Wearable sensors have the potential to enable clinical-grade ambulatory health monitoring outside the clinic. Technological advances have enabled development of devices that can measure vital signs with great precision and significant progress has been made towards extracting clinically meaningful information from these devices in research studies. However, translating measurement accuracies achieved in the controlled settings such as the lab and clinic to unconstrained environments such as the home remains a challenge. In this paper, we present a novel wearable computing platform for unobtrusive collection of labeled datasets and a new paradigm for continuous development, deployment and evaluation of machine learning models to ensure robust model performance as we transition from the lab to home. Using this system, we train activity classification models across two studies and track changes in model performance as we go from constrained to unconstrained settings.

Scoping Study of Machine Learning Techniques for Visualization and Analysis of Multi-source Data in Nuclear Safeguards

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

Cui, Yonggang

In implementation of nuclear safeguards, many different techniques are being used to monitor operation of nuclear facilities and safeguard nuclear materials, ranging from radiation detectors, flow monitors, video surveillance, satellite imagers, digital seals to open source search and reports of onsite inspections/verifications. Each technique measures one or more unique properties related to nuclear materials or operation processes. Because these data sets have no or loose correlations, it could be beneficial to analyze the data sets together to improve the effectiveness and efficiency of safeguards processes. Advanced visualization techniques and machine-learning based multi-modality analysis could be effective tools in such integratedmore » analysis. In this project, we will conduct a survey of existing visualization and analysis techniques for multi-source data and assess their potential values in nuclear safeguards.« less

Estimation of tool wear during CNC milling using neural network-based sensor fusion

NASA Astrophysics Data System (ADS)

Ghosh, N.; Ravi, Y. B.; Patra, A.; Mukhopadhyay, S.; Paul, S.; Mohanty, A. R.; Chattopadhyay, A. B.

2007-01-01

Cutting tool wear degrades the product quality in manufacturing processes. Monitoring tool wear value online is therefore needed to prevent degradation in machining quality. Unfortunately there is no direct way of measuring the tool wear online. Therefore one has to adopt an indirect method wherein the tool wear is estimated from several sensors measuring related process variables. In this work, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM). Features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindle current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge. Novel strategies such as, signal level segmentation for temporal registration, feature space filtering, outlier removal, and estimation space filtering have been proposed. The proposed approach has been validated by both laboratory and industrial implementations.

Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations.

PubMed

Zhang, Cunji; Yao, Xifan; Zhang, Jianming; Jin, Hong

2016-05-31

Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM) is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z) are acquired during milling operations, and the raw signals are de-noised by wavelet analysis. These features of de-noised signals are extracted in the time, frequency and time-frequency domains. The key features are selected based on Pearson's Correlation Coefficient (PCC). The Neuro-Fuzzy Network (NFN) is adopted to predict the tool wear and Remaining Useful Life (RUL). In comparison with Back Propagation Neural Network (BPNN) and Radial Basis Function Network (RBFN), the results show that the NFN has the best performance in the prediction of tool wear and RUL.

Detection of periods of food intake using Support Vector Machines.

PubMed

Lopez-Meyer, Paulo; Schuckers, Stephanie; Makeyev, Oleksandr; Sazonov, Edward

2010-01-01

Studies of obesity and eating disorders need objective tools of Monitoring of Ingestive Behavior (MIB) that can detect and characterize food intake. In this paper we describe detection of food intake by a Support Vector Machine classifier trained on time history of chews and swallows. The training was performed on data collected from 18 subjects in 72 experiments involving eating and other activities (for example, talking). The highest accuracy of detecting food intake (94%) was achieved in configuration where both chews and swallows were used as predictors. Using only swallowing as a predictor resulted in 80% accuracy. Experimental results suggest that these two predictors may be used for differentiation between periods of resting and food intake with a resolution of 30 seconds. Proposed methods may be utilized for development of an accurate, inexpensive, and non-intrusive methodology to objectively monitor food intake in free living conditions.

Automation and robotics technology for intelligent mining systems

NASA Technical Reports Server (NTRS)

Welsh, Jeffrey H.

1989-01-01

The U.S. Bureau of Mines is approaching the problems of accidents and efficiency in the mining industry through the application of automation and robotics to mining systems. This technology can increase safety by removing workers from hazardous areas of the mines or from performing hazardous tasks. The short-term goal of the Automation and Robotics program is to develop technology that can be implemented in the form of an autonomous mining machine using current continuous mining machine equipment. In the longer term, the goal is to conduct research that will lead to new intelligent mining systems that capitalize on the capabilities of robotics. The Bureau of Mines Automation and Robotics program has been structured to produce the technology required for the short- and long-term goals. The short-term goal of application of automation and robotics to an existing mining machine, resulting in autonomous operation, is expected to be accomplished within five years. Key technology elements required for an autonomous continuous mining machine are well underway and include machine navigation systems, coal-rock interface detectors, machine condition monitoring, and intelligent computer systems. The Bureau of Mines program is described, including status of key technology elements for an autonomous continuous mining machine, the program schedule, and future work. Although the program is directed toward underground mining, much of the technology being developed may have applications for space systems or mining on the Moon or other planets.

Micro-machined calorimetric biosensors

DOEpatents

Doktycz, Mitchel J.; Britton, Jr., Charles L.; Smith, Stephen F.; Oden, Patrick I.; Bryan, William L.; Moore, James A.; Thundat, Thomas G.; Warmack, Robert J.

2002-01-01

A method and apparatus are provided for detecting and monitoring micro-volumetric enthalpic changes caused by molecular reactions. Micro-machining techniques are used to create very small thermally isolated masses incorporating temperature-sensitive circuitry. The thermally isolated masses are provided with a molecular layer or coating, and the temperature-sensitive circuitry provides an indication when the molecules of the coating are involved in an enthalpic reaction. The thermally isolated masses may be provided singly or in arrays and, in the latter case, the molecular coatings may differ to provide qualitative and/or quantitative assays of a substance.

[Anesthesia simulators and training devices].

PubMed

Hartmannsgruber, M; Good, M; Carovano, R; Lampotang, S; Gravenstein, J S

1993-07-01

Simulators and training devices are used extensively by educators in 'high-tech' occupations, especially those requiring an understanding of complex systems and co-ordinated psychomotor skills. Because of advances in computer technology, anaesthetised patients can now be realistically simulated. This paper describes several training devices and a simulator currently being employed in the training of anaesthesia personnel at the University of Florida. This Gainesville Anesthesia Simulator (GAS) comprises a patient mannequin, anaesthesia gas machine, and a full set of normally operating monitoring instruments. The patient can spontaneously breathe, has audible heart and breath sounds, and palpable pulses. The mannequin contains a sophisticated lung model that consumes and eliminates gas according to physiological principles. Interconnected computers controlling the physical signs of the mannequin enable the presentation of a multitude of clinical signs. In addition, the anaesthesia machine, which is functionally intact, has hidden fault activators to challenge the user to correct equipment malfunctions. Concealed sensors monitor the users' actions and responses. A robust data acquisition and control system and a user-friendly scripting language for programming simulation scenarios are key features of GAS and make this system applicable for the training of both the beginning resident and the experienced practitioner. GAS enhances clinical education in anaesthesia by providing a non-threatening environment that fosters learning by doing. Exercises with the simulator are supported by sessions on a number of training devices. These present theoretical and practical interactive courses on the anaesthesia machine and on monitors. An extensive system, for example, introduces the student to the physics and clinical application of transoesophageal echocardiography.(ABSTRACT TRUNCATED AT 250 WORDS)

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

PubMed Central

Zhao, Rui; Yan, Ruqiang; Wang, Jinjiang; Mao, Kezhi

2017-01-01

In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks (LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods. PMID:28146106

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks.

PubMed

Zhao, Rui; Yan, Ruqiang; Wang, Jinjiang; Mao, Kezhi

2017-01-30

In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.

Functionality of empirical model-based predictive analytics for the early detection of hemodynamic instabilty.

PubMed

Summers, Richard L; Pipke, Matt; Wegerich, Stephan; Conkright, Gary; Isom, Kristen C

2014-01-01

Background. Monitoring cardiovascular hemodynamics in the modern clinical setting is a major challenge. Increasing amounts of physiologic data must be analyzed and interpreted in the context of the individual patient’s pathology and inherent biologic variability. Certain data-driven analytical methods are currently being explored for smart monitoring of data streams from patients as a first tier automated detection system for clinical deterioration. As a prelude to human clinical trials, an empirical multivariate machine learning method called Similarity-Based Modeling (“SBM”), was tested in an In Silico experiment using data generated with the aid of a detailed computer simulator of human physiology (Quantitative Circulatory Physiology or “QCP”) which contains complex control systems with realistic integrated feedback loops. Methods. SBM is a kernel-based, multivariate machine learning method that that uses monitored clinical information to generate an empirical model of a patient’s physiologic state. This platform allows for the use of predictive analytic techniques to identify early changes in a patient’s condition that are indicative of a state of deterioration or instability. The integrity of the technique was tested through an In Silico experiment using QCP in which the output of computer simulations of a slowly evolving cardiac tamponade resulted in progressive state of cardiovascular decompensation. Simulator outputs for the variables under consideration were generated at a 2-min data rate (0.083Hz) with the tamponade introduced at a point 420 minutes into the simulation sequence. The functionality of the SBM predictive analytics methodology to identify clinical deterioration was compared to the thresholds used by conventional monitoring methods. Results. The SBM modeling method was found to closely track the normal physiologic variation as simulated by QCP. With the slow development of the tamponade, the SBM model are seen to disagree while the simulated biosignals in the early stages of physiologic deterioration and while the variables are still within normal ranges. Thus, the SBM system was found to identify pathophysiologic conditions in a timeframe that would not have been detected in a usual clinical monitoring scenario. Conclusion. In this study the functionality of a multivariate machine learning predictive methodology that that incorporates commonly monitored clinical information was tested using a computer model of human physiology. SBM and predictive analytics were able to differentiate a state of decompensation while the monitored variables were still within normal clinical ranges. This finding suggests that the SBM could provide for early identification of a clinical deterioration using predictive analytic techniques. predictive analytics, hemodynamic, monitoring.

Spinoff 2013

NASA Technical Reports Server (NTRS)

2014-01-01

Topics covered include: Innovative Software Tools Measure Behavioral Alertness; Miniaturized, Portable Sensors Monitor Metabolic Health; Patient Simulators Train Emergency Caregivers; Solar Refrigerators Store Life-Saving Vaccines; Monitors Enable Medication Management in Patients' Homes; Handheld Diagnostic Device Delivers Quick Medical Readings; Experiments Result in Safer, Spin-Resistant Aircraft; Interfaces Visualize Data for Airline Safety, Efficiency; Data Mining Tools Make Flights Safer, More Efficient; NASA Standards Inform Comfortable Car Seats; Heat Shield Paves the Way for Commercial Space; Air Systems Provide Life Support to Miners; Coatings Preserve Metal, Stone, Tile, and Concrete; Robots Spur Software That Lends a Hand; Cloud-Based Data Sharing Connects Emergency Managers; Catalytic Converters Maintain Air Quality in Mines; NASA-Enhanced Water Bottles Filter Water on the Go; Brainwave Monitoring Software Improves Distracted Minds; Thermal Materials Protect Priceless, Personal Keepsakes; Home Air Purifiers Eradicate Harmful Pathogens; Thermal Materials Drive Professional Apparel Line; Radiant Barriers Save Energy in Buildings; Open Source Initiative Powers Real-Time Data Streams; Shuttle Engine Designs Revolutionize Solar Power; Procedure-Authoring Tool Improves Safety on Oil Rigs; Satellite Data Aid Monitoring of Nation's Forests; Mars Technologies Spawn Durable Wind Turbines; Programs Visualize Earth and Space for Interactive Education; Processor Units Reduce Satellite Construction Costs; Software Accelerates Computing Time for Complex Math; Simulation Tools Prevent Signal Interference on Spacecraft; Software Simplifies the Sharing of Numerical Models; Virtual Machine Language Controls Remote Devices; Micro-Accelerometers Monitor Equipment Health; Reactors Save Energy, Costs for Hydrogen Production; Cameras Monitor Spacecraft Integrity to Prevent Failures; Testing Devices Garner Data on Insulation Performance; Smart Sensors Gather Information for Machine Diagnostics; Oxygen Sensors Monitor Bioreactors and Ensure Health and Safety; Vision Algorithms Catch Defects in Screen Displays; and Deformable Mirrors Capture Exoplanet Data, Reflect Lasers.

Shaft instantaneous angular speed for blade vibration in rotating machine

NASA Astrophysics Data System (ADS)

Gubran, Ahmed A.; Sinha, Jyoti K.

2014-02-01

Reliable blade health monitoring (BHM) in rotating machines like steam turbines and gas turbines, is a topic of research since decades to reduce machine down time, maintenance costs and to maintain the overall safety. Transverse blade vibration is often transmitted to the shaft as torsional vibration. The shaft instantaneous angular speed (IAS) is nothing but the representing the shaft torsional vibration. Hence the shaft IAS has been extracted from the measured encoder data during machine run-up to understand the blade vibration and to explore the possibility of reliable assessment of blade health. A number of experiments on an experimental rig with a bladed disk were conducted with healthy but mistuned blades and with different faults simulation in the blades. The measured shaft torsional vibration shows a distinct difference between the healthy and the faulty blade conditions. Hence, the observations are useful for the BHM in future. The paper presents the experimental setup, simulation of blade faults, experiments conducted, observations and results.

Detection of Cutting Tool Wear using Statistical Analysis and Regression Model

NASA Astrophysics Data System (ADS)

Ghani, Jaharah A.; Rizal, Muhammad; Nuawi, Mohd Zaki; Haron, Che Hassan Che; Ramli, Rizauddin

2010-10-01

This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals. A statistical-based method called Integrated Kurtosis-based Algorithm for Z-Filter technique, called I-kaz was used for developing a regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out using a CNC turning machine Colchester Master Tornado T4 in dry cutting condition. A Kistler 9255B dynamometer was used to measure the cutting force signals, which were transmitted, analyzed, and displayed in the DasyLab software. Various force signals from machining operation were analyzed, and each has its own I-kaz 3D coefficient. This coefficient was examined and its relationship with flank wear lands (VB) was determined. A regression model was developed due to this relationship, and results of the regression model shows that the I-kaz 3D coefficient value decreases as tool wear increases. The result then is used for real time tool wear monitoring.

High-pressure microscopy for tracking dynamic properties of molecular machines.

PubMed

Nishiyama, Masayoshi

2017-12-01

High-pressure microscopy is one of the powerful techniques to visualize the effects of hydrostatic pressures on research targets. It could be used for monitoring the pressure-induced changes in the structure and function of molecular machines in vitro and in vivo. This review focuses on the dynamic properties of the assemblies and machines, analyzed by means of high-pressure microscopy measurement. We developed a high-pressure microscope that is optimized both for the best image formation and for the stability to hydrostatic pressure up to 150 MPa. Application of pressure could change polymerization and depolymerization processes of the microtubule cytoskeleton, suggesting a modulation of the intermolecular interaction between tubulin molecules. A novel motility assay demonstrated that high hydrostatic pressure induces counterclockwise (CCW) to clockwise (CW) reversals of the Escherichia coli flagellar motor. The present techniques could be extended to study how molecular machines in complicated systems respond to mechanical stimuli. Copyright © 2017 Elsevier B.V. All rights reserved.

Antibiotic Residues in Milk from Three Popular Kenyan Milk Vending Machines.

PubMed

Kosgey, Amos; Shitandi, Anakalo; Marion, Jason W

2018-05-01

Milk vending machines (MVMs) are growing in popularity in Kenya and worldwide. Milk vending machines dispense varying quantities of locally sourced, pasteurized milk. The Kenya Dairy Board has a regulatory framework, but surveillance is weak because of several factors. Milk vending machines' milk is not routinely screened for antibiotics, thereby increasing potential for antibiotic misuse. To investigate, a total of 80 milk samples from four commercial providers ( N = 25), street vendors ( N = 21), and three MVMs ( N = 34) were collected and screened in Eldoret, Kenya. Antibiotic residue surveillance occurred during December 2016 and January 2017 using Idexx SNAP ® tests for tetracyclines, sulfamethazine, beta-lactams, and gentamicin. Overall, 24% of MVM samples and 24% of street vendor samples were presumably positive for at least one antibiotic. No commercial samples were positive. Research into cost-effective screening methods and increased monitoring by food safety agencies are needed to uphold hazard analysis and critical control point for improving antibiotic stewardship throughout the Kenyan private dairy industry.

