False data injection attack detection in smart grid

Smart grid is a distributed and autonomous energy delivery infrastructure that constantly monitors the operational state of its overall network using smart techniques and state estimation. State estimation is a powerful technique that is used to determine the overall operational state of the system based on a limited set of measurements collected through metering systems. Cyber-attacks pose serious risks to a smart grid state estimation that can cause disruptions and power outages resulting in huge economical losses and are therefore a big concern to a reliable national grid operation. False data injection attacks (FDIAs), engineered on the basis of the knowledge of the network configuration, are difficult to detect using the traditional data detection mechanisms. These detection schemes have been found vulnerable and failed to detect these FDIAs. FDIAs specifically target the state data and can manipulate the state measurements in such a way that these false measurements appear real to the main control systems. This research work explores the possibility of FDIA detection using state estimation in a distributed and partitioned smart grid. In order to detect FDIAs we use measurements for residual-based testing which creates an objective function; and the probability of erroneous data is determined from this residual test. In this test, a preset threshold is determined based on the prior history of the state data. FDIA cases are simulated within a smart grid considering that the Chi-square detection state estimator fails in identifying such attacks. We compute the objective function using the standard weighted least problem and then test the objective function against the value in the Chi-square table. The gain matrix and the Jacobian matrix are computed. The state variables are computed in the form of a voltage magnitude. The state variables are computed after the inception of an attack to assess these state magnitude results. Different sizes of partitioning are used to improve the overall sensitivity of the Chi-square results. Our additional estimator is based on a Kalman estimation that consists of the state prediction and state correction steps. In the first step, it obtains the state and matrix covariance prediction, and in the second step, it calculates the Kalman gain and the state and matrix covariance update steps. The set of points is created for the state vector x at a time instant t. The initial vector and covariance matrix are based on a priori knowledge of the historical estimates. A set of sigma points is estimated by the state update function. Sigma points refer to the minimal set of sampling points that are selected and transformed using nonlinear function, and the new mean and the covariance are formed out of these transformed points. The idea behind this is that it is easier to compute a Gaussian distribution than an arbitrary nonlinear function. The filter gain, the mean and the covariance are used to estimate the next state. Our simulation results show that the combination of Kalman estimation and distributed state estimation improves the overall stability index and vulnerability assessment score of the smart grid. We built a stability index table for a smart grid based on the state estimates value after the inception of an FDIA. The vulnerability assessment score of the smart grid is based on common vulnerability scoring system (CVSS) and state estimates under the influence of an FDIA. The simulations are conducted in the MATPOWER program and different electrical bus systems such as IEEE 14, 30, 39, 118 and 300 are tested. All the contributions have been published in reputable journals and conferences.Doctor of Philosoph

The substring inclusion constraint longest common subsequence problem can be solved in quadratic time

AbstractIn this paper, we study some variants of the Constrained Longest Common Subsequence (CLCS) problem, namely, the substring inclusion CLCS (Substring-IC-CLCS) problem and a generalized version thereof. In the Substring-IC-CLCS problem, we are to find a longest common subsequence (LCS) of two given strings containing a third constraint string (given) as a substring. Previous solution to this problem runs in cubic time, i.e, O(nmk) time, where n,m and k are the length of the 3 input strings. In this paper, we present simple O(nm) time algorithms to solve the Substring-IC-CLCS problem. We also study the Generalized Substring-IC-LCS problem where we are given two strings of length n and m respectively and an ordered list of p strings and the goal is to find an LCS containing each of them as a substring in the order they appear in the list. We present an O(nmp) algorithm for this generalized version of the problem

Enhancement of Methane Concentration by Removing Contaminants from Biogas Mixtures Using Combined Method of Absorption and Adsorption

We report a laboratory scale combined absorption and adsorption chemical process to remove contaminants from anaerobically produced biogas using cafeteria (food), vegetable, fruit, and cattle manure wastes. Iron oxide (Fe2O3), zero valent iron (Feo), and iron chloride (FeCl2) react with hydrogen sulfide (H2S) to deposit colloidal sulfur. Silica gel, sodium sulfate (Na2SO4), and calcium oxide (CaO) reduce the water vapour (H2O) and carbon dioxide (CO2). It is possible to upgrade methane (CH4) above 95% in biogas using chemical or physical absorption or adsorption process. The removal efficiency of CO2, H2S, and H2O depends on the mass of removing agent and system pH. The results showed that Ca(OH)2 solutions are capable of reducing CO2 below 6%. The H2S concentration was reduced to 89%, 90%, 86%, 85%, and 96% for treating with 10 g of FeCl2, Feo (with pH), Fe2O3, Feo, and activated carbon, respectively. The H2O concentration was reduced to 0.2%, 0.7%, 0.2%, 0.2%, and 0.3% for treating raw biogas with 10 g of silica gel and Na2SO4 for runs R1, R2, R3, R4, and R5, respectively. Thus, given the successful contaminant elimination, the combined absorption and adsorption process is a feasible system for biogas purification

Assessment of new real-time in-situ optical coherence tomography instrumentation and techniques for diagnosing and monitoring oral and cutaneous lesions

