Sampling Plan Using Process Loss Index Using Multiple Dependent State Sampling Under Neutrosophic Statistics

This paper presents the designing of a sampling plan using the process loss consideration for the multiple dependent state sampling under the neutrosophic statistics. The operating characteristics under the neutrosophic statistical interval method (NSIM) are developed to find the neutrosophic plan parameters of the proposed sampling plan. A non-linear optimization under NSIM is used to find the optimal neutrosophic plan parameters under the given conditions. The advantages of the proposed sampling plan are discussed over the existing sampling plans. A real example having some uncertain observations is given for the illustration purpose

A New S-2 Control Chart Using Multiple Dependent State Repetitive Sampling

The combined application of multiple dependent state sampling and the repetitive group sampling (RGS) is an efficient sampling scheme for industrial process monitoring as it combines the advantages of both the sampling schemes. In this paper, a new variance control chart has been proposed, when the interesting quality characteristic follows the normal distribution using the combination of these two efficient sampling schemes called multiple dependent state repetitive sampling. The control chart coefficients and parameters have been estimated through simulation for the in-control process by considering the target in-control average run lengths under different process settings. The efficiency of the proposed chart has been determined by computing the out-of-control ARL for different shift levels. The advantages of the proposed monitoring scheme have been discussed and compared with the existing RGS scheme and the single sampling scheme. A simulated example and a real industrial data have been included to demonstrate the application of the proposed monitoring scheme. It has been observed that the proposed chart is a valuable addition to the toolkit of the quality monitoring personnel.11Ysciescopu

EXTREME RANKED REPETITIVE SAMPLING CONTROL CHARTS

ABSTRACT In this paper, we proposed a new ranked data control chart using repetitive sampling criterion to increase the performance of detecting any shift in mean process. For the comparisons target, the average run length (ARL) of the proposed control chart based on repetitive extreme ranked set sampling computed using exact and estimated parameters. The results showed that the ARL affected negatively by the parameter estimation. Moreover, the performances of the proposed control chart is evaluated and compared with similar control chart that obtained by using different sampling schemes such as the simple random sampling, ranked set sampling, extreme ranked set sampling and repetitive ranked set sampling.. The results showed that the ranked data based control chart outperform the classical control chart in terms of the ARL

Finnish pupils’ success in mathematics: factors that contribute to Finnish pupils’ success in mathematics

This report focuses on the factors that contribute positively to Finnish pupils’ success in mathematics and considers the implications for policy and practice in England. Includes examples of items from the PISA and TIMSS test

Measurements, Models, Systems and Design

531 s.

Usability of Upper Limb Electromyogram Features as Muscle Fatigue Indicators for Better Adaptation of Human-Robot Interactions

Human-robot interaction (HRI) is the process of humans and robots working together to accomplish a goal with the objective of making the interaction beneficial to humans. Closed loop control and adaptability to individuals are some of the important acceptance criteria for human-robot interaction systems. While designing an HRI interaction scheme, it is important to understand the users of the system and evaluate the capabilities of humans and robots. An acceptable HRI solution is expected to be adaptable by detecting and responding to the changes in the environment and its users. Hence, an adaptive robotic interaction will require a better sensing of the human performance parameters. Human performance is influenced by the state of muscular and mental fatigue during active interactions. Researchers in the field of human-robot interaction have been trying to improve the adaptability of the environment according to the physical state of the human participants. Existing human-robot interactions and robot assisted trainings are designed without sufficiently considering the implications of fatigue to the users. Given this, identifying if better outcome can be achieved during a robot-assisted training by adapting to individual muscular status, i.e. with respect to fatigue, is a novel area of research. This has potential applications in scenarios such as rehabilitation robotics. Since robots have the potential to deliver a large number of repetitions, they can be used for training stroke patients to improve their muscular disabilities through repetitive training exercises. The objective of this research is to explore a solution for a longer and less fatiguing robot-assisted interaction, which can adapt based on the muscular state of participants using fatigue indicators derived from electromyogram (EMG) measurements. In the initial part of this research, fatigue indicators from upper limb muscles of healthy participants were identified by analysing the electromyogram signals from the muscles as well as the kinematic data collected by the robot. The tasks were defined to have point-to-point upper limb movements, which involved dynamic muscle contractions, while interacting with the HapticMaster robot. The study revealed quantitatively, which muscles were involved in the exercise and which muscles were more fatigued. The results also indicated the potential of EMG and kinematic parameters to be used as fatigue indicators. A correlation analysis between EMG features and kinematic parameters revealed that the correlation coefficient was impacted by muscle fatigue. As an extension of this study, the EMG collected at the beginning of the task was also used to predict the type of point-to-point movements using a supervised machine learning algorithm based on Support Vector Machines. The results showed that the movement intention could be detected with a reasonably good accuracy within the initial milliseconds of the task. The final part of the research implemented a fatigue-adaptive algorithm based on the identified EMG features. An experiment was conducted with thirty healthy participants to test the effectiveness of this adaptive algorithm. The participants interacted with the HapticMaster robot following a progressive muscle strength training protocol similar to a standard sports science protocol for muscle strengthening. The robotic assistance was altered according to the muscular state of participants, and, thus, offering varying difficulty levels based on the states of fatigue or relaxation, while performing the tasks. The results showed that the fatigue-based robotic adaptation has resulted in a prolonged training interaction, that involved many repetitions of the task. This study showed that using fatigue indicators, it is possible to alter the level of challenge, and thus, increase the interaction time. In summary, the research undertaken during this PhD has successfully enhanced the adaptability of human-robot interaction. Apart from its potential use for muscle strength training in healthy individuals, the work presented in this thesis is applicable in a wide-range of humanmachine interaction research such as rehabilitation robotics. This has a potential application in robot-assisted upper limb rehabilitation training of stroke patients