Condition monitoring of a prototype turbine. Description of the system and main results

NASA Astrophysics Data System (ADS)

Valero, C.; Egusquiza, E.; Presas, A.; Valentin, D.; Egusquiza, M.; Bossio, M.

2017-04-01

The fast change in new renewable energy is affecting directly the required operating range of hydropower plants. According to the present demand of electricity, it is necessary to generate different levels of power. Because of its ease to regulate and its huge storage capacity of energy, hydropower is the unique energy source that can adapt to the demand. Today, the required operating range of turbine units is expected to extend from part load to overload. These extreme operations points can cause several pressure pulsations, cavitation and vibrations in different parts of the machine. To determine the effects on the machine, vibration measurements are necessary in actual machines. Vibrations can be used for machinery protection and to identify problems in the machine (diagnosis). In this paper, some results obtained in a hydropower plant are presented. The variation of global levels and vibratory signatures has been analysed as function as gross head, transducer location and operating points.

Display-And-Alarm Circuit For Accelerometer

NASA Technical Reports Server (NTRS)

Bozeman, Richard J., Jr.

1995-01-01

Compact accelerometer assembly consists of commercial accelerometer retrofit with display-and-alarm circuit. Provides simple means for technician attending machine to monitor vibrations. Also simpifies automatic safety shutdown by providing local alarm or shutdown signal when vibration exceeds preset level.

Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy.

PubMed

Hueso, Miguel; Vellido, Alfredo; Montero, Nuria; Barbieri, Carlo; Ramos, Rosa; Angoso, Manuel; Cruzado, Josep Maria; Jonsson, Anders

2018-02-01

Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients.

Inductive System Health Monitoring

NASA Technical Reports Server (NTRS)

Iverson, David L.

2004-01-01

The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS uses nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. IMS is able to monitor the system by comparing real time operational data with these classes. We present a description of learning and monitoring method used by IMS and summarize some recent IMS results.

Extrudable Gel-Forming Bioabsorbable Hemostatic Tissue Adhesives for Traumatic and Burn Wounds

DTIC Science & Technology

1996-12-01

DAMD17-96-1-6241 6. AUTHOR(S) Shalaby W. Shalaby, Ph.D. 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS( ES ) B. PERFORMING ORGANIZATION Poly-Med...Incorporated REPORT NUMBER Anderson, South Carolina 29625 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS( ES ) 10. SPONSORING/MONITORING Commander AGENCY...the wound breaking strength. This was done using a Satec universal testing machine. Details of the animal protocol are given in Appendix A. The

Improvement of Computer Software Quality through Software Automated Tools.

DTIC Science & Technology

1986-08-30

information that are returned from the tools to the human user, and the forms in which these outputs are presented. Page 2 of 4 STAGE OF DEVELOPMENT: What... AUTOMIATED SOFTWARE TOOL MONITORING SYSTEM APPENDIX 2 2-1 INTRODUCTION This document and Automated Software Tool Monitoring Program (Appendix 1) are...t Output Output features provide links from the tool to both the human user and the target machine (where applicable). They describe the types

High-Speed Edge Trimming of CFRP and Online Monitoring of Performance of Router Tools Using Acoustic Emission

PubMed Central

Prakash, Rangasamy; Krishnaraj, Vijayan; Zitoune, Redouane; Sheikh-Ahmad, Jamal

2016-01-01

Carbon fiber reinforced polymers (CFRPs) have found wide-ranging applications in numerous industrial fields such as aerospace, automotive, and shipping industries due to their excellent mechanical properties that lead to enhanced functional performance. In this paper, an experimental study on edge trimming of CFRP was done with various cutting conditions and different geometry of tools such as helical-, fluted-, and burr-type tools. The investigation involves the measurement of cutting forces for the different machining conditions and its effect on the surface quality of the trimmed edges. The modern cutting tools (router tools or burr tools) selected for machining CFRPs, have complex geometries in cutting edges and surfaces, and therefore a traditional method of direct tool wear evaluation is not applicable. An acoustic emission (AE) sensing was employed for on-line monitoring of the performance of router tools to determine the relationship between AE signal and length of machining for different kinds of geometry of tools. The investigation showed that the router tool with a flat cutting edge has better performance by generating lower cutting force and better surface finish with no delamination on trimmed edges. The mathematical modeling for the prediction of cutting forces was also done using Artificial Neural Network and Regression Analysis. PMID:28773919

A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing.

PubMed

Oresko, Joseph J; Duschl, Heather; Cheng, Allen C

2010-05-01

Cardiovascular disease (CVD) is the single leading cause of global mortality and is projected to remain so. Cardiac arrhythmia is a very common type of CVD and may indicate an increased risk of stroke or sudden cardiac death. The ECG is the most widely adopted clinical tool to diagnose and assess the risk of arrhythmia. ECGs measure and display the electrical activity of the heart from the body surface. During patients' hospital visits, however, arrhythmias may not be detected on standard resting ECG machines, since the condition may not be present at that moment in time. While Holter-based portable monitoring solutions offer 24-48 h ECG recording, they lack the capability of providing any real-time feedback for the thousands of heart beats they record, which must be tediously analyzed offline. In this paper, we seek to unite the portability of Holter monitors and the real-time processing capability of state-of-the-art resting ECG machines to provide an assistive diagnosis solution using smartphones. Specifically, we developed two smartphone-based wearable CVD-detection platforms capable of performing real-time ECG acquisition and display, feature extraction, and beat classification. Furthermore, the same statistical summaries available on resting ECG machines are provided.

A Comparative Experimental Study on the Use of Machine Learning Approaches for Automated Valve Monitoring Based on Acoustic Emission Parameters

NASA Astrophysics Data System (ADS)

Ali, Salah M.; Hui, K. H.; Hee, L. M.; Salman Leong, M.; Al-Obaidi, M. A.; Ali, Y. H.; Abdelrhman, Ahmed M.

2018-03-01

Acoustic emission (AE) analysis has become a vital tool for initiating the maintenance tasks in many industries. However, the analysis process and interpretation has been found to be highly dependent on the experts. Therefore, an automated monitoring method would be required to reduce the cost and time consumed in the interpretation of AE signal. This paper investigates the application of two of the most common machine learning approaches namely artificial neural network (ANN) and support vector machine (SVM) to automate the diagnosis of valve faults in reciprocating compressor based on AE signal parameters. Since the accuracy is an essential factor in any automated diagnostic system, this paper also provides a comparative study based on predictive performance of ANN and SVM. AE parameters data was acquired from single stage reciprocating air compressor with different operational and valve conditions. ANN and SVM diagnosis models were subsequently devised by combining AE parameters of different conditions. Results demonstrate that ANN and SVM models have the same results in term of prediction accuracy. However, SVM model is recommended to automate diagnose the valve condition in due to the ability of handling a high number of input features with low sampling data sets.

Towards Intelligent Environments: An Augmented Reality–Brain–Machine Interface Operated with a See-Through Head-Mount Display

PubMed Central

Takano, Kouji; Hata, Naoki; Kansaku, Kenji

2011-01-01

The brain–machine interface (BMI) or brain–computer interface is a new interface technology that uses neurophysiological signals from the brain to control external machines or computers. This technology is expected to support daily activities, especially for persons with disabilities. To expand the range of activities enabled by this type of interface, here, we added augmented reality (AR) to a P300-based BMI. In this new system, we used a see-through head-mount display (HMD) to create control panels with flicker visual stimuli to support the user in areas close to controllable devices. When the attached camera detects an AR marker, the position and orientation of the marker are calculated, and the control panel for the pre-assigned appliance is created by the AR system and superimposed on the HMD. The participants were required to control system-compatible devices, and they successfully operated them without significant training. Online performance with the HMD was not different from that using an LCD monitor. Posterior and lateral (right or left) channel selections contributed to operation of the AR–BMI with both the HMD and LCD monitor. Our results indicate that AR–BMI systems operated with a see-through HMD may be useful in building advanced intelligent environments. PMID:21541307

Monitoring machining conditions by infrared images

NASA Astrophysics Data System (ADS)

Borelli, Joao E.; Gonzaga Trabasso, Luis; Gonzaga, Adilson; Coelho, Reginaldo T.

2001-03-01

During machining process the knowledge of the temperature is the most important factor in tool analysis. It allows to control main factors that influence tool use, life time and waste. The temperature in the contact area between the piece and the tool is resulting from the material removal in cutting operation and it is too difficult to be obtained because the tool and the work piece are in motion. One way to measure the temperature in this situation is detecting the infrared radiation. This work presents a new methodology for diagnosis and monitoring of machining processes with the use of infrared images. The infrared image provides a map in gray tones of the elements in the process: tool, work piece and chips. Each gray tone in the image corresponds to a certain temperature for each one of those materials and the relationship between the gray tones and the temperature is gotten by the previous of infrared camera calibration. The system developed in this work uses an infrared camera, a frame grabber board and a software composed of three modules. The first module makes the image acquisition and processing. The second module makes the feature image extraction and performs the feature vector. Finally, the third module uses fuzzy logic to evaluate the feature vector and supplies the tool state diagnostic as output.

Offline detection of broken rotor bars in AC induction motors

NASA Astrophysics Data System (ADS)

Powers, Craig Stephen

ABSTRACT. OFFLINE DETECTION OF BROKEN ROTOR BARS IN AC INDUCTION MOTORS. The detection of the broken rotor bar defect in medium- and large-sized AC induction machines is currently one of the most difficult tasks for the motor condition and monitoring industry. If a broken rotor bar defect goes undetected, it can cause a catastrophic failure of an expensive machine. If a broken rotor bar defect is falsely determined, it wastes time and money to physically tear down and inspect the machine only to find an incorrect diagnosis. Previous work in 2009 at Baker/SKF-USA in collaboration with the Korea University has developed a prototype instrument that has been highly successful in correctly detecting the broken rotor bar defect in ACIMs where other methods have failed. Dr. Sang Bin and his students at the Korea University have been using this prototype instrument to help the industry save money in the successful detection of the BRB defect. A review of the current state of motor conditioning and monitoring technology for detecting the broken rotor bar defect in ACIMs shows improved detection of this fault is still relevant. An analysis of previous work in the creation of this prototype instrument leads into the refactoring of the software and hardware into something more deployable, cost effective and commercially viable.

Structural health monitoring for bolt loosening via a non-invasive vibro-haptics human-machine cooperative interface

NASA Astrophysics Data System (ADS)

Pekedis, Mahmut; Mascerañas, David; Turan, Gursoy; Ercan, Emre; Farrar, Charles R.; Yildiz, Hasan

2015-08-01

For the last two decades, developments in damage detection algorithms have greatly increased the potential for autonomous decisions about structural health. However, we are still struggling to build autonomous tools that can match the ability of a human to detect and localize the quantity of damage in structures. Therefore, there is a growing interest in merging the computational and cognitive concepts to improve the solution of structural health monitoring (SHM). The main object of this research is to apply the human-machine cooperative approach on a tower structure to detect damage. The cooperation approach includes haptic tools to create an appropriate collaboration between SHM sensor networks, statistical compression techniques and humans. Damage simulation in the structure is conducted by releasing some of the bolt loads. Accelerometers are bonded to various locations of the tower members to acquire the dynamic response of the structure. The obtained accelerometer results are encoded in three different ways to represent them as a haptic stimulus for the human subjects. Then, the participants are subjected to each of these stimuli to detect the bolt loosened damage in the tower. Results obtained from the human-machine cooperation demonstrate that the human subjects were able to recognize the damage with an accuracy of 88 ± 20.21% and response time of 5.87 ± 2.33 s. As a result, it is concluded that the currently developed human-machine cooperation SHM may provide a useful framework to interact with abstract entities such as data from a sensor network.

Design of online monitoring and forecasting system for electrical equipment temperature of prefabricated substation based on WSN

NASA Astrophysics Data System (ADS)

Qi, Weiran; Miao, Hongxia; Miao, Xuejiao; Xiao, Xuanxuan; Yan, Kuo

2016-10-01

In order to ensure the safe and stable operation of the prefabricated substations, temperature sensing subsystem, temperature remote monitoring and management subsystem, forecast subsystem are designed in the paper. Wireless temperature sensing subsystem which consists of temperature sensor and MCU sends the electrical equipment temperature to the remote monitoring center by wireless sensor network. Remote monitoring center can realize the remote monitoring and prediction by monitoring and management subsystem and forecast subsystem. Real-time monitoring of power equipment temperature, history inquiry database, user management, password settings, etc., were achieved by monitoring and management subsystem. In temperature forecast subsystem, firstly, the chaos of the temperature data was verified and phase space is reconstructed. Then Support Vector Machine - Particle Swarm Optimization (SVM-PSO) was used to predict the temperature of the power equipment in prefabricated substations. The simulation results found that compared with the traditional methods SVM-PSO has higher prediction accuracy.

Classification of Variable Objects in Massive Sky Monitoring Surveys

NASA Astrophysics Data System (ADS)

Woźniak, Przemek; Wyrzykowski, Łukasz; Belokurov, Vasily

2012-03-01

The era of great sky surveys is upon us. Over the past decade we have seen rapid progress toward a continuous photometric record of the optical sky. Numerous sky surveys are discovering and monitoring variable objects by hundreds of thousands. Advances in detector, computing, and networking technology are driving applications of all shapes and sizes ranging from small all sky monitors, through networks of robotic telescopes of modest size, to big glass facilities equipped with giga-pixel CCD mosaics. The Large Synoptic Survey Telescope will be the first peta-scale astronomical survey [18]. It will expand the volume of the parameter space available to us by three orders of magnitude and explore the mutable heavens down to an unprecedented level of sensitivity. Proliferation of large, multidimensional astronomical data sets is stimulating the work on new methods and tools to handle the identification and classification challenge [3]. Given exponentially growing data rates, automated classification of variability types is quickly becoming a necessity. Taking humans out of the loop not only eliminates the subjective nature of visual classification, but is also an enabling factor for time-critical applications. Full automation is especially important for studies of explosive phenomena such as γ-ray bursts that require rapid follow-up observations before the event is over. While there is a general consensus that machine learning will provide a viable solution, the available algorithmic toolbox remains underutilized in astronomy by comparison with other fields such as genomics or market research. Part of the problem is the nature of astronomical data sets that tend to be dominated by a variety of irregularities. Not all algorithms can handle gracefully uneven time sampling, missing features, or sparsely populated high-dimensional spaces. More sophisticated algorithms and better tools available in standard software packages are required to facilitate the adoption of machine learning in astronomy. The goal of this chapter is to show a number of successful applications of state-of-the-art machine learning methodology to time-resolved astronomical data, illustrate what is possible today, and help identify areas for further research and development. After a brief comparison of the utility of various machine learning classifiers, the discussion focuses on support vector machines (SVM), neural nets, and self-organizing maps. Traditionally, to detect and classify transient variability astronomers used ad hoc scan statistics. These methods will remain important as feature extractors for input into generic machine learning algorithms. Experience shows that the performance of machine learning tools on astronomical data critically depends on the definition and quality of the input features, and that a considerable amount of preprocessing is required before standard algorithms can be applied. However, with continued investments of effort by a growing number of astro-informatics savvy computer scientists and astronomers the much-needed expertise and infrastructure are growing faster than ever.

Compliance with school nutrition policies in Ontario and Alberta: An assessment of secondary school vending machine data from the COMPASS study.

