Blockchain-based data transmission control for Tactical Data Link

Tactical Data Link(TDL)is a communication system that utilizes a particular message format and a protocol to transmit data via wireless channels in an instant,automatic,and secure way.So far,TDL has shown its excellence in military applications.Current TDL adopts a distributed architecture to enhance anti-destruction capacity.However,It still faces a problem of data inconsistency and thus cannot well support cooperation across multiple militarily domains.To tackle this problem,we propose to leverage blockchain to build an automatic and adaptive data transmission control scheme for TDL.It achieves automatic data transmission and realizes information consistency among different TDL entities.Besides,applying smart contracts based on blockchain further enables adjusting data transmission policies automatically.Security analysis and experimental results based on simulations illustrate the effectiveness and efficiency of our proposed scheme.

TrustControl:Trusted Private Data Usage Control Based on Security Enhanced TrustZone

The past decade has seen the rapid development of data in many areas.Data has enormous commercial potential as a new strategic resource that may efficiently boost technical growth and service innovation.However,individuals are becoming increasingly concerned about data misuse and leaks.To address these issues,in this paper,we propose TrustControl,a trusted data usage control system to control,process,and protect data usage without revealing privacy.A trusted execution environment(TEE)is exploited to process confidential user data.First of all,we design a secure and reliable remote attestation mechanism for ARM TrustZone,which can verify the security of the TEE platform and function code,thus guaranteeing data processing security.Secondly,to address the security problem that the raw data may be misused,we design a remote dynamic code injection method to regulate that data can only be processed for the expected purpose.Our solution focuses on protecting the sensitive data of the data owner and the function code of the data user to prevent data misuse and leakage.Furthermore,we implement the prototype system of TrustControl on TrustZone-enabled hardware.Real-world experiment results demonstrate that the proposed Trust-Control is secure and the performance overhead of introducing our prototype system is very low.

Real Time Control System for Metro Railways Using PLC & SCADA

This paper proposes to adopt SCADA and PLC technology for the improvement of the performance of real time signaling&train control systems in metro railways.The main concern of this paper is to minimize the failure in automated metro railways system operator and integrate the information coming from Operational Control Centre(OCC),traction SCADA system,traction power control,and power supply system.This work presents a simulated prototype of an automated metro train system operator that uses PLC and SCADA for the real time monitoring and control of the metro railway systems.Here,SCADA is used for the visualization of an automated process operation and then the whole opera-tion is regulated with the help of PLC.The PLC used in this process is OMRON(NX1P2-9024DT1)and OMRON’s Sysmac studio programming software is used for developing the ladder logic of PLC.The metro railways system has deployed infrastructure based on SCADA from the power supply system,and each station’s traction power control is connected to the OCC remotely which commands all of the stations and has the highest command priority.An alarm is triggered in the event of an emergency or system congestion.This proposed system overcomes the drawbacks of the current centralized automatic train control(CATC)system.This system provides prominent benefits like augmenting services which may enhance a network’s full load capacity and networkflexibility,which help in easy modification in the existing program at any time.

A call for enhanced data-driven insights into wind energy flow physics

With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations,machine learning(ML)models are poised to advance our understanding of the physics underpinning the interaction between the atmospheric boundary layer and wind turbine arrays,the generated wakes and their interactions,and wind energy harvesting.However,the majority of the existing ML models for predicting wind turbine wakes merely recreate Computational fluid dynamics(CFD)simulated data with analogous accuracy but reduced computational costs,thus providing surrogate models rather than enhanced data-enabled physics insights.Although ML-based surrogate models are useful to overcome current limitations associated with the high computational costs of CFD models,using ML to unveil processes from experimental data or enhance modeling capabilities is deemed a potential research direction to pursue.In this letter,we discuss recent achievements in the realm of ML modeling of wind turbine wakes and operations,along with new promising research strategies.

Automated Detection of Contaminated Radar Image Pixels in Mountain Areas

In mountain areas, radar observations are often contaminated （1） by echoes from high-speed moving vehicles and （2） by point-wise ground clutter under either normal propagation （NP） or anomalous propagation （AP） conditions. Level Ⅱ data are collected from KMTX （Salt Lake City, Utah） radar to analyze these two types of contamination in the mountain area around the Great Salt Lake. Human experts provide the ＂ground truth＂ for possible contamination of either type on each individual pixel. Common features are then extracted for contaminated pixels of each type. For example, pixels contaminated by echoes from high-speed moving vehicles are characterized by large radial velocity and spectrum width. Echoes from a moving train tend to have larger velocity and reflectivity but smaller spectrum width than those from moving vehicles on highways. These contaminated pixels are only seen in areas of large terrain gradient （in the radial direction along the radar beam）. The same is true for the second type of contamination - pointwise ground clutters. Six quality control （QC） parameters are selected to quantify the extracted features. Histograms are computed for each QC parameter and grouped for contaminated pixels of each type and also for non-contaminated pixels. Based on the computed histograms, a fuzzy logical algorithm is developed for automated detection of contaminated pixels. The algorithm is tested with KMTX radar data under different （clear and rainy） weather conditions.

