Estimation of Extreme Coastal Wave Heightsfrom Time Series of Wave Data

A method is presented to extrapolate a time series of wave data to extreme wave heights. The 15-year time series of deepwater wave data collected for 34 min every hour from 1988 to 2002 in the South Pacific Ocean, Australia, is analyzed to generate a set of storm peak wave heights by use of the Peaks-Over-Threshold method. The probability distribution is calculated by grouping the observod storm peak wave heights into a number of wave height classes and assigning a probability to each wave height class. The observed probability distribution is then fitted to eight different probability distribution functions and found to be fitted best by the Weibull distribution （a = 1.17）, nearly best by the FT-Ⅰ, quite well by the exponential, and poorly by the lognormal function based on the criterion of the sum of squares of the errors, SSE （H）. The effect of the threshold wave height on the estimated extreme wave height is also studied and is found insignificant in this study. The 95 % prediction intervals of the best-fit FT-Ⅰ , exponential and Weibull functions are also derived.

A Data Security Framework for Cloud Computing Services

Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies,such as the Internet of Things.With increasing industry adoption and migration of traditional computing services to the cloud,one of the main challenges in cybersecurity is to provide mechanisms to secure these technologies.This work proposes a Data Security Framework for cloud computing services(CCS)that evaluates and improves CCS data security from a software engineering perspective by evaluating the levels of security within the cloud computing paradigm using engineering methods and techniques applied to CCS.This framework is developed by means of a methodology based on a heuristic theory that incorporates knowledge generated by existing works as well as the experience of their implementation.The paper presents the design details of the framework,which consists of three stages:identification of data security requirements,management of data security risks and evaluation of data security performance in CCS.

On Human Autonomic Nervous Activity Related to Behavior, Daily and Regional Changes Based on Big Data Measurement via Smartphone

This research uses a large amount of autonomic nervous system data (approximately 100,000 entries) to investigate the relationship between human autonomic nervous activity and behaviors, daily and regional changes. Data were measured via a heart rate variability analysis system that utilizes the camera of smartphones. This system was developed by the authors during previous research. The relations between autonomic nervous system and behaviors, total power and sympathetic nervous activity were found to rise after waking, while during leisure time, the total power rises and sympathetic nervous activity is inhibited. Concerning the relationship between autonomic nervous system and day of the week, it was found that total power decreases from the middle through the latter part of the week (namely, Wednesday, Thursday, and Friday), while it rises on Saturday, while the sympathetic nervous activity is suppressed on Saturday. Regarding the relationship between autonomic nervous system and region, it was found that total power is lower in the Kanto region of Japan than in others. This study also shows statistical proof (using a large amount of measurement data) to ideas held by the public for years. Thus, the data can be considered meaningful to the society, and the authors hope that it helps to improve work-life balance.

On Human Autonomic Nervous Activity Related to Weather Conditions Based on Big Data Measurement via Smartphone

This research uses our previously-developed smartphone camera-based heart rate change analysis system to survey the correlation between weather patterns and the autonomic nervous activity across a big data set of approximately 200,000 entries. The results showed a trend in which a significant decrease was seen in sympathetic nervous activity in both males and females—the higher the temperature. In addition, a significant increase was seen in the sympathetic nervous system in both males and females—the higher the atmospheric pressure. Lastly, a significant decrease was seen in the sympathetic nervous system in both males and females—the more precipitation there was. These results accord with prior research and with human biological phenomena, and we were able to use a data set of approximately 200,000 entries to statistically demonstrate our hypothesis. We believe this represents a valuable set of reference data for use in the health care.

Macro-Analysis on Across-Fault Crustal Deformation Measurement Data Along the Northern Edge of the Qinghai-Xizang Block

Based on the arrangement of the across-fault measurement data along the northern edge of the Qinghai-Xizang block,we divide the deformation into different types and probe the nature of various fault movements based on these types.The recent situation of tectonic movement of main structural belts and seismicity in this area are expounded.From the above,it is concluded that across-fault measurement can reflect not only the conditions of fault movement nearby but also the change of regional stress fields; not only is this a method to obtain regional seismogenic information and to conduct short-term prediction but it is also involved with large scale space-time prediction of moderate and strong earthquakes on the basis of the macro characteristics of fractures.