PubMed

Vine, Michelle M; Harrington, Daniel W; Butler, Alexandra; Patte, Karen; Godin, Katelyn; Leatherdale, Scott T

2017-04-20

We investigated the extent to which a sample of Ontario and Alberta secondary schools are being compliant with their respective provincial nutrition policies, in terms of the food and beverages sold in vending machines. This observational study used objective data on drinks and snacks from vending machines, collected over three years of the COMPASS study (2012/2013-2014/2015 school years). Drink (e.g., sugar-containing carbonated/non-carbonated soft drinks, sports drinks, etc.) and snack (e.g., chips, crackers, etc.) data were coded by number of units available, price, and location of vending machine(s) in the school. Univariate and bivariate analyses were undertaken using R version 3.2.3. In order to assess policy compliancy over time, nutritional information of products in vending machines was compared to nutrition standards set out in P/PM 150 in Ontario, and those set out in the Alberta Nutrition Guidelines for Children and Youth (2012) in Alberta. Results reveal a decline over time in the proportion of schools selling sugar-containing carbonated soft drinks (9% in 2012/2013 vs. 3% in 2014/2015), crackers (26% vs. 17%) and cake products (12% vs. 5%) in vending machines, and inconsistent changes in the proportion selling chips (53%, 67% and 65% over the three school years). Conversely, results highlight increases in the proportion of vending machines selling chocolate bars (7% vs. 13%) and cookies (21% vs. 40%) between the 2012/2013 and 2014/2015 school years. Nutritional standard policies were not adhered to in the majority of schools with respect to vending machines. There is a need for investment in formal monitoring and evaluation of school policies, and the provision of information and tools to support nutrition policy implementation.

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

PubMed

Newton, Richard; Wernisch, Lorenz

2017-08-11

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

Measured energy savings and performance of power-managed personal computers and monitors

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

Nordman, B.; Piette, M.A.; Kinney, K.

1996-08-01

Personal computers and monitors are estimated to use 14 billion kWh/year of electricity, with power management potentially saving $600 million/year by the year 2000. The effort to capture these savings is lead by the US Environmental Protection Agency`s Energy Star program, which specifies a 30W maximum demand for the computer and for the monitor when in a {open_quote}sleep{close_quote} or idle mode. In this paper the authors discuss measured energy use and estimated savings for power-managed (Energy Star compliant) PCs and monitors. They collected electricity use measurements of six power-managed PCs and monitors in their office and five from two othermore » research projects. The devices are diverse in machine type, use patterns, and context. The analysis method estimates the time spent in each system operating mode (off, low-, and full-power) and combines these with real power measurements to derive hours of use per mode, energy use, and energy savings. Three schedules are explored in the {open_quotes}As-operated,{close_quotes} {open_quotes}Standardized,{close_quotes} and `Maximum` savings estimates. Energy savings are established by comparing the measurements to a baseline with power management disabled. As-operated energy savings for the eleven PCs and monitors ranged from zero to 75 kWh/year. Under the standard operating schedule (on 20% of nights and weekends), the savings are about 200 kWh/year. An audit of power management features and configurations for several dozen Energy Star machines found only 11% of CPU`s fully enabled and about two thirds of monitors were successfully power managed. The highest priority for greater power management savings is to enable monitors, as opposed to CPU`s, since they are generally easier to configure, less likely to interfere with system operation, and have greater savings. The difficulties in properly configuring PCs and monitors is the largest current barrier to achieving the savings potential from power management.« less

Clinical data miner: an electronic case report form system with integrated data preprocessing and machine-learning libraries supporting clinical diagnostic model research.

PubMed

Installé, Arnaud Jf; Van den Bosch, Thierry; De Moor, Bart; Timmerman, Dirk

2014-10-20

Using machine-learning techniques, clinical diagnostic model research extracts diagnostic models from patient data. Traditionally, patient data are often collected using electronic Case Report Form (eCRF) systems, while mathematical software is used for analyzing these data using machine-learning techniques. Due to the lack of integration between eCRF systems and mathematical software, extracting diagnostic models is a complex, error-prone process. Moreover, due to the complexity of this process, it is usually only performed once, after a predetermined number of data points have been collected, without insight into the predictive performance of the resulting models. The objective of the study of Clinical Data Miner (CDM) software framework is to offer an eCRF system with integrated data preprocessing and machine-learning libraries, improving efficiency of the clinical diagnostic model research workflow, and to enable optimization of patient inclusion numbers through study performance monitoring. The CDM software framework was developed using a test-driven development (TDD) approach, to ensure high software quality. Architecturally, CDM's design is split over a number of modules, to ensure future extendability. The TDD approach has enabled us to deliver high software quality. CDM's eCRF Web interface is in active use by the studies of the International Endometrial Tumor Analysis consortium, with over 4000 enrolled patients, and more studies planned. Additionally, a derived user interface has been used in six separate interrater agreement studies. CDM's integrated data preprocessing and machine-learning libraries simplify some otherwise manual and error-prone steps in the clinical diagnostic model research workflow. Furthermore, CDM's libraries provide study coordinators with a method to monitor a study's predictive performance as patient inclusions increase. To our knowledge, CDM is the only eCRF system integrating data preprocessing and machine-learning libraries. This integration improves the efficiency of the clinical diagnostic model research workflow. Moreover, by simplifying the generation of learning curves, CDM enables study coordinators to assess more accurately when data collection can be terminated, resulting in better models or lower patient recruitment costs.

Development of a sterilizing in-place application for a production machine using Vaporized Hydrogen Peroxide.

PubMed

Mau, T; Hartmann, V; Burmeister, J; Langguth, P; Häusler, H

2004-01-01

The use of steam in sterilization processes is limited by the implementation of heat-sensitive components inside the machines to be sterilized. Alternative low-temperature sterilization methods need to be found and their suitability evaluated. Vaporized Hydrogen Peroxide (VHP) technology was adapted for a production machine consisting of highly sensitive pressure sensors and thermo-labile air tube systems. This new kind of "cold" surface sterilization, known from the Barrier Isolator Technology, is based on the controlled release of hydrogen peroxide vapour into sealed enclosures. A mobile VHP generator was used to generate the hydrogen peroxide vapour. The unit was combined with the air conduction system of the production machine. Terminal vacuum pumps were installed to distribute the gas within the production machine and for its elimination. In order to control the sterilization process, different physical process monitors were incorporated. The validation of the process was based on biological indicators (Geobacillus stearothermophilus). The Limited Spearman Karber Method (LSKM) was used to statistically evaluate the sterilization process. The results show that it is possible to sterilize surfaces in a complex tube system with the use of gaseous hydrogen peroxide. A total microbial reduction of 6 log units was reached.

Review of smoothing methods for enhancement of noisy data from heavy-duty LHD mining machines

NASA Astrophysics Data System (ADS)

Wodecki, Jacek; Michalak, Anna; Stefaniak, Paweł

2018-01-01

Appropriate analysis of data measured on heavy-duty mining machines is essential for processes monitoring, management and optimization. Some particular classes of machines, for example LHD (load-haul-dump) machines, hauling trucks, drilling/bolting machines etc. are characterized with cyclicity of operations. In those cases, identification of cycles and their segments or in other words - simply data segmentation is a key to evaluate their performance, which may be very useful from the management point of view, for example leading to introducing optimization to the process. However, in many cases such raw signals are contaminated with various artifacts, and in general are expected to be very noisy, which makes the segmentation task very difficult or even impossible. To deal with that problem, there is a need for efficient smoothing methods that will allow to retain informative trends in the signals while disregarding noises and other undesired non-deterministic components. In this paper authors present a review of various approaches to diagnostic data smoothing. Described methods can be used in a fast and efficient way, effectively cleaning the signals while preserving informative deterministic behaviour, that is a crucial to precise segmentation and other approaches to industrial data analysis.

Thermophysical Modeling of Novel Machinable Ceramic Materials

DTIC Science & Technology

2009-11-01

ORGANIZATION REPORT NUMBER Gonzalo Gutierrez. Departamento de Fisica , Facultad de Ciencias. L’niversidad de Chile. Casilla 653, Santiago, Chile...Walter Orellana, Departamento de Fisica , Universidad Andres Belio, Chile www.gnm.cl 9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES

Constraint monitoring in TOSCA

NASA Technical Reports Server (NTRS)

Beck, Howard

1992-01-01

The Job-Shop Scheduling Problem (JSSP) deals with the allocation of resources over time to factory operations. Allocations are subject to various constraints (e.g., production precedence relationships, factory capacity constraints, and limits on the allowable number of machine setups) which must be satisfied for a schedule to be valid. The identification of constraint violations and the monitoring of constraint threats plays a vital role in schedule generation in terms of the following: (1) directing the scheduling process; and (2) informing scheduling decisions. This paper describes a general mechanism for identifying constraint violations and monitoring threats to the satisfaction of constraints throughout schedule generation.

Analysis and Processing the 3D-Range-Image-Data for Robot Monitoring

NASA Astrophysics Data System (ADS)

Kohoutek, Tobias

2008-09-01

Industrial robots are commonly used for physically stressful jobs in complex environments. In any case collisions with heavy and high dynamic machines need to be prevented. For this reason the operational range has to be monitored precisely, reliably and meticulously. The advantage of the SwissRanger® SR-3000 is that it delivers intensity images and 3D-information simultaneously of the same scene that conveniently allows 3D-monitoring. Due to that fact automatic real time collision prevention within the robots working space is possible by working with 3D-coordinates.

Structural health monitoring of wind turbine blades

NASA Astrophysics Data System (ADS)

Rumsey, Mark A.; Paquette, Joshua A.

2008-03-01

As electric utility wind turbines increase in size, and correspondingly, increase in initial capital investment cost, there is an increasing need to monitor the health of the structure. Acquiring an early indication of structural or mechanical problems allows operators to better plan for maintenance, possibly operate the machine in a de-rated condition rather than taking the unit off-line, or in the case of an emergency, shut the machine down to avoid further damage. This paper describes several promising structural health monitoring (SHM) techniques that were recently exercised during a fatigue test of a 9 meter glass-epoxy and carbon-epoxy wind turbine blade. The SHM systems were implemented by teams from NASA Kennedy Space Center, Purdue University and Virginia Tech. A commercial off-the-shelf acoustic emission (AE) NDT system gathered blade AE data throughout the test. At a fatigue load cycle rate around 1.2 Hertz, and after more than 4,000,000 fatigue cycles, the blade was diagnostically and visibly failing at the out-board blade spar-cap termination point at 4.5 meters. For safety reasons, the test was stopped just before the blade completely failed. This paper provides an overview of the SHM and NDT system setups and some current test results.

An optical motion measuring system for laterally oscillated fatigue tests

NASA Technical Reports Server (NTRS)

Tripp, John S.; Tcheng, Ping; Murri, Gretchen B.; Sharpe, Scott

1993-01-01

This paper describes an optical system developed for materials testing laboratories at NASA Langley Research Center (LaRC) for high resolution monitoring of the transverse displacement and angular rotation of a test specimen installed in an axial-tension bending machine (ATB) during fatigue tests. It consists of a small laser, optics, a motorized mirror, three photodiodes, electronic detection and counting circuits, a data acquisition system, and a personal computer. A 3-inch by 5-inch rectangular plate attached to the upper grip of the test machine serves as a target base for the optical system. The personal computer automates the fatigue test procedure, controls data acquisition, performs data reduction, and provides user displays. The data acquisition system also monitors signals from up to 16 strain gages mounted on the test specimen. The motion measuring system is designed to continuously monitor and correlate the amplitude of the oscillatory motion with the strain gage signals in order to detect the onset of failure of the composite test specimen. A prototype system has been developed and tested which exceeds the design specifications of +/- 0.01 inch displacement accuracy, and +/- 0.25 deg angular accuracy at a sampling rate of 100 samples per second.

Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses.

PubMed

Mathur, Neha; Glesk, Ivan; Buis, Arjan

2016-10-01

Monitoring of the interface temperature at skin level in lower-limb prosthesis is notoriously complicated. This is due to the flexible nature of the interface liners used impeding the required consistent positioning of the temperature sensors during donning and doffing. Predicting the in-socket residual limb temperature by monitoring the temperature between socket and liner rather than skin and liner could be an important step in alleviating complaints on increased temperature and perspiration in prosthetic sockets. In this work, we propose to implement an adaptive neuro fuzzy inference strategy (ANFIS) to predict the in-socket residual limb temperature. ANFIS belongs to the family of fused neuro fuzzy system in which the fuzzy system is incorporated in a framework which is adaptive in nature. The proposed method is compared to our earlier work using Gaussian processes for machine learning. By comparing the predicted and actual data, results indicate that both the modeling techniques have comparable performance metrics and can be efficiently used for non-invasive temperature monitoring. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Gleaning knowledge from data in the intensive care unit.

PubMed

Pinsky, Michael R; Dubrawski, Artur

2014-09-15

It is often difficult to accurately predict when, why, and which patients develop shock, because signs of shock often occur late, once organ injury is already present. Three levels of aggregation of information can be used to aid the bedside clinician in this task: analysis of derived parameters of existing measured physiologic variables using simple bedside calculations (functional hemodynamic monitoring); prior physiologic data of similar subjects during periods of stability and disease to define quantitative metrics of level of severity; and libraries of responses across large and comprehensive collections of records of diverse subjects whose diagnosis, therapies, and course is already known to predict not only disease severity, but also the subsequent behavior of the subject if left untreated or treated with one of the many therapeutic options. The problem is in defining the minimal monitoring data set needed to initially identify those patients across all possible processes, and then specifically monitor their responses to targeted therapies known to improve outcome. To address these issues, multivariable models using machine learning data-driven classification techniques can be used to parsimoniously predict cardiorespiratory insufficiency. We briefly describe how these machine learning approaches are presently applied to address earlier identification of cardiorespiratory insufficiency and direct focused, patient-specific management.

Feasibility of a portable pedal exercise machine for reducing sedentary time in the workplace.

PubMed

Carr, Lucas J; Walaska, Kristen A; Marcus, Bess H

2012-05-01

Sedentary time is independently associated with an increased risk of metabolic disease. Worksite interventions designed to decrease sedentary time may serve to improve employee health. The purpose of this study is to test the feasibility and use of a pedal exercise machine for reducing workplace sedentary time. Eighteen full-time employees (mean age+SD 40.2+10.7 years; 88% female) working in sedentary occupations were recruited for participation. Demographic and anthropometric data were collected at baseline and 4 weeks. Participants were provided access to a pedal exercise machine for 4 weeks at work. Use of the device was measured objectively by exercise tracking software, which monitors pedal activity and provides the user real-time feedback (eg, speed, time, distance, calories). At 4 weeks, participants completed a feasibility questionnaire. Participants reported sitting 83% of their working days. Participants used the pedal machines an average of 12.2+6.6 out of a possible 20 working days and pedalled an average of 23.4+20.4 min each day used. Feasibility data indicate that participants found the machines feasible for use at work. Participants also reported sedentary time at work decreased due to the machine. Findings from this study suggest that this pedal machine may be a feasible tool for reducing sedentary time while at work. These findings hold public health significance due to the growing number of sedentary jobs in the USA and the potential of the device for use in large-scale worksite health programmes.

Stellite-based classification of tillage practices in the U.S.

NASA Astrophysics Data System (ADS)

Azzari, G.; Lobell, D. B.

2017-12-01

The number of applications based on Machine learning algorithms applied to satellite images has been increasing steadily in last few years. While in the context of agricultural monitoring these techiques are most commonly used for land cover type and crop classification, they also show a great potential for monitoring management practices. In this study, we present some preliminary results on classifying tillage practices in the U.S. midwest using Landsat 8 and Sentinel 2 data.

Diffraction measurements using the LHC Beam Loss Monitoring System

NASA Astrophysics Data System (ADS)

Kalliokoski, Matti

2017-03-01

The Beam Loss Monitoring (BLM) system of the Large Hadron Collider protects the machine from beam induced damage by measuring the absorbed dose rates of beam losses, and by triggering beam dump if the rates increase above the allowed threshold limits. Although the detection time scales are optimized for multi-turn losses, information on fast losses can be recovered from the loss data. In this paper, methods in using the BLM system in diffraction studies are discussed.

