scholarly journals Cyber Attacks and Faults Discrimination in Intelligent Electronic Device-Based Energy Management Systems

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 650
Author(s):  
B. M. Ruhul Amin ◽  
M. J. Hossain ◽  
Adnan Anwar ◽  
Shafquat Zaman
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Detection Rate ◽  
Electronic Device ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Research Work ◽  
Load Flow ◽  
Cyber Attacks ◽  
Real Time System

Intelligent electronic devices (IEDs) along with advanced information and communication technology (ICT)-based networks are emerging in the legacy power grid to obtain real-time system states and provide the energy management system (EMS) with wide-area monitoring and advanced control capabilities. Cyber attackers can inject malicious data into the EMS to mislead the state estimation process and disrupt operations or initiate blackouts. A machine learning algorithm (MLA)-based approach is presented in this paper to detect false data injection attacks (FDIAs) in an IED-based EMS. In addition, stealthy construction of FDIAs and their impact on the detection rate of MLAs are analyzed. Furthermore, the impacts of natural disturbances such as faults on the system are considered, and the research work is extended to distinguish between cyber attacks and faults by using state-of-the-art MLAs. In this paper, state-of-the-art MLAs such as Random Forest, OneR, Naive Bayes, SVM, and AdaBoost are used as detection classifiers, and performance parameters such as detection rate, false positive rate, precision, recall, and f-measure are analyzed for different case scenarios on the IEEE benchmark 14-bus system. The experimental results are validated using real-time load flow data from the New York Independent System Operator (NYISO).

scholarly journals Alkalmazott mesterséges intelligencia felhasználási területei és biztonsági kérdései – Mesterséges intelligencia a gyakorlatban

2020 ◽  
Vol 1 (1) ◽  
pp. 35-42
Author(s):  
Péter Ekler ◽  
Dániel Pásztor
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Anomaly Detection ◽  
Real Time ◽  
Learning Algorithm ◽  
Sensor Data ◽  
Test Field ◽  
Real Time System ◽  
Data Set ◽  
Wide Range

Összefoglalás. A mesterséges intelligencia az elmúlt években hatalmas fejlődésen ment keresztül, melynek köszönhetően ma már rengeteg különböző szakterületen megtalálható valamilyen formában, rengeteg kutatás szerves részévé vált. Ez leginkább az egyre inkább fejlődő tanulóalgoritmusoknak, illetve a Big Data környezetnek köszönhető, mely óriási mennyiségű tanítóadatot képes szolgáltatni. A cikk célja, hogy összefoglalja a technológia jelenlegi állapotát. Ismertetésre kerül a mesterséges intelligencia történelme, az alkalmazási területek egy nagyobb része, melyek központi eleme a mesterséges intelligencia. Ezek mellett rámutat a mesterséges intelligencia különböző biztonsági réseire, illetve a kiberbiztonság területén való felhasználhatóságra. A cikk a jelenlegi mesterséges intelligencia alkalmazások egy szeletét mutatja be, melyek jól illusztrálják a széles felhasználási területet. Summary. In the past years artificial intelligence has seen several improvements, which drove its usage to grow in various different areas and became the focus of many researches. This can be attributed to improvements made in the learning algorithms and Big Data techniques, which can provide tremendous amount of training. The goal of this paper is to summarize the current state of artificial intelligence. We present its history, introduce the terminology used, and show technological areas using artificial intelligence as a core part of their applications. The paper also introduces the security concerns related to artificial intelligence solutions but also highlights how the technology can be used to enhance security in different applications. Finally, we present future opportunities and possible improvements. The paper shows some general artificial intelligence applications that demonstrate the wide range usage of the technology. Many applications are built around artificial intelligence technologies and there are many services that a developer can use to achieve intelligent behavior. The foundation of different approaches is a well-designed learning algorithm, while the key to every learning algorithm is the quality of the data set that is used during the learning phase. There are applications that focus on image processing like face detection or other gesture detection to identify a person. Other solutions compare signatures while others are for object or plate number detection (for example the automatic parking system of an office building). Artificial intelligence and accurate data handling can be also used for anomaly detection in a real time system. For example, there are ongoing researches for anomaly detection at the ZalaZone autonomous car test field based on the collected sensor data. There are also more general applications like user profiling and automatic content recommendation by using behavior analysis techniques. However, the artificial intelligence technology also has security risks needed to be eliminated before applying an application publicly. One concern is the generation of fake contents. These must be detected with other algorithms that focus on small but noticeable differences. It is also essential to protect the data which is used by the learning algorithm and protect the logic flow of the solution. Network security can help to protect these applications. Artificial intelligence can also help strengthen the security of a solution as it is able to detect network anomalies and signs of a security issue. Therefore, the technology is widely used in IT security to prevent different type of attacks. As different BigData technologies, computational power, and storage capacity increase over time, there is space for improved artificial intelligence solution that can learn from large and real time data sets. The advancements in sensors can also help to give more precise data for different solutions. Finally, advanced natural language processing can help with communication between humans and computer based solutions.