Robust state of charge estimation of lithium-ion battery via mixture kernel mean p-power error loss LSTM with heap-based-optimizer

The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,which is capable of estimating the future changing states of a nonlinear system.Since the BMS usually works under complicated operating conditions,i.e the real measurement data used for model training may be corrupted by non-Gaussian noise,and thus the performance of the original LSTM with the mean square error(MSE)loss may deteriorate.Therefore,a novel LSTM with mixture kernel mean p-power error(MKMPE)loss,called MKMPE-LSTM,is developed by using the MKMPE loss to replace the MSE as the learning criterion in LSTM framework,which can achieve robust SOC estimation under the measurement data contaminated with non-Gaussian noises(or outliers)because of the MKMPE containing the p-order moments of the error distribution.In addition,a meta-heuristic algorithm,called heap-based-optimizer(HBO),is employed to optimize the hyper-parameters(mainly including learning rate,number of hidden layer neuron and value of p in MKMPE)of the proposed MKMPE-LSTM model to further improve its flexibility and generalization performance,and a novel hybrid model(HBO-MKMPE-LSTM)is established for SOC estimation under non-Gaussian noise cases.Finally,several tests are performed under various cases through a benchmark to evaluate the performance of the proposed HBO-MKMPE-LSTM model,and the results demonstrate that the proposed hybrid method can provide a good robustness and accuracy under different non-Gaussian measurement noises,and the SOC estimation results in terms of mean square error(MSE),root MSE(RMSE),mean absolute relative error(MARE),and determination coefficient R2are less than 0.05%,3%,3%,and above 99.8%at 25℃,respectively.

Simulation of fire emergency evacuation in a large-passenger-flow subway station based on the on-site measured data of Shenzhen Metro

Because of its large capacity,high efficiency and energy savings,the subway has gradually become the primary mode of transportation for citizens.A high density of passengers exists within a large-passenger-flow subway station,and the number of casualties and injuries during a fire emergency is substantial.In this paper,Pathfinder software and on-site measured data of Pingzhou station in Shenzhen(China)were utilized to simulate a fire emergency evacuation in a large-passenger-flow subway station.The Required Safe Egress Time(RSET),number of passengers and flow rates of stairs and escalators were analysed for three fire evacuation scenarios:train fire,platform fire and hall fire.The evacuation time of the train fire,which was 1173 s,was the longest,and 3621 occupants needed to evacuate when the train was fully loaded.Occupants could not complete the evacuation within 6 mins in all three fire evacuation scenarios,which does not meet the current standard requirements and codes.By changing the number of passengers and the number of stairs for evacuation,the flow rate capacity and evacuation time were explored,which have reference values for safety management and emergency evacuation plan optimization during peak hours of subway operation.

Back-n white neutron source at CSNS and its applications

Back-streaming neutrons from the spallation target of the China Spallation Neutron Source(CSNS)that emit through the incoming proton channel were exploited to build a white neutron beam facility(the so-called Back-n white neutron source),which was completed in March 2018.The Back-n neutron beam is very intense,at approximately 29107 n/cm2/s at 55 m from the target,and has a nominal proton beam with a power of 100 kW in the CSNS-I phase and a kinetic energy of 1.6 GeV and a thick tungsten target in multiple slices with modest moderation from the cooling water through the slices.In addition,the excellent energy spectrum spanning from 0.5 eV to 200 MeV,and a good time resolution related tothe time-of-flight measurements make it a typical white neutron source for nuclear data measurements;its overall performance is among that of the best white neutron sources in the world.Equipped with advanced spectrometers,detectors,and application utilities,the Back-n facility can serve wide applications,with a focus on neutron-induced cross-sectional measurements.This article presents an overview of the neutron beam characteristics,the experimental setups,and the ongoing applications at Backn.

ON SINGULAR EQUATIONS INVOLVING FRACTIONAL LAPLACIAN

We study the existence and the regularity of solutions for a class of nonlocal equations involving the fractional Laplacian operator with singular nonlinearity and Radon measure data.

