Smart Grid Cyber Security： An Overview of Threats and Countermeasures

The smart grid is the next generation of power and distribution systems. The integration of advanced network, communications, and computing techniques allows for the enhancement of efficiency and reliability. The smart grid interconnects the flow of information via the power line, intelligent metering, renewable and distributed energy systems, and a monitoring and controlling infrastructure. For all the advantages that these components come with, they remain at risk to a spectrum of physical and digital attacks. This paper will focus on digital vulnerabilities within the smart grid and how they may be exploited to form full fledged attacks on the system. A number of countermeasures and solutions from the literature will also be reported, to give an overview of the options for dealing with such problems. This paper serves as a triggering point for future research into smart grid cyber security.

Open Journal of Applied Biosensor: Point-of-Care Biosensing and Environment Monitoring

Open Journal of Applied Biosensor: Point-of-Care Biosensing and Environment

Recent Advances in Detection of Ochratoxin-A

Ochratoxin-A[7-(L-β-phenylalanylcarbonyl)-carboxyl-5-chloro-8-hydroxy-3,4-dihydro-3R-methyl-isocumarin, OTA] is a common food contaminant mycotoxin that enters the human body through the consumption of improperly stored food products. Upon ingestion, it leads to immuno-suppression and immuno-toxicity. OTA has been known to produce nephrotoxic, teratogenic, and carcinogenic activity (via oxidative DNA damage) in several species. This review introduces potentials of electrochemical biosensor to provide breakthroughs in OTA detection through improved selectivity and sensitivity and also the current approaches for detecting OTA in food products.

System Verification of Hardware Optimization Based on Edge Detection

Nowadays, digital camera based remote controllers are widely used in people’s daily lives. It is known that the edge detection process plays an essential role in remote controlled applications. In this paper, a system verification platform of hardware optimization based on the edge detection is proposed. The Field-Programmable Gate Array (FPGA) validation is an important step in the Integrated Circuit (IC) design workflow. The Sobel edge detection algorithm is chosen and optimized through the FPGA verification platform. Hardware optimization techniques are used to create a high performance, low cost design. The Sobel edge detection operator is designed and mounted through the system Advanced High-performance Bus (AHB). Different FPGA boards are used for evaluation purposes. It is proved that with the proposed hardware optimization method, the hardware design of the Sobel edge detection operator can save 6% of on-chip resources for the Sobel core calculation and 42% for the whole frame calculation.

An Average Distance Based Self-Relocation and Self-Healing Algorithm for Mobile Sensor Networks

The sensing coverage of a wireless sensor network is an important measure of the quality of service. It is desirable to develop energy efficient methods for relocating mobile sensors in order to achieve optimum sensing coverage. This paper introduces an average distance based self-relocation and self-healing algorithm for randomly deployed mobile sensor networks. No geo-location or relative location information is needed by this algorithm thereby no hardware such as GPS is required. The tradeoff is that sensors need to move longer distance in order to achieve certain coverage. Simulations are conducted in order to evaluate the proposed relocation and self-healing algorithms. An average of 94% coverage is achieved in the cases that we are examined with or without obstacles.

Generalized Attack Model for Networked Control Systems, Evaluation of Control Methods

Networked Control Systems (NCSs) have been implemented in several different industries. The integration with advanced communication networks and computing techniques allows for the enhancement of efficiency of industrial control systems. Despite all the advantages that NCSs bring to industry, they remain at risk to a spectrum of physical and cyber-attacks. In this paper, we elaborate on security vulnerabilities of NCSs, and examine how these vulnerabilities may be exploited when attacks occur. A general model of NCS designed with three different controllers, i.e., proportional-integral-derivative (PID) controllers, Model Predictive control (MPC) and Emotional Learning Controller (ELC) are studied. Then three different types of attacks are applied to evaluate the system performance. For the case study, a networked pacemaker system using the Zeeman nonlinear heart model (ZHM) as the plant combined with the above-mentioned controllers to test the system performance when under attacks. The results show that with Emotional Learning Controller (ELC), the pacemaker is able to track the ECG signal with high fidelity even under different attack scenarios.

A Novel Technique for Detection of Time Delay Switch Attack on Load Frequency Control

In this paper, we focus on the estimation of time delays caused by adversaries in the sensing loop (SL). Based on the literature review, time delay switch (TDS) attacks could make any control system, in particular a power control system, unstable. Therefore, future smart grids will have to use advanced methods to provide better situational awareness of power grid states keeping smart grids reliable and safe from TDS attacks. Here, we introduce a simple method for preventing time delay switch attack on networked control systems. The method relies on an estimator that will estimate and track time delays introduced by an adversary. Knowing the maximum tolerable time delay of the plant’s optimal controller for which the plant remains stable, a time-delay detector issues an alarm signal when the estimated time delay is larger than the minimum one and directs the system to alarm state. In an alarm state, the plant operates under the control of an emergency controller that is local to the plant and remains in this mode until the networked control system state is restored. This method is an inexpensive and simple way to guarantee that an industrial control system remains stable and secure.

