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Because of its fast scintillation peaked at 220 nm with a decay time of less than 0.6 ns, barium fluoride crystals have attracted broad interest in the community pursuing ultrafast detectors for future high energy physics (HEP) experiments and GHz hard X-ray imaging for future X-ray free-electron laser (XFEL) facilities. A crucial issue for its application is the spectral response of photodetectors down to vacuum ultraviolet (VUV). In this article, we compare quantum efficiency (QE) and photon detection efficiency (PDE) spectra measured for several ultraviolet (UV) photodetectors down to 200 nm. Their figures of merit on the detection efficiency for the fast component and the suppression of the slow component are also discussed. [ABSTRACT FROM AUTHOR]

Fuzzy Petri nets (FPNs) are a useful instrument for modelling expert systems to conduct knowledge representation and reasoning. Many studies have been carried out for improving the performance of FPNs in terms of their accurate representation of knowledge and power of approximate reasoning. Nevertheless, the current representation methods with FPNs are unable to handle the uncertain linguistic knowledge given by domain experts and the reliability of their judgments. In addition, the existing reasoning algorithms have no way to capture the interrelationship of the propositions with the same output transition. Therefore, we present a new type of FPNs, called 2-dimensional uncertain linguistic Petri nets (2DULPNs). The 2-dimensional uncertain linguistic variables (2DULVs) and Choquet integral are combined for knowledge representation and reasoning for the first time. The truth degrees of propositions, thresholds and certainty values of linguistic production rules are denoted as 2DULVs. Some new aggregated operators based on Choquet integral are proposed and used in the approximate reasoning to capture the interactions among antecedent propositions. Finally, an equipment fault diagnosis example is provided to illustrate the correctness and effectiveness of the proposed 2DULPN model. [ABSTRACT FROM AUTHOR]

Because of their potential low cost, bright light, and fast decay time, LuAG:Ce ceramic scintillators have attracted a broad interest in the high-energy physics community. One crucial issue for their application in future high-energy physics experiments is their radiation hardness against neutrons and protons expected at future hadron colliders. We report optical and scintillation performance of 1-mm LuAG:Ce ceramic samples doped with Mg2+ (and Ca2+) and their radiation damage induced by hadrons. While Mg2+ co-doping improves their light output, Ca2+ co-doping improves their fast to total (F/T) ratio. LuAG:Ce ceramic samples were irradiated at the Los Alamos Neutron Science Center (LANSCE), Los Alamos, NM, USA, by neutrons up to $6.7\times 10^{15}\,\,\text{n}_{\mathrm {eq}}$ /cm2 and by 24-GeV and 800-MeV protons at CERN PS-IRRAD up to $1.2\times 10^{15}$ p/cm2 and at LANSCE up to $2.3\times 10^{14}$ p/cm2, respectively. All samples show excellent radiation hardness with more than 90% of light after irradiation. The RIAC values induced by neutrons are found to be a factor of 2 smaller than lutetium–yttrium oxyorthosilicate (LYSO:Ce) crystals. The RIAC values induced by protons are also found a factor of 2 smaller than LYSO:Ce crystals in LuAG:Ce ceramic samples with good optical quality. Research and development will continue to develop LuAG:Ce scintillating ceramics with improved optical quality for future investigation. [ABSTRACT FROM AUTHOR]

Channel estimation is challenging for the reconfigurable intelligent surface (RIS)-aided wireless communications. Since the number of coefficients of the cascaded channel among the base station (BS), the RIS, and the user equipment (UE), is the product of the number of BS antennas, the number of RIS elements, and the number of UEs, the pilot overhead can be prohibitively high. In this paper, we propose a two-timescale channel estimation framework to exploit the property that the BS-RIS channel is high-dimensional but quasi-static, while the RIS-UE channel is mobile but low-dimensional. Specifically, to estimate the quasi-static BS-RIS channel, we propose a dual-link pilot transmission scheme, where the BS transmits downlink pilots and receives uplink pilots reflected by the RIS. Then, we propose a coordinate descent-based algorithm to recover the BS-RIS channel. Since the quasi-static BS-RIS channel is estimated less frequently than the mobile channel is, the average pilot overhead can be reduced from a long-term perspective. Although the mobile RIS-UE channel has to be frequently estimated in a small timescale, the associated pilot overhead is low thanks to its low dimension. Simulation results show that the proposed two-timescale channel estimation framework can achieve accurate channel estimation with low pilot overhead. [ABSTRACT FROM AUTHOR]