A Study on Optimal Power Solution through Optimization Technique in Solar Power

2021 ◽  
Vol 23 (06) ◽  
pp. 565-587
Author(s):  
C. Shilaja ◽  
◽  
G. Nalinashini ◽  
N. Balaji ◽  
K. Sujatha ◽  
...  
Keyword(s):  
Power Loss ◽  
Optimal Power Flow ◽  
Learning Algorithm ◽  
Optimization Technique ◽  
Research Work ◽  
Active Power ◽  
Outcome Analysis ◽  
Real Time System ◽  
Optimal Power

This research work tries to deal with renewable energy [RE] sources where the RE is integrated into the power supply system. In order to proceed with this work, windy, as well as sunny areas, were selected. For this purpose, a persistence-extreme-based learning algorithm was used. Both the long-term and long-term forecasting of solar insolation and wind speed was done in the selected area using the proposed algorithm. The solar and wind power penetration of the system helps in solving the optimal power-flow issue in almost twelve different cases. The outcome analysis was done through active power loss and V (voltage) deviation. From the outcome, it was found that the V-deviation was high during both the long and short term due to the horizons of solar integration and wind at the same time the active power loss was less when compared to V-deviation. The proposed method was done in Andhra Pradesh belongs to the South part of India where 14 bus systems and 123 IND [Indian utility real-time system] were selected for the study. The simulation process was done using MATLAB (2013) version A.

scholarly journals Patch Based Multiple Instance Learning Algorithm for Object Tracking

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Zhenjie Wang ◽  
Lijia Wang ◽  
Hua Zhang
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Multiple Instance Learning ◽  
Partial Occlusion ◽  
Learning Rates ◽  
Classification Score ◽  
Object Based ◽  
Illumination Changes

To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance learning (P-MIL) algorithm is proposed. The algorithm divides an object into many blocks. Then, the online MIL algorithm is applied on each block for obtaining strong classifier. The algorithm takes account of both the average classification score and classification scores of all the blocks for detecting the object. In particular, compared with the whole object based MIL algorithm, the P-MIL algorithm detects the object according to the unoccluded patches when partial occlusion occurs. After detecting the object, the learning rates for updating weak classifiers’ parameters are adaptively tuned. The classifier updating strategy avoids overupdating and underupdating the parameters. Finally, the proposed method is compared with other state-of-the-art algorithms on several classical videos. The experiment results illustrate that the proposed method performs well especially in case of illumination changes or pose variations and partial occlusion. Moreover, the algorithm realizes real-time object tracking.

IMPLEMENTING BACK-PROPAGATION- THROUGH-TIME LEARNING ALGORITHM USING CELLULAR NEURAL NETWORKS

1999 ◽  
Vol 09 (06) ◽  
pp. 1041-1074 ◽  
Author(s):  
TAO YANG ◽  
LEON O. CHUA
Keyword(s):  
Real Time ◽  
Time Complexity ◽  
Large Scale ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Back Propagation ◽  
Cellular Neural Network ◽  
Small Scale ◽  
Von Neumann ◽  
On Line

In a programmable (multistage) cellular neural network (CNN) structure, the CPU is a CNN universal chip which supports massively parallel computations on patterns and images, including videos. In this paper, we decompose the structure of a class of simultaneous recurrent networks (SRN) into a CNN program and run it on a von Neumann-like stored program CNN structure. To train the SRN, we map the back-propagation-through-time (BTT) learning algorithm into a sequence of CNN subroutines to achieve real-time performance via a CNN universal chip. By computing in parallel, the CNN universal chip can be programmed to implement in real time the BTT learning algorithm, which has a very high time complexity. An estimate of the time complexity of the BTT learning algorithm based on the CNN universal chip is presented. For small-scale problems, our simulation results show that a CNN implementation of the BTT learning algorithm for a two-dimensional SRN is at least 10,000 times faster than that based on state-of-the-art sequential workstations. For the few large-scale problems which we have so far simulated, the CNN implemented BTT learning algorithm maintained virtually the same time complexity with a learning time of a few seconds, while those implemented on state-of-the-art sequential workstations dramatically increased their time complexity, often requiring several days of running time. Several examples are presented to demonstrate how efficiently a CNN universal chip can speed up the learning algorithm for both off-line and on-line applications.

