Showing papers in "Advances in Electrical and Electronic Engineering in 2020"

Performance Modeling and Analysis of WDM Optical Networks under Wavelength Continuity Constraint using MILP

Abstract: Optical networks exploit the Wavelength Division Multiplexing (WDM) to meet the ever-growing bandwidth demands of upcoming communication applications. This is achieved by dividing the enormous transmission bandwidth of fiber into smaller communication channels. The major problem with WDM network design is to find an optimal path between two end users and allocate an available wavelength to the chosen path for the successful data transmission. This communication over a WDM network is carried out through lightpaths. The merging of all these lightpaths in an optical network generates a virtual topology which is suitable for the optimal network design to meet the increasing traffic demands. But, this virtual topology design is an NP-hard problem. This paper aims to explore Mixed Integer Linear Programming (MILP) framework to solve this design issue. The comparative results of the proposed and existing mathematical models show that the proposed algorithm outperforms with the various performance parameters. Finally, it is concluded that network congestion is reduced marginally in the overall performance of the network.

Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artificial Neural Network

Abstract: Short Term Load Forecasting (STLF) can predict load from several minutes to week plays a vital role to address challenges such as optimal generation, economic scheduling, dispatching and contingency analysis. This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) technique to perform STFL but long training time and convergence issues caused by bias, variance and less generalization ability, make this algorithm unable to accurately predict future loads. This issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint partitions, small bags, replica small bags and disjoint bags) which help in reducing variance and increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of this method by taking mean improves the overall performance. This method of combining several predictors known as Ensemble Artificial Neural Network (EANN) outperforms the ANN and Bagging method by further increasing the generalization ability and STLF accuracy.

Efficient Solar Integrated Doubly Fed Induction Generator for Wind Energy Harnessing

Abstract: Doubly-Fed Induction Generator (DFIG) is a dual input conventional asynchronous device connected across both stator and rotor, which also functions as an electrical transducer consuming up-to 30 % of the generated power during the rotor injection. This is the major concern with respect to the DFIG in terms of effective power generation. The proposed work provides the solution for this undesirable power loss by connecting the DFIG with an array of solar Photovoltaic (PV) panel through a conventional direct current bus using a boost converter. This research setup can work on the basis of indirect vector control algorithms applied at the rotor and grid converter level in order to maintain the constant DC voltage under arbitrary conditions. Even the variations in voltage and frequency will be controlled with this novel approach. A simulation model of the 300KW solar PV array and a 2MW DFIG system is developed using MATLAB/Simulink environment. Solar and wind energy sources incorporate Maximum Power Point Tracking (MPPT) through their control algorithms resulting in the add on 30-80 KW of solar power for rotor injection compared to the existing system in order to reduce the power rating of the converters and power consumption from the grid.

Frequency Dependent Alterations of S. Cerevisiae Proliferation Due to LF EMF Exposure

Abstract: The presented paper deals with low frequency electromagnetic field application on Saccharomyces cerevisiae cells. Experiments performed through wide frequency range have shown selective frequency dependent biological response, which could be successfully predicted by ion parametric resonance theory proposed by V. V. Lednev. Although observed results give satisfying answer to questions whether or not electromagnetic fields could affect cell cultures even at non-thermal levels, the research presented herein opens a multitude of questions about the exact physical mechanisms underlying the observed microorganism behavior, as the theory discussed within the scope of this article is still not completely unambiguous.

A Survey of Multistage Interconnection Networks

Abstract: Multistage interconnection networks are being used in computer and communications. Multiprocessor architectures for parallel computing exercise these interconnection networks for connecting various processing elements and transfer data between sub-systems of a digital system. The vast diversity of the field poses an obstacle to realize different kinds of interconnection networks and their relationship. This paper consists of an extensive survey of multistage interconnection networks. A broad classification of multistage interconnection networks based on network functionality, reliability and fault tolerance is presented in order to emphasize the important principles which differentiate the network architectures. For each class of network, significant results are given and the basic design principles are explained. The various multistage interconnection networks design provide high performance, availability, throughput, lower latency, less power consumption along with improved fault-tolerance and reliability. However, there is a rising demand for new fault-tolerant and reliable multistage interconnection networks.