Fuzzy Petri nets (FPNs) are a graphical-based knowledge representation and reasoning tool widely used in many fields for intelligent decision making. However, the traditional FPNs could not accurately represent domain experts’ uncertain and ambiguous knowledge with the increasing complexity of knowledge base systems. Additionally, the task of assessing the truth degrees of input places is limited to small-scale expert groups in current practices. In response to these problems, we develop a new type of FPN model, called Pythagorean FPNs (PFPNs), for knowledge representation and reasoning in the large group context. Pythagorean fuzzy sets are introduced to capture imprecise and ambiguous knowledge and a large group truth determination method is proposed to get the truth degrees of input places with massive data. Finally, an application example regarding security risk assessment is given to show the effectiveness and advantages of our proposed PFPNs. [ABSTRACT FROM AUTHOR]

Lu2(1–x)Y2xSiO5 (LYSO) crystals readout by a silicon photomultiplier (SiPM) will be used in the barrel timing layer of a precision timing detector for the Compact Muon Solenoid (CMS) experiment at the High-Luminosity Large Hadron Collider (HL-LHC). This detector is expected to be exposed up to 5-Mrad γ-ray dose, 2.5 × 1013 charged hadrons/cm2, and 3 × 1014 neq/cm2 at the HL-LHC for 10 years of operation. We present results of the photocurrent in LYSO+SiPM packages induced by Co-60 γ-rays and Cf-252 neutrons of the expected dose rate and neutron flux, respectively. The γ-ray-induced readout noise is about 30 keV, which is negligible when compared to the 4.2-MeV signal from the minimum ionization particle signals. The neutron-induced readout noise is about 7 keV, which is more than a factor of 4 smaller than that from the γ-rays. [ABSTRACT FROM AUTHOR]

Quality function deployment (QFD) is an efficient planning and problem-solving method to convert customer requirements (CRs) into the engineering characteristics (ECs) of a product or service. Nevertheless, in the traditional QFD, the assessment of relationships between CRs and ECs and the prioritization of ECs have been criticized for various reasons in previous studies. This paper aims to propose a novel QFD method integrating extended hesitant fuzzy linguistic term sets (EHFLTSs) and prospect theory to overcome the limitations of the traditional QFD. First, EHFLTSs are used for the elicitation of hesitant linguistic assessment information of QFD team members. Considering the interrelations between CRs, Choquet integral is applied to obtain the aggregated relationship evaluation results. An extended prospect theory is suggested to derive the ranking orders of ECs. Finally, the efficiency and effectiveness of the presented QFD method are illustrated by an example of an electric vehicle manufacturing company. [ABSTRACT FROM AUTHOR]

Failure mode and effect analysis (FMEA), as a proactive reliability management technique, has been widely employed to reduce the risk of systems and assure the quality of products in various industries. Nevertheless, the traditional FMEA method shows many weaknesses when used in real-life scenarios. This causes the dilemma for practitioners that it is not great as expected. Many alternative risk priority models have been developed to enhance the capacity of FMEA, but the majority of them focus on how to acquire a complete ranking of failure modes. In this article, we propose a novel FMEA approach using hesitant uncertain linguistic Z numbers (HULZNs) and density-based spatial clustering of applications with noise (DBSCAN) algorithm to assess and cluster the risk of failure modes. The HULZNs are adopted to represent the uncertain and hesitant risk evaluation information of FMEA team members. The normal DBSCAN algorithm is improved to cluster the recognized failure modes into different risk classes. Moreover, the weights of FMEA experts are dynamically obtained with a weight adjustment method. Finally, a practical case of a geothermal power plant is given to demonstrate the effectiveness and validity of our introduced FMEA approach. [ABSTRACT FROM AUTHOR]

