Ending Privacy’s Gremlin: Stopping the Data-Broker Loophole to the Fourth Amendment’s Search Warrant Requirement

Advances in technology require upgrades in the law. One such area involves data brokers, which have thus far gone unregulated. Data brokers use artificial intelligence to aggregate information into data profiles about individual Americans derived from consumer use of the internet and connected devices. Data profiles are then sold for profit. Government investigators use a legal loophole to purchase this data instead of obtaining a search warrant, which the Fourth Amendment would otherwise require. Consumers have lacked a reasonable means to fight or correct the information data brokers collect. Americans may not even be aware of the risks of data aggregation, which upends the test of reasonable expectations used in a search warrant analysis. Data aggregation should be controlled and regulated, which is the direction some privacy laws take. Legislatures must step forward to safeguard against shadowy data-profiling practices, whether abroad or at home. In the meantime, courts can modify their search warrant analysis by including data privacy principles.

Traffic Flow Data Forecasting Based on Interval Type-2 Fuzzy Sets Theory

This paper proposes a long-term forecasting scheme and implementation method based on the interval type-2 fuzzy sets theory for traffic flow data. The type-2 fuzzy sets have advantages in modeling uncertainties because their membership functions are fuzzy. The scheme includes traffic flow data preprocessing module, type-2 fuzzification operation module and long-term traffic flow data forecasting output module, in which the Interval Approach acts as the core algorithm. The central limit theorem is adopted to convert point data of mass traffic flow in some time range into interval data of the same time range (also called confidence interval data) which is being used as the input of interval approach. The confidence interval data retain the uncertainty and randomness of traffic flow, meanwhile reduce the influence of noise from the detection data. The proposed scheme gets not only the traffic flow forecasting result but also can show the possible range of traffic flow variation with high precision using upper and lower limit forecasting result. The effectiveness of the proposed scheme is verified using the actual sample application. © 2014 Chinese Association of Automation.

Design of Central Management & Control Unit for Onboard High-Speed Data Handling System

The Main Optical Telescope (MOT) is an important payload of the Space Solar Telescope (SST) with various instruments and observation modes. Its real-time data handling and management and control tasks are arduous. Based on the advanced techniques of foreign countries, an improved structure of onboard data handling systems feasible for SST, is proposed. This article concentrated on the development of a Central Management & Control Unit (MCU) based on FPGA and DSP. Through reconfigurating the FPGA and DSP programs, the prototype could perform different tasks. Thus the inheritability of the whole system is improved. The completed dual-channel prototype proves that the system meets all requirements of the MOT. Its high reliability and safety features also meet the requirements under harsh conditions such as mine detection.

Modeling viscosity of methane,nitrogen,and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques

Accurate gas viscosity determination is an important issue in the oil and gas industries.Experimental approaches for gas viscosity measurement are timeconsuming,expensive and hardly possible at high pressures and high temperatures(HPHT).In this study,a number of correlations were developed to estimate gas viscosity by the use of group method of data handling(GMDH)type neural network and gene expression programming(GEP)techniques using a large data set containing more than 3000 experimental data points for methane,nitrogen,and hydrocarbon gas mixtures.It is worth mentioning that unlike many of viscosity correlations,the proposed ones in this study could compute gas viscosity at pressures ranging between 34 and 172 MPa and temperatures between 310 and 1300 K.Also,a comparison was performed between the results of these established models and the results of ten wellknown models reported in the literature.Average absolute relative errors of GMDH models were obtained 4.23%,0.64%,and 0.61%for hydrocarbon gas mixtures,methane,and nitrogen,respectively.In addition,graphical analyses indicate that the GMDH can predict gas viscosity with higher accuracy than GEP at HPHT conditions.Also,using leverage technique,valid,suspected and outlier data points were determined.Finally,trends of gas viscosity models at different conditions were evaluated.

