In practice, measurement results are sometimes described by an estimate, which is not the best one as defined in the GUM. Such alternative estimates arise when the result of a measurement is not corrected for all systematic effects. No recommendation exists in the GUM for associating an uncertainty with an uncorrected estimate.
A common choice in guidelines and in the literature is the uncertainty for an alternative estimate y′. It arises from the expected quadratic loss, on which, also in the GUM, the standard uncertainty u(y), and the best estimate y are based. However, such an uncertainty is not a standard uncertainty and we establish, it may not be used for uncertainty propagation.
One consequence is, for example, that pairs (y′, u(y′)) are not to be used in calibration certificates.
Evan Krell, Alaa Sheta, Arun Prassanth Ramaswamy Balasubramanian and Scott A. King
The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.
Oded Koren, Carina Antonia Hallin, Nir Perel and Dror Bendet
Big data research has become an important discipline in information systems research. However, the flood of data being generated on the Internet is increasingly unstructured and non-numeric in the form of images and texts. Thus, research indicates that there is an increasing need to develop more efficient algorithms for treating mixed data in big data for effective decision making. In this paper, we apply the classical K-means algorithm to both numeric and categorical attributes in big data platforms. We first present an algorithm that handles the problem of mixed data. We then use big data platforms to implement the algorithm, demonstrating its functionalities by applying the algorithm in a detailed case study. This provides us with a solid basis for performing more targeted profiling for decision making and research using big data. Consequently, the decision makers will be able to treat mixed data, numerical and categorical data, to explain and predict phenomena in the big data ecosystem. Our research includes a detailed end-to-end case study that presents an implementation of the suggested procedure. This demonstrates its capabilities and the advantages that allow it to improve the decision-making process by targeting organizations’ business requirements to a specific cluster[s]/profiles[s] based on the enhancement outcomes.
Miguel Costa, Daniel Oliveira, Sandro Pinto and Adriano Tavares
The lack of attention during the driving task is considered as a major risk factor for fatal road accidents around the world. Despite the ever-growing trend for autonomous driving which promises to bring greater road-safety benefits, the fact is today’s vehicles still only feature partial and conditional automation, demanding frequent driver action. Moreover, the monotony of such a scenario may induce fatigue or distraction, reducing driver awareness and impairing the regain of the vehicle’s control. To address this challenge, we introduce a non-intrusive system to monitor the driver in terms of fatigue, distraction, and activity. The proposed system explores state-of-the-art sensors, as well as machine learning algorithms for data extraction and modeling. In the domain of fatigue supervision, we propose a feature set that considers the vehicle’s automation level. In terms of distraction assessment, the contributions concern (i) a holistic system that covers the full range of driver distraction types and (ii) a monitoring unit that predicts the driver activity causing the faulty behavior. By comparing the performance of Support Vector Machines against Decision Trees, conducted experiments indicated that our system can predict the driver’s state with an accuracy ranging from 89% to 93%.
Peter Pavlasek, Jan Rybař, Stanislav Ďuriš and Jakub Palenčar
Au/Pt thermocouples are considered as an alternative to High Temperature Platinum Resistance Thermometers and are one of the prime candidates to replace them as the interpolating instrument of the International Temperature Scale of 1990 (ITS-90) in the temperature range between about 660 °C and 962 °C. This work presents the results of investigation of two Au/Pt thermocouples that used exclusively quartz glass (SiO2) as insulation material. Measurements in fixed points of Zn, Al, and Ag were realized on these thermocouples as well with interchanged inner insulation made of high purity aluminium oxide (Al2O3). The conducted experiments tested the performance of Au/Pt thermocouples with the use of different insulation materials. The measured electromotive forces were found to be sensitive to the replacement of the quartz glass by aluminium oxide as an insulation material of the Au/Pt thermocouples. This change of insulation has resulted in a temperature increase up to about 0.5 K measured at the freezing point of silver. The decreasing insulation resistance of quartz glass at higher temperatures is believed to be the source of thermoelectric instability.
Fault prognosis plays a key role in the framework of Condition-Based Maintenance (CBM). Limited by the inherent disadvantages, most traditional intelligent algorithms perform not very well in fault prognosis of hydraulic pumps. In order to improve the prediction accuracy, a novel methodology for fault prognosis of hydraulic pump based on the bispectrum entropy and the deep belief network is proposed in this paper. Firstly, the bispectrum features of vibration signals are analyzed, and a bispectrum entropy method based on energy distribution is proposed to extract the effective feature for prognostics. Then, the Deep Belief Network (DBN) model based on the Restrict Boltzmann Machine (RBM) is proposed as the prognostics model. For the purpose of accurately predicting the trends and the random fluctuations during the performance degradation of the hydraulic pump, the Quantum Particle Swarm Optimization (QPSO) is introduced to search for the optimal value of initial parameters of the network. Finally, analysis of the hydraulic pump degradation experiment demonstrates that the proposed algorithm has a satisfactory prognostics performance and is feasible to meet the requirements of CBM.