The regression based deep neural networks have achieved state-of-the-arts performance on depth 3D hand pose estimation task. This paper focuses on improving the regression mapping between features and pose joints. Inspired by the distribution modeling ability of Variational Autoencoders, we introduce an auxiliary variable into the regression network. During training, the auxiliary variable is modeled by an inference distribution that learns the underlying structural kinematics of human hand. Different with other regression methods on hand poses, our network estimates the pose joints from input depth features and the learned auxiliary variable as well. We show that by introducing the auxiliary variable, the regression is benefited from 1) regularization modeled by inference distribution; and 2) prior information carried by the auxiliary model. The effectiveness of the proposed regression method is evaluated with extensively self-comparative experiments and in comparison with other regression methods on hand pose datasets. The proposed network is easy to train in an end-to-end manner and can work with various feature extraction methods. We apply the proposed regression method to an existing hand pose estimation system, and improves the estimation accuracy by 18.35% and 16.65% on public hand pose datasets. [ABSTRACT FROM AUTHOR]

Millimeter-wave massive multiple-input multiple-output (MIMO) can use a lens antenna array to considerably reduce the number of radio frequency (RF) chains, but channel estimation is challenging due to the number of RF chains is much smaller than that of antennas. By exploiting the sparsity of beamspace channels, the beamspace channel estimation can be formulated as a sparse signal recovery problem, which can be solved by the classical iterative algorithm named approximate message passing (AMP), and its corresponding version learned AMP (LAMP) realized by a deep neural network (DNN). However, these existing schemes cannot achieve satisfactory estimation accuracy. To improve the channel estimation performance, we propose a prior-aided Gaussian mixture LAMP (GM-LAMP) based beamspace channel estimation scheme in this paper. Specifically, based on the prior information that beamspace channel elements can be modeled by the Gaussian mixture distribution, we first derive a new shrinkage function to refine the AMP algorithm. Then, by replacing the original shrinkage function in the LAMP network with the derived Gaussian mixture shrinkage function, a prior-aided GM-LAMP network is developed to estimate the beamspace channel more accurately. Simulation results by using both the theoretical channel model and the ray-tracing based channel dataset show that, the proposed GM-LAMP network can achieve better channel estimation accuracy than existing schemes. [ABSTRACT FROM AUTHOR]

One crucial issue for the application of scintillation crystals in future high-energy physics experiments is radiation damage in a severe radiation environment, such as the high luminosity large hadron collider. While radiation damage induced by ionization dose in inorganic scintillators is well understood, investigations are ongoing to understand radiation damage induced by hadrons, including both charged hadrons and neutrons. Aiming at understanding neutron-induced radiation damage in fast inorganic scintillators, lutetium yttrium oxyorthosilicate (LYSO)/lutetium fine silicate (LFS), BaF2, and PWO crystals were irradiated at Los Alamos Neutron Science Center by a combination of particles, including neutrons, protons, and γ-rays. The results indicate that LYSO/LFS and BaF2 crystal plates are radiation hard up to a 1-MeV equivalent neutrons fluence of 9 × 1015 equivalent neutron (neq)/cm2, but not PWO, and the neutron-induced radiation damage in LYSO crystals is a factor of ten less than protons. [ABSTRACT FROM AUTHOR]

Gigahertz hard X-ray imaging presents an unprecedented challenge to both timing and spatial resolutions for inorganic scintillator-based front imagers. A beam test with 30-keV X-rays from the Advanced Photon Source (APS) showed that 5-mm BaF2 plates resolve well 30-keV X-ray septuplets of 27-ps width and 2.83-ns spacing. Pixelated crystal screens with a pitch down to 400~μm were fabricated by mechanic slicing for BaF2, BaF2:Y, and LYSO crystals. Their spatial resolution and detection efficiency for hard X-rays are defined by the pitch and thickness, respectively. Thicker monolithic crystal screens show poorer spatial resolution, which may be improved using a small optical aperture with a loss in both efficiency and dynamic range for hard X-rays. Future plans include pursuing smaller crystal pitches by laser slicing and research and development on novel ultrafast inorganic scintillators. [ABSTRACT FROM AUTHOR]