Hybrid Cryptography Algorithm for Securing Wireless Sensor Networks from Node Clone Attack

Abstract: Security in WSNs is the most significant because sensor nodes deployed are in an unattended environment, and the information is communicated over insecure wireless channels. Therefore, there is a need to put enhanced security measures for securing sensor nodes as well as ensure secured transmission of information. These resource-constrained networks suffer from various attacks namely, node clone attack, Sybil attack, and DoS attack. In this paper, we proposed a hybrid cryptography algorithm, which secured WSNs from node clone attack. The proposed algorithm uses a combination of symmetric (AES) and asymmetric (ECC) cryptography techniques along with the hash function. Further, the proposed algorithm checks the integrity of messages during communication in the sensor network. The performance of the proposed hybrid algorithm was examined through the various metrics like communication, computation, and storage overheads. The comparison of the results validates the efficiency of the proposed hybrid algorithm. The proposed hybrid algorithm provides a secure and efficient solution to the energy sensor networks.

A Holistic Survey on Disaster and Disruption in Optical Communication Network

Abstract: This paper is a review of optical fiber communications and also presents the different types of disasters and different protection schemes to combat these disasters. In the optical communication network, the failure of a network caused by disasters (e.g. predictable disaster, unpredictable disaster, Intentional attacks) leads to the failure of several optical communication channels and huge data loss. Most of the previous works on optical network survivability assume that the failures are going to occur in future, and the network is made survivable to ensure connectivity in events of failures. With the advancements in technology, the predictions of tornados, cyclone and landslides are becoming more accurate by using some early warning systems. Significant improvement in optical communication network survivability can be achieved using our proposed protection scheme in the event of a major earthquake, cloud burst and landslides. The review concludes that a large amount of data could be lost even if there is a very short disruption in the optical backbone network.

Two-terminal Reliability Analysis for Time-evolving and Predictable Delay-tolerant Networks

Abstract: Several techniques are available to evaluate the two-terminal reliability (2TR) of static networks; however, the advent of dynamic networks in recent past, e.g., Delay Tolerant Networks (DTNs), has made this task extremely challenging due to their peculiar characteristics with an associated disruptive operational environment. Recently, a Cartesian product-based method has been proposed to enumerate time-stamped-minimal path sets (TS-MPS)-a precursor to compute the 2TR of such networks. However, it cannot be used to generate time-stamped-minimal cut sets (TS-MCS). TS-MCS cannot only be used as an alternative to generate 2TR but also to compute other unexplored reliability metrics in DTNs, e.g., the weakest link. To propose a novel approach to enumerate both TS-MPS and TS-MCS of a dynamic network, thereby computing the 2TR of such networks. The proposed technique converts the time aggregated graph model of a dynamic network into a Line Graph (LG) while maintaining the time-varying graph’s node reachability information. This LG is used thereafter to generate TS-MCS as well as TS-MPS to compute 2TR of the network. The DTN examples are presented to show the efficacy and salient features of our algorithm to obtain 2TR of such networks. The terminologies and techniques used for studying/analyzing network reliability of static networks can be extended to dynamic networks as well, e.g., the notion of minimal path sets to TS-MPS or minimal cut sets to TS-MCS, to assess their network reliability-a potential area of furthering network reliability research.

Microstrip Antenna-inception, Progress and Current-state of The Art Review

Abstract: Microstrip antenna has gained significant attention for a large number of communication systems due to its adaptable features and compatibility. The major objective of this manuscript is to assess the microstrip antenna technology for structure adaptability, the feature based performance capabilities, design and versatility. The major reviews conducted in every decade on antenna technology highlighted the significance of microstrip antenna technology. Moreover, a review of the articles on microstrip antenna published in ‘IEEE Transaction on Antenna and Propagation’ revealed that these antennas can primarily be used instead of other types of antennas. The available presentation of microstrip antennas includes features, such as small size, flatness (low profiles), wide-ranging impedance bandwidth, high gain, and circular polarization. A chronological assessment of the major endeavors in the microstrip antenna study conducted during the last four decades, is highlighted. This manuscript focuses on the cutting-edge developments in the microstrip antenna technology and facilitates various modern designated extents, which provide the readers an insight into the tractability presented by microstrip antennas and their prospective benefits as compared to different types of other antennas like lens antenna, dish antenna, horn antenna, etc. The recent advancements in manufacturing techniques of microstrip antenna are reviewed. A large number of publications on the applications of microstrip antennas, such as communication, sensing, energy harvesting, biomedical relevance etc. are also included to show their importance at present. This article presents a state of the art evaluation of the research carried out on microstrip antennas from the time of their inception to till date. It consolidates the information for the researchers working in this field and will be helpful in enhancing the efforts for the research.

