Window Width Research Articles

Energy-filtered random-phase states as microcanonical thermal pure quantum states

We propose a method to calculate finite-temperature properties of a quantum many-body system for a microcanonical ensemble by introducing a pure quantum state named here an energy-filtered random-phase state, which is also a potentially promising application of near-term quantum computers. In our formalism, a microcanonical ensemble is specified by two parameters, i.e., the energy of the system and its associated energy window. Accordingly, the density of states is expressed as a sum of Gaussians centered at the target energy with its spread corresponding to the width of the energy window. We then show that the thermodynamic quantities such as entropy and temperature are calculated by evaluating the trace of the time-evolution operator and the trace of the time-evolution operator multiplied by the Hamiltonian of the system. We also describe how these traces can be evaluated using random diagonal-unitary circuits appropriate to quantum computation. The pure quantum state representing our microcanonical ensemble is related to a state of the form introduced by Wall and Neuhauser for the filter diagonalization method [M. R. Wall and D. Neuhauser, J. Chem. Phys. 102, 8011 (1995)], and therefore we refer to it as an energy-filtered random-phase state. The energy-filtered random-phase state is essentially a Fourier transform of a time-evolved state whose initial state is prepared as a random-phase state, and the cut-off time in the time-integral for the Fourier transform sets the inverse of the width of the energy window. The proposed method is demonstrated numerically by calculating thermodynamic quantities for the one-dimensional spin-1/2 Heisenberg model on small clusters up to 28 qubits, showing that the method is most efficient for the target energy around which the dense distribution of energy eigenstates is found.

A reduced-scale experimental investigation of facade flame behavior ejected from a top-hung window of fire compartment

In case of a room fire, facade flame ejected from a window could lead to catastrophic consequence. Top-hung window is a common feature in modern buildings. This paper investigates, for the first time, the ejected facade flame behavior from a top-hung window. Experiments are performed by a cubic scale model compartment with a top-hung window of four opening angles and four dimensions under various heat release rates (HRR). A total of 119 test conditions are involved to quantify generally the ejected facade flame behavior and morphological characteristics, characterized by its vertical height and horizontal depth (normal to facade). It is found that the position of neutral plane between the inflow and outflow at the top-hung window increases with opening angle, while being smaller as the window width is larger or the window height is smaller. The critical HRR for flame ejection is higher as the top-hung window opening angle is larger. The facade flame depth increases, while the flame height decreases, with increasing of opening angle of the top-hung window. A new characteristic length taking into account both the top-hung window dimensions and window opening angle is deduced by the mass balance analysis of the inflow and outflow as well as ventilation factor through the top-hung window. New dimensionless models are established to describe the critical HRR for flame ejection, the height of the facade flame and flame depth as a function of the newly derived top-hung window ventilation factor as well as characteristic length, showing good fitting of experimental results. The present study provides the basic data, understanding and model of facade flame characteristics from a top-hung window of a fire compartment, which is essential in estimation of its risk and adverse impact to urban environment as a new supplementary over previous knowledge limited for freely opened windows.

Detecting Discontinuities From In Situ Space Measurements: Method and FPGA Implementation

AbstractThe analysis in real time of space data variability is essential for scientists and space mission controllers. Automated tools designed to extract key descriptors of variability are needed and solutions to adapt such algorithms for on‐board computers are rare. This paper describes the design of an automated system for detecting directional discontinuities of a physical quantity and its implementation in Field‐Programmable Gate Array (FPGA). The system is currently adapted for solar wind or terrestrial magnetosheath magnetic field directional discontinuities, that is, sharp changes of the magnetic field directionality. Our detection algorithm uses analysis windows of adjustable width and averaging procedures in order to reduce the effects of random fluctuations. A sliding‐window approach is designed for continuous monitoring and detection of magnetic directional discontinuities. A software implementation of the algorithm was tested using in‐situ magnetic field measurements, and emphasized improvements of performance when using analysis windows of adjustable width. The FPGA implementation of the detection algorithm is built on DILIGENT Nexys 4 DDR featuring a commercial Xilinx Artix‐7 device and is designed to be ported to space qualified infrastructure. The FPGA system was tested with synthetic and laboratory signals, and provides results in very good agreement with the software implementation. The FPGA system provides an efficient real‐time monitoring solution using minimal computational and energy resources, and reducing the main on‐board computer utilization.