Asymptotic Calculation of the Wave Trough Exceedance Probabilities in A Nonlinear Sea

This paper concerns the calculation of the wave trough exceedance probabilities in a nonlinear sea. The calculations have been carried out by incorporating a second order nonlinear wave model into an asymptotic method. This is a new approach for the calculation of the wave trough exceedance probabilities, and, as all of the calculations are performed in the probability domain, avoids the need for long time-domain simulations. The proposed asymptotic method has been applied to calculate the wave trough depth exceedance probabilities of a sea state with the surface elevation data measured at the coast of Yura in the Japan Sea. It is demonstrated that the proposed new method can offer better predictions than the theoretical Rayleigh wave trough depth distribution model. The calculated results by using the proposed new method have been further compared with those obtained by using the Arhan and Plaisted nonlinear distribution model and the Toffoli et al.’s wave trough depth distribution model, and its accuracy has been once again substantiated. The research findings obtained from this study demonstrate that the proposed asymptotic method can be readily utilized in the process of designing various kinds of ocean engineering structures.

ADAPTIVE FUSION ALGORITHMS BASED ON WEIGHTED LEAST SQUARE METHOD

Weighted fusion algorithms, which can be applied in the area of multi-sensor data fusion, are advanced based on weighted least square method. A weighted fusion algorithm, in which the relationship between weight coefficients and measurement noise is established, is proposed by giving attention to the correlation of measurement noise. Then a simplified weighted fusion algorithm is deduced on the assumption that measurement noise is uncorrelated. In addition, an algorithm, which can adjust the weight coefficients in the simplified algorithm by making estimations of measurement noise from measurements, is presented. It is proved by emulation and experiment that the precision performance of the multi-sensor system based on these algorithms is better than that of the multi-sensor system based on other algorithms.

Calorimetric power measurements in the EAST ECRH system

In this paper, the measurement method of calorimetric power for an electron cyclotron resonance heating（ECRH） system for EAST is presented. This method requires measurements of the water flow through the cooling circuits and the input and output water temperatures in each cooling circuit. Usually, the inlet water temperature stability is controlled to obtain more accurate results.The influence of the inlet water temperature change on the measurement results is analyzed for the first time in this paper. Also, a novel temperature calibration method is proposed. This kind of calibration method is accurate and effective, and can be easily implemented.

Estimate the influence of snow grain size and black carbon on albedo

Estimation of the influence of snow grain size and black carbon on albedo is essential in obtaining the accurate albedo. In this paper, field measurement data, including snow grain size, snow depth and density was obtained. Black carbon samples were collected from the snow surface. A simultaneous observation using Analytical Spectral Devices was employed in the Qiyi Glacier located in the Qilian Mountain. Analytical Spectral Devices spectrum data were used to analyze spectral re- flectance of snow for different grain size and black carbon content. The measurements were compared with the results obtained from the Snow, Ice, and Aerosol Radiation model, and the simulation was found to correlate well with the ob- served data. However, the simulated albedo was near to 0.98 times of the measured albedo, so the other factors were as- sumed to be constant using the corrected Snow, Ice, and Aerosol Radiation model to estimate the influence of measured snow grain size and black carbon on albedo. Field measurements were controlled to fit the relationship between the snow grain size and black carbon in order to estimate the influence of these factors on the snow albedo.

An adaptive algorithm for pass adaptation in plate rolling

A new algorithm for pass adaptation in plate rolling is developedto improve thickness accuracy of plate products. The feature of thealgorithm is that it uses the measured data rather than the schedulecalculated data in adaptation, which leads to notable improvem- entin prediction accuracy of the rolling parameters and thicknessaccuracy of products can be improved according. Results show thatthis adaptive algorithm is effective in practice.

Kinetic assessment of measured mass flow rates and streamwise pressure distributions in microchannel gas flows

Measured mass flow rates and streamwise pressure distributions of gas flowing through microchannels were reported by many researchers. Assessment of these data is crucial before they are used in the examination of slip models and numerical schemes, and in the design of microchannel elements in various MEMS devices. On the basis of kinetic solutions of the mass flow rates and pressure distributions in microchannel gas flows, the measured data available are properly normalized and then are compared with each other. The 69 normalized data of measured pressure distributions are in excellent agreement, and 67 of them are within 1 ± 0.05. The normalized data of mass flow-rates ranging between 0.95 and 1 agree well with each other as the inlet Knudsen number Kni 〈 0.02, but they scatter between 0.85 and 1.15 as Kni 〉 0.02 with. to some extent, a very interesting bifurcation trend.