Fast ReRoute Model with Realization of Path and Bandwidth Protection Scheme in SDN

Abstract: The paper proposes a Fast ReRoute model with the realization of path and bandwidth protection scheme, which can be used in MPLS for SDN. The task of calculating the set of disjoint primary and backup paths during fast rerouting was reduced to solving the optimization problem of Integer Linear Programming. The advantage of the proposed solution is the possibility of implementing 1:1, 1:2, …, 1:n path protection schemes without the introduction of an additional set of control (routing) variables, which contributes to reducing the dimension of the optimization problem being solved and the computational complexity of its practical implementation. The criterion of optimality of routing solutions contributes to the formation of the primary and backup disjoint paths with the highest possible bandwidth. In this case, the path with the maximum bandwidth will correspond to the primary path, while the remaining paths will be used as backup ones in decreasing order of their bandwidth. The total number of calculated disjoint paths depends on the selected redundancy scheme. The study conducted showed the efficiency and adequacy of the proposed Fast ReRoute model when using various redundancy schemes.

A Novel Dynamic Weight Allocation Method for Assessing the Health Status of Remote Terminal Unit in Distribution Automation System Based on AHM and GRA

Abstract: The Remote Terminal Units (RTUs) in the Distribution Automation System (DAS) are widely used in the field in recent years. It is lack of sample data in different operation status that makes the RTU maintenance improper. The weight allocation for the monitoring indicators in the health status evaluation needs to be identified efficiently and properly. A systematic health status assessment indicator system is constructed firstly. Then, a hybrid indicator weight allocation algorithm based on the Attribute Hierarchy Model (AHM) and grey relational degree (GRA) is proposed to identify the elementary item in the weight allocation under small sample condition. The final indicator weight would be dynamically adjusted according to the equilibrium coefficient, which is determined by the indicator condition parameter. The simulation results show that the weight allocation can be effectively and reasonably adjusted according to the indicator value even under small sample condition. The expert experience and objective data laws are combined and used in our proposed dynamic weight allocation method. It can be used to assess the health status of RTU in the electrical power distribution network.

Two-axis Gimbal System Stabilization Using Adaptive Feedback Linearization

Abstract: This paper is devoted to adaptive feedback linearization based stabilization of a two-axis gimbal platform. A Lyapunov based adaptation mechanism is proposed for stabilization of a two axes gimbal system through a feedback linearized control structure. The uncertainty of the products of inertia is incorporated in the design procedure. The performed computer simulations demonstrate the robust performance of the proposed two-axis gimbal stabilizer considering the cross-coupling effects between inner and outer gimbals. The proposed control structure could be utilized in guided missiles, spacecraft, and navigation systems.

Forecasting of Energy Consumption and Production Using Recurrent Neural Networks

Abstract: Energy forecasting for both consumption and production is a challenging task as it involves many variable factors. It is necessary to calculate the actual production of energy and its consumption as it is very beneficial in maintaining demand and supply. The reliability and smooth functioning of any electrical system are dependent on this management. In this article, the Recurrent Neural Network (RNN) based algorithm is used for energy forecasting. The algorithm is used for making three days ahead prediction of energy for both generation and consumption in Estonia. A comparison is also made between our proposed algorithm and the forecasting algorithm used by Estonian energy regulatory authority. The results of both algorithms indicate that our proposed algorithm has lower Root Mean Square Error (RMSE) and is giving better forecasting.

Track Maneuvering using PID Control for Self-driving Cars

Abstract: In this paper, a Proportional-Integral-Differential (PID) controller that facilitates track maneuvering for self-driving cars is proposed. Three different design approaches are used to find and tune the controller hyperparameters. One of them is “WAF-Tune”, which is an ad hoc trial-and-error based technique that is specifically proposed in this paper for this specific application. The proposed controller uses only the Cross-Track-Error (CTE) as an input to the controller, whereas the output is the steering command. Extensive simulation studies in complex tracks with many sharp turns have been carried out to evaluate the performance of the proposed controller at different speeds. The analysis shows that the proposed technique outperforms the other ones. The usefulness and the shortcomings of the proposed tuning mechanism are also discussed in details.