Because of their ultrafast scintillation with subnanosecond decay time, barium fluoride (BaF2) crystals have attracted broad interest in the high energy physics and nuclear physics communities. One crucial issue, however, is its slow scintillation component with 600-ns decay time, which causes pile-up in a high rate environment. Previous studies show that the slow component can be suppressed effectively by rare earth doping. In this paper, we report investigations on a set of $\Phi \,\,18\times21$ mm3 BaF2 cylinders doped with different yttrium levels grown at Beijing Glass Research Institute (BGRI), from which the optimized yttrium doping level was determined. $\text{A}\Phi \,\,40\times160$ mm3 BaF2 ingot with 5 atomic % (at.%) yttrium doping was consequently grown at BGRI and was used to cut one $25\times 25\times100$ mm3 crystal and several thin slices. Their optical and scintillation properties were measured at Caltech. The results show that yttrium doping effectively suppresses the slow component while maintaining its ultrafast light unchanged. Research and development will continue to develop large-size BaF2:Y crystals with improved optical quality for a fast BaF2:Y crystal calorimeter for Mu2e-II. [ABSTRACT FROM AUTHOR]

This study develops an improved failure mode and effects analysis (FMEA) method using two-dimensional uncertain linguistic variables (2DULVs) and alternative queuing method (AQM). The 2DULVs are employed to represent the evaluations provided by FMEA team members on the weights of risk factors and the risk of failure modes in the form of pairwise comparisons. The two-dimensional uncertain linguistic best worst method (2DUL-BWM) is used to derive the weights of risk factors. The two-dimensional uncertain linguistic AQM (2DUL-AQM) is proposed for determining the risk ranking of the identified failure modes. Finally, the maintenance of a water treatment plant is presented as an example to demonstrate the applicability and effectiveness of the proposed FMEA method. By comparing its performance with those of other existing methods, the proposed FMEA is shown to be more advantageous in ranking the risk of failure modes. [ABSTRACT FROM AUTHOR]

Channel estimation is challenging for millimeter-wave massive MIMO with hybrid precoding, since the number of radio frequency chains is much smaller than that of antennas. Conventional compressive sensing based channel estimation schemes suffer from severe resolution loss due to the channel angle quantization. To improve the channel estimation accuracy, we propose an iterative reweight-based superresolution channel estimation scheme in this paper. By optimizing an objective function through the gradient descent method, the proposed scheme can iteratively move the estimated angle of arrivals/departures towards the optimal solutions, and finally realize the superresolution channel estimation. In the optimization, a weight parameter is used to control the tradeoff between the sparsity and the data fitting error. In addition, a singular value decomposition-based preconditioning is developed to reduce the computational complexity of the proposed scheme. Simulation results verify the better performance of the proposed scheme than conventional solutions. [ABSTRACT FROM AUTHOR]

Gigahertz hard X-ray imaging for the proposed matter-radiation interaction in extreme project presents an unprecedented challenge to front imager in both speed and radiation hardness. Novel fast scintillators are to be developed to face these challenges. This paper presents an investigation on the optical and scintillation properties for a set of fast inorganic scintillators. Transmittance, emission, light output, and decay time were measured. Based on this investigation, we plan to take two approaches to develop inorganic scintillators with subnanoseconds of decay time for the gigahertz hard X-ray imaging. One is yttrium-doped barium fluoride single crystals, and another is based on gallium-doped ZnO nanoparticles. [ABSTRACT FROM AUTHOR]