Cloud Control Systems

The concept of cloud control systems is discussed in this paper, which is an extension of networked control systems (NCSs). With the development of internet of things (IOT), the technology of NCSs has played a key role in IOT. At the same time, cloud computing is developed rapidly, which provides a perfect platform for big data processing, controller design and performance assessment. The research on cloud control systems will give new contribution to the control theory and applications in the near future. © 2014 Chinese Association of Automation.

Hourly traffic flow forecasting using a new hybrid modelling method

Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.

A Novel Induction Algorithm for DM

DM usually means an efficient knowledge discovery from database, and the immune algorithm is a biological theory-based and global searching algorithm. A novel induction algorithm is proposed here which integrates a power of individual immunity and an evolutionary mechanism of population. This algorithm does not take great care of discovering some classifying information, but unknown knowledge or a predication on higher level rules. Theoretical analysis and simulations both show that this algorithm is prone to the stabilization of a population and the improvement of entire capability, and also keeping a high degree of preciseness during the rule induction.

Full aperture imaging algorithm for highly squinted TOPS SAR

Aiming at solving the azimuth signal aliasing problem in the Doppler domain, the azimuth time aliasing problem after range cell migration correction (RCMC) and the severe range and the azimuth coupling problem in both the phase and envelope for highly squinted terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR), a novel full aperture imaging algorithm is presented. An unaliased two-dimensional (2-D) spectrum is first obtained by the azimuth preprocessing; the modified range migration algorithm (RMA) is then used to complete RCMC; and finally the azimuth signal is focused in the Doppler domain by spectral analysis (SPECAN) and deramping. Simulations and real data processing results validate the effectiveness of the proposed algorithm. © 2016 Beijing Institute of Aerospace Information.

Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G context.However,during a flood,sensor nodes may be washed up or become faulty,which seriously affects network connectivity.To address this issue,Unmanned Aerial Vehicles(UAVs)could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction.In light of this,we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels.The framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a flood.Besides,an algorithm hybridized with Group Method Data Handling(GMDH)and Particle Swarm Optimization(PSO)is proposed to predict forthcoming floods in an intelligent collaborative environment.The proposed water-level prediction model is trained based on the real dataset obtained fromthe Selangor River inMalaysia.The performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination(R2),correlation coefficient(R),RootMean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and BIAS are provided.

Best compromising crashworthiness design of automotive S-rail using TOPSIS and modified NSGAⅡ

In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.

Global Solar Radiation Maps of Saudi Arabia

This paper uses Abductive network to predict global solar radiation in any location in the Kingdom of Saudi Arabia （KSA） based on sunshine duration, month number, latitude, longitude, and altitude of the location. Results indicate good agreement between measured and predicted GSR values for each of the 35 locations with known GSR values. Finally, the data from all available stations are used to train an abductive network to estimate the GSR values anywhere in the Kingdom based on latitude and longitude. GSR values are estimated using the developed method at 25 additional locations throughout the kingdom and used with the measured data from the 35 available measurement stations to draw a comprehensive contour map of GSR values for KSA.

White-box machine-learning models for accurate interfacial tension prediction in hydrogen-brine mixtures

The severity of climate change and global warming necessitates the need for a transition from traditional hydrocarbon-based energy sources to renewable energy sources.One intrinsic challenge with renewable energy sources is their intermittent nature,which can be addressed by transforming excess energy into hydrogen and storing it safely for future use.To securely store hydrogen underground,a comprehensive knowledge of the interactions between hydrogen and residing fluids is required.Interfacial tension is an important variable influenced by cushion gases such as CO_(2) and CH4.This research developed explicit correlations for approximating the interfacial tension of a hydrogen–brine mixture using two advanced machine-learning techniques:gene expression programming and the group method of data handling.The interfacial tension of a hydrogen–brine mixture was considered to be heavily influenced by temperature,pressure,water salinity,and the average critical temperature of the gas mixture.The results indicated a higher performance of the group method of data handling-based correlation,showing an average absolute relative error of 4.53%.Subsequently,Pearson,Spearman,and Kendall methods were used to assess the influence of individual input variables on the outputs of the correlations.Analysis showed that the temperature and the average critical temperature of the gas mixture had considerable inverse impacts on the estimated interfacial tension values.Finally,the reliability of the gathered databank and the scope of application for the proposed correlations were verified using the leverage approach by illustrating 97.6%of the gathered data within the valid range of the Williams plot.