Active microwave sensing of the atmosphere, chapter 4

The use of active microwave systems to study atmospheric phenomena is studied. Atmospheric pollution, weather prediction, climate and weather modification, weather danger and disaster warning, and atmospheric processes and interactions are covered

Improving the design process for factories:modelling human performance variation

Theprocess of manufacturing system design frequently includes modeling, and usually, this means applying a technique such as discrete event simulation (DES). However, the computer tools currently available to apply this technique enable only a superficial representation of the people that operate within the systems. This is a serious limitation because the performance of people remains central to the competitiveness of many manufacturing enterprises. Therefore, this paper explores the use of probability density functions to represent the variation of worker activity times within DES models

Ground reference data for sugarcane biomass estimation in São Paulo state, Brazil

In order to make effective decisions on sustainable development, it is essential for sugarcane-producing countries to take into account sugarcane acreage and sugarcane production dynamics. The availability of sugarcane biophysical data along the growth season is key to an effective mapping of such dynamics, especially to tune agronomic models and to cross-validate indirect satellite measurements. Here, we introduce a dataset comprising 3,500 sugarcane observations collected from October 2014 until October 2015 at four fields in the São Paulo state (Brazil). The campaign included both non-destructive measurements of plant biometrics and destructive biomass weighing procedures. The acquisition plan was designed to maximize cost-effectiveness and minimize field-invasiveness, hence the non-destructive measurements outnumber the destructive ones. To compensate for such imbalance, a method to convert the measured biometrics into biomass estimates, based on the empirical adjustment of allometric models, is proposed. In addition, the paper addresses the precisions associated to the ground measurements and derived metrics. The presented growth dynamics and associated precisions can be adopted when designing new sugarcane measurement campaigns.5FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2013/50942-

Characterization of Novel Di-, Tri-, and Tetranucleotide Microsatellite Primers Suitable for Genotyping Various Plant Pathogenic Fungi with Special Emphasis on Fusaria and Mycospherella graminicola

The goals of this investigation were to identify and evaluate the use of polymorphic microsatellite marker (PMM) analysis for molecular typing of seventeen plant pathogenic fungi. Primers for di-, tri-, and tetranucleotide loci were designed directly from the recently published genomic sequence of Mycospherlla graminicola and Fusarium graminearum. A total of 20 new microsatellite primers as easy-to-score markers were developed. Microsatellite primer PCR (MP-PCR) yielded highly reproducible and complex genomic fingerprints, with several bands ranging in size from 200 to 3000 bp. Of the 20 primers tested, only (TAGG)4, (TCC)5 and (CA)7T produced a high number of polymorphic bands from either F. graminearum or F. culmorum. (ATG)5 led to successful amplifications in M. graminicola isolates collected from Germany. Percentage of polymorphic bands among Fusarium species ranged from 9 to 100%. Cluster analysis of banding patterns of the isolates corresponded well to the established species delineations based on morphology and other methods of phylogenetic analysis. The current research demonstrates that the newly designed microsatellite primers are reliable, sensitive and technically simple tools for assaying genetic variability in plant pathogenic fungi