Cloud Computing: A model Construct of Real-Time Monitoring for Big Dataset Analytics Using Apache Spark

NASA Astrophysics Data System (ADS)

Alkasem, Ameen; Liu, Hongwei; Zuo, Decheng; Algarash, Basheer

2018-01-01

The volume of data being collected, analyzed, and stored has exploded in recent years, in particular in relation to the activity on the cloud computing. While large-scale data processing, analysis, storage, and platform model such as cloud computing were previously and currently are increasingly. Today, the major challenge is it address how to monitor and control these massive amounts of data and perform analysis in real-time at scale. The traditional methods and model systems are unable to cope with these quantities of data in real-time. Here we present a new methodology for constructing a model for optimizing the performance of real-time monitoring of big datasets, which includes a machine learning algorithms and Apache Spark Streaming to accomplish fine-grained fault diagnosis and repair of big dataset. As a case study, we use the failure of Virtual Machines (VMs) to start-up. The methodology proposition ensures that the most sensible action is carried out during the procedure of fine-grained monitoring and generates the highest efficacy and cost-saving fault repair through three construction control steps: (I) data collection; (II) analysis engine and (III) decision engine. We found that running this novel methodology can save a considerate amount of time compared to the Hadoop model, without sacrificing the classification accuracy or optimization of performance. The accuracy of the proposed method (92.13%) is an improvement on traditional approaches.

Crack identification for rigid pavements using unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Bahaddin Ersoz, Ahmet; Pekcan, Onur; Teke, Turker

2017-09-01

Pavement condition assessment is an essential piece of modern pavement management systems as rehabilitation strategies are planned based upon its outcomes. For proper evaluation of existing pavements, they must be continuously and effectively monitored using practical means. Conventionally, truck-based pavement monitoring systems have been in-use in assessing the remaining life of in-service pavements. Although such systems produce accurate results, their use can be expensive and data processing can be time consuming, which make them infeasible considering the demand for quick pavement evaluation. To overcome such problems, Unmanned Aerial Vehicles (UAVs) can be used as an alternative as they are relatively cheaper and easier-to-use. In this study, we propose a UAV based pavement crack identification system for monitoring rigid pavements’ existing conditions. The system consists of recently introduced image processing algorithms used together with conventional machine learning techniques, both of which are used to perform detection of cracks on rigid pavements’ surface and their classification. Through image processing, the distinct features of labelled crack bodies are first obtained from the UAV based images and then used for training of a Support Vector Machine (SVM) model. The performance of the developed SVM model was assessed with a field study performed along a rigid pavement exposed to low traffic and serious temperature changes. Available cracks were classified using the UAV based system and obtained results indicate it ensures a good alternative solution for pavement monitoring applications.

Wearable health monitoring using capacitive voltage-mode Human Body Communication.

PubMed

Maity, Shovan; Das, Debayan; Sen, Shreyas

2017-07-01

Rapid miniaturization and cost reduction of computing, along with the availability of wearable and implantable physiological sensors have led to the growth of human Body Area Network (BAN) formed by a network of such sensors and computing devices. One promising application of such a network is wearable health monitoring where the collected data from the sensors would be transmitted and analyzed to assess the health of a person. Typically, the devices in a BAN are connected through wireless (WBAN), which suffers from energy inefficiency due to the high-energy consumption of wireless transmission. Human Body Communication (HBC) uses the relatively low loss human body as the communication medium to connect these devices, promising order(s) of magnitude better energy-efficiency and built-in security compared to WBAN. In this paper, we demonstrate a health monitoring device and system built using Commercial-Off-The-Shelf (COTS) sensors and components, that can collect data from physiological sensors and transmit it through a) intra-body HBC to another device (hub) worn on the body or b) upload health data through HBC-based human-machine interaction to an HBC capable machine. The system design constraints and signal transfer characteristics for the implemented HBC-based wearable health monitoring system are measured and analyzed, showing reliable connectivity with >8× power savings compared to Bluetooth low-energy (BTLE).

A Method of High Throughput Monitoring Crop Physiology Using Chlorophyll Fluorescence and Multispectral Imaging.

PubMed

Wang, Heng; Qian, Xiangjie; Zhang, Lan; Xu, Sailong; Li, Haifeng; Xia, Xiaojian; Dai, Liankui; Xu, Liang; Yu, Jingquan; Liu, Xu

2018-01-01

We present a high throughput crop physiology condition monitoring system and corresponding monitoring method. The monitoring system can perform large-area chlorophyll fluorescence imaging and multispectral imaging. The monitoring method can determine the crop current condition continuously and non-destructively. We choose chlorophyll fluorescence parameters and relative reflectance of multispectral as the indicators of crop physiological status. Using tomato as experiment subject, the typical crop physiological stress, such as drought, nutrition deficiency and plant disease can be distinguished by the monitoring method. Furthermore, we have studied the correlation between the physiological indicators and the degree of stress. Besides realizing the continuous monitoring of crop physiology, the monitoring system and method provide the possibility of machine automatic diagnosis of the plant physiology. Highlights: A newly designed high throughput crop physiology monitoring system and the corresponding monitoring method are described in this study. Different types of stress can induce distinct fluorescence and spectral characteristics, which can be used to evaluate the physiological status of plants.

Failure prediction using machine learning and time series in optical network.

PubMed

Wang, Zhilong; Zhang, Min; Wang, Danshi; Song, Chuang; Liu, Min; Li, Jin; Lou, Liqi; Liu, Zhuo

2017-08-07

In this paper, we propose a performance monitoring and failure prediction method in optical networks based on machine learning. The primary algorithms of this method are the support vector machine (SVM) and double exponential smoothing (DES). With a focus on risk-aware models in optical networks, the proposed protection plan primarily investigates how to predict the risk of an equipment failure. To the best of our knowledge, this important problem has not yet been fully considered. Experimental results showed that the average prediction accuracy of our method was 95% when predicting the optical equipment failure state. This finding means that our method can forecast an equipment failure risk with high accuracy. Therefore, our proposed DES-SVM method can effectively improve traditional risk-aware models to protect services from possible failures and enhance the optical network stability.

A machine for the preliminary investigation of design features influencing the wear behaviour of knee prostheses.

PubMed

McGloughlin, T M; Murphy, D M; Kavanagh, A G

2004-01-01

Degradation of tibial inserts in vivo has been found to be multifactorial in nature, resulting in a complex interaction of many variables. A range of kinematic conditions occurs at the tibio-femoral interface, giving rise to various degrees of rolling and sliding at this interface. The movement of the tibio-femoral contact point may be an influential factor in the overall wear of ultra-high molecular weight polyethylene (UHMWPE) tibial components. As part of this study a three-station wear-test machine was designed and built to investigate the influence of rolling and sliding on the wear behaviour of specific design aspects of contemporary knee prostheses. Using the machine, it is possible to monitor the effect of various slide roll ratios on the performance of contemporary bearing designs from a geometrical and materials perspective.

Energy-efficient algorithm for classification of states of wireless sensor network using machine learning methods

NASA Astrophysics Data System (ADS)

Yuldashev, M. N.; Vlasov, A. I.; Novikov, A. N.

2018-05-01

This paper focuses on the development of an energy-efficient algorithm for classification of states of a wireless sensor network using machine learning methods. The proposed algorithm reduces energy consumption by: 1) elimination of monitoring of parameters that do not affect the state of the sensor network, 2) reduction of communication sessions over the network (the data are transmitted only if their values can affect the state of the sensor network). The studies of the proposed algorithm have shown that at classification accuracy close to 100%, the number of communication sessions can be reduced by 80%.

A Shellcode Detection Method Based on Full Native API Sequence and Support Vector Machine

NASA Astrophysics Data System (ADS)

Cheng, Yixuan; Fan, Wenqing; Huang, Wei; An, Jing

2017-09-01

Dynamic monitoring the behavior of a program is widely used to discriminate between benign program and malware. It is usually based on the dynamic characteristics of a program, such as API call sequence or API call frequency to judge. The key innovation of this paper is to consider the full Native API sequence and use the support vector machine to detect the shellcode. We also use the Markov chain to extract and digitize Native API sequence features. Our experimental results show that the method proposed in this paper has high accuracy and low detection rate.

A tool for modeling concurrent real-time computation

NASA Technical Reports Server (NTRS)

Sharma, D. D.; Huang, Shie-Rei; Bhatt, Rahul; Sridharan, N. S.

1990-01-01

Real-time computation is a significant area of research in general, and in AI in particular. The complexity of practical real-time problems demands use of knowledge-based problem solving techniques while satisfying real-time performance constraints. Since the demands of a complex real-time problem cannot be predicted (owing to the dynamic nature of the environment) powerful dynamic resource control techniques are needed to monitor and control the performance. A real-time computation model for a real-time tool, an implementation of the QP-Net simulator on a Symbolics machine, and an implementation on a Butterfly multiprocessor machine are briefly described.

Tool wear modeling using abductive networks

NASA Astrophysics Data System (ADS)

Masory, Oren

1992-09-01

A tool wear model based on Abductive Networks, which consists of a network of `polynomial' nodes, is described. The model relates the cutting parameters, components of the cutting force, and machining time to flank wear. Thus real time measurements of the cutting force can be used to monitor the machining process. The model is obtained by a training process in which the connectivity between the network's nodes and the polynomial coefficients of each node are determined by optimizing a performance criteria. Actual wear measurements of coated and uncoated carbide inserts were used for training and evaluating the established model.

Camera positioning and calibration techniques for integrating traffic surveillance video systems with machine-vision vehicle detection devices.

DOT National Transportation Integrated Search

2002-12-01

The Virginia Department of Transportation, like many other transportation agencies, has invested significantly in extensive closed circuit television (CCTV) systems to monitor freeways in urban areas. Although these systems have proven very effective...

40 CFR 63.5895 - How do I monitor and collect data to demonstrate continuous compliance?

Code of Federal Regulations, 2012 CFR

2012-07-01

... combination of an individual resin or gel coat, application method, and controls meets its applicable emission... pultrusion machines, you must record all times that wet area enclosures doors or covers are open and there is...

Computer Simulation Of An In-Process Surface Finish Sensor.

NASA Astrophysics Data System (ADS)

Rakels, Jan H.

1987-01-01

It is generally accepted, that optical methods are the most promising for the in-process measurement of surface finish. These methods have the advantages of being non-contacting and fast data acquisition. Furthermore, these optical instruments can be easily retrofitted on existing machine-tools. In the Micro-Engineering Centre at the University of Warwick, an optical sensor has been developed which can measure the rms roughness, slope and wavelength of turned and precision ground surfaces during machining. The operation of this device is based upon the Kirchhoff-Fresnel diffraction integral. Application of this theory to ideal turned and ground surfaces is straightforward, and indeed the calculated diffraction patterns are in close agreement with patterns produced by an actual optical instrument. Since it is mathematically difficult to introduce real machine-tool behaviour into the diffraction integral, a computer program has been devised, which simulates the operation of the optical sensor. The program produces a diffraction pattern as a graphical output. Comparison between computer generated and actual diffraction patterns of the same surfaces show a high correlation. The main aim of this program is to construct an atlas, which maps known machine-tool errors versus optical diffraction patterns. This atlas can then be used for machine-tool condition diagnostics. It has been found that optical monitoring is very sensitive to minor defects. Therefore machine-tool detoriation can be detected before it is detrimental.

Performance of Activity Classification Algorithms in Free-living Older Adults

PubMed Central

Sasaki, Jeffer Eidi; Hickey, Amanda; Staudenmayer, John; John, Dinesh; Kent, Jane A.; Freedson, Patty S.

2015-01-01

Purpose To compare activity type classification rates of machine learning algorithms trained on laboratory versus free-living accelerometer data in older adults. Methods Thirty-five older adults (21F and 14M ; 70.8 ± 4.9 y) performed selected activities in the laboratory while wearing three ActiGraph GT3X+ activity monitors (dominant hip, wrist, and ankle). Monitors were initialized to collect raw acceleration data at a sampling rate of 80 Hz. Fifteen of the participants also wore the GT3X+ in free-living settings and were directly observed for 2-3 hours. Time- and frequency- domain features from acceleration signals of each monitor were used to train Random Forest (RF) and Support Vector Machine (SVM) models to classify five activity types: sedentary, standing, household, locomotion, and recreational activities. All algorithms were trained on lab data (RFLab and SVMLab) and free-living data (RFFL and SVMFL) using 20 s signal sampling windows. Classification accuracy rates of both types of algorithms were tested on free-living data using a leave-one-out technique. Results Overall classification accuracy rates for the algorithms developed from lab data were between 49% (wrist) to 55% (ankle) for the SVMLab algorithms, and 49% (wrist) to 54% (ankle) for RFLab algorithms. The classification accuracy rates for SVMFL and RFFL algorithms ranged from 58% (wrist) to 69% (ankle) and from 61% (wrist) to 67% (ankle), respectively. Conclusion Our algorithms developed on free-living accelerometer data were more accurate in classifying activity type in free-living older adults than our algorithms developed on laboratory accelerometer data. Future studies should consider using free-living accelerometer data to train machine-learning algorithms in older adults. PMID:26673129

Performance of Activity Classification Algorithms in Free-Living Older Adults.

PubMed

Sasaki, Jeffer Eidi; Hickey, Amanda M; Staudenmayer, John W; John, Dinesh; Kent, Jane A; Freedson, Patty S

2016-05-01

The objective of this study is to compare activity type classification rates of machine learning algorithms trained on laboratory versus free-living accelerometer data in older adults. Thirty-five older adults (21 females and 14 males, 70.8 ± 4.9 yr) performed selected activities in the laboratory while wearing three ActiGraph GT3X+ activity monitors (in the dominant hip, wrist, and ankle; ActiGraph, LLC, Pensacola, FL). Monitors were initialized to collect raw acceleration data at a sampling rate of 80 Hz. Fifteen of the participants also wore GT3X+ in free-living settings and were directly observed for 2-3 h. Time- and frequency-domain features from acceleration signals of each monitor were used to train random forest (RF) and support vector machine (SVM) models to classify five activity types: sedentary, standing, household, locomotion, and recreational activities. All algorithms were trained on laboratory data (RFLab and SVMLab) and free-living data (RFFL and SVMFL) using 20-s signal sampling windows. Classification accuracy rates of both types of algorithms were tested on free-living data using a leave-one-out technique. Overall classification accuracy rates for the algorithms developed from laboratory data were between 49% (wrist) and 55% (ankle) for the SVMLab algorithms and 49% (wrist) to 54% (ankle) for the RFLab algorithms. The classification accuracy rates for SVMFL and RFFL algorithms ranged from 58% (wrist) to 69% (ankle) and from 61% (wrist) to 67% (ankle), respectively. Our algorithms developed on free-living accelerometer data were more accurate in classifying the activity type in free-living older adults than those on our algorithms developed on laboratory accelerometer data. Future studies should consider using free-living accelerometer data to train machine learning algorithms in older adults.

Performance Measurement, Visualization and Modeling of Parallel and Distributed Programs

NASA Technical Reports Server (NTRS)

Yan, Jerry C.; Sarukkai, Sekhar R.; Mehra, Pankaj; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

This paper presents a methodology for debugging the performance of message-passing programs on both tightly coupled and loosely coupled distributed-memory machines. The AIMS (Automated Instrumentation and Monitoring System) toolkit, a suite of software tools for measurement and analysis of performance, is introduced and its application illustrated using several benchmark programs drawn from the field of computational fluid dynamics. AIMS includes (i) Xinstrument, a powerful source-code instrumentor, which supports both Fortran77 and C as well as a number of different message-passing libraries including Intel's NX Thinking Machines' CMMD, and PVM; (ii) Monitor, a library of timestamping and trace -collection routines that run on supercomputers (such as Intel's iPSC/860, Delta, and Paragon and Thinking Machines' CM5) as well as on networks of workstations (including Convex Cluster and SparcStations connected by a LAN); (iii) Visualization Kernel, a trace-animation facility that supports source-code clickback, simultaneous visualization of computation and communication patterns, as well as analysis of data movements; (iv) Statistics Kernel, an advanced profiling facility, that associates a variety of performance data with various syntactic components of a parallel program; (v) Index Kernel, a diagnostic tool that helps pinpoint performance bottlenecks through the use of abstract indices; (vi) Modeling Kernel, a facility for automated modeling of message-passing programs that supports both simulation -based and analytical approaches to performance prediction and scalability analysis; (vii) Intrusion Compensator, a utility for recovering true performance from observed performance by removing the overheads of monitoring and their effects on the communication pattern of the program; and (viii) Compatibility Tools, that convert AIMS-generated traces into formats used by other performance-visualization tools, such as ParaGraph, Pablo, and certain AVS/Explorer modules.