Head and neck cancer is the sixth most common cancer worldwide, with 686,328 new cases per year. Most head and neck cancers are squamous cell carcinomas of the oral cavity and oropharynx, and are burdened by high mortality (50% at 5 years from diagnosis), notwithstanding recent progress in treatment methods. The vast majority of oro-pharyngeal cancers are late diagnosed, with significant adverse effects on cure, morbidity and prognosis. There is general consensus that earlier diagnosis contributes to better outcome measures. Current diagnostic standards consist of clinical examination and surgical biopsy, which are associated with delayed presentation, diagnosis and greater mortality. There is an unmet need for effective diagnostic techniques to aid early identification of cancers. Optical coherence tomography (OCT) is one of a number of non-invasive real-time imaging systems, introduced during the last two decades aiming to provide tissue information similar to conventional histopathological examination. The technique is similar to a B-mode ultrasound section, but employs a scanning near infrared light source rather than ultrasound waves, generating cross-sectional images of the sample tissue in an X-Z orientation. In this study, I investigated a modified OCT oral instrument (VivoSight® Michelson Diagnostics Ltd, Orpington, Kent, UK) with adapted probe for intraoral use. The new oral instrument was not CE marked, was uncalibrated and consequently a non-standard instrument. Therefore, prior to clinical application, the new instrument required calibration and comparison with the conventional instrument to assess and confirm performance in image quality and resolution in X, Y, and Z-planes. A series of laboratory engineering standards were created and compared by scanning with both instruments in X, Y & Z planes. A second series of experiments were conducted using porcine tissue as models for human tissue, confirming the similarities of fact and artefact observable when the two instruments were applied to challenging imaging scenarios, in particular, the effects of dissimilar target tissue refractive indices on the OCT image. The effects (tissue dimensional changes) of fixing samples in formal-incontaining media and tissue processing were also then investigated using this non-invasive measuring technique

State estimation within ied based smart grid using kalman estimates

State Estimation is a traditional and reliable technique within power distribution and control systems. It is used for building a topology of the power grid network based on state measurements and current operational state of different nodes & buses. The protection of sensors and measurement units such as Intelligent Electronic Devices (IED) in Central Energy Management System (CEMS) against False Data Injection Attacks (FDIAs) is a big concern to grid operators. These are special kind of cyber-attacks that are directed towards the state & measurement data in such a way that mislead the CEMS into making incorrect decisions and create generation load imbalance. These are known to bypass the traditional bad data detection systems within central estimators. This paper presents the use of an additional novel state estimator based on Kalman filter along with traditional Distributed State Estimation (DSE) which is based on Weighted Least Square (WLS). Kalman filter is a feedback control mechanism that constantly updates itself based on state prediction and state correction technique and shows improvement in the estimates. The additional estimator output is compared with the results of DSE in order to identify anomalies and injection of false data. We evaluated our methodology by simulating proposed technique using MATPOWER over IEEE-14, IEEE-30, IEEE-118, IEEE-300 bus. The results clearly demonstrate the superiority of the proposed method over traditional state estimation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Prevalent distribution of conscious processes on either side of the brain

Objectives. The brain has an intrinsic tendency for the lateralization of its functions. For instance, the left hemisphere assists in the comprehension and motor expression of language. What remains uncertain is whether conscious processes are also more prevalent in one hemisphere of the brain than the other. The epistemic goal of this research was to address this particular issue. Materials and Methods. We observed the rare pathological event of proximal occlusion of the middle cerebral artery (MCA), which halts blood flow to the central two-thirds of a hemisphere, and examined its effects on consciousness. We aggregated individual scores for eyes-opening and limb-motor responses from the Glasgow Coma Scale (GCS) to determine the conscious level, and used brain computed tomography imaging to identify the whole-territory infarcts of MCA. Results. Being a rare condition, we managed to recruit 35 patients from two centers (average age: 64.54 ± 13 years, 45.71% females). Whole-territory infarcts of the MCA occurred more frequently in the left hemisphere (22 versus 13, frequency: 62.85%). Unconsciousness was also more common with left hemisphere infarctions (16 versus 2 unconscious patients of the right hemisphere, Frequency: 72%, GCS: 2/10=3/22 cases, GCS: 5/10=1/22 cases, GCS: 6-7/10=12/22 cases). The difference in unconsciousness proved significant in Fisher’s exact analysis (p-value = 0.001) and remained independent of age (p-value=0.7247) and gender (p-value=0.3145). Moreover, six conscious patients with left hemisphere involvement exhibited a loss of conscious control for normal responses, implying a strong link between consciousness and cognition. Unconsciousness also correlated with stroke outcomes (16 Unconscious: 56.25% deceased within the hospital). Conclusion. Conscious processes are more predominant in the brain\u27s left hemisphere. Our observations indicate that only a gross unilateral insult to the brain can lead to unconsciousness

The Role Of Teachers In Embedding Islamic Values And Ethics In Education: A Literature Review