Fuzzy Petri nets (FPNs) are a vital modeling technique for the construction of knowledge-based systems, which have been commonly used in many fields, such as fault diagnosis, risk assessment, workflow management, and disassembly process planning. However, the conventional FPNs have been blamed for the following reasons: 1) the representation parameters in FPNs cannot precisely model experts' experience since it is difficult to manage the fuzziness and randomness of knowledge assessments simultaneously, and 2) the weight coefficients in the existing approximate reasoning algorithms are hardly enough to reflect the associated weights of reordered places. In response, we propose a new type of FPNs, called cloud reasoning Petri nets (CRPNs) based on the concept of interval clouds and the hybrid averaging operator. The cloud production rules in a knowledge-based system are modeled by CRPNs, where the truth degrees of places, the certainty factors of rules, and the thresholds of transitions are represented by interval clouds. Moreover, a matrix operation-based reasoning algorithm is proposed to improve the efficiency of calculating final truth degrees, in which both local and ordered weight coefficients are taken into consideration. Finally, a practical example concerning a power system is provided to demonstrate the usefulness and advantages of the proposed CRPN model. [ABSTRACT FROM AUTHOR]

Future high-energy physics experiments at the energy and intensity frontiers will face a challenge of severe radiation environment from both ionization dose and charged and neutral hadrons. The high-luminosity large hadron collider, for example, will present an environment, where up to 130 Mrad ionization dose, 3 \times 10^14 charged hadrons/cm2 and 5 \times 10^15 neutrons/cm2 are expected. In this paper, we report our investigation on charged hadron-induced radiation damage in BaF2, LYSO/LFS, and PWO crystals up to 3 \times 10^15 protons/cm2 by using 800-MeV protons at the Los Alamos Neutron Science Center. Comparison is made between radiation damages induced by protons and ionization dose alone. [ABSTRACT FROM AUTHOR]

Failure mode and effect analysis (FMEA) is a well-known engineering technique to recognize and reduce possible failures for quality and reliability improvement in products and services. It is a group-oriented method usually conducted by a multidisciplinary and cross-functional expert panel. In this paper, we explore two key issues inherent to the FMEA practice: the representation of diversified risk assessments of FMEA team members and the determination of priority ranking of failure modes. Specifically, a framework integrating cloud model, a new cognitive model for coping with fuzziness and randomness, and preference ranking organization method for enrichment evaluation (PROMETHEE) method, a powerful and flexible outranking decision making method, is developed for managing the group behaviors in FMEA. Moreover, FMEA team members’ weights are objectively derived taking advantage of the risk assessment information. Finally, we illustrate the new risk priority model with a healthcare risk analysis case, and further validate its effectiveness via sensitivity and comparison discussions. [ABSTRACT FROM PUBLISHER]

Fuzzy Petri net (FPN), as one type of high-level Petri nets, has attracted a lot of attention over the recent decade due to its adequacy for knowledge representation and logic reasoning. However, in the FPN literature, the truth degrees of input places are usually given directly or supposed by researchers. No or little research has been performed on the determination of initial marking vector for a specific FPN. In this correspondence paper, we introduce a group decision-making model using hesitant 2-tuple linguistic term sets to obtain the initial truth values of FPNs based on domain experts’ knowledge and gathered data. As is illustrated by the numerical example, the proposed framework can well capture domain experts’ diversity judgements and derive initial truth degrees for an FPN under different types of uncertainties. [ABSTRACT FROM AUTHOR]