Ondřej Klempíř, Radim Krupička, Eduard Bakštein and Robert Jech
Deep brain stimulation (DBS) is an internationally accepted form of treatment option for selected patients with Parkinson’s disease and dystonia. Intraoperative extracellular microelectrode recordings (MER) are considered as the standard electrophysiological method for the precise positioning of the DBS electrode into the target brain structure. Pre-processing of MERs is a key phase in clinical analysis, with intraoperative microelectrode recordings being prone to several artifact groups (up to 25 %). The aim of this methodological article is to provide a convolutional neural network (CNN) processing pipeline for the detection of artifacts in an MER. We applied continuous wavelet transform (CWT) to generate an over-complete time–frequency representation. We demonstrated that when attempting to find artifacts in an MER, the new CNN + CWT provides a high level of accuracy (ACC = 88.1 %), identifies individual classes of artifacts (ACC = 75.3 %) and also offers artifact time onset detail, which can lead to a reduction in false positives/negatives. In summary, the presented methodology is capable of identifying and removing various artifacts in a comprehensive database of MER and represents a substantial improvement over the existing methodology. We believe that this approach will assist in the proposal of interesting clinical hypotheses and will have neurologically relevant effects.
Peng Hu, Zhongyuan Zhou, Jinpeng Li, Xiang Zhou, Mingjie Sheng, Peng Li and Qi Zhou
More and more EMC tests have shown that the radiated emission problems of the equipment under test mainly concentrate on the intercon- nected power cables and cable connectors. Measurement of shielding performance is a prerequisite for quantitative and qualitative evaluation of the frequency-dependent characteristic of braided-shield power cables and cable connectors. Due to the asymmetric geometric structures of these cable assemblies, compared with the coaxial and symmetrical communication cables, the commonly used transfer impedance testing methods may not be suitable. In view of this, several improved simple and effective measurement methods, including transfer impedance and shield reduction factor testing methods, were proposed in recent years. These methods, based on the equivalent circuit model of the characteristic parameters, provide good repeatability for the measurement of shielding performance. This paper presents an overview analysis of various measurement techniques for shielding performance of power cables and cable connectors, highlights some of its equivalence principle in measurement setups, and showcases a brief comparison between transfer impedance and shield reduction factor.
Recently there has been great interest in photoplethysmogram signal processing. However, its minimally necessary sampling frequency for accurate heart rate variability parameters is ambiguous. In the present paper frequency-modulated 1.067 Hz cosine wave modelled the variable PPG in silico. The five-minute-long, 1 ms resolution master-signals were decimated (D) at 2-500 ms, then cubic spline interpolated (I) back to 1 ms resolution. The mean pulse rate, standard deviation, root mean square of successive pulse rate differences (RMSSD), and spectral components were computed by Varian 2.3 and compared to the master-series via relative accuracy error. Also Poincaré-plot morphology was assessed. Mean pulse rate is accurate down to 303 ms (D) and 400 ms (I). In low-variability series standard deviation required at least 5 ms (D) and 100 ms (I). RMSSD needed 10 ms (D), and 303 ms (I) in normal, whereas 2 ms (D) and 100 ms (I) in low- variability series. In the frequency domain 5 ms (D) and 100 ms (I) are required. 2 ms (D) and 100 ms (I) preserved the Poincaré-plot morphology. The minimal sampling frequency of PPG for accurate HRV analysis is higher than expected from the signal bandwidth and sampling theorem. Interpolation improves accuracy. The ratio of sampling error and expected variability should be considered besides the inherent sensitivity of the given parameter, the interpolation technique, and the pulse rate detection method.
Sou Nobukawa, Haruhiko Nishimura and Teruya Yamanishi
Many recent studies have applied to spike neural networks with spike-timing-dependent plasticity (STDP) to machine learning problems. The learning abilities of dopamine-modulated STDP (DA-STDP) for reward-related synaptic plasticity have also been gathering attention. Following these studies, we hypothesize that a network structure combining self-organized STDP and reward-related DA-STDP can solve the machine learning problem of pattern classification. Therefore, we studied the ability of a network in which recurrent spiking neural networks are combined with STDP for non-supervised learning, with an output layer joined by DA-STDP for supervised learning, to perform pattern classification. We confirmed that this network could perform pattern classification using the STDP effect for emphasizing features of the input spike pattern and DA-STDP supervised learning. Therefore, our proposed spiking neural network may prove to be a useful approach for machine learning problems.