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

Saleh, Z; Tang, X; Song, Y

Purpose: To investigate the long term stability and viability of using EPID-based daily output QA via in-house and vendor driven protocol, to replace conventional QA tools and improve QA efficiency. Methods: Two Varian TrueBeam machines (TB1&TB2) equipped with electronic portal imaging devices (EPID) were employed in this study. Both machines were calibrated per TG-51 and used clinically since Oct 2014. Daily output measurement for 6/15 MV beams were obtained using SunNuclear DailyQA3 device as part of morning QA. In addition, in-house protocol was implemented for EPID output measurement (10×10 cm fields, 100 MU, 100cm SID, output defined over an ROImore » of 2×2 cm around central axis). Moreover, the Varian Machine Performance Check (MPC) was used on both machines to measure machine output. The EPID and DailyQA3 based measurements of the relative machine output were compared and cross-correlated with monthly machine output as measured by an A12 Exradin 0.65cc Ion Chamber (IC) serving as ground truth. The results were correlated using Pearson test. Results: The correlations among DailyQA3, in-house EPID and Varian MPC output measurements, with the IC for 6/15 MV were similar for TB1 (0.83–0.95) and TB2 (0.55–0.67). The machine output for the 6/15MV beams on both machines showed a similar trend, namely an increase over time as indicated by all measurements, requiring a machine recalibration after 6 months. This drift is due to a known issue with pressurized monitor chamber which tends to leak over time. MPC failed occasionally but passed when repeated. Conclusion: The results indicate that the use of EPID for daily output measurements has the potential to become a viable and efficient tool for daily routine LINAC QA, thus eliminating weather (T,P) and human setup variability and increasing efficiency of the QA process.« less

Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer

PubMed Central

Pérez-López, Carlos; Català, Andreu; Moreno Arostegui, Joan M.; Cabestany, Joan; Bayés, Àngels; Alcaine, Sheila; Mestre, Berta; Prats, Anna; Crespo, M. Cruz; Counihan, Timothy J.; Browne, Patrick; Quinlan, Leo R.; ÓLaighin, Gearóid; Sweeney, Dean; Lewy, Hadas; Azuri, Joseph; Vainstein, Gabriel; Annicchiarico, Roberta; Costa, Alberto; Rodríguez-Molinero, Alejandro

2017-01-01

Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy. PMID:28199357

RELIABILITY TESTING OF AN ON-HARVESTER COTTON WEIGHT MEASUREMENT SYSTEM

USDA-ARS?s Scientific Manuscript database

A system for weighing seed cotton onboard stripper harvesters was developed and installed on several producer owned and operated machines. The weight measurement system provides critical information to producers when in the process of calibrating yield monitors or conducting on-farm research. The ...

Machine-vision-based roadway health monitoring and assessment : development of a shape-based pavement-crack-detection approach.

DOT National Transportation Integrated Search

2016-01-01

State highway agencies (SHAs) routinely employ semi-automated and automated image-based methods for network-level : pavement-cracking data collection, and there are different types of pavement-cracking data collected by SHAs for reporting and : manag...

Next Generation Parallelization Systems for Processing and Control of PDS Image Node Assets

NASA Astrophysics Data System (ADS)

Verma, R.

2017-06-01

We present next-generation parallelization tools to help Planetary Data System (PDS) Imaging Node (IMG) better monitor, process, and control changes to nearly 650 million file assets and over a dozen machines on which they are referenced or stored.

Synthesis of actual knowledge on machine-tool monitoring methods and equipment

NASA Astrophysics Data System (ADS)

Tanguy, J. C.

1988-06-01

Problems connected with the automatic supervision of production were studied. Many different automatic control devices are now able to identify defects in the tools, but the solutions proposed to detect optimal limits in the utilization of a tool are not satisfactory.

Request queues for interactive clients in a shared file system of a parallel computing system

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

Bent, John M.; Faibish, Sorin

Interactive requests are processed from users of log-in nodes. A metadata server node is provided for use in a file system shared by one or more interactive nodes and one or more batch nodes. The interactive nodes comprise interactive clients to execute interactive tasks and the batch nodes execute batch jobs for one or more batch clients. The metadata server node comprises a virtual machine monitor; an interactive client proxy to store metadata requests from the interactive clients in an interactive client queue; a batch client proxy to store metadata requests from the batch clients in a batch client queue;more » and a metadata server to store the metadata requests from the interactive client queue and the batch client queue in a metadata queue based on an allocation of resources by the virtual machine monitor. The metadata requests can be prioritized, for example, based on one or more of a predefined policy and predefined rules.« less

Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations

PubMed Central

Zhang, Cunji; Yao, Xifan; Zhang, Jianming; Jin, Hong

2016-01-01

Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM) is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z) are acquired during milling operations, and the raw signals are de-noised by wavelet analysis. These features of de-noised signals are extracted in the time, frequency and time–frequency domains. The key features are selected based on Pearson’s Correlation Coefficient (PCC). The Neuro-Fuzzy Network (NFN) is adopted to predict the tool wear and Remaining Useful Life (RUL). In comparison with Back Propagation Neural Network (BPNN) and Radial Basis Function Network (RBFN), the results show that the NFN has the best performance in the prediction of tool wear and RUL. PMID:27258277

Supervisory control and diagnostics system for the mirror fusion test facility: overview and status 1980

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

McGoldrick, P.R.

1981-01-01

The Mirror Fusion Test Facility (MFTF) is a complex facility requiring a highly-computerized Supervisory Control and Diagnostics System (SCDS) to monitor and provide control over ten subsystems; three of which require true process control. SCDS will provide physicists with a method of studying machine and plasma behavior by acquiring and processing up to four megabytes of plasma diagnostic information every five minutes. A high degree of availability and throughput is provided by a distributed computer system (nine 32-bit minicomputers on shared memory). Data, distributed across SCDS, is managed by a high-bandwidth Distributed Database Management System. The MFTF operators' control roommore » consoles use color television monitors with touch sensitive screens; this is a totally new approach. The method of handling deviations to normal machine operation and how the operator should be notified and assisted in the resolution of problems has been studied and a system designed.« less

RESTful M2M Gateway for Remote Wireless Monitoring for District Central Heating Networks

PubMed Central

Cheng, Bo; Wei, Zesan

2014-01-01

In recent years, the increased interest in energy conservation and environmental protection, combined with the development of modern communication and computer technology, has resulted in the replacement of distributed heating by central heating in urban areas. This paper proposes a Representational State Transfer (REST) Machine-to-Machine (M2M) gateway for wireless remote monitoring for a district central heating network. In particular, we focus on the resource-oriented RESTful M2M gateway architecture, and present an uniform devices abstraction approach based on Open Service Gateway Initiative (OSGi) technology, and implement the resource mapping mechanism between resource address mapping mechanism between RESTful resources and the physical sensor devices, and present the buffer queue combined with polling method to implement the data scheduling and Quality of Service (QoS) guarantee, and also give the RESTful M2M gateway open service Application Programming Interface (API) set. The performance has been measured and analyzed. Finally, the conclusions and future work are presented. PMID:25436650

RESTful M2M gateway for remote wireless monitoring for district central heating networks.

PubMed

Cheng, Bo; Wei, Zesan

2014-11-27

In recent years, the increased interest in energy conservation and environmental protection, combined with the development of modern communication and computer technology, has resulted in the replacement of distributed heating by central heating in urban areas. This paper proposes a Representational State Transfer (REST) Machine-to-Machine (M2M) gateway for wireless remote monitoring for a district central heating network. In particular, we focus on the resource-oriented RESTful M2M gateway architecture, and present an uniform devices abstraction approach based on Open Service Gateway Initiative (OSGi) technology, and implement the resource mapping mechanism between resource address mapping mechanism between RESTful resources and the physical sensor devices, and present the buffer queue combined with polling method to implement the data scheduling and Quality of Service (QoS) guarantee, and also give the RESTful M2M gateway open service Application Programming Interface (API) set. The performance has been measured and analyzed. Finally, the conclusions and future work are presented.

A new technique for rapid assessment of eutrophication status of coastal waters using a support vector machine

NASA Astrophysics Data System (ADS)

Kong, Xianyu; Che, Xiaowei; Su, Rongguo; Zhang, Chuansong; Yao, Qingzhen; Shi, Xiaoyong

2017-05-01

There is an urgent need to develop efficient evaluation tools that use easily measured variables to make rapid and timely eutrophication assessments, which are important for marine health management, and to implement eutrophication monitoring programs. In this study, an approach for rapidly assessing the eutrophication status of coastal waters with three easily measured parameters (turbidity, chlorophyll a and dissolved oxygen) was developed by the grid search (GS) optimized support vector machine (SVM), with trophic index TRIX classification results as the reference. With the optimized penalty parameter C =64 and the kernel parameter γ =1, the classification accuracy rates reached 89.3% for the training data, 88.3% for the cross-validation, and 88.5% for the validation dataset. Because the developed approach only used three easy-to-measure variables, its application could facilitate the rapid assessment of the eutrophication status of coastal waters, resulting in potential cost savings in marine monitoring programs and assisting in the provision of timely advice for marine management.

Process development and monitoring in stripping of a highly transparent polymeric paint with ns-pulsed fiber laser

NASA Astrophysics Data System (ADS)

Jasim, Halah A.; Demir, Ali Gökhan; Previtali, Barbara; Taha, Ziad A.

2017-08-01

Laser paint removal was studied with ns-pulsed fiber laser on the combination of 20 μm-thick, white polymeric paint and Al alloy substrate. The response of paint to single pulse ablation was evaluated to measure the ablated zone dimensions. With this information, the effect of overlap, number of passes and pulse repetition rate was evaluated to investigate machining depth. Optical emission spectroscopy was used to investigate the machining behaviour as well as to propose monitoring strategies. The results showed that despite the high transparency of the paint, complete paint removal can be achieved with reduced substrate damage (Sa = 1.3 μm). The emission spectroscopy can be used to identify removal completion as well as the reach of substrate material. The observations were also used to explain a paint removal mechanism based on thermal expansion of the paint and mechanical action provided by the plasma expansion from the substrate material.

FLUKA Monte Carlo simulations and benchmark measurements for the LHC beam loss monitors

NASA Astrophysics Data System (ADS)

Sarchiapone, L.; Brugger, M.; Dehning, B.; Kramer, D.; Stockner, M.; Vlachoudis, V.

2007-10-01

One of the crucial elements in terms of machine protection for CERN's Large Hadron Collider (LHC) is its beam loss monitoring (BLM) system. On-line loss measurements must prevent the superconducting magnets from quenching and protect the machine components from damages due to unforeseen critical beam losses. In order to ensure the BLM's design quality, in the final design phase of the LHC detailed FLUKA Monte Carlo simulations were performed for the betatron collimation insertion. In addition, benchmark measurements were carried out with LHC type BLMs installed at the CERN-EU high-energy Reference Field facility (CERF). This paper presents results of FLUKA calculations performed for BLMs installed in the collimation region, compares the results of the CERF measurement with FLUKA simulations and evaluates related uncertainties. This, together with the fact that the CERF source spectra at the respective BLM locations are comparable with those at the LHC, allows assessing the sensitivity of the performed LHC design studies.

Statistical analysis on the signals monitoring multiphase flow patterns in pipeline-riser system

NASA Astrophysics Data System (ADS)

Ye, Jing; Guo, Liejin

2013-07-01

The signals monitoring petroleum transmission pipeline in offshore oil industry usually contain abundant information about the multiphase flow on flow assurance which includes the avoidance of most undesirable flow pattern. Therefore, extracting reliable features form these signals to analyze is an alternative way to examine the potential risks to oil platform. This paper is focused on characterizing multiphase flow patterns in pipeline-riser system that is often appeared in offshore oil industry and finding an objective criterion to describe the transition of flow patterns. Statistical analysis on pressure signal at the riser top is proposed, instead of normal prediction method based on inlet and outlet flow conditions which could not be easily determined during most situations. Besides, machine learning method (least square supported vector machine) is also performed to classify automatically the different flow patterns. The experiment results from a small-scale loop show that the proposed method is effective for analyzing the multiphase flow pattern.

Fish swarm intelligent to optimize real time monitoring of chips drying using machine vision

NASA Astrophysics Data System (ADS)

Hendrawan, Y.; Hawa, L. C.; Damayanti, R.

2018-03-01

This study attempted to apply machine vision-based chips drying monitoring system which is able to optimise the drying process of cassava chips. The objective of this study is to propose fish swarm intelligent (FSI) optimization algorithms to find the most significant set of image features suitable for predicting water content of cassava chips during drying process using artificial neural network model (ANN). Feature selection entails choosing the feature subset that maximizes the prediction accuracy of ANN. Multi-Objective Optimization (MOO) was used in this study which consisted of prediction accuracy maximization and feature-subset size minimization. The results showed that the best feature subset i.e. grey mean, L(Lab) Mean, a(Lab) energy, red entropy, hue contrast, and grey homogeneity. The best feature subset has been tested successfully in ANN model to describe the relationship between image features and water content of cassava chips during drying process with R2 of real and predicted data was equal to 0.9.

Spatiotemporal modeling of node temperatures in supercomputers

DOE PAGES

Storlie, Curtis Byron; Reich, Brian James; Rust, William Newton; ...

2016-06-10

Los Alamos National Laboratory (LANL) is home to many large supercomputing clusters. These clusters require an enormous amount of power (~500-2000 kW each), and most of this energy is converted into heat. Thus, cooling the components of the supercomputer becomes a critical and expensive endeavor. Recently a project was initiated to investigate the effect that changes to the cooling system in a machine room had on three large machines that were housed there. Coupled with this goal was the aim to develop a general good-practice for characterizing the effect of cooling changes and monitoring machine node temperatures in this andmore » other machine rooms. This paper focuses on the statistical approach used to quantify the effect that several cooling changes to the room had on the temperatures of the individual nodes of the computers. The largest cluster in the room has 1,600 nodes that run a variety of jobs during general use. Since extremes temperatures are important, a Normal distribution plus generalized Pareto distribution for the upper tail is used to model the marginal distribution, along with a Gaussian process copula to account for spatio-temporal dependence. A Gaussian Markov random field (GMRF) model is used to model the spatial effects on the node temperatures as the cooling changes take place. This model is then used to assess the condition of the node temperatures after each change to the room. The analysis approach was used to uncover the cause of a problematic episode of overheating nodes on one of the supercomputing clusters. Lastly, this same approach can easily be applied to monitor and investigate cooling systems at other data centers, as well.« less

On line instrument systems for monitoring steam turbogenerators

NASA Astrophysics Data System (ADS)

Clapis, A.; Giorgetti, G.; Lapini, G. L.; Benanti, A.; Frigeri, C.; Gadda, E.; Mantino, E.

A computerized real time data acquisition and data processing for the diagnosis of malfunctioning of steam turbogenerator systems is described. Pressure, vibration and temperature measurements are continuously collected from standard or special sensors including startup or stop events. The architecture of the monitoring system is detailed. Examples of the graphics output are presented. It is shown that such a system allows accurate diagnosis and the possibility of creating a data bank to describe the dynamic characteristics of the machine park.

An Information Processing Analysis of the Function of Conceptual Understanding in the Learning of Arithmetic Procedures

DTIC Science & Technology

1988-08-01

J. R. (1986). Knowledge compilation: The general learning mechanism. In R. S . Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning...REPORTApproval for public release, 2b. DECLASSIFICATION I DOWNGRADING SCHEDULE d i s tri bution un limi ted 4. PERFORMING ORGANIZATION REPORT NUMBER( S ) S ...MONITORING ORGANIZATION REPORT NUMBER( S ) UPITT/LRDC/ONR/KUL-88-03 6a. NAME OF PERFORMING ORGANIZATION I6b. OFFICE SYMBOL 7a. NAME OF MONITORING

The Use of Thermal Spraying to Enhance the Bonding Characteristics of a Urethane Coated Propeller

DTIC Science & Technology

1999-05-03

NAME(S) AND ADDRESS( ES ) 8. PERFORMING ORGANIZATION REPORT NUMBER U.S. Naval Academy USNA Trident Scholar project report Annapolis, MD no. 265 (1999...9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS( ES ) 10. SPONSORING/MONITORING AGENCY REPORT NUMBER 11. SUPPLEMENTARY NOTES Accepted by the U.S...TEST: A 90-degree Peel Test was used to examine the bond strength of the specimens. A SATEC tensile test machine was used with a 2000 lb. load cell

Development and experimentation of an eye/brain/task testbed

NASA Technical Reports Server (NTRS)

Harrington, Nora; Villarreal, James

1987-01-01

The principal objective is to develop a laboratory testbed that will provide a unique capability to elicit, control, record, and analyze the relationship of operator task loading, operator eye movement, and operator brain wave data in a computer system environment. The ramifications of an integrated eye/brain monitor to the man machine interface are staggering. The success of such a system would benefit users of space and defense, paraplegics, and the monitoring of boring screens (nuclear power plants, air defense, etc.)