Nowadays, failure mode and effect analysis (FMEA) is a widely used reliability analysis technique for products, processes, and services because of its relative simplicity. The conventional risk priority number method, however, has been criticized as having many limitations, such as in the assessment of failure modes, the weighting of risk factors, and the prioritization of failure modes. In this paper, we aim to develop a new risk priority model for FMEA by integrating hesitant 2-tuple linguistic term sets and an extended QUALIFLEX approach. The concept of hesitant 2-tuple linguistic term sets is first presented to express various uncertainties in the assessment information of FMEA team members. Borrowing the idea of grey relational analysis, a multiple objective optimization model is constructed to determine the relative weights of risk factors with incomplete weight information. The extended QUALIFLEX approach with an inclusion comparison method is then suggested to determine the risk ranking of failure modes identified in FMEA. Finally, the practicality and effectiveness of the proposed FMEA are demonstrated through a case study, and the results show that the new risk priority approach is useful and flexible for handling complicated FMEA problems and can yield a reasonable and credible priority ranking of failure modes. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) is usually applied to multispectral fluorescence imaging for fluorescence unmixing. Unfortunately, most NMF-based fluorescence unmixing methods fail to take advantage of spatial information in data. Besides, NMF is an inherently ill-posed problem, which gets worse in the sparse acquisition of multispectral data (from a small number of spectral bands) due to its insufficient measurements and severe discontinuities in spectral emissions. To overcome these limitations by exploiting the spatial difference between multiple-target fluorophores and background autofluorescence (AF), we propose improved normalized cut to automatically classify all multispectral pixels into target fluorophores and background AF groups. We then initialize NMF by extracting the endmember spectra of target/background fluorescent components in the two groups, and impose a L1/2 -norm partial sparseness constraint on merely the abundances of target fluorophores within hierarchical alternating least squares framework of NMF. Experimental results based on synthetic and in vivo fluorescence data show the superiority of the proposed algorithm with respect to other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This paper proposes a linguistic reasoning Petri net (LRPN) model and develops an ordered weighted linguistic reasoning (OWLR) algorithm for knowledge representation and reasoning. Linguistic production rules in the knowledge base of a decision support system are modeled by LRPNs, where the truth degrees of the propositions in the linguistic production rules and the certainty factors of the rules are represented by linguistic 2-tuples. Moreover, both local and global weights of knowledge rules are taken into account in the linguistic reasoning process. The developed OWLR algorithm can allow the rule-based expert systems modeled with LRPNs to execute knowledge reasoning in a more flexible and intelligent manner. Finally, a case study regarding production rescheduling is presented to show the effectiveness and benefits of the proposed LRPN model and the linguistic reasoning approach. [ABSTRACT FROM PUBLISHER]

A series–parallel autoregulated rectifying circuit is proposed for a multioutput dc/dc light-emitting diode (LED) driver. The series–parallel autoregulated characteristics balance the charge in the secondary blocking capacitors automatically. Hence, with the proposed rectifier and secondary-side multiwinding transformer, a multioutput LED driver with precise currentsharing capability is achieved even for multiple LED strings with different voltages. The operating principle of the rectifier is analyzed with a sinusoidal current source and is verified with a four-output LLC dc/dc LED driver. This proposed rectifier can be applied to other converters featuring a high-frequency current source link at the primary side. [ABSTRACT FROM PUBLISHER]

Although a promising tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) still suffer from some deficiencies. First, the parameters in current FPN models, such as weight, threshold, and certainty factor do not accurately represent increasingly complex knowledge-based expert systems and do not capture the dynamic nature of fuzzy knowledge. Second, the fuzzy rules of most existing knowledge inference frameworks are static and cannot be adjusted dynamically according to variations of antecedent propositions. To address these problems, we present a new type of FPN model, dynamic adaptive fuzzy Petri nets, for knowledge representation and reasoning. We also propose a max-algebra based parallel reasoning algorithm so that the reasoning process can be implemented automatically. As illustrated by a numerical example, the proposed model can well represent the experts' diverse experience and can implement the knowledge reasoning dynamically. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to propose a new risk priority model for prioritizing failures in failure mode and effects analysis (FMEA) on the basis of fuzzy evidential reasoning (FER) and belief rule-based (BRB) methodology. The technique is particularly intended to resolve some of the shortcomings in fuzzy FMEA (i.e., fuzzy rule-based) approaches. In the proposed approach, risk factors like occurrence (O), severity (S), and detection (D), along with their relative importance weights, are described using fuzzy belief structures. The FER approach is used to capture and aggregate the diversified, uncertain assessment information given by the FMEA team members; the BRB methodology is used to model the uncertainty, and nonlinear relationships between risk factors and corresponding risk level; and the inference of the rule-based system is implemented using the weighted average-maximum composition algorithm. The Dempster rule of combination is then used to aggregate all relevant rules for assessing and prioritizing the failure modes that have been identified in FMEA. A case study concerning an ocean going fishing vessel in a marine industry is provided and conducted using the proposed model to illustrate its potential applications and benefits. [ABSTRACT FROM PUBLISHER]