scholarly journals Towards real-time tracking of persons in distress phase situations using emotional physiological signals

2019 ◽  
Author(s):  
◽  
Abdultaofeek Abayomi
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Emergency Services ◽  
Recognition Performance ◽  
Research Work ◽  
Physiological Signals ◽  
Image Processing Technique ◽  
Real Time System ◽  
Human Emotion ◽  
Real Time Tracking

This research work investigates physiological signals based human emotion and its incorporation in an affective system architecture for real-time tracking of persons in distress phase situations to prevent the occurrence of casualties. In a casualty situation, a mishap has already occurred leading to life, limb and valuables being in a state of peril. However, in a distress phase situation, there is a high likelihood that a tragedy is about to occur unless an immediate assistance is rendered. The distress phase situations include the spate of kidnapping, human trafficking and terrorism related crimes that could lead to casualty such as loss of lives, properties, finances and destruction of infrastructure. These situations are of global concern and worldwide phenomenon that necessitate a system that could mitigate the alarming trend of social crimes. The novel idea of deploying a combination of data and knowledge driven approaches using wearable sensor devices supported by machine learning methods could prove useful as a preventive mechanism in a distress phase situation. Such a system could be achieved through modelling human emotion recognition, including the harvesting and recognising human emotion physiological signals. Different methods have been applied in emotion recognition domain because the extraction of relevant discriminating features has been identified as an unresolved and one of the most daunting aspects of physiological signals based human emotion recognition system. In this thesis, emotion physiological signals, image processing technique and shallow learning based on radial basis function neural network were used to construct a system for real-time tracking of persons in distress phase situations. The system was tested using the Database for Emotion Analysis using Physiological Signal (DEAP) to ascertain the recognition performance that could be achieved. Emotion representations such as Arousal, Valence, Dominance and Liking have been creatively mapped to different conditions of human safety and survival state like happy phase, distress phase and casualty phase in a real-time system for tracking of persons. The constructed system can practically benefit security agencies, emergency services, rescue teams and restore confidence to both the potential victims and their family by proactively providing assistance in an emergency event of a distress phase situation. Moreover, the system would prove beneficial in stemming the tide of the identified societal crimes and tragedies by thwarting the successful progress of a distress phase situation through an application of information communication technology to address critical societal challenges. The performance of the recognition algorithmic component of the constructed system gives accuracy that outperforms the state of the art results based on deep learning techniques.

scholarly journals A Time Refinement Framework Based on iUML-B State Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Han Peng ◽  
Xiaoli Zhang ◽  
Guozhen Cao ◽  
Zhouzhou Liu ◽  
Yuejuan Jing ◽  
...  
Keyword(s):  
Real Time ◽  
Timed Automata ◽  
System Modeling ◽  
Research Work ◽  
Time Constraints ◽  
Formal Modeling ◽  
Time System ◽  
Time Model ◽  
Real Time System ◽  
Time Systems

Event-B is a formal modeling language that is very suitable for software engineering, but it lacks the ability of modeling time. Researchers have proposed some methods for modeling time constraints in Event-B. The limitations with existing methods are that, first of all, the existing research work lacks a systematic time refinement framework based on Event-B; secondly, the existing methods only model time in the Event-B framework and cannot be smoothly converted to automata-based models such as timed automata that facilitate the verification of time properties. These limitations make it more difficult to model and verify real-time systems with Event-B because it is very time-consuming to prove time properties in the Event-B framework. In this paper, we firstly proposed a systematic time refinement framework to express and refine time constraints in Event-B. Secondly, we also proposed various vertical refinement patterns and horizontal extension patterns to guide modelers to refine the Event-B real-time model step by step. Finally, we use a real-time system case to demonstrate the practicality of our method. The experimental results show that the proposed method can make the real-time system modeling in Event-B more convenient and the models are easier to convert to the timed automata model, thereby facilitating the verification of various time properties.