Tapered Optical Fiber Sensor for Discrimination of Strain and Temperature

Abstract: A simple Mach-Zehnder interferometer (MZI) based on a standard single-mode fiber for simultaneous strain and temperature sensing was proposed and demonstrated experimentally. The interferometer was fabricated by electrical arc discharge method in which a portion of the standard single-mode fiber was heated and stretched. Any special type of fiber or a particular splicer was not required to form the MZI and it indicated high resolution. To find the modes that contributed to the interference spectrum, Fourier analysis was done and the spatial frequency versus normalized intensity plot showed that LP02 was the excited dominant higher order cladding mode. Coefficient matrix was realized for simultaneous measurement of strain and temperature ability of such an MZI. For discrete measurements, a resolution of 0.79 °C in temperature and 1.18 micro-strain can be achieved for 10 pm wavelength resolution. This extremely simple, low-cost sensor easily fabricated with good repeatability is a good candidate in diverse sensing applications.

Grid Integration of Renewable Energy Sources

Abstract: The course will consider large-scale integration of renewable generation in electricity grids. Wind and solar (photovoltaic and thermal) technologies will be discussed, in the context of their influence on grid operation and control. Wind and solar forecasting will be introduced. Impacts of variability will be considered, with both local (voltage) and grid-wide (frequency regulation) effects being addressed. Methods of accounting for renewable uncertainty in optimal generation dispatch will be developed. The use of energy storage for offsetting variability will also be discussed. At the local level, the course will consider the design of renewable-based microgrid energy systems.

Fixing the bugs in software projects from Software Repositories for Improvisation of Quality

Abstract: The study of bugs that are reported and close may indicate the growth and working of a software project. It may also indicate the quality of the project. As software projects grow, the number of bugs reported generally increases each year. To maintain quality, the developers have to resolve and close these increasing numbers of bugs. The present paper discusses the relations between bugs being reported and bugs being closed. It also discusses some parameters related to the study of bugs. In this paper, new parameters have been introduced that help in the improvement of quality and this is the novelty of paper. The research mainly covers the problem of finding the relation of collaborative growth with the quality of software. The paper also covers improvements in parameters like the rate of bug fixing. It also discusses the significance of these parameters. We have suggested a new parameter called the bug closing rate. And this can be calculated in two ways.

Switching Losses Analysis of a Constructed Solar DC-DC Static Boost Converter

Abstract: The DC-DC converter is majorly used in several renewable energy applications. It is usually relevant in a hard-switching operating mode at the cost of increasing power losses and declining efficiency. Power losses are comprised of switching losses and conduction losses, which affect the reliability and speed up the aging of the switch. Therefore, soft-switching techniques are inescapable to reduce electromagnetic interference EMI, minimize losses, and enhance power conversion efficiency. Among the sundry techniques of soft-switching, passive snubbers are uncomplicated and vigorous, besides it has been spotlighted as a finer alternative compared to the active snubbers that involve extra switches and an additional control circuit. This paper investigates the power loss of a conventional solar DC-DC static converter designed and controlled through Maximum Power Point Tracking (MPPT). It evaluates the switch's temperature in the hard-switching operating mode. Besides, this paper presents a new research initiative that aims to allow a zero switching and stabilizing the temperature of the switch through a novel approach of design for RLD and RCD snubber cells. This new design allows the switch to achieve soft-switching, by abolishing the voltage stress, minimizing the power losses, and stabilizing the junction temperature. This snubber has a simple structure with a few components and ease of control, which helps to upgrade the power conversion efficiency through controlling the high voltage and current stress in the switch. In this treatise, elements of the snubber are designed and adjusted for maximum reliability through the simulation in OrCAD environment. Furthermore, the effectiveness of the model is approved through experimental results on a 1600 W conventional boost to validate the proposal.

Mitigation of Harmonics from Output of Cascaded H Bridge Multilevel Inverter Using SHE PWM and AI Technique: A Review

Abstract: An extensive use of fossil fuel has led to an extreme increment of carbon footprint in the world globally. Glaciers are melting on the earth’s poles due to the greenhouse effect. Many such natural indications urge us to adopt renewable energy sources. In most cases, renewable power is generated in electrical form, which does not suit the existing grid requirements. Cascaded H bridge with multilevel inverter topology is promising in this context. The output of the multilevel inverter is near to sinusoidal. It has added advantages of low device stress, no need for step-up transformers, low common-mode voltage, near sinusoidal input current, low switching frequency, and reduced harmonics. The direct output of Cascaded H bridge multilevel inverters may not be suitable for many applications like integrating solar panel output with an existing grid where harmonic reduction is necessary. Various modulation techniques are available like sinusoidal pulse width modulation, space vector modulation, and selective harmonic elimination. Among the listed modulation techniques, selective harmonic elimination can be implemented with the low switching frequency, and it is suitable where low electromagnetic interference, low switching loss, and good power quality are required. To reduce the harmonics, one must solve the non-linear transcendental output equations of the cascaded H-bridge Multilevel inverter (CHB-MLI). Various Artificial Intelligent (AI) algorithms are introduced, and researchers have worked on eliminating harmonics or minimizing Total Harmonics Distortion (THD) from the output of CHB-MLI. This paper gives a review of the harmonic reduction using some of the well-known explored AI algorithms. It also provides insight into some of the unexplored algorithms in this area.