SEASONAL CIRCULATION IN THE NORTHWESTERN TROPICAL PACIFIC OCEAN DIAGNOSED WITH THE ASSIMILATION DATA FROM 1989 THROUGH 1997

An assimilation data set based on the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model version 3 (MOM3) and the NODC XBT data set is used to examine the circulation and its variabilities in the western tropical Pacific, with special emphasis on the seasonal variations. It is shown that the assimilated and observed mean velocities and transports of the major flows in the western tropical Pacific agree well. The flows in the north Pacific, including the North Equatorial Current (NEC), Kuroshio, Mindanao Current (MC) and north Equatorial Countercurrent (NECC) west of 140°E display the seasonal cycles almost in the same phase, with the biggest transport in spring and the smallest in autumn. The phase of the NECC seasonal cycle east of 140°E is opposite to that in the west. Besides of the annual cycle, there seems to be a semi-annual fluctuation of the NECC transport possibly resulting from the phase lag between seasonal cycles of the NEC and NGCC. Strong in summer during the southeast monsoon, the seasonal cycle of the Indonesian Throughflow (ITF) is closely linked with those of both the MC and the New Guinea Coastal Current/Undercurrent (NGCC/NGCUC), but not as strong as that in observations probably caused by the superimposed seasonal and interannual variations. Variations on the interannual time scale are also discussed, but only indistinct interannual variations of the flows related to the ENSO are revealed during 1989-1997. Transport of the NEC, Kuroshio and NECC are slightly larger in the E1 Nino years when that of the ITF is weaker, while the MC has little ENSO-related variation. There were also quasi-biennial signals superimposing the ENSO-like oscillations in the flows, but their relationships with the ENSO are still unclear.

Predicting beach profile evolution with group method data handling-type neural networks on beaches with seawalls

A major goal of coastal engineering is to develop models for the reliable prediction of short-and longterm near shore evolution.The most successful coastal models are numerical models,which allow flexibility in the choice of initial and boundary conditions.In the present study,evolutionary algorithms(EAs)are employed for multi-objective Pareto optimum design of group method data handling(GMDH)-type neural networks that have been used for bed evolution modeling in the surf zone for reflective beaches,based on the irregular wave experiments performed at the Hydraulic Laboratory of Imperial College(London,UK).The input parameters used for such modeling are significant wave height,wave period,wave action duration,reflection coefficient,distance from shoreline and sand size.In this way,EAs with an encoding scheme are presented for evolutionary design of the generalized GMDH-type neural networks,in which the connectivity configurations in such networks are not limited to adjacent layers.Also,multi-objective EAs with a diversity preserving mechanism are used for Pareto optimization of such GMDH-type neural networks.The most important objectives of GMDH-type neural networks that are considered in this study are training error(TE),prediction error(PE),and number of neurons(N).Different pairs of these objective functions are selected for two-objective optimization processes.Therefore,optimal Pareto fronts of such models are obtained in each case,which exhibit the trade-offs between the corresponding pair of the objectives and,thus,provide different non-dominated optimal choices of GMDH-type neural network model for beach profile evolution.The results showed that the present model has been successfully used to optimally prediction of beach profile evolution on beaches with seawalls.

New data processing method for improving the measurement accuracy in FTP

During the 3D shape measurement,there are noises in the images that are obtained by the capture system.The traditional method,Fourier transform profilometry(FTP) technique,improves the accuracy only by the filtering method in the frequency domain.In this paper,the curve fitting method is used for the light field distribution calculation before the filtering process applied in the frequency domain by choosing a suitable filter window,and then the higher quality of the basic frequency component signal is got.This method can avoid the frequency overlapping caused by the noise,so the improvement of the measuring accuracy of FTP is realized.