Passenger Flow Prediction of Scenic Spots in Jilin Province Based on Convolutional Neural Network and Improved Quantile Regression Long Short-Term Memory Network

Passenger flow is an important benchmark for measuring tourism benefits, and accurate tourism passenger flow prediction is of great significance to the government and related tourism enterprises and can promote the sustainable development of China’s tourism industry. For daily passenger flow time series data, a passenger flow forecasting method based on convolutional neural network (CNN) and improved quantile regression long short-term memory network (QRLSTM), denoted as CNN-IQRLSTM, is proposed with reconstructed correlation features and in the form of sliding windows as inputs. First, four discrete variables such as whether the day is a weekend and holiday are created by time; then, a sliding window of width 42 is used to pass the passenger flow data into the network sequentially; finally, the loss function of the sparse Laplacian improved QRLSTM is introduced for passenger flow prediction, and the point prediction and interval prediction results under different quartiles are obtained. The application of quantile regression captures the overall picture of the data, enhances the robustness, fit, predictive power and nonlinear processing capability of neural networks, and fills the gap between quantile regression and neural network methods in the field of passenger flow prediction. CNN can effectively handle complex input data, and the improved nonlinear QR model can provide passenger flow quantile prediction information. The method is applied to the tourism traffic prediction of four 5A scenic spots in Jilin Province, and the effectiveness of the method is verified. The results show that the method proposed in this paper fits best in point prediction and has higher prediction accuracy. The MAPE of the Changbai Mountain dataset was 0.07, the MAPE of the puppet palace museum dataset was 0.05, the fit of the Sculpture Park dataset reached 93%, and the fit of the net moon lake dataset was as high as 99%. Meanwhile, the interval prediction results show that the method has a larger interval coverage as well as a smaller interval average width, which improves the prediction efficiency. In 95% of the interval predictions, the interval coverage of Changbai Mountain data is 99% and the interval average width is 0.49. It is a good reference value for the management of different scenic spots.

Data Processing Method of Mine Wind Speed Monitoring Based on an Improved Fuzzy C-Means Clustering Algorithm

Analyzing and processing mine wind speed monitoring data is the key to realizing intelligent ventilation and real-time calculation of the ventilation network. According to the characteristics of the artificial regulation of a mine ventilation system, a local regression fuzzy C clustering algorithm is proposed in this paper, which combines local outlier processing with global air volume state analysis. Firstly, the algorithm uses the robust local weighted regression principle to analyze and preprocess the data locally, determines the risk degree of the abnormal data according to the identified times of outliers, determines the clustering number according to the clustering validity function, and analyzes the global air volume fluctuation according to the clustering results. The results show that most outliers are identified in data preprocessing. Still, the processing of dense outliers is weak, related to the window width setting and weighting multiple. The number of clusters can represent the fluctuation of the ventilation state and the pre-processed cluster centers are 4.4% lower than the original data because most of the outliers are higher than the average data. According to the law of air volume balance, the clustering results can pave the way for the global deduction of mine wind speed. There is an implicit relationship between data preprocessing and the clustering process, and when intensive outliers are not eliminated, they may be identified as separate clusters. The research of this paper points out the direction of mine wind speed data analysis, which can provide a theoretical basis for intelligent mine ventilation and real-time calculation of the ventilation network.

The second-order local polynomial Fourier transform as instantaneous frequency and chirp rate estimator

Statistical properties, bias and variance, of the second-order local polynomial Fourier transform (LPFT) in the instantaneous frequency (IF) and chirp rate (CR) estimation are considered. We have derived expressions based on the error linearization around the exact value of signal parameters as it is commonly done in this field. However, we have also demonstrated the limits of this methodology. Two particularly important frequency modulated (FM) signals are considered as test cases: quadratic and cubic phase signals. Approximative expression for the bias and variance in the latter case are also derived. It is demonstrated that the optimal design parameter of the LPFT, i.e., window width, must be optimized independently for the IF and CR estimation to produce the minimum mean squared error (MSE). The derived expressions are checked by simulations.