Synchronization of nonlinear multi-agent systems using a non-fragile sampled data control approach and its application to circuit systems

The main aim of this work is to design a non-fragile sampled data control(NFSDC) scheme for the asymptotic synchronization criteria for interconnected coupled circuit systems(multi-agent systems, MASs). NFSDC is used to conduct synchronization analysis of the considered MASs in the presence of time-varying delays. By constructing suitable Lyapunov functions, sufficient conditions are derived in terms of linear matrix inequalities(LMIs) to ensure synchronization between the MAS leader and follower systems. Finally, two numerical examples are given to show the effectiveness of the proposed control scheme and less conservation of the proposed Lyapunov functions.

Derivation of Regression Coefficients for Sea Surface Temperature Retrieval over East Asia

Among the regression-based algorithms for deriving SST from satellite measurements, regionally optimized algorithms normally perform better than the corresponding global algorithm. In this paper, three algorithms are considered for SST retrieval over the East Asia region （15°-55°N, 105°-170°E）, including the multi-channel algorithm （MCSST）, the quadratic algorithm （QSST）, and the Pathfinder algorithm （PFSST）. All algorithms are derived and validated using collocated buoy and Geostationary Meteorological Satellite （GMS-5） observations from 1997 to 2001. An important part of the derivation and validation of the algorithms is the quality control procedure for the buoy SST data and an improved cloud screening method for the satellite brightness temperature measurements. The regionally optimized MCSST algorithm shows an overall improvement over the global algorithm, removing the bias of about -0.13℃ and reducing the root-mean-square difference （rmsd） from 1.36℃ to 1.26℃. The QSST is only slightly better than the MCSST. For both algorithms, a seasonal dependence of the remaining error statistics is still evident. The Pathfinder approach for deriving a season-specific set of coefficients, one for August to October and one for the rest of the year, provides the smallest rmsd overall that is also stable over time.

Networking dual-surveillance/dual-pair-tele-paths for critical urban areas

Providing a reliable and efficient communication infrastructure for critical regions is generally a strategy used to enhance regional safety or to sustain regional development. For such purposes, the spider-web network is prototyped as a cellular telecommunication infrastructure for its advantages including being interference-free, possessing fault-tolerance, having security management, and countering radio's multipath effects. Besides, it is used to develop area-based-street-SCADA (supervisory control and data acquisition) networks composed of regular optimal degree-3 prototyped sub-networks with the concepts of dual-surveillance, operational order and Hamiltonian laceability, and it flexibly fits various circulation configurations. Moreover, it can offer integrative response capabilities even after two paths' SCADA networks have been ruined.

Wind turbine fault detection based on SCADA data analysis using ANN

Wind energy is one of the fast growing sources of power production currently, and there is a great demand to reduce the cost of operation and maintenance. Most wind farms have installed supervisory control and data acquisition （SCADA） systems for system control and logging data. However, the collected data are not used effectively. This paper proposes a fault detection method for main bearing wind turbine based on existing SCADA data using an artificial neural network （ANN）. The ANN model for the normal behavior is established, and the difference between theoretical and actual values of the parameters is then calculated. Thus the early stage of main bearing fault can be identified to let the operator have sufficient time to make more informed decisions for maintenance.

Optimal Threshold Policies for Robust Data Center Control

With the simultaneous rise of energy costs and demand for cloud computing, efficient control of data centers becomes crucial. In the data center control problem, one needs to plan at every time step how many servers to switch on or off in order to meet stochastic job arrivals while trying to minimize electricity consumption. This problem becomes particularly challenging when servers can be of various types and jobs from different classes can only be served by certain types of server, as it is often the case in real data centers. We model this problem as a robust Markov decision process(i.e., the transition function is not assumed to be known precisely). We give sufficient conditions(which seem to be reasonable and satisfied in practice) guaranteeing that an optimal threshold policy exists. This property can then be exploited in the design of an efficient solving method, which we provide.Finally, we present some experimental results demonstrating the practicability of our approach and compare with a previous related approach based on model predictive control.