Twin-T Oscillator Containing Polymer Coated Parallel Plate Capacitor for Sea Water Salinity Sensing

This paper presents the development of a Twin-T oscillator comprising polymer coated parallel plates as a sensor for ocean water salinity monitoring.This sensor employs a parallel plate capacitor design, with sea water serving as the medium between plates. Novalac resin and a proprietary commercial polymer (AccufloTW) were investigated as corrosion protective coatings for the copper electrodes of the capacitor. Electrochemical Impedance Spectroscopy (EIS) was employed to evaluate corrosion inhibition of polymer coatingin sea water. A detection circuit was designed and simulated using P-spice and then implemented in Printed Circuit Board (PCB). EIS results indicate that Accuflo exhibits better corrosion inhibition in ocean water than Novolac. Further, the use of Twin-T oscillator based detection circuit resulted in enhanced sensitivity and better detection limit. Experiments performed using ocean water samples resulted in oscillator frequency shift of 410 Hertz/power supply unit (Hz/PSU). Oscillator frequency drift was reduced using frequency-to-voltage converters and sensitivity of 10 mV/PSU was achieved.

System-on-a-Chip (SoC) Based Hardware Acceleration for Video Codec

Nowadays, from home monitoring to large airport security, a lot of digital video surveillance systems have been used. Digital surveillance system usually requires streaming video processing abilities. As an advanced video coding method, H.264 is introduced to reduce the large video data dramatically (usually by 70X or more). However, computational overhead occurs when coding and decoding H.264 video. In this paper, a System-on-a-Chip (SoC) based hardware acceleration solution for video codec is proposed, which can also be used for other software applications. The characteristics of the video codec are analyzed by using the profiling tool. The Hadamard function, which is the bottleneck of H.264, is identified not only by execution time but also another two attributes, such as cycle per loop and loop round. The Co-processor approach is applied to accelerate the Hadamard function by transforming it to hardware. Performance improvement, resource costs and energy consumption are compared and analyzed. Experimental results indicate that 76.5% energy deduction and 8.09X speedup can be reached after balancing these three key factors.

Othogonal Waveform Design for Multiple-Input Multiple-Output (MIMO) Radar

An effective numerical approach is developed for orthogonal waveform design for Multiple-Input Multiple-Output (MIMO) radar. The Doppler shift tolerance is considered in the design cost function. The design results indicate that the Doppler? tolerance of the designed orthogonal waveforms is markedly improved.

Characterization of the retinal vasculature in fundus photos using the PanOptic iExaminer system

Background:The goal was to characterize retinal vasculature by quantitative analysis of arteriole-to-venule(AM)ratio and vessel density in fundus photos taken with the PanOptic iExaminer System.Methods:The PanOptic ophthalmoscope equipped with a smartphone was used to acquire fundus photos centered on the optic nerve head.Two fundus photos of a total of 19 eyes from 10 subjects were imaged.Retinal vessels were analyzed to obtain the AN ratio.In addition,the vessel tree was extracted using deep learning U-NET,and vessel density was processed by the percentage of pixels within vessels over the entire image.Results:All images were successfully processed for the AN ratio and vessel density.There was no significant difference of averaged AN ratio between the first(0.77±0.09)and second(0.77±0.10)measurements(P=0.53).There was no significant difference of averaged vessel density(%)between the first(6.11±1.39)and second(6.12±1.40)measurements(P=0.85).Conclusions:Quantitative analysis of the retinal vasculature was feasible in fundus photos taken using the PanOptic ophthalmoscope.The device appears to provide sufficient image quality for analyzing AN ratio and vessel density with the benefit of portability,easy data transferring,and low cost of the device,which could be used for pre-clinical screening of systemic,cerebral and ocular diseases.

Mechanical Design with Experimental Verification of a Lightweight Exoskeleton Chair

In this study,a human-chair model was developed as the basis for a wearable-chair design.A prototype chair,HUST-EC,based on the model was fabricated and evaluated.Employing the optimization under the golden divisional method,an optimized simulation of the operating mode with the lowest chair height was implemented.A novel multi-link support structure has been established with parameters optimized using Matlab software.The stress analysis of the solid models was conducted to ensure the adequate support from the designed chair for the user.Ten subjects participated in the evaluation experiment,who performed both static tasks and dynamic tasks.The experimental results consisted of subjective evaluation and objective evaluation.The experimental data demonstrate that(1)the HUST-EC can effectively reduce the activation level of related muscles at a variety of tasks;(2)the plantar pressure was reduced by 54%–67%;(3)the angle between the upper body and the vertical axis was reduced by 59%–77%;(4)the subjective scores for chair comfortability,portability,and stability were all higher than 7.The results further revealed that the designed chair can reduce the musculoskeletal burden and may improve work efficiency.