Voltage and Current Measurements in HIFX Diodes

DTIC Science & Technology

1977-08-01

Laboratories High- Intensity Flash X Ray Pacility. Sensitivities of these monitors have been measured to an accuracy of 10 percent or better by improved...importance of voltage (V) and current (1) monitors as a diagnostic tool for pulsed-electron beam machines such as High-Intensity Flash X Ray (HIFX) is well...15.4 2.7 109515. .2 7. - 3. 172.6 6.0 2.30 36. 4T. H. Martin, K. R. Prestwicht and D. L. Johnson, Summary of th e Hermes Flash X -Ray Program, Sandia

Research of a smart cutting tool based on MEMS strain gauge

NASA Astrophysics Data System (ADS)

Zhao, Y.; Zhao, Y. L.; Shao, YW; Hu, T. J.; Zhang, Q.; Ge, X. H.

2018-03-01

Cutting force is an important factor that affects machining accuracy, cutting vibration and tool wear. Machining condition monitoring by cutting force measurement is a key technology for intelligent manufacture. Current cutting force sensors exist problems of large volume, complex structure and poor compatibility in practical application, for these problems, a smart cutting tool is proposed in this paper for cutting force measurement. Commercial MEMS (Micro-Electro-Mechanical System) strain gauges with high sensitivity and small size are adopted as transducing element of the smart tool, and a structure optimized cutting tool is fabricated for MEMS strain gauge bonding. Static calibration results show that the developed smart cutting tool is able to measure cutting forces in both X and Y directions, and the cross-interference error is within 3%. Its general accuracy is 3.35% and 3.27% in X and Y directions, and sensitivity is 0.1 mV/N, which is very suitable for measuring small cutting forces in high speed and precision machining. The smart cutting tool is portable and reliable for practical application in CNC machine tool.

A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface

PubMed Central

Su, Yi; Routhu, Sudhamayee; Moon, Kee S.; Lee, Sung Q.; Youm, WooSub; Ozturk, Yusuf

2016-01-01

All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP) oscillation and stimulate the target area at the same time. PMID:27669264

Review of the energy check of an electron-only linear accelerator over a 6 year period: sensitivity of the technique to energy shift.

PubMed

Biggs, Peter J

2003-04-01

The calibration and monthly QA of an electron-only linear accelerator dedicated to intra-operative radiation therapy has been reviewed. Since this machine is calibrated prior to every procedure, there was no necessity to adjust the output calibration at any time except, perhaps, when the magnetron is changed, provided the machine output is reasonably stable. This gives a unique opportunity to study the dose output of the machine per monitor unit, variation in the timer error, flatness and symmetry of the beam and the energy check as a function of time. The results show that, although the dose per monitor unit varied within +/- 2%, the timer error within +/- 0.005 MU and the asymmetry within 1-2%, none of these parameters showed any systematic change with time. On the other hand, the energy check showed a linear drift with time for 6, 9, and 12 MeV (2.1, 3.5, and 2.5%, respectively, over 5 years), while at 15 and 18 MeV, the energy check was relatively constant. It is further shown that based on annual calibrations and RPC TLD checks, the energy of each beam is constant and that therefore the energy check is an exquisitely sensitive one. The consistency of the independent checks is demonstrated.

A novel spatter detection algorithm based on typical cellular neural network operations for laser beam welding processes

NASA Astrophysics Data System (ADS)

Nicolosi, L.; Abt, F.; Blug, A.; Heider, A.; Tetzlaff, R.; Höfler, H.

2012-01-01

Real-time monitoring of laser beam welding (LBW) has increasingly gained importance in several manufacturing processes ranging from automobile production to precision mechanics. In the latter, a novel algorithm for the real-time detection of spatters was implemented in a camera based on cellular neural networks. The latter can be connected to the optics of commercially available laser machines leading to real-time monitoring of LBW processes at rates up to 15 kHz. Such high monitoring rates allow the integration of other image evaluation tasks such as the detection of the full penetration hole for real-time control of process parameters.

Creating Situational Awareness in Spacecraft Operations with the Machine Learning Approach

NASA Astrophysics Data System (ADS)

Li, Z.

2016-09-01

This paper presents a machine learning approach for the situational awareness capability in spacecraft operations. There are two types of time dependent data patterns for spacecraft datasets: the absolute time pattern (ATP) and the relative time pattern (RTP). The machine learning captures the data patterns of the satellite datasets through the data training during the normal operations, which is represented by its time dependent trend. The data monitoring compares the values of the incoming data with the predictions of machine learning algorithm, which can detect any meaningful changes to a dataset above the noise level. If the difference between the value of incoming telemetry and the machine learning prediction are larger than the threshold defined by the standard deviation of datasets, it could indicate the potential anomaly that may need special attention. The application of the machine-learning approach to the Advanced Himawari Imager (AHI) on Japanese Himawari spacecraft series is presented, which has the same configuration as the Advanced Baseline Imager (ABI) on Geostationary Environment Operational Satellite (GOES) R series. The time dependent trends generated by the data-training algorithm are in excellent agreement with the datasets. The standard deviation in the time dependent trend provides a metric for measuring the data quality, which is particularly useful in evaluating the detector quality for both AHI and ABI with multiple detectors in each channel. The machine-learning approach creates the situational awareness capability, and enables engineers to handle the huge data volume that would have been impossible with the existing approach, and it leads to significant advances to more dynamic, proactive, and autonomous spacecraft operations.

SU-E-T-113: Dose Distribution Using Respiratory Signals and Machine Parameters During Treatment

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

Imae, T; Haga, A; Saotome, N

Purpose: Volumetric modulated arc therapy (VMAT) is a rotational intensity-modulated radiotherapy (IMRT) technique capable of acquiring projection images during treatment. Treatment plans for lung tumors using stereotactic body radiotherapy (SBRT) are calculated with planning computed tomography (CT) images only exhale phase. Purpose of this study is to evaluate dose distribution by reconstructing from only the data such as respiratory signals and machine parameters acquired during treatment. Methods: Phantom and three patients with lung tumor underwent CT scans for treatment planning. They were treated by VMAT while acquiring projection images to derive their respiratory signals and machine parameters including positions ofmore » multi leaf collimators, dose rates and integrated monitor units. The respiratory signals were divided into 4 and 10 phases and machine parameters were correlated with the divided respiratory signals based on the gantry angle. Dose distributions of each respiratory phase were calculated from plans which were reconstructed from the respiratory signals and the machine parameters during treatment. The doses at isocenter, maximum point and the centroid of target were evaluated. Results and Discussion: Dose distributions during treatment were calculated using the machine parameters and the respiratory signals detected from projection images. Maximum dose difference between plan and in treatment distribution was −1.8±0.4% at centroid of target and dose differences of evaluated points between 4 and 10 phases were no significant. Conclusion: The present method successfully evaluated dose distribution using respiratory signals and machine parameters during treatment. This method is feasible to verify the actual dose for moving target.« less

Runtime Verification of C Programs

NASA Technical Reports Server (NTRS)

Havelund, Klaus

2008-01-01

We present in this paper a framework, RMOR, for monitoring the execution of C programs against state machines, expressed in a textual (nongraphical) format in files separate from the program. The state machine language has been inspired by a graphical state machine language RCAT recently developed at the Jet Propulsion Laboratory, as an alternative to using Linear Temporal Logic (LTL) for requirements capture. Transitions between states are labeled with abstract event names and Boolean expressions over such. The abstract events are connected to code fragments using an aspect-oriented pointcut language similar to ASPECTJ's or ASPECTC's pointcut language. The system is implemented in the C analysis and transformation package CIL, and is programmed in OCAML, the implementation language of CIL. The work is closely related to the notion of stateful aspects within aspect-oriented programming, where pointcut languages are extended with temporal assertions over the execution trace.

Energy landscapes for a machine-learning prediction of patient discharge

NASA Astrophysics Data System (ADS)

Das, Ritankar; Wales, David J.

2016-06-01

The energy landscapes framework is applied to a configuration space generated by training the parameters of a neural network. In this study the input data consists of time series for a collection of vital signs monitored for hospital patients, and the outcomes are patient discharge or continued hospitalisation. Using machine learning as a predictive diagnostic tool to identify patterns in large quantities of electronic health record data in real time is a very attractive approach for supporting clinical decisions, which have the potential to improve patient outcomes and reduce waiting times for discharge. Here we report some preliminary analysis to show how machine learning might be applied. In particular, we visualize the fitting landscape in terms of locally optimal neural networks and the connections between them in parameter space. We anticipate that these results, and analogues of thermodynamic properties for molecular systems, may help in the future design of improved predictive tools.

Control and protection system for an installation for the combined production of electrical and thermal energy

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

Agazzone, U.; Ausiello, F.P.

1981-06-23

A power-generating installation comprises a plurality of modular power plants each comprised of an internal combustion engine connected to an electric machine. The electric machine is used to start the engine and thereafter operates as a generator supplying power to an electrical network common to all the modular plants. The installation has a control and protection system comprising a plurality of control modules each associated with a respective plant, and a central unit passing control signals to the modules to control starting and stopping of the individual power plants. Upon the detection of abnormal operation or failure of its associatedmore » power plant, each control module transmits an alarm signal back to the central unit which thereupon stops, or prevents the starting, of the corresponding power plant. Parameters monitored by each control module include generated current and inter-winding leakage current of the electric machine.« less

Integration of passive driver-assistance systems with on-board vehicle systems

NASA Astrophysics Data System (ADS)

Savchenko, V. V.; Poddubko, S. N.

2018-02-01

Implementation in OIAS such functions as driver’s state monitoring and high-precision calculation of the current navigation coordinates of the vehicle, modularity of the OIAS construction and the possible increase in the functionality through integration with other onboard systems has a promising development future. The development of intelligent transport systems and their components allows setting and solving fundamentally new tasks for the safety of human-to-machine transport systems, and the automatic analysis of heterogeneous information flows provides a synergistic effect. The analysis of cross-modal information exchange in human-machine transport systems, from uniform methodological points of view, will allow us, with an accuracy acceptable for solving applied problems, to form in real time an integrated assessment of the state of the basic components of the human-to-machine system and the dynamics in changing situation-centered environment, including the external environment, in their interrelations.

Study of five cell salvage machines in coronary artery surgery.

PubMed

Burman, J F; Westlake, A S; Davidson, S J; Rutherford, L C; Rayner, A S; Wright, A M; Morgan, C J; Pepper, J R

2002-06-01

We evaluated the effectiveness, ease of use and safety of five machines for blood salvage during coronary artery surgery. All were equally effective in concentrating red cells. We measured haemoglobin, packed cell volume, free haemoglobin, white cells, neutrophil elastase, platelets, thrombin-antithrombin complex (TAT), prothrombin activation peptide F1.2, fibrin degradation product (d-dimers), tissue plasminogen activator (tPA) and heparin in wound blood, in washed cell suspensions and in a unit of bank blood prepared for each patient. All machines were equally safe and easy to use and were equally effective in removing heparin and the physiological components measured. There were no adverse effects on patients. Clotting factors are severely depleted both in salvaged blood, even before washing, and in bank blood. Cell savers are a valuable adjunct to coronary artery surgery, but careful monitoring of coagulation is required when the volumes of either bank blood or salvaged blood are large.

Monitoring and Modeling Performance of Communications in Computational Grids

NASA Technical Reports Server (NTRS)

Frumkin, Michael A.; Le, Thuy T.

2003-01-01

Computational grids may include many machines located in a number of sites. For efficient use of the grid we need to have an ability to estimate the time it takes to communicate data between the machines. For dynamic distributed grids it is unrealistic to know exact parameters of the communication hardware and the current communication traffic and we should rely on a model of the network performance to estimate the message delivery time. Our approach to a construction of such a model is based on observation of the messages delivery time with various message sizes and time scales. We record these observations in a database and use them to build a model of the message delivery time. Our experiments show presence of multiple bands in the logarithm of the message delivery times. These multiple bands represent multiple paths messages travel between the grid machines and are incorporated in our multiband model.

Design and Implementation of a Hypothermic Machine Perfusion Device for Clinical Preservation of Isolated Organs

PubMed Central

Shen, Fei; Yan, Ruqiang

2017-01-01

The imbalance between limited organ supply and huge potential need has hindered the development of organ-graft techniques. In this paper a low-cost hypothermic machine perfusion (HMP) device is designed and implemented to maintain suitable preservation surroundings and extend the survival life of isolated organs. Four necessary elements (the machine perfusion, the physiological parameter monitoring, the thermostatic control and the oxygenation apparatus) involved in this HMP device are introduced. Especially during the thermostatic control process, a modified Bayes estimation, which introduces the concept of improvement factor, is realized to recognize and reduce the possible measurement errors resulting from sensor faults and noise interference. Also, a fuzzy-PID controller contributes to improve the accuracy and reduces the computational load using the DSP. Our experiments indicate that the reliability of the instrument meets the design requirements, thus being appealing for potential clinical preservation applications. PMID:28587173

Hybrid Cloud Computing Environment for EarthCube and Geoscience Community

NASA Astrophysics Data System (ADS)

Yang, C. P.; Qin, H.

2016-12-01

The NSF EarthCube Integration and Test Environment (ECITE) has built a hybrid cloud computing environment to provides cloud resources from private cloud environments by using cloud system software - OpenStack and Eucalyptus, and also manages public cloud - Amazon Web Service that allow resource synchronizing and bursting between private and public cloud. On ECITE hybrid cloud platform, EarthCube and geoscience community can deploy and manage the applications by using base virtual machine images or customized virtual machines, analyze big datasets by using virtual clusters, and real-time monitor the virtual resource usage on the cloud. Currently, a number of EarthCube projects have deployed or started migrating their projects to this platform, such as CHORDS, BCube, CINERGI, OntoSoft, and some other EarthCube building blocks. To accomplish the deployment or migration, administrator of ECITE hybrid cloud platform prepares the specific needs (e.g. images, port numbers, usable cloud capacity, etc.) of each project in advance base on the communications between ECITE and participant projects, and then the scientists or IT technicians in those projects launch one or multiple virtual machines, access the virtual machine(s) to set up computing environment if need be, and migrate their codes, documents or data without caring about the heterogeneity in structure and operations among different cloud platforms.

A Novel Vaping Machine Dedicated to Fully Controlling the Generation of E-Cigarette Emissions

PubMed Central

Soulet, Sébastien; Pairaud, Charly; Lalo, Hélène

2017-01-01

The accurate study of aerosol composition and nicotine release by electronic cigarettes is a major issue. In order to fully and correctly characterize aerosol, emission generation has to be completely mastered. This study describes an original vaping machine named Universal System for Analysis of Vaping (U-SAV), dedicated to vaping product study, enabling the control and real-time monitoring of applied flow rate and power. Repeatability and stability of the machine are demonstrated on flow rate, power regulation and e-liquid consumption. The emission protocol used to characterize the vaping machine is based on the AFNOR-XP-D90-300-3 standard (15 W power, 1 Ω atomizer resistance, 100 puffs collected per session, 1.1 L/min airflow rate). Each of the parameters has been verified with two standardized liquids by studying mass variations, power regulation and flow rate stability. U-SAV presents the required and necessary stability for the full control of emission generation. The U-SAV is recognised by the French association for standardization (AFNOR), European Committee for Standardization (CEN) and International Standards Organisation (ISO) as a vaping machine. It can be used to highlight the influence of the e-liquid composition, user behaviour and nature of the device, on the e-liquid consumption and aerosol composition. PMID:29036888

A Novel Vaping Machine Dedicated to Fully Controlling the Generation of E-Cigarette Emissions.