THE SYSTEM OF MODEL CONTROL AND DATA ACQUISITION IN THE HUAIHE RIVER FLOOD CONTROL MODEL

The area of the Huaihe river flood control model is 4000m2. A computer system is employed to control models and collect experimental data.It can control 7 inflows which simulating 9 typical years flood process, a tail water level, 6 sluices, 11 outlets of breaking dike, and collect the water level of 37 points and velocity of 32 points.After running long time,the system has ben proved that it possesses steady character and exact accuracy.It is essential precondition for the experimental study of the Huaibe river find control model.

A blockchain-based transaction system for private data sharing and trading

To address the private data management problems and realize privacy-preserving data sharing,a blockchain-based transaction system named Ecare featuring information transparency,fairness and scalability is proposed.The proposed system formulates multiple private data access control strategies,and realizes data trading and sharing through on-chain transactions,which makes transaction records transparent and immutable.In our system,the private data are encrypted,and the role-based account model ensures that access to the data requires owner’s authorization.Moreover,a new consensus protocol named Proof of Transactions(PoT)proposed by ourselves has been used to improve consensus efficiency.The value of Ecare is not only that it aggregates telemedicine,data transactions,and other features,but also that it translates these actions into transaction events stored in the blockchain,making them transparent and immutable to all participants.The proposed system can be extended to more general big data privacy protection and data transaction scenarios.

Data Driven Model-Free Adaptive Control Method for Quadrotor Trajectory Tracking Based on Improved Sliding Mode Algorithm

In order to solve the problems of dynamic modeling and complicated parameters identification of trajectory tracking control of the quadrotor,a data driven model-free adaptive control method based on the improved sliding mode control(ISMC)algorithm is designed,which does not depend on the precise dynamic model of the quadrotor.The design of the general sliding mode control(SMC)algorithm depends on the mathematical model of the quadrotor and has chattering problems.In this paper,according to the dynamic characteristics of the quadrotor,an adaptive update law is introduced and a saturation function is used to improve the SMC.The proposed control strategy has an inner and an outer loop control structures.The outer loop position control provides the required reference attitude angle for the inner loop.The inner loop attitude control ensures rapid convergence of the attitude angle.The effectiveness and feasibility of the algorithm are verified by mathematical simulation.The mathematical simulation results show that the designed model-free adaptive control method of the quadrotor is effective,and it can effectively realize the trajectory tracking control of the quadrotor.The design of the controller does not depend on the kinematic and dynamic models of the unmanned aerial vehicle(UAV),and has high control accuracy,stability,and robustness.

Research and Realization of Ten-print Data Quality Control Techniques for Imperial Scale Automated Fingerprint Identification System

As the first individualization-information processing equipment put into practical service worldwide,Automated Fingerprint Identification System(AFIS)has always been regarded as the first choice in individualization of criminal suspects or those who died in mass disasters.By integrating data within the existing regional large-scale AFIS database,many countries are constructing an ultra large state-of-the-art AFIS(or Imperial Scale AFIS)system.Therefore,it is very important to develop a series of ten-print data quality controlling process for this system of this type,which would insure a substantial matching efficiency,as the pouring data come into this imperial scale being.As the image quality of ten-print data is closely relevant to AFIS matching proficiency,a lot of police departments have allocated huge amount of human and financial resources over this issue by carrying out manual verification works for years.Unfortunately,quality control method above is always proved to be inadequate because it is an astronomical task involved,in which it has always been problematic and less affiant for potential errors.Hence,we will implement quality control in the above procedure with supplementary-acquisition effect caused by the delay of feedback instructions sent from the human verification teams.In this article,a series of fingerprint image quality supervising techniques has been put forward,which makes it possible for computer programs to supervise the ten-print image quality in real-time and more accurate manner as substitute for traditional manual verifications.Besides its prominent advantages in the human and financial expenditures,it has also been proved to obviously improve the image quality of the AFIS ten-print database,which leads up to a dramatic improvement in the AFIS-matching accuracy as well.