Deep learning-based quantitative estimation of lymphedema-induced fibrosis using three-dimensional computed tomography images

In lymphedema, proinflammatory cytokine-mediated progressive cascades always occur, leading to macroscopic fibrosis. However, no methods are practically available for measuring lymphedema-induced fibrosis before its deterioration. Technically, CT can visualize fibrosis in superficial and deep locations. For standardized measurement, verification of deep learning (DL)-based recognition was performed. A cross-sectional, observational cohort trial was conducted. After narrowing window width of the absorptive values in CT images, SegNet-based semantic segmentation model of every pixel into 5 classes (air, skin, muscle/water, fat, and fibrosis) was trained (65%), validated (15%), and tested (20%). Then, 4 indices were formulated and compared with the standardized circumference difference ratio (SCDR) and bioelectrical impedance (BEI) results. In total, 2138 CT images of 27 chronic unilateral lymphedema patients were analyzed. Regarding fibrosis segmentation, the mean boundary F1 score and accuracy were 0.868 and 0.776, respectively. Among 19 subindices of the 4 indices, 73.7% were correlated with the BEI (partial correlation coefficient: 0.420–0.875), and 13.2% were correlated with the SCDR (0.406–0.460). The mean subindex of Index 2 left( {frac{{P_{Fibrosis, in, Affected} - P_{Fibrosis, in, Unaffected} }}{{P_{Limb, in, Unaffected} }}} right) presented the highest correlation. DL has potential applications in CT image-based lymphedema-induced fibrosis recognition. The subtraction-type formula might be the most promising estimation method.

THE EFFECT OF PICKING UNCERTAINTY WINDOW INTERVAL ON HYPOCENTER MICRO-EARTHQUAKE (MEQ) LOCATION USING GEIGER METHOD

Identification of the initial phase of the primary (P) waves at each seismic station is often inconsistent and implies the operator’s subjectivity, due to the high noise level. Errors in identifying the initial phase of the P waves can significantly bias the location of the hypocenter. In this study, the data used is one micro-earthquake (MEQ) event recorded by 8 seismic stations. At each seismic station, the P waves arrival time was measured repeatedly, to obtain the picking uncertainty time window interval of the P waves arrival time. The P waves arrival time data was processed using the Geiger method to obtain the MEQ hypocenter location. Based on the processing results, the determination of the arrival time of the P waves depends on the width of the time window and the amplitude scale used. The picking uncertainty time window interval will be narrower for arrival time observations with an enlarged time window and amplitude scale. Time window intervalin the range of 0,007-0,049 seconds, in this study significantly refracted the MEQ hypocenter location. The results of determining the location of the MEQ hypocenter using the Geiger method only produced two variants of the RMS error value with a difference of 0.001 seconds. However, the difference in the RMS error value is associated with a shift in the epicenter in the range of 2 – 21,1 meters and a shift in the elevation of the hypocenter in the range of 3-15 meters.

Towards accurate vibration prediction of pump-turbine runners in deep partial load operation

Within the XFLEX HYDRO project, operating power plants in low load conditions is being investigated as one of the possibilities to enhance the flexibility of hydropower. Therefore, the structural dynamic behaviour of a pump-turbine runner is numerically analysed in deep partial load (DPL) operating points. Based on highly resolved unsteady CFD simulations performed prior to this work, the pressure fluctuations are extracted and mapped onto the structure for subsequent Finite Element (FE) analyses. Herein, an efficient hybrid methodology combining purely structural and coupled Fluid-Structure-Interaction (FSI) modelling is applied. The obtained simulation results are compared to experimental measurements. Herein, the influence of several factors such as the CFD mesh size, the turbulence model, the simulated time window width, and the structural simulation type is studied. As a result, the numerical outcome is validated with good accuracy. The importance of highly resolved CFD simulations with appropriate turbulence modelling is highlighted. Besides, the necessity of coupled acoustic-structural analyses to accurately resolve the dynamic response to excitation by rotor stator interaction (RSI) pressure shapes is demonstrated despite the chaotically dominated nature of the structural vibrations. The results of this work can be used to extend the operating range of the pump-turbine runner and, most importantly, to calibrate numerical models to predict the structural vibrations accurately and efficiently in DPL and finally ensure safe operation of these components.