PubMed

Soulet, Sébastien; Pairaud, Charly; Lalo, Hélène

2017-10-14

The accurate study of aerosol composition and nicotine release by electronic cigarettes is a major issue. In order to fully and correctly characterize aerosol, emission generation has to be completely mastered. This study describes an original vaping machine named Universal System for Analysis of Vaping (U-SAV), dedicated to vaping product study, enabling the control and real-time monitoring of applied flow rate and power. Repeatability and stability of the machine are demonstrated on flow rate, power regulation and e-liquid consumption. The emission protocol used to characterize the vaping machine is based on the AFNOR-XP-D90-300-3 standard (15 W power, 1 Ω atomizer resistance, 100 puffs collected per session, 1.1 L/min airflow rate). Each of the parameters has been verified with two standardized liquids by studying mass variations, power regulation and flow rate stability. U-SAV presents the required and necessary stability for the full control of emission generation. The U-SAV is recognised by the French association for standardization (AFNOR), European Committee for Standardization (CEN) and International Standards Organisation (ISO) as a vaping machine. It can be used to highlight the influence of the e-liquid composition, user behaviour and nature of the device, on the e-liquid consumption and aerosol composition.

Flowmeter determines mix ratio for viscous adhesives

NASA Technical Reports Server (NTRS)

Lemons, C. R.

1967-01-01

Flowmeter determines mix ratio for continuous flow mixing machine used to produce an adhesive from a high viscosity resin and aliphatic amine hardener pumped through separate lines to a rotary blender. The flowmeter uses strain gages in the two flow paths and monitors their outputs with appropriate instrumentation.

Autonomous Energy Grids | Grid Modernization | NREL

Science.gov Websites

control themselves using advanced machine learning and simulation to create resilient, reliable, and affordable optimized energy systems. Current frameworks to monitor, control, and optimize large-scale energy of optimization theory, control theory, big data analytics, and complex system theory and modeling to

Coaxial CVD diamond detector for neutron diagnostics at ShenGuang III laser facility.

PubMed

Yu, Bo; Liu, Shenye; Chen, Zhongjing; Huang, Tianxuan; Jiang, Wei; Chen, Bolun; Pu, Yudong; Yan, Ji; Zhang, Xing; Song, Zifeng; Tang, Qi; Hou, Lifei; Ding, Yongkun; Zheng, Jian

2017-06-01

A coaxial, high performance diamond detector has been developed for neutron diagnostics of inertial confinement fusion at ShenGuangIII laser facility. A Φ10 mm × 1 mm "optical grade" chemical-vapor deposition diamond wafer is assembled in coaxial-designing housing, and the signal is linked to a SubMiniature A connector by the cathode cone. The coaxial diamond detector performs excellently for neutron measurement with the full width at half maximum of response time to be 444 ps for a 50 Ω measurement system. The average sensitivity is 0.677 μV ns/n for 14 MeV (DT fusion) neutrons at an electric field of 1000 V/mm, and the linear dynamic range is beyond three orders of magnitude. The ion temperature results fluctuate widely from the neutron time-of-flight scintillator detector results because of the short flight length. These characteristics of small size, large linear dynamic range, and insensitive to x-ray make the diamond detector suitable to measure the neutron yield, ion temperature, and neutron emission time.

Secondary electron emission from electrically charged fluorinated-ethylene-propylene Teflon for normal and non-normal electron incidence. M.S. Thesis; [spacecraft thermal coatings

NASA Technical Reports Server (NTRS)

Budd, P. A.

1981-01-01

The secondary electron emission coefficient was measured for a charged polymer (FEP-Teflon) with normally and obliquely incident primary electrons. Theories of secondary emission are reviewed and the experimental data is compared to these theories. Results were obtained for angles of incidence up to 60 deg in normal electric fields of 1500 V/mm. Additional measurements in the range from 50 to 70 deg were made in regions where the normal and tangential fields were approximately equal. The initial input angles and measured output point of the electron beam could be analyzed with computer simulations in order to determine the field within the chamber. When the field is known, the trajectories can be calculated for impacting electrons having various energies and angles of incidence. There was close agreement between the experimental results and the commonly assumed theoretical model in the presence of normal electric fields for angles of incidence up to 60 deg. High angle results obtained in the presence of tangential electric fields did not agree with the theoretical models.

Electro-migration of impurities in TlBr

NASA Astrophysics Data System (ADS)

Kim, Ki Hyun; Kim, Eunlim; Kim, H.; Tappero, R.; Bolotnikov, A. E.; Camarda, G. S.; Hossain, A.; Cirignano, L.; James, R. B.

2013-10-01

We observed the electro-migration of Cu, Ag, and Au impurities that exist in positive-ion states in TlBr detectors under electric field strengths typically used for device operation. The migration occurred predominantly through bulk- and specific-channels, which are presumed to be a network of grain and sub-grain boundaries. The electro-migration velocity of Cu, Ag, and Au in TlBr is about 4-8 × 10-8 cm/s at room temperature under an electric field of 500-800 V/mm. The instability and polarization effects of TlBr detectors might well be correlated with the electro-migration of residual impurities in TlBr, which alters the internal electric field over time. The effect may also have been due to migration of the electrode material itself, which would allow for the possibility of a better choice for contact material and for depositing an effective diffusion barrier. From our findings, we suggest that applying our electro-migration technique for purifying material is a promising new way to remove electrically active metallic impurities in TlBr crystals, as well as other materials.

Electrically responsive materials based on polycarbazole/sodium alginate hydrogel blend for soft and flexible actuator application.

PubMed

Sangwan, Watchara; Petcharoen, Karat; Paradee, Nophawan; Lerdwijitjarud, Wanchai; Sirivat, Anuvat

2016-10-20

The electromechanical properties, namely the storage modulus sensitivity and bending, of sodium alginate (SA) hydrogels and polycarbazole/sodium alginate (PCB/SA) hydrogel blends under applied electric field was investigated. The electromechanical properties of the pristine SA were studied under effects of crosslinking types and SA molecular weights, whereas the PCB/SA hydrogel blends were studied under the effect of PCB concentrations. The storage modulus sensitivity and bending of the pristine SA as crosslinked by the ionic crosslinking agent were found to be higher than those of the covalent crosslinking. The storage modulus sensitivity and deflection of the SA increased monotonically with increasing molecular weight. The highest electromechanical response of the PCB/SA hydrogel blends was obtained from the blend with 0.10% v/v PCB as it provided surprisingly the highest ever storage modulus sensitivity, (G'-G'0)/G'0 where G'0 and G' are the storage modulus without and with applied electric field, respectively, at 18.5 under applied electric field strength of 800V/mm. Copyright © 2016 Elsevier Ltd. All rights reserved.

Study of a non-equilibrium plasma pinch with application for microwave generation

NASA Astrophysics Data System (ADS)

Al Agry, Ahmad Farouk

The Non-Equilibrium Plasma Pinch (NEPP), also known as the Dense Plasma Focus (DPF) is well known as a source of energetic ions, relativistic electrons and neutrons as well as electromagnetic radiation extending from the infrared to X-ray. In this dissertation, the operation of a 15 kJ, Mather type, NEPP machine is studied in detail. A large number of experiments are carried out to tune the machine parameters for best performance using helium and hydrogen as filling gases. The NEPP machine is modified to be able to extract the copious number of electrons generated at the pinch. A hollow anode with small hole at the flat end, and a mock magnetron without biasing magnetic field are built. The electrons generated at the pinch are very difficult to capture, therefore a novel device is built to capture and transport the electrons from the pinch to the magnetron. The novel cup-rod-needle device successfully serves the purpose to capture and transport electrons to monitor the pinch current. Further, the device has the potential to field emit charges from its needle end acting as a pulsed electron source for other devices such as the magnetron. Diagnostics tools are designed, modeled, built, calibrated, and implemented in the machine to measure the pinch dynamics. A novel, UNLV patented electromagnetic dot sensors are successfully calibrated, and implemented in the machine. A new calibration technique is developed and test stands designed and built to measure the dot's ability to track the impetus signal over its dynamic range starting and ending in the noise region. The patented EM-dot sensor shows superior performance over traditional electromagnetic sensors, such as Rogowski coils. On the other hand, the cup-rod structure, when grounded on the rod side, serves as a diagnostic tool to monitor the pinch current by sampling the actual current, a quantity that has been always very challenging to measure without perturbing the pinch. To the best of our knowledge, this method of measuring the pinch current is unique and has never been done before. Agreement with other models is shown. The operation of the NEPP machine with the hole in the center of the anode and the magnetron connected including the cup-rod structure is examined against the NEPP machine signature with solid anode. Both cases showed excellent agreement. This suggests that the existence of the hole and the diagnostic tool inside the anode have negligible effects on the pinch.

Application of an onboard processor to the OAO C spacecraft

NASA Technical Reports Server (NTRS)

Stewart, W. N.; Hartenstein, R. G.; Trevathan, C.

1972-01-01

The design of a stored program computer for spacecraft use and its application on the fourth Orbiting Astronomical Observatory (OAO) is reported. The computer is a medium scale, parallel machine with a memory capacity of 16384 words of 18 bits each. It possesses a comprehensive instruction repertoire and operates on 45 W of power (including the dc-to-dc converter). The machine operates at a 500-kHz rate and executes an add instruction in 10 microseconds. Its primary functions on OAO C will be auxiliary command storage, spacecraft monitoring and malfunction reporting, data compression and status summary, and possible performance of emergency corrective action for certain anomalous situations.

FPGA-based fused smart-sensor for tool-wear area quantitative estimation in CNC machine inserts.

PubMed

Trejo-Hernandez, Miguel; Osornio-Rios, Roque Alfredo; de Jesus Romero-Troncoso, Rene; Rodriguez-Donate, Carlos; Dominguez-Gonzalez, Aurelio; Herrera-Ruiz, Gilberto

2010-01-01

Manufacturing processes are of great relevance nowadays, when there is a constant claim for better productivity with high quality at low cost. The contribution of this work is the development of a fused smart-sensor, based on FPGA to improve the online quantitative estimation of flank-wear area in CNC machine inserts from the information provided by two primary sensors: the monitoring current output of a servoamplifier, and a 3-axis accelerometer. Results from experimentation show that the fusion of both parameters makes it possible to obtain three times better accuracy when compared with the accuracy obtained from current and vibration signals, individually used.

Design of Remote Monitoring System of Irrigation based on GSM and ZigBee Technology

NASA Astrophysics Data System (ADS)

Xiao xi, Zheng; Fang, Zhao; Shuaifei, Shao

2018-03-01

To solve the problems of low level of irrigation and waste of water resources, a remote monitoring system for farmland irrigation based on GSM communication technology and ZigBee technology was designed. The system is composed of sensors, GSM communication module, ZigBee module, host computer, valve and so on. The system detects and closes the pump and the electromagnetic valve according to the need of the system, and transmits the monitoring information to the host computer or the user’s Mobile phone through the GSM communication network. Experiments show that the system has low power consumption, friendly man-machine interface, convenient and simple. It can monitor agricultural environment remotely and control related irrigation equipment at any time and place, and can better meet the needs of remote monitoring of farmland irrigation.

Micro-patterned graphene-based sensing skins for human physiological monitoring

NASA Astrophysics Data System (ADS)

Wang, Long; Loh, Kenneth J.; Chiang, Wei-Hung; Manna, Kausik

2018-03-01

Ultrathin, flexible, conformal, and skin-like electronic transducers are emerging as promising candidates for noninvasive and nonintrusive human health monitoring. In this work, a wearable sensing membrane is developed by patterning a graphene-based solution onto ultrathin medical tape, which can then be attached to the skin for monitoring human physiological parameters and physical activity. Here, the sensor is validated for monitoring finger bending/movements and for recognizing hand motion patterns, thereby demonstrating its future potential for evaluating athletic performance, physical therapy, and designing next-generation human-machine interfaces. Furthermore, this study also quantifies the sensor’s ability to monitor eye blinking and radial pulse in real-time, which can find broader applications for the healthcare sector. Overall, the printed graphene-based sensing skin is highly conformable, flexible, lightweight, nonintrusive, mechanically robust, and is characterized by high strain sensitivity.

Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques

PubMed Central

Markides, Andreas; Skillman, Severin; Acton, Sahr Thomas; Elsaleh, Tarek; Hassanpour, Masoud; Ahrabian, Alireza; Kenny, Mark; Klein, Stuart; Rostill, Helen; Nilforooshan, Ramin; Barnaghi, Payam

2018-01-01

The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients’ routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%. PMID:29723236

Dynamic virtual machine allocation policy in cloud computing complying with service level agreement using CloudSim

NASA Astrophysics Data System (ADS)

Aneri, Parikh; Sumathy, S.

2017-11-01

Cloud computing provides services over the internet and provides application resources and data to the users based on their demand. Base of the Cloud Computing is consumer provider model. Cloud provider provides resources which consumer can access using cloud computing model in order to build their application based on their demand. Cloud data center is a bulk of resources on shared pool architecture for cloud user to access. Virtualization is the heart of the Cloud computing model, it provides virtual machine as per application specific configuration and those applications are free to choose their own configuration. On one hand, there is huge number of resources and on other hand it has to serve huge number of requests effectively. Therefore, resource allocation policy and scheduling policy play very important role in allocation and managing resources in this cloud computing model. This paper proposes the load balancing policy using Hungarian algorithm. Hungarian Algorithm provides dynamic load balancing policy with a monitor component. Monitor component helps to increase cloud resource utilization by managing the Hungarian algorithm by monitoring its state and altering its state based on artificial intelligent. CloudSim used in this proposal is an extensible toolkit and it simulates cloud computing environment.

Streamline integration as a method for two-dimensional elliptic grid generation

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

Wiesenberger, M., E-mail: Matthias.Wiesenberger@uibk.ac.at; Held, M.; Einkemmer, L.

We propose a new numerical algorithm to construct a structured numerical elliptic grid of a doubly connected domain. Our method is applicable to domains with boundaries defined by two contour lines of a two-dimensional function. Furthermore, we can adapt any analytically given boundary aligned structured grid, which specifically includes polar and Cartesian grids. The resulting coordinate lines are orthogonal to the boundary. Grid points as well as the elements of the Jacobian matrix can be computed efficiently and up to machine precision. In the simplest case we construct conformal grids, yet with the help of weight functions and monitor metricsmore » we can control the distribution of cells across the domain. Our algorithm is parallelizable and easy to implement with elementary numerical methods. We assess the quality of grids by considering both the distribution of cell sizes and the accuracy of the solution to elliptic problems. Among the tested grids these key properties are best fulfilled by the grid constructed with the monitor metric approach. - Graphical abstract: - Highlights: • Construct structured, elliptic numerical grids with elementary numerical methods. • Align coordinate lines with or make them orthogonal to the domain boundary. • Compute grid points and metric elements up to machine precision. • Control cell distribution by adaption functions or monitor metrics.« less

Automated electronic monitoring of circuit pressures during continuous renal replacement therapy: a technical report.

PubMed

Zhang, Ling; Baldwin, Ian; Zhu, Guijun; Tanaka, Aiko; Bellomo, Rinaldo

2015-03-01

Automated electronic monitoring and analysis of circuit pressures during continuous renal replacement therapy (CRRT) has the potential to predict failure and allow intervention to optimise function. Current CRRT machines can measure and store pressure readings for downloading into databases and for analysis. We developed a procedure to obtain such data at intervals of 1 minute and analyse them using the Prismaflex CRRT machine, and we present an example of such analysis. We obtained data on pressures obtained at intervals of 1 minute in a patient with acute kidney injury and sepsis treated with continuous haemofiltration at 2 L/hour of ultrafiltration and a blood flow of 200 mL/minute. Data analysis identified progressive increases in transmembrane pressure (TMP) and prefilter pressure (PFP) from time 0 until 33 hours or clotting. TMP increased from 104 mmHg to 313 mmHg and PFP increased from from 131 mmHg to 185 mmHg. Effluent pressure showed a progressive increase in the negative pressure applied to achieve ultrafiltration from 0 mmHg to -168 mmHg. The inflection point for such changes was also identified. Blood pathway pressures for access and return remained unchanged throughout. Automated electronic monitoring of circuit pressure during CRRT is possible and provides useful information on the evolution of circuit clotting.

Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors

PubMed Central

Liu, Kai-Chun; Chan, Chia-Tai

2017-01-01

The proportion of the aging population is rapidly increasing around the world, which will cause stress on society and healthcare systems. In recent years, advances in technology have created new opportunities for automatic activities of daily living (ADL) monitoring to improve the quality of life and provide adequate medical service for the elderly. Such automatic ADL monitoring requires reliable ADL information on a fine-grained level, especially for the status of interaction between body gestures and the environment in the real-world. In this work, we propose a significant change spotting mechanism for periodic human motion segmentation during cleaning task performance. A novel approach is proposed based on the search for a significant change of gestures, which can manage critical technical issues in activity recognition, such as continuous data segmentation, individual variance, and category ambiguity. Three typical machine learning classification algorithms are utilized for the identification of the significant change candidate, including a Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Naive Bayesian (NB) algorithm. Overall, the proposed approach achieves 96.41% in the F1-score by using the SVM classifier. The results show that the proposed approach can fulfill the requirement of fine-grained human motion segmentation for automatic ADL monitoring. PMID:28106853

Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques.

PubMed

Enshaeifar, Shirin; Zoha, Ahmed; Markides, Andreas; Skillman, Severin; Acton, Sahr Thomas; Elsaleh, Tarek; Hassanpour, Masoud; Ahrabian, Alireza; Kenny, Mark; Klein, Stuart; Rostill, Helen; Nilforooshan, Ramin; Barnaghi, Payam

2018-01-01

The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients' routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%.

A real-time posture monitoring method for rail vehicle bodies based on machine vision

NASA Astrophysics Data System (ADS)

Liu, Dongrun; Lu, Zhaijun; Cao, Tianpei; Li, Tian

2017-06-01

Monitoring vehicle operation conditions has become significantly important in modern high-speed railway systems. However, the operational impact of monitoring the roll angle of vehicle bodies has principally been limited to tilting trains, while few studies have focused on monitoring the running posture of vehicle bodies during operation. We propose a real-time posture monitoring method to fulfil real-time monitoring requirements, by taking rail surfaces and centrelines as detection references. In realising the proposed method, we built a mathematical computational model based on space coordinate transformations to calculate attitude angles of vehicles in operation and vertical and lateral vibration displacements of single measuring points. Moreover, comparison and verification of reliability between system and field results were conducted. Results show that monitoring of the roll angles of car bodies obtained through the system exhibit variation trends similar to those converted from the dynamic deflection of bogie secondary air springs. The monitoring results of two identical conditions were basically the same, highlighting repeatability and good monitoring accuracy. Therefore, our monitoring results were reliable in reflecting posture changes in running railway vehicles.

In-situ monitoring and assessment of post barge-bridge collision damage for minimizing traffic delay and detour : final report.

DOT National Transportation Integrated Search

2016-07-31

This report presents a novel framework for promptly assessing the probability of barge-bridge : collision damage of piers based on probabilistic-based classification through machine learning. The main : idea of the presented framework is to divide th...

Identification and Triage of Compromised Virtual Machines

DTIC Science & Technology

2014-09-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA APPLIED CYBER OPERATIONS CAPSTONE PROJECT REPORT Approved for public release...ADDRESS(ES) Naval Postgraduate School Monterey, CA 93943-5000 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING /MONITORING AGENCY NAME(S...IN APPLIED CYBER OPERATIONS from the NAVAL POSTGRADUATE SCHOOL September 2014 Authors: John Paulenich Chukwuemeka Agbedo

Smart Sensors Gather Information for Machine Diagnostics

NASA Technical Reports Server (NTRS)

2014-01-01

Stennis Space Center was interested in using smart sensors to monitor components on test stands and avert equipment failures. Partnering with St. Paul, Minnesota-based Lion Precision through a Cooperative Agreement, the team developed a smart sensor and the associated communication protocols. The same sensor is now commercially available for manufacturing.

A comparison of non-parametric techniques to estimate incident photosynthetically active radiation from MODIS for monitoring primary production

NASA Astrophysics Data System (ADS)

Brown, M. G. L.; He, T.; Liang, S.

2016-12-01

Satellite-derived estimates of incident photosynthetically active radiation (PAR) can be used to monitor global change, are required by most terrestrial ecosystem models, and can be used to estimate primary production according to the theory of light use efficiency. Compared with parametric approaches, non-parametric techniques that include an artificial neural network (ANN), support vector machine regression (SVM), an artificial bee colony (ABC), and a look-up table (LUT) do not require many ancillary data as inputs for the estimation of PAR from satellite data. In this study, a selection of machine learning methods to estimate PAR from MODIS top of atmosphere (TOA) radiances are compared to a LUT approach to determine which techniques might best handle the nonlinear relationship between TOA radiance and incident PAR. Evaluation of these methods (ANN, SVM, and LUT) is performed with ground measurements at seven SURFRAD sites. Due to the design of the ANN, it can handle the nonlinear relationship between TOA radiance and PAR better than linearly interpolating between the values in the LUT; however, training the ANN has to be carried out on an angular-bin basis, which results in a LUT of ANNs. The SVM model may be better for incorporating multiple viewing angles than the ANN; however, both techniques require a large amount of training data, which may introduce a regional bias based on where the most training and validation data are available. Based on the literature, the ABC is a promising alternative to an ANN, SVM regression and a LUT, but further development for this application is required before concrete conclusions can be drawn. For now, the LUT method outperforms the machine-learning techniques, but future work should be directed at developing and testing the ABC method. A simple, robust method to estimate direct and diffuse incident PAR, with minimal inputs and a priori knowledge, would be very useful for monitoring global change of primary production, particularly of pastures and rangeland, which have implications for livestock and food security. Future work will delve deeper into the utility of satellite-derived PAR estimation for monitoring primary production in pasture and rangelands.

Design and validation of wireless system for oil monitoring base on optical sensing unit

NASA Astrophysics Data System (ADS)

Niu, Liqun; Wang, Weiming; Zhang, Shuaishuai; Li, Zhirui; Yu, Yan; Huang, Hui

2017-04-01

According to the situation of oil leakage and the development of oil detection technology, a wireless monitoring system, combining with the sensor technology, optical measurement technology, and wireless technology, is designed. In this paper, the architecture of a wireless system is designed. In the hardware, the collected data, acquired by photoelectric conversion and analog to digital conversion equipment, will be sent to the upper machine where they are saved and analyzed. The experimental results reveals that the wireless system has the characteristics of higher precision, more real-time and more convenient installation, it can reflect the condition of the measuring object truly and implement the dynamic monitoring for a long time on-site, stability—thus it has a good application prospect in the oil monitoring filed.

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

NASA Astrophysics Data System (ADS)

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

2018-07-01

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

Automated Management of Exercise Intervention at the Point of Care: Application of a Web-Based Leg Training System

PubMed Central

2015-01-01

Background Recent advances in information and communication technology have prompted development of Web-based health tools to promote physical activity, the key component of cardiac rehabilitation and chronic disease management. Mobile apps can facilitate behavioral changes and help in exercise monitoring, although actual training usually takes place away from the point of care in specialized gyms or outdoors. Daily participation in conventional physical activities is expensive, time consuming, and mostly relies on self-management abilities of patients who are typically aged, overweight, and unfit. Facilitation of sustained exercise training at the point of care might improve patient engagement in cardiac rehabilitation. Objective In this study we aimed to test the feasibility of execution and automatic monitoring of several exercise regimens on-site using a Web-enabled leg training system. Methods The MedExercise leg rehabilitation machine was equipped with wireless temperature sensors in order to monitor its usage by the rise of temperature in the resistance unit (Δt°). Personal electronic devices such as laptop computers were fitted with wireless gateways and relevant software was installed to monitor the usage of training machines. Cloud-based software allowed monitoring of participant training over the Internet. Seven healthy participants applied the system at various locations with training protocols typically used in cardiac rehabilitation. The heart rates were measured by fingertip pulse oximeters. Results Exercising in home chairs, in bed, and under an office desk was made feasible and resulted in an intensity-dependent increase of participants’ heart rates and Δt° in training machine temperatures. Participants self-controlled their activities on smart devices, while a supervisor monitored them over the Internet. Individual Δt° reached during 30 minutes of moderate-intensity continuous training averaged 7.8°C (SD 1.6). These Δt° were used as personalized daily doses of exercise with automatic email alerts sent upon achieving them. During 1-week training at home, automatic notifications were received on 4.4 days (SD 1.8). Although the high intensity interval training regimen was feasible on-site, it was difficult for self- and remote management. Opportunistic leg exercise under the desk, while working with a computer, and training in bed while viewing television were less intensive than dosed exercise bouts, but allowed prolonged leg mobilization of 73.7 minutes/day (SD 29.7). Conclusions This study demonstrated the feasibility of self-control exercise training on-site, which was accompanied by online monitoring, electronic recording, personalization of exercise doses, and automatic reporting of adherence. The results suggest that this technology and its applications are useful for the delivery of Web-based exercise rehabilitation and cardiac training programs at the point of care. PMID:28582243

2007 SB14 Source Reduction Plan/Report

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

Chang, L

2007-07-24

Aqueous solutions (mixed waste) generated from various LLNL operations, such as debris washing, sample preparation and analysis, and equipment maintenance and cleanout, were combined for storage in the B695 tank farm. Prior to combination the individual waste streams had different codes depending on the particular generating process and waste characteristics. The largest streams were CWC 132, 791, 134, 792. Several smaller waste streams were also included. This combined waste stream was treated at LLNL's waste treatment facility using a vacuum filtration and cool vapor evaporation process in preparation for discharge to sanitary sewer. Prior to discharge, the treated waste streammore » was sampled and the results were reviewed by LLNL's water monitoring specialists. The treated solution was discharged following confirmation that it met the discharge criteria. A major source, accounting for 50% for this waste stream, is metal machining, cutting and grinding operations in the engineering machine shops in B321/B131. An additional 7% was from similar operations in B131 and B132S. This waste stream primarily contains metal cuttings from machined parts, machining coolant and water, with small amounts of tramp oil from the machining and grinding equipment. Several waste reduction measures for the B321 machine shop have been taken, including the use of a small point-of-use filtering/tramp-oil coalescing/UV-sterilization coolant recycling unit, and improved management techniques (testing and replenishing) for coolants. The recycling unit had some operational problems during 2006. The machine shop is planning to have it repaired in the near future. A major source, accounting for 50% for this waste stream, is metal machining, cutting and grinding operations in the engineering machine shops in B321/B131. An additional 7% was from similar operations in B131 and B132S. This waste stream primarily contains metal cuttings from machined parts, machining coolant and water, with small amounts of tramp oil from the machining and grinding equipment. Several waste reduction measures for the B321 machine shop have been taken, including the use of a small point-of-use filtering/tramp-oil coalescing/UV-sterilization coolant recycling unit, and improved management techniques (testing and replenishing) for coolants. The recycling unit had some operational problems during 2006. The machine shop is planning to have it repaired in the near future. Quarterly waste generation data prepared by the Environmental Protection Department's P2 Team are regularly provided to engineering shops as well as other facilities so that generators can track the effectiveness of their waste minimization efforts.« less

Diagnostics of flexible workpiece using acoustic emission, acceleration and eddy current sensors in milling operation

NASA Astrophysics Data System (ADS)

Filippov, A. V.; Tarasov, S. Yu.; Filippova, E. O.; Chazov, P. A.; Shamarin, N. N.; Podgornykh, O. A.

2016-11-01

Monitoring of the edge clamped workpiece deflection during milling has been carried our using acoustic emission, accelerometer and eddy current sensors. Such a monitoring is necessary in precision machining of vital parts used in air-space engineering where a majority of them made by milling. The applicability of the AE, accelerometers and eddy current sensors has been discussed together with the analysis of measurement errors. The appropriate sensor installation diagram has been proposed for measuring the workpiece elastic deflection exerted by the cutting force.

Analysis of Oxygen, Anaesthesia Agent and Flows in Anaesthesia Machine

PubMed Central

Garg, Rakesh; Gupta, Ramesh Chand

2013-01-01

The technical advancement in the anaesthesia workstations has made the peri-operative anaesthesia more safer. Apart from other monitoring options, respiratory gas analysis has become an integral part of the modern anaesthesia workstations. Monitoring devices, such as an oxygen analyser with an audible alarm, carbon dioxide analyser, a vapour analyser, whenever a volatile anaesthetic is delivered have also been recommended by various anaesthesia societies. This review article discusses various techniques for analysis of flow, volumes and concentration of various anaesthetic agents including oxygen, nitrous oxide and volatile anaesthetic agents. PMID:24249881

An adaptive deep learning approach for PPG-based identification.

PubMed

Jindal, V; Birjandtalab, J; Pouyan, M Baran; Nourani, M

2016-08-01

Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.

System for monitoring an industrial or biological process

DOEpatents

Gross, Kenneth C.; Wegerich, Stephan W.; Vilim, Rick B.; White, Andrew M.

1998-01-01

A method and apparatus for monitoring and responding to conditions of an industrial process. Industrial process signals, such as repetitive manufacturing, testing and operational machine signals, are generated by a system. Sensor signals characteristic of the process are generated over a time length and compared to reference signals over the time length. The industrial signals are adjusted over the time length relative to the reference signals, the phase shift of the industrial signals is optimized to the reference signals and the resulting signals output for analysis by systems such as SPRT.

Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems

NASA Technical Reports Server (NTRS)

Hearn, Tristan A.

2015-01-01

This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.

System for monitoring an industrial or biological process

DOEpatents

Gross, K.C.; Wegerich, S.W.; Vilim, R.B.; White, A.M.

1998-06-30

A method and apparatus are disclosed for monitoring and responding to conditions of an industrial process. Industrial process signals, such as repetitive manufacturing, testing and operational machine signals, are generated by a system. Sensor signals characteristic of the process are generated over a time length and compared to reference signals over the time length. The industrial signals are adjusted over the time length relative to the reference signals, the phase shift of the industrial signals is optimized to the reference signals and the resulting signals output for analysis by systems such as SPRT. 49 figs.

OTVE turbopump condition monitoring, task E.5

NASA Technical Reports Server (NTRS)

Coleman, Paul T.; Collins, J. J.

1989-01-01

Recent work has been carried out on development of isotope wear analysis and optical and eddy current technologies to provide bearing wear measurements and real time monitoring of shaft speed, shaft axial displacement and shaft orbit of the Orbit Transfer Vehicle hydrostatic bearing tester. Results show shaft axial displacement can be optically measured (at the same time as shaft orbital motion and speed) to within 0.3 mils by two fiberoptic deflectometers. Evaluation of eddy current probes showed that, in addition to measuring shaft orbital motion, they can be used to measure shaft speed without having to machine grooves on the shaft surface as is the usual practice for turbomachinery. The interim results of this condition monitoring effort are presented.

Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition

PubMed Central

Shenoy, Varun N.; Aalami, Oliver O.

2017-01-01

Biometric measurements captured from medical devices, such as blood pressure gauges, glucose monitors, and weighing scales, are essential to tracking a patient’s health. Trends in these measurements can accurately track diabetes, cardiovascular issues, and assist medication management for patients. Currently, patients record their results and date of measurement in a physical notebook. It may be weeks before a doctor sees a patient’s records and can assess the health of the patient. With a predicted 6.8 billion smartphones in the world by 20221, health monitoring platforms, such as Apple’s HealthKit2, can be leveraged to provide the right care at the right time. This research presents a mobile application that enables users to capture medical monitor data and send it to their doctor swiftly. A key contribution of this paper is a robust engine that can recognize digits from medical monitors with an accuracy of 98.2%. PMID:29854226

Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition.

PubMed

Shenoy, Varun N; Aalami, Oliver O

2017-01-01

Biometric measurements captured from medical devices, such as blood pressure gauges, glucose monitors, and weighing scales, are essential to tracking a patient's health. Trends in these measurements can accurately track diabetes, cardiovascular issues, and assist medication management for patients. Currently, patients record their results and date of measurement in a physical notebook. It may be weeks before a doctor sees a patient's records and can assess the health of the patient. With a predicted 6.8 billion smartphones in the world by 2022 1 , health monitoring platforms, such as Apple's HealthKit 2 , can be leveraged to provide the right care at the right time. This research presents a mobile application that enables users to capture medical monitor data and send it to their doctor swiftly. A key contribution of this paper is a robust engine that can recognize digits from medical monitors with an accuracy of 98.2%.