Designing an Intelligent Control Philosophy in Reservoirs of Water Transfer Networks in Supervisory Control and Data Acquisition System Stations

In this paper, a hybrid neural-genetic fuzzy system is proposed to control the flow and height of water in the reservoirs of water transfer networks. These controls will avoid probable water wastes in the reservoirs and pressure drops in water distribution networks. The proposed approach combines the artificial neural network, genetic algorithm, and fuzzy inference system to improve the performance of the supervisory control and data acquisition stations through a new control philosophy for instruments and control valves in the reservoirs of the water transfer networks. First, a multi-core artificial neural network model, including a multi-layer perceptron and radial based function, is proposed to forecast the daily consumption of the water in a reservoir. A genetic algorithm is proposed to optimize the parameters of the artificial neural networks. Then, the online height of water in the reservoir and the output of artificial neural networks are used as inputs of a fuzzy inference system to estimate the flow rate of the reservoir inlet. Finally, the estimated inlet flow is translated into the input valve position using a transform control unit supported by a nonlinear autoregressive exogenous model. The proposed approach is applied in the Tehran water transfer network. The results of this study show that the usage of the proposed approach significantly reduces the deviation of the reservoir height from the desired levels.

Development and case study of a science-based software platform to support policy making on air quality

This article describes the development and implementations of a novel software platform that supports real-time, science-based policy making on air quality through a user-friendly interface. The software, RSM-VAT, uses a response surface modeling（RSM） methodology and serves as a visualization and analysis tool（VAT） for three-dimensional air quality data obtained by atmospheric models. The software features a number of powerful and intuitive data visualization functions for illustrating the complex nonlinear relationship between emission reductions and air quality benefits. The case study of contiguous U.S.demonstrates that the enhanced RSM-VAT is capable of reproducing the air quality model results with Normalized Mean Bias 〈 2% and assisting in air quality policy making in near real time.

Non-fragile switched H_∞ control for morphing aircraft with asynchronous switching

This paper deals with the problem of non-fragile linear parameter-varying（LPV） H_∞ control for morphing aircraft with asynchronous switching.The switched LPV model of morphing aircraft is established by Jacobian linearization approach according to the nonlinear model.The data missing is taken into account in the link from sensors to controllers and the link from controllers to actuators,which satisfies Bernoulli distribution.The non-fragile switched LPV controllers are constructed with consideration of the uncertainties of controllers and asynchronous switching phenomenon.The parameter-dependent Lyapunov functional method and mode-dependent average dwell time（MDADT） method are combined to guarantee the stability and prescribed performance of the system.The sufficient conditions on the solvability of the problem are derived in the form of linear matrix inequalities（LMI）.In order to achieve higher efficiency of the designing process,an algorithm is applied to divide the whole set into subsets automatically.Simulation results are provided to verify the effectiveness and superiority of the method in the paper.

Development of an Internet and Fuzzy Based Control System of Manufacturing Process

The aim of this work is to develop an Internet and fuzzy based control and data acquisition system for an industrial process plant which can ensure remote running and fuzzy control of a cement factory. Cases studies of the proposed system application in three cement factories in Algeria, SCAEK（Setif）, SCIMAT（Batna）, and SCT（Tebessa）, are discussed. The remote process control consists of alarms generated during running of the processes while maintaining and synchronizing different regulation loops thus ensuring automatic running of processes smoothly. In addition, fuzzy control of the kiln and the other two mills ensures that the system is operational at all times with minimal downtime. The process control system contains different operator station（OP）, alarms table and a provision to monitor trends analysis. The operator can execute any operation according to his authorised access assigned by the system administrator using user administration tool. The Internet technology is used for human security by avoiding all times presence of operators at site for maintenance. Further, in case of a breakdown, the problem would be remotely diagnosed and resolved avoiding requirement of an expert on site thus eliminating traveling cost, security risks, visa formalities, etc. These trips are difficult to organize（costs, visas, risks）. So the enterprise can reduce downtimes and travel costs. In order to realize a process control system guided by operators in the main control room or through Internet, the process control is based on programming in PCS 7 utilizing Cemat library and Fuzzy Control＋＋ Siemens tools.

Risk assessment framework for power control systems with PMU-based intrusion response system

Cyber threats are serious concerns for power systems.For example,hackers may attack power control systems via interconnected enterprise networks.This paper proposes a risk assessment framework to enhance the resilience of power systems against cyber attacks.The duality element relative fuzzy evaluation method is employed to evaluate identified security vulnerabilities within cyber systems of power systems quantitatively.The attack graph is used to identify possible intrusion scenarios that exploit multiple vulnerabilities.An intrusion response system(IRS)is developed to monitor the impact of intrusion scenarios on power system dynamics in real time.IRS calculates the conditional Lyapunov exponents(CLEs)on line based on the phasor measurement unit data.Power system stability is predicted through the values of CLEs.Control actions based on CLEs will be suggested if power system instability is likely to happen.A generic wind farm control system is used for case study.The effectiveness of IRS is illustrated with the IEEE 39 bus system model.