The purpose of this paper is to investigate the role of teachers in embedding Islamic values and ethics in education. The rationale of this study is by understanding how Islamic Education teachers should play their roles in embedding Islamic values and ethics, could perhaps contribute to better ways of students’ behavior development. In this regard, the study has focused on exploring how the roles of  Islamic Education teachers, and the challenges that affect the teachers in promoting Islamic values and ethics in education. The study used a library research design. The data were collected from research articles published in either international or national accredited journals. The research findings show although the elements of Islamic values and ethics seem to be discussed in Islamic Education, students show low Islamic values and ethics in their behavior. The study recommends several approaches to improve the teaching of Islamic values and ethics in schools which include training for teachers to improve the methodology of teaching and providing teaching and learning facilities. It is also suggested that an open discussion is conducted between parents, teachers, students, policymakers, and religious leaders to develop a guideline on what should be included in the syllabus and how to cultivate Islamic values and ethics in education effectively.   Keywords: Role Of Teacher, Islamic Values, Ethic

An Improved Deep Learning Model for Electricity Price Forecasting

Accurate electricity price forecasting (EPF) is important for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity market. Besides that, EPF becomes critically important for effective planning and efficient operation of a power system due to deregulation of electricity industry. However, accurate EPF is very challenging due to complex nonlinearity in the time series-based electricity prices. Hence, this work proposed two-fold contributions which are (1) effective time series preprocessing module to ensure feasible time-series data is fitted in the deep learning model, and (2) an improved long short-term memory (LSTM) model by incorporating linear scaled hyperbolic tangent (LiSHT) layer in the EPF. In this work, the time series pre-processing module adopted linear trend of the correlated features of electricity price series and the time series are tested by using Augmented Dickey Fuller (ADF) test method. In addition, the time series are transformed using boxcox transformation method in order to satisfy the stationarity property. Then, an improved LSTM prediction module is proposed to forecast electricity prices where LiSHT layer is adopted to optimize the parameters of the heterogeneous LSTM. This study is performed using the Australian electricity market price, load and renewable energy supply data. The experimental results obtained show that the proposed EPF framework performed better compared to previous techniques

Frequency of Tension-Type Headache in Patients with Migraine: A Single-Center Cross-Sectional Study

ABSTRACT Background and objective: Migraine is a common headache disorder characterized by recurrent episodes of moderate to severe headaches which are usually unilateral. Migraine is the second most common headache disorder after tension-type headache. The objective of this study was to determine the frequency of tension-type headache in patients with migraine. Methods: This cross-sectional study was carried out at Pakistan Institute of Medical Sciences, Islamabad for a period of six months between 1st July 2018 and 31st December 2018. This study enrolled patients above the age of 12 years that were diagnosed with “Migraine without aura” or “Migraine with aura”. The patients were subsequently asked for presence of features of tension-type headache. The criteria published by International Headache Society, ICHD-3 was used for making the diagnosis of migraine and tension-type headache. The data was analyzed using SPSS version 17. In the case of numerical variables, the mean and standard deviation were calculated. In the case of categorical variables, the frequency and percentage were calculated. All data were presented in tables and figures. Results: One-hundred-forty-two patients participated in the study. The age range was between 14 and 72 years. The mean age was 30.12 years. Female patients were 76.1 percent. Eighty patients were married, and 15.5 percent patients did not receive education; 72.5 percent patients were from urban background. Seventy-five (52.8%) patients had migraine with aura while 67 (47.2%) patients had migraine without aura. Twenty-nine (20.4%) patients of migraine had coexistent tension-type headache while 113 (79.6%) patients of migraine did not have tension-type headache. Conclusion: Tension type headache was an infrequent finding in our study population of migraine patients

Adaptive Three Layer Hybrid Reconfigurable Intelligent Surface for 6G Wireless Communication: Trade-offs and Performance

A potential candidate technology for the development of future 6G networks has been recognized as Reconfigurable Intelligent Surface (RIS). However, due to the variation in radio link quality, traditional passive RISs only accomplish a minimal signal gain in situations with strong direct links between user equipment (UE) and base station (BS). In order to get over this fundamental restriction of smaller gain, the idea of active RISs might be a suitable solution. In contrast to current passive RIS, which simply reflects and directs signals without any additional amplification, active RISs have the ability to enhance reflected signals by the incorporation of amplifiers inside its elements. However, with additional amplifiers, apart from the relatively complex attributes of RIS-assisted arrangements, the additional energy consumption of such technologies is often disregarded. So, there might be a tradeoff between the additional energy consumption for the RIS technologies and the overall gain acquired by deploying this potential advancement. The objective of this work is to provide a primary idea of a three-layer hybrid RIS-assisted configuration that is responsive to both active and passive RIS, as well as an additional dormant or inactive state. The single RIS structure should be capable of adjusting its overall configuration in response to fluctuations in transmit power and radio link quality. Furthermore, our fabricated passive RIS-assisted structure verifies a portion of the proposed idea, with simulations highlighting its advantages over standalone passive or active RIS-assisted technologies.Comment: Accepted for presentation and publication at the 8th IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob) Conferenc