Pinned OS/Services： A Case Study of XML Parsing on Intel SCC

Nowadays, we are heading towards integrating hundreds to thousands of cores on a single chip. However, traditional system software and middleware are not well suited to manage and provide services at such large scale. To improve the scalability and adaptability of operating system and middleware services on future many-core platform, we propose the pinned OS/services. By porting each OS and runtime system （middleware） service to a separate core （special hardware acceleration）, we expect to achieve maximal performance gain and energy efficiency in many-core environments. As a case study, we target on XML （Extensible Markup Language）, the commonly used data transfer/store standard in the world. We have successfully implemented and evaluated the design of porting XML parsing service onto Intel 48-core Single-Chip Cloud Computer （SCC） platform. The results show that it can provide considerable energy saving. However, we also identified heavy performance penalties introduced from memory side, making the parsing service bloated. Hence, as a further step, we propose the memory-side hardware accelerator for XML parsing. With specified hardware design, we can further enhance the performance gain and energy efficiency, where the performance can be improved by 20% with 12.27% energy reduction.

Characterization of the retinal vasculature in fundus photos using the PanOptic iExaminer system

Background:The goal was to characterize retinal vasculature by quantitative analysis of arteriole-to-venule(A/V)ratio and vessel density in fundus photos taken with the PanOptic iExaminer System.Methods:The PanOptic ophthalmoscope equipped with a smartphone was used to acquire fundus photos centered on the optic nerve head.Two fundus photos of a total of 19 eyes from 10 subjects were imaged.Retinal vessels were analyzed to obtain the A/V ratio.In addition,the vessel tree was extracted using deep learning U-NET,and vessel density was processed by the percentage of pixels within vessels over the entire image.Results:All images were successfully processed for the A/V ratio and vessel density.There was no significant difference of averaged A/V ratio between the first(0.77±0.09)and second(0.77±0.10)measurements(P=0.53).There was no significant difference of averaged vessel density(%)between the first(6.11±1.39)and second(6.12±1.40)measurements(P=0.85).Conclusions:Quantitative analysis of the retinal vasculature was feasible in fundus photos taken using the PanOptic ophthalmoscope.The device appears to provide sufficient image quality for analyzing A/V ratio and vessel density with the benefit of portability,easy data transferring,and low cost of the device,which could be used for pre-clinical screening of systemic,cerebral and ocular diseases.

Online power quality disturbance detection by support vector machine in smart meter

Power quality assessment is an important performance measurement in smart grids.Utility companies are interested in power quality monitoring even in the low level distribution side such as smart meters.Addressing this issue,in this study,we propose segregation of the power disturbance from regular values using one-class support vector machine(OCSVM).To precisely detect the power disturbances of a voltage wave,some practical wavelet filters are applied.Considering the unlimited types of waveform abnormalities,OCSVM is picked as a semisupervised machine learning algorithm which needs to be trained solely on a relatively large sample of normal data.This model is able to automatically detect the existence of any types of disturbances in real time,even unknown types which are not available in the training time.In the case of existence,the disturbances are further classified into different types such as sag,swell,transients and unbalanced.Being light weighted and fast,the proposed technique can be integrated into smart grid devices such as smart meter in order to perform a real-time disturbance monitoring.The continuous monitoring of power quality in smart meters will give helpful insight for quality power transmission and management.

Fixed-time constrained acceleration reconstruction scheme for robotic exoskeleton via neural networks

Accurate acceleration acquisition is a critical issue in the robotic exoskeleton system,but it is difficult to directly obtain the acceleration via the existing sensing systems.The existing algorithm-based acceleration acquisition methods put more attention on finite-time convergence and disturbance suppression but ignore the error constraint and initial state irrelevant techniques.To this end,a novel radical bias function neural network(RBFNN)based fixed-time reconstruction scheme with error constraints is designed to realize high-performance acceleration estimation.In this scheme,a novel exponential-type barrier Lyapunov function is proposed to handle the error constraints.It also provides a unified and concise Lyapunov stability-proof template for constrained and non-constrained systems.Moreover,a fractional power sliding mode control law is designed to realize fixed-time convergence,where the convergence time is irrelevant to initial states or external disturbance,and depends only on the chosen parameters.To further enhance observer robustness,an RBFNN with the adaptive weight matrix is proposed to approximate and attenuate the completely unknown disturbances.Numerical simulation and human sub ject experimental results validate the unique properties and practical robustness.

Weather-based interruption prediction in the smart grid utilizing chronological data

This unique study will demonstrate a combined effect of weather parameters on the total number of power distribution interruptions in a region.Based on common weather conditions,a theoretical model can predict interruptions and risk assessment with immediate weather conditions.Using daily and hourly weather data,the created models will predict the number of daily or by-shift interruptions.The weather and environmental conditions to be addressed will include rain,wind,temperature,lightning density,humidity,barometric pressure,snow and ice.Models will be developed to allow broad applications.Statistical and deterministic simulations of the models using the data collected will be conducted by employing existing software,and the results will be used to refine the models.Models developed in this study will be used to predict power interruptions in areas that can be readily monitored,thus validating the models.The application has resulted in defining the predicted number of interruptions in a region with a specific confidence level.Reliability is major concern for every utility.Prediction and timely action to minimize the outage duration improves reliability.Use of this predictor model with existing smart grid self-healing technology is proposed.