In contrast to hybrid electric cars (HECs), the issues concerning cost, volume, and reliability are even more rigorous when developing hybrid electric scooters (HESs). Therefore, the drive topology and control strategy used in HEC cannot be applied to HES directly. This paper presents a single-stage bidirectional dclac converter based on a general full-bridge inverter. The converter is designed for a low-voltage brushless dc motor/alternator (BLDCM/A) used in HESs, in which the additional bulky inductor and power switches are eliminated in the proposed design. In order to increase the reliability of the commutation process, a cost-effective sensorless control scheme for the motor and alternator commutation is developed. The commutation signals are extracted directly from the average terminal voltages, in which the motor neutral voltage, multistage analog filters, analog-to-digital converters, and the complex digital phase-shift (delay) circuits are eliminated. In addition, instead of using the complex flux-weakening control technique, the winding-changeover technique is exploited to extend the range of the operating speed. With attractive features, such as low cost and ease of implementation, the proposed approach is particularly suitable for electric bikes, electric scooters, HESs, etc. Theoretical analysis and several experiments are conducted to justify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents the analysis, design, and implementation of a high performance and cost effective sensorless control scheme for the extensively used brushless dc motors. In an effort to decrease cost and increase ease of implementation, the commutation signals are obtained without the motor neutral voltage, multistage analog filters, A/D converters, or the complex digital phase shift (delay) circuits which are indispensable in the conventional sensorless control algorithms. In the proposed method, instead of detecting the zero crossing point of the nonexcited motor back electromagnetic force (EMF) or the average motor terminal to neutral voltage, the commutation signals are extracted directly from the specific average line to line voltages with simple RC circuits and comparators. In contrast to conventional methods, the neutral voltage is not needed; therefore, the commutation signals are insensitive to the common mode noise. Moreover, the complex phase shift circuit can be eliminated. As a result, the proposed control algorithm can be easily interfaced with the cost effective commercial Hall effect sensor based commutation integrated circuits. Due to its inherent low cost, the proposed control algorithm is particularly suitable for cost sensitive products such as air purifiers, air blowers, cooling fans, and related home appliances. Theoretical analysis and experiments are conducted over a wide operating speed range and different back EMF waveforms to justify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Because of its fast scintillation and low cost, undoped CsI crystal has been used to construct crystal calorimeters for high-energy physics experiments. Undoped CsI crystal samples from three vendors were investigated at the Caltech Crystal Laboratory. In addition to the 30-ns fast scintillation peaked at 310 nm, a slow scintillation component peaked at 450 nm was found with a decay time of a few microseconds. The intensity of the slow component may be varied from the seed to the tail end, so it affects crystal’s light response uniformity. It was also found that the slow component is highly correlated with the radiation-induced readout noise and may be eliminated spectroscopically by using an optical filter. The slow component is believed to be related to impurities and/or defects in the crystal. [ABSTRACT FROM AUTHOR]

Failure mode and effects analysis (FMEA) is a systematic and proactive risk prevention and management tool used to improve the reliability and safety of a system, process or service. However, many defects around the traditional FMEA affect the effectiveness of its practical applications. When dealing with the FMEA problems, the first and the most important step is to collect the risk assessment information of experts regarding failure modes. Moreover, clustering failure modes is more adaptable to actual needs, especially under a tight resource constraint. In this article, a new FMEA model using double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) and k-means clustering is developed to evaluate and cluster the risk of failure modes. First, the DHHFLTSs are utilized to describe the complex linguistic assessments on failure modes given by FMEA team members. Next, an improved k-means algorithm is proposed and used to cluster failure modes into priority classes. Finally, a case study concerning the risk evaluation over floating offshore wind turbine is provided to illustrate the effectiveness and usefulness of our proposed FMEA. The results show that the new risk classification approach is more reasonable and practical for risk management decision making. [ABSTRACT FROM AUTHOR]