The European Standard EN 15757 Concerning Specifications for Relative Humidity: Suggested Improvements for Its Revision

The European Standard EN 15757: 2010 ‘Conservation of Cultural Property—Specifications for temperature and relative humidity to limit climate-induced mechanical damage in organic hygroscopic materials’ is a guide specifying the allowed limits of variability of the indoor climate, in particular relative humidity (RH) to preserve cultural heritage objects and collections composed of climate-vulnerable materials. This paper is finalized to provide useful elements to improve the Standard at its next revision, based on focused research. The methodologies and the mathematical tools used are performed on 18 case studies representing different buildings, climates, and use, including heated and unheated buildings, museums, churches, concert halls, archives, and storage rooms. The first aim is to compare the method based on the centred moving average suggested by Annex A of EN15757 with an alternative method based on percentile interpolation to calculate the reference RH values, and in particular the safe band of RH variability, as well as the upper and lower risky bands. It has been found that the two methods provided the same results, but the latter is easier to manage. The second aim is to verify if the duration of the record necessary for the determination of the safe band is really 13 months of measurements as required by the Standard to account for the specific request of the centred moving average with a 30-day time window. This paper demonstrates that the same goal may be reached with a 12-month record, but extracting from the record itself the two periods required by the time window, i.e., the last 15 days of the year will be copied before the start of the record, and the same with the first 15 days after the end. The third aim is to test if the particular choice of the width of the time window is influential on the width of the safe band, and to assess the relationship between the width of the safe band and the width of the time window. The results show that the safe band logarithmically depends on the length of the time window, so it is crucial to respect the 30-day window established by the Standard.

A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models

Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions.

Investigation of anode shaping process during co-rotating electrochemical machining of convex structure on inner surface

A novel co-rotating electrochemical machining method is proposed for fabricating convex structures on the inner surface of a revolving part. The electrodes motion and material removal method of co-rotating electrochemical machining are different from traditional electrochemical machining. An equivalent kinematic model is established to analyze the novel electrodes motion, since the anode and cathode rotate in the same direction while the cathode simultaneously feeds along the line of centres. According to the kinematic equations of the electrodes and Faraday’s law, a material removal model is established to simulate the evolution of the anode profile in co-rotating electrochemical machining. The simulation results indicate that the machining accuracy of the convex structure is strongly affected by the angular velocity ratio and the radius of the cathode tool. An increase of the angular velocity ratio can improve the machining accuracy of a convex structure. A small difference in the radius of the cathode tool will cause changes in the shape of the sidewalls of the convex structure. The width of the cathode window affects only the width of the convex structure and the inclination α of the sidewall. A relation between the width of the cathode window and the width of the convex structure was obtained. The formation process for a convex structure under electrochemical dissolution was revealed. Based on the simulation results, the optimal angular velocity ratio and cathode radius were selected for an experimental verification, and 12 convex structures were simultaneously fabricated on the inner surface of a thin-walled revolving part. The experimental results are in good agreement with the simulation results, which verifies the correctness of the theoretical analysis. Therefore, inner surface co-rotating electrochemical machining is an effective method for fabricating convex structures on the inner surface of a revolving part.

Polarimetric Direction of Arrival Estimations Based on Adaptive Linear Time-Frequency Transforms