The BS-MFC service and system evolutions within Copernicus Marine Service

2021 ◽  
Author(s):  
Atanas Palazov ◽  
Stefania A. Ciliberti ◽  
Rita Lecci ◽  
Marilaure Gregoire ◽  
Joanna Staneva ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Black Sea ◽  
Satellite Data ◽  
State Of The Art ◽  
Core Model ◽  
The Black Sea ◽  
Time System ◽  
Real Time System ◽  
Black Sea Region

<p>The BS-MFC (Black Sea Monitoring and Forecasting Centre) since 2016 is guaranteeing production and delivery of high quality ocean analysis, forecast and reanalysis fields for essential variables, biogeochemical quantities and waves in the Black Sea region within the Copernicus Marine Service. A reliable and robust service infrastructure serves both the production systems and data delivery, through ad hoc technical interfaces, for an efficient update of the catalogue, which includes 22 datasets for physical variables, 22 for biogeochemical variables and 4 for waves. Additionally, a Local Service Desk is in charge for ensuring connections among BS-MFC, CMEMS and Users with the scope to support end-users in using BS-MFC data for downstream applications from the technical and scientific perspectives. The production centres are the core of the BS-MFC: Physics, Biogeochemistry and Waves units implemented, over the Copernicus 2 Programme, state-of-the-art and advanced numerical approaches to improve the quality of the near real time and multi year products. In the 2020, in particular, the BS-Physics team proposed a new reanalysis product, based on new version of the hydrodynamical core model, based on NEMO v3.6, with assimilation of CMEMS observations (e.g. insitu temperature and salinity profiles, including also historical dataset provided by SeaDataNet, and sea level anomaly satellite data) and forced by ECMWF ERA5. The BS-Physics team is working also on preparing the new version of the near real time system, that will provide spatial high resolution analysis and forecast products, using a new version based on NEMO v4.0, online coupled to data assimilation scheme, with optimal interface with the Mediterranean Sea. BS-Biogeochemistry team updated the overall catalogue, with new near real time system, based on NEMO v3.6 online coupled to BAHMBI model, with new carbonate model, able to assimilate new chlorophyll satellite data provided by the CMEMS OC TAC; regarding multi year product, the BS-Biogeochemistry team delivered new datasets, generated by the new NEMO-BAHMBI coupled system forced by ECMWF ERA5 – totally aligned with the near real time system, without data assimilation – for reconstructing the past biogeochemical sea state in the Black Sea. BS-Waves team updated the overall catalogue as well, with new near real time system based on state-of-the-art WAM Cycle 6.0, one-way coupled with hourly currents fields provided by the BS-Physics near real time system; a new reanalysis, from 1979 to 2019, has been also delivered, based on same core model as the near real time system, forced by ECMWF ERA5 atmospheric forcing, and able to assimilate the significant wave height provided by CMEMS SL TAC. Systems are monitored through a product quality dashboard, based on standards inherited from GODAE/Oceanpredict and MERSEA/MyOcean (which includes CLASS 1, 2 and 4 metrics).</p><p> </p>

A Systematic Survey of Multiprocessor Real-Time Scheduling and Synchronization Protocol

2022 ◽  
Vol 12 ◽  
Author(s):  
Ajitesh Kumar
Keyword(s):  
Real Time ◽  
Rapid Development ◽  
Research Work ◽  
Resource Scheduling ◽  
Multiprocessor Scheduling ◽  
Shared Resources ◽  
Time System ◽  
Real Time System ◽  
Real Time Scheduling ◽  
Time Scheduling

Background: Nowadays, there is an immense increase in the demand for high power computation of real-time workloads and the trend towards multi-core and multiprocessor CPUs. The real-time system needs to be implemented upon multiprocessor platforms. Introduction: The nature of processors in an embedded real-time system is changing day by day. The two most significant challenges in a multiprocessor environment are scheduling and synchronization. The popularity of real-time multi-core systems has exploded in recent years, driving the rapid development of a variety of methods for multiprocessor scheduling of essential tasks, on the other hand, these systems have constraints when it comes to maintaining synchronization in order to access shared resources. Method: This research work presents a systematic review of different existing scheduling algorithms and synchronization protocols for shared resources in a real-time multiprocessor environment. The manuscript also presents a study based on various metrics of resource scheduling and comparison among different resource scheduling techniques. Result and Conclusion: The survey classifies open issues, key challenges, and likely useful research directions. Finally, we accept that there is still a lot of capacity in getting better resource management and further maintaining the overall quality. The paper considers such a future path of research in this field.

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