Building geospatial infrastructure

Many visions for geospatial technology have been advanced over the past half century.Initially researchers saw the handling of geospatial data as the major problem to be overcome.The vision of geographic information systems arose as an early international consensus.Later visions included spatial data infrastructure,Digital Earth,and a nervous system for the planet.With accelerating advances in information technology,a new vision is needed that reflects today’s focus on open and multimodal access,sharing,engagement,the Web,Big Data,artificial intelligence,and data science.We elaborate on the concept of geospatial infrastructure,and argue that it is essential if geospatial technology is to contribute to the solution of problems facing humanity.

On-line measurement of temperature and water vapor in CH_4 /air premixed flame using near-infrared diode laser

We establish a single diode laser sensor system to obtain temperature and water concentration in CH4/air premixed flame.Line-of-sight properties are analyzed,but line-of-sight results are not path average values for temperature measurements.The measurements are performed on a flat burner based on scannedwavelength direct absorption spectroscopy using two adjacent water lines at 7153.75 and 7154.35 cm 1.Real-time results are acquired using a data acquisition card with a Labview data processing program.The standard uncertainties of the temperature and water concentration measurements are 2.3% and 5.1%,respectively.

X-ray photon-counting detector based on a micro-channel plate for pulsar navigation

The pulse time of arrival （TOA） is a determining parameter for accurate timing and positioning in X-ray pulsar navigation. The pulse TOA can be calculated by comparing the measured arrival time with the predicted arrival time of the X-ray pulse for pulsar. In this study, in order to research the measurement of pulse arrival time, an experimental system is set up. The experimental system comprises a simulator of the X-ray pulsar, an X-ray detector, a time-measurement system, and a data-processing system. An X-ray detector base is proposed on the basis of the micro-channel plate （MCP）, which is sensitive to soft X-ray in the 1–10 keV band. The MCP-based detector, the structure and principle of the experimental system, and results of the pulse profile are described in detail. In addition, a discussion of the effects of different X-ray pulse periods and the quantum efficiency of the detector on pulse-profile signal-to-noise ratio （SNR） is presented. Experimental results reveal that the SNR of the measured pulse profile becomes enhanced as the quantum efficiency of the detector increases. The SNR of the pulse profile is higher when the period of the pulse is smaller at the same integral.

Developing a robust correlation for prediction of sweet and sour gas hydrate formation temperature

There are numerous correlations and thermodynamic models for predicting the natural gas hydrate formation condition but still the lack of a simple and unifying general model that addresses a broad ranges of gas mixture.This study was aimed to develop a user-friendly universal correlation based on hybrid group method of data handling(GMDH)for prediction of hydrate formation temperature of a wide range of natural gas mixtures including sweet and sour gas.To establish the hybrid GMDH,the total experimental data of 343 were obtained from open articles.The selection of input variables was based on the hydrate structure formed by each gas species.The modeling resulted in a strong algorithm since the squared correlation coefficient(R2)and root mean square error(RMSE)were 0.9721 and 1.2152,respectively.In comparison to some conventional correlation,this model represented not only the outstanding statistical parameters but also its absolute superiority over others.In particular,the result was encouraging for sour gases concentrated at H2S to the extent that the model outstrips all available thermodynamic models and correlations.Leverage statistical approach was applied on datasets to the discovery of the defected and doubtful experimental data and suitability of the model.According to this algorithm,approximately all the data points were in the proper range of the model and the proposed hybrid GMDH model was statistically reliable.

Continuous generation of dissipative spatial solitons in two-dimensional Ginzburg–Landau models with elliptical shaped potentials

We report on the rich dynamics of two-dimensional fundamental solitons coupled and interacting on the top of an elliptical shaped potential in a two-dimensional Ginzburg-Landau model. Under the elliptical shaped poten- tial, the solitons display unique and dynamic properties, such as the generation of straight-line arrays, emission of either one elliptical shaped soliton or several elliptical ring soliton arrays, and soliton decay. When changing the depth and sharpness of the external potential and fixing other parameters of the potential, various scenarios of soliton dynamics are also revealed. These results suggest some possible applications for all-optical data-processing schemes, such as the routing of light signals in optical communication devices.