A spatially polarized time-frequency distribution (SPTFD) based on dual-polarized double-fed antenna arrays is adapted to deal with polarization-unstable signals. A linear time-frequency (TF) representation was used for an instantaneous frequency (IF) estimate, primarily due to its simplicity and immunity to cross-interference. Using a set of linear TF transformations using Gaussian windows and Fourier oscillation kernels, the IF estimated window widths of multiple unstable signals are obtained. This paper introduces a new method for estimating the direction of arrival (DOA) of polarized waves using adaptive linear time-frequency transforms. In this paper, a narrowband far-field point source on the receiving array is analyzed. The source signal is split into two orthogonally polarized components. The optimal window is determined by the first derivative of the IF; for this purpose, we take a simple algorithm for solving the derivative and optimize it. In developing TF-adaptive and fully automatic TF display technology, the first method is to use the time-adaptive window for minimizing the IF estimate mean square error (MSE) sum at each moment, while the second procedure is to adjust according to time and frequency and minimize estimate MSE sum at each position in the TF region. Due to its combination with signal polarization, the spatial time-frequency distribution (STFD) gains more freedom and thus perfects the phonon space estimation of noise and signal. On the SPTFD platform, polarized time-frequency multiple signal classification (PTF-MUSIC) is used for the estimation of signal direction of arrival, which outperforms conventional time-frequency MUSIC. Using the example of a synthesized signal, this method outperforms conventional techniques in DOA estimation.

Case of pneumatosis cystoides intestinalis with intra-abdominal free air developed during treatment with voglibose.

Contrast-enhanced computed tomography (CT) scan (portal phase) at the onset. Emphysema is detected in the ileal wall (b, c: Arrows) and free air is detected in the abdominal cavity (a: Arrowhead). CT scan imaging settings: (a-c) window level (WL) 60 and window width (WW) 300. (d-f) WL 0 and WW 433. By changing the imaging settings, intestinal emphysema and free air can be more easily identified.

Dynamic relational event modeling: Testing, exploring, and applying.

The relational event model (REM) facilitates the study of network evolution in relational event history data, i.e., time-ordered sequences of social interactions. In real-life social networks it is likely that network effects, i.e., the parameters that quantify the relative importance of drivers of these social interaction sequences, change over time. In these networks, the basic REM is not appropriate to understand what drives network evolution. This research extends the REM framework with approaches for testing and exploring time-varying network effects. First, we develop a Bayesian approach to test whether network effects change during the study period. We conduct a simulation study that illustrates that the Bayesian test accurately quantifies the evidence between a basic ('static') REM or a dynamic REM. Secondly, in the case of the latter, time-varying network effects can be studied by means of a moving window that slides over the relational event history. A simulation study was conducted that illustrates that the accuracy and precision of the estimates depend on the window width: narrower windows result in greater accuracy at the cost of lower precision. Third, we develop a Bayesian approach for determining window widths using the empirical network data and conduct a simulation study that illustrates that estimation with empirically determined window widths achieves both good accuracy for time intervals with important changes and good precision for time intervals with hardly any changes in the effects. Finally, in an empirical application, we illustrate how the approaches in this research can be used to test for and explore time-varying network effects of face-to-face contacts at the workplace.

Truck routing and platooning optimization considering drivers’ mandatory breaks

Truck platooning has been touted as one of the most promising technologies to improve fuel efficiency thanks to the reduced air drag of the digitally connected truck’s slipstream in a trainlike convoy. The benefits of truck platooning lie in the reduced fuel consumption and labor cost, and the enhanced driving experience. However, these benefits cannot be realized without an appropriate routing, scheduling, and platooning plan of trucks subject to practical constraints, especially the drivers’ break requirement for long-haul journeys. This study addresses the truck routing and platooning problem considering drivers’ mandatory breaks as well as other characteristics such as the state-and-position-dependent fuel-saving rates of platooning, trucks’ designated intermediate relays, and platoon size limit. The problem is to route the trucks to their respective destinations on time using the least amount of fuel by maximizing the formations of fuel-saving platoons over entire trips while satisfying the break time requirement of drivers. A mixed-integer linear programming (MILP) model is first developed for the proposed problem. A hybrid algorithm integrating the partial-MILP approach and iterated neighborhood search with tailored search operators is proposed to address the problem. Various randomly generated networks are used in numerical experiments to examine the effectiveness and efficiency of our proposed model and solution method. An extensive sensitivity analysis is also conducted to explore the impacts of several major influential factors, i.e., the drivers’ mandatory break times, the fuel reduction rate of the leading truck of platoons, as well as the width of the service time window, on the system performance and derive the managerial insights.