This paper studies a strategic level decision problem for disaster relief facility network planning in metropolises. This problem is concerned with some long-term decisions on locating the emergency shelters, locating the supply and medical centers, and maintaining fleets of ambulances and transportation vehicles. These established facilities and deployed vehicles constitute a humanitarian logistic network for rapid responses and reliefs. An integer programming model is proposed to minimize the total cost of establishing such a disaster response network. A Lagrangian relaxation method is developed for solving the problem in large scales. To illustrate the proposed method’s usage in practice, a demo example about its application in Shanghai is given. Some numerical experiments are also performed to validate the effectiveness and the efficiency of the proposed model and method. [ABSTRACT FROM PUBLISHER]

Bilayer barium titanate (BaTiO3, BTO)/bismuth ferrite (BiFeO3, BFO) capacitive devices were fabricated and systematically analyzed from the aspects of thin-film material crystallography and electrical characterization. The tetragonal phase (T) of BTO with the reducing thickness (150 nm) was demonstrated through carefully adjusting the conditions of sputtering process and post-thermal treatments. The thickness dependence of polarization, energy density, permittivity, and leakage current was investigated in the BTO/BFO bilayer system. It showed that 150-nm BFO film is an optimal thickness, which led to the substantial increase in energy density up to 920 mJ/cm3 with the charge/discharge efficiency of 79.7% at 10 V. The permittivity of 150-nm BFO bilayer device was found to be 258, which is much higher than the single -layer BFO and other BTO/BFO devices with different BFO thicknesses. Leakage current could be reduced by three orders with the increasing thickness of BFO layer from 30 to 225 nm. In addition, the leakage current of BTO/BFO bilayer capacitors was 102 times lower than BFO single layer with the same total thickness. The result showed that the leakage current of BFO material could be significantly reduced in the bilayer system. The performance of BTO/BFO bilayer capacitors indicates that it is a promising technique for the implementation of nanoscale, lead-free, and nonelectrochemical thin-film energy storage-related applications. [ABSTRACT FROM AUTHOR]

In addition to La-doped barium fluoride (BaF2) crystals, La/Ce co-doped BaF2 crystals were also investigated at the California Institute of Technology, Pasadena, CA, USA. Strong cerium-induced emissions peaked at 305 and 320 nm with a decay time of 25 ns were observed, in addition to the fast and slow scintillation at 220 and 300 nm, respectively. The La/Ce co-doping was also found effective in suppressing the slow component in BaF2. Compared to La-doped crystals, the La/Ce co-doped crystals have a better light output in both 50- and 2500-ns gate. The fast/slow ratio of La/Ce co-doped crystal was found to be about 1/1, similar to La-doping, which is also considered not sufficient for pile-up suppression. A 20-cm-long La/Ce co-doped BaF2 crystal shows also excellent optical quality and light response uniformity. [ABSTRACT FROM AUTHOR]

Because of its fast scintillation component with subnanosecond decay time, BaF2 crystals are considered as a candidate for ultrafast crystal calorimeters for future high energy physics experiments at the energy and intensity frontiers. Undoped BaF2, however, has a slow scintillation component with 600 ns decay time, which causes pile-up. In the part I of this paper, we report investigations on La doping in BaF2 crystals to suppress the slow component. While typical fast/slow (F/S) ratio observed in undoped BaF2 is 1/5, La-doped BaF2 crystals were found to improve this ratio to about 1/1. The overall F/S ratio, however, is considered not sufficient for pile-up suppression. [ABSTRACT FROM AUTHOR]