Measuring the inequality using energy consumption data in China

With the support by the National Natural Science Foundation of China,the research team led by Prof.Wei Chu(魏楚)and Prof.Zheng Xinye(郑新业)at the Department of Energy Economics,School of Economics,Renmin University of China,measures the inequality using the household energy

Water storage changes and balances in Africa observed by GRACE and hydrologic models

Continental water storage plays a major role in Earth＇s climate system. However, temporal and spatial variations of continental water are poorly known, particularly in Africa. Gravity Recovery and Climate Experiment （GRACE） satellite mission provides an opportunity to estimate terrestrial water storage （TWS） variations at both continental and river-basin scales. In this paper, seasonal and secular variations of TWS within Africa for the period from January 2003 to July 2013 are assessed using monthly GRACE coefficients from three processing centers （Centre for Space Research, the German Research Centre for Geo- sciences, and NASA＇s Jet Propulsion Laboratory）. Monthly grids from Global Land Data Assimilation System （GLDAS）-I and from the Tropical Rainfall Measuring Mission （TRMM）- 3B43 models are also used in order to understand the reasons of increasing or decreasing water storage. Results from GRACE processing centers show similar TWS estimates at seasonal timescales with some differences concerning inter-annual trend variations. The largest annual signals of GRACE TWS are observed in Zambezi and Okavango River basins and in Volta River Basin. An increasing trend of 11.60 mm/a is found in Zambezi River Basin and of 9 mm/a in Volta River Basin. A phase shift is found between rainfall and GRACE TWS （GRACE TWS is preceded by rainfall} by 2-3 months in parts of south central Africa. Comparing GLDAS rainfall with TRMM model, it is found that GLDAS has a dry bias from TRMM model.

Entropy and Renormalized Solutions for Nonlinear Elliptic Problem Involving Variable Exponent and Measure Data

We give an existence result of entropy and renormalized solutions for strongly nonlinear elliptic equations in the framework of Sobolev spaces with variable exponents of the type： -div （a（x, u,▽u）＋φ（u））＋g（x, u,▽u）=μ, where the right-hand side belongs to L^1（Ω）＋W^-1,p＇（x）（Ω）, -div（a（x, u,▽u）） is a Leray-Lions operator defined from W^-1,p＇（x）（Ω） into its dual and φ∈C^0（R,R^N）. The function g（x, u,▽u） is a non linear lower order term with natural growth with respect to ｜▽u｜ satisfying the sign condition, that is, g（x, u,▽u）u ≥ 0.

Online Pattern Recognition and Data Correction of PMU Data Under GPS Spoofing Attack

Smart grids are increasingly dependent on data with the rapid development of communication and measurement.As one of the important data sources of smart grids,phasor measurement unit(PMU)is facing the high risk from attacks.Compared with cyber attacks,global position system(GPS)spoofing attacks(GSAs)are easier to implement because they can be exploited by portable devices,without the need to access the physical system.Therefore,this paper proposes a novel method for pattern recognition of GSA and an additional function of the proposed method is the data correction to the phase angle difference(PAD)deviation.Specifically,this paper analyzes the effect of GSA on PMU measurement and gives two common patterns of GSA,i.e.,the step attack and the ramp attack.Then,the method of estimating the PAD deviation across a transmission line introduced by GSA is proposed,which does not require the line parameters.After obtaining the estimated PAD deviations,the pattern of GSA can be recognized by hypothesis tests and correlation coefficients according to the statistical characteristics of the estimated PAD deviations.Finally,with the case studies,the effectiveness of the proposed method is demonstrated,and the success rate of the pattern recognition and the online performance of the proposed method are analyzed.

Sensor data compression based on MapReduce

A compression algorithm is proposed in this paper for reducing the size of sensor data. By using a dictionary-based lossless compression algorithm, sensor data can be compressed efficiently and interpreted without decompressing. The correlation between redundancy of sensor data and compression ratio is explored. Further, a parallel compression algorithm based on MapReduce [1] is proposed. Meanwhile, data partitioner which plays an important role in performance of MapReduce application is discussed along with performance evaluation criteria proposed in this paper. Experiments demonstrate that random sampler is suitable for highly redundant sensor data and the proposed compression algorithms can compress those highly redundant sensor data efficiently.