Real-time remaining useful life prediction based on adaptive kernel window width density

Remaining useful life (RUL) prediction plays an important role in improving the availability and productivity of systems. To improve the accuracy of real-time RUL prediction during system operation, we propose a modeling method for real-time RUL prediction based on adaptive kernel window width density. First, a non-parametric kernel density estimation (KDE) real-time RUL prediction model is proposed, and a window width model with adaptive kernel window width density is established by introducing a local density factor in the window width selection. The local density of sample points is calculated by the k-nearest neighbor distance, and the KDE is performed by adaptively selecting the window width value according to the local density of sample points in the region of non-uniform distribution of sample points. As the monitoring data changes in real time, the kernel density estimates of known samples are used to recursively update the kernel density estimates of new samples. Moreover, the logarithmic transformation of random variables and space mapping are used in the establishment of the RUL prediction model. A model of logarithmic kernel diffeomorphism transformation is established to solve the boundary shift problem of kernel estimation in the prediction to improve the prediction accuracy. Finally, the validity of the method is verified through case studies, and the accuracy of the model is judged using evaluation quasi-measures.

The Effect of an Energy Window with an Ellipsoid Phantom on the Differential Defect Contrast on Myocardial SPECT Images.

Good quality single-photon emission computed tomography (SPECT) images are required to achieve a perfect diagnosis and determine the severity of defects within the myocardial wall. There are many techniques that can support the diagnosis of defect formations in acquired images and contribute to avoiding errors before image construction. The main aim of this study was to determine the effect of energy width (15%, 20%, and 25%) on defect contrast in myocardial SPECT images correlated with the decentralization of positioning of a phantom. A phantom of polyethylene plastic was used to mimic the myocardial wall of the left ventricle. The phantom consists of two chambers, inner and outer. Two rectangular pieces of plastic were placed in anterior and inferior locations in the mid-region of the myocardial phantom to simulate myocardial infarction (defects). The average defect contrast for all phantom positions using 15% to 20% energy was (1.2, 1.6) for the anterior region and (1.1, 2) for the inferior region, respectively. Additionally, the energy window width was >25% with a large displacement of the positioning off center, leading to loss of the defect contrast in myocardial SPECT images, particularly in the inferior region. The study showed decreasing defect contrast in both locations, anterior and inferior, with increasing energy window width correlated with eccentricity positioning of the phantom on an imaging table.

Precession and Jitter in FRB 180916B

ABSTRACT Recent CHIME/FRB observations of the periodic repeating fast radio bursts (FRB) 180916B have produced a homogeneous sample of 44 bursts. These permit a redetermination of the modulation period and phase window, in agreement with earlier results. If the periodicity results from the precession of an accretion disc, in analogy with those of Her X-1, SS 433, and many other superorbital periods, the width of the observable phase window indicates that the disc axis jitters by an angle of about 0.14 of the inclination angle, similar to the ratio of 0.14 in the well-observed jittering jet source SS 433.

The optimum model of horizontal canopies on reducing building energy consumption

Shading windows that are exposed to sunlight is an important method of reducing cooling energy in the hot months of the year and thus reducing annual energy consumption. One of the best ways to shade windows on the south walls is to use the horizontal canopy, which is located outside the building and above the windows. In this study, the horizontal canopy above the south wall window is optimized for different weather conditions. In order to optimize the horizontal canopy, various parameters that affect shading and solar radiation received by a window with a fixed surface of one square meter have been investigated in Kochi, India climate. These parameters are canopy dimensions, window dimensions, latitude and window location. The results include the optimal dimensions of the canopy and window as a coefficient of window height. A study of parameters on shading in Kochi climate equal to windows width Ww = 10 and e = 0, g = 0.3, pa = 0.3, provided the optimal canopy dimensions. In hot months, an optimal horizontal canopy over the south window at a rate of 505 MJ/m2 provides significant savings compared to a south window without a canopy. Radiant energy received without canopies and with canopies during the cold months. In cold months, the horizontal canopy on the south window consumes 62 MJ/m2, which is a waste of energy compared to the south windows without canopies.