A Study of Wireless Body Area Networks and its Routing Protocols for Healthcare Environment

Abstract: The increased cost of treatments in the health care industry and advancements in technologies have led to a promising area of development in Wireless Sensor Networks and semiconductor technologies. Wireless Body Area Networks is a subset of WSN in which sensor nodes are placed on the human body or implanted inside the body to determine various physical parameters of the human body. This information is forwarded to the medical centers or central servers through gateways. The direct advantage of this technology is the existence of portable health monitoring applications as well as location independent monitoring applications. But, still, the existence of smart hospitals needs a lot of focused research related to practical problems faced by patients as well as practitioners. The aim of this paper is to present an essential depiction of WBAN development in both medical and non- medical applications. The important features of various wireless technologies supported by WBAN have also been presented. It is apparent that to determine the overall performance of a network in terms of different parameters like temperature, power consumption, throughput and delay, etc., a significant role is played by the routing protocols. Since WBAN directly deals with the human body and hence implementation of a new protocol is a challenging task before researchers, this paper reviews each category of routing protocols and their corresponding limitations. A comparison among routing protocols will guide researchers in implementing a specific protocol for targeted application. The paper also focuses on the future of WBAN which will provide the research areas for further exploration. It is found that QoS aware protocols are employed specifically for critical applications. If we consider radiation imparted from the sensors and tissue protection of the human body, the thermal aware routing protocol is the solution. Another important conclusion of this paper is that the various protocols do not provide an optimal solution for selecting the forward node during routing and this solution primarily depends on the residual energy of the nodes and distance of the node from the sink. A study of protocols developed from 2004 onwards till date shows that implementation of WBAN using integration of IoT, EoT, and fog computing has been the emerging topic of research in recent years.

A Novel Strategy of Maximum Power Point Tracking for Photovoltaic Panels Based on Fuzzy Logic Algorithm

Abstract: From the perspective of renewable energy industry investment, absorbing maximum power from renewable sources is a vital factor. Hence, an algorithm is required to change the operating point of renewable energy sources in different environment conditions accordingly. This paper proposes a novel algorithm for tracking the Maximum Power Point (MPP) of a Photovoltaic (PV) panel. In this paper, an auxiliary parameter based on the voltage and power of the PV panel is suggested. By adopting this parameter, independence of irradiation and temperature, the interval between the operating point and the MPP can be estimated. Furthermore, the range of the MPP voltage variations is calculated with respect to various irradiations and temperatures. Then, a novel fuzzy logic Maximum Power Point Tracking (MPPT) algorithm is proposed based on the introduced parameter and voltage variations interval of the MPP. The proposed algorithm has appropriate respond to environment condition changes with proper speed and accuracy. In addition, unlike Hill Climbing (HC) and Perturb and Observe (P&O), the proposed method has no chattering in steady state. The abovementioned claims are successfully validated via the software MATLAB/Simulink.

Control Strategy of a Solid State Transformer for the Grid-side Converter

Abstract: This paper presents a decoupled control technique for balancing the power and voltage through grid side converter using a solid state transformer. Decoupling control is essentially a voltage oriented control technique with the objective of eliminating cross-coupling elements. Use of this decouple technique, allows bi-directional power flow control for both active and reactive power, thereby maintaining steady state DC interference voltage. The performance of this scheme is analyzed & the results are obtained from the Matlab/Simulink model. From the above analysis, it can be concluded that the decoupled control strategy can easily eliminate the cross- coupled element of a solid state transformer for the grid side converter.

A Rectangular Microstrip Patch Antenna for Dual Band Wireless Applications

Abstract: In this paper, the design of a dual-band microstrip patch antenna with operating frequencies of 2.16 GHz and 2.79 GHz is proposed. The proposed antenna design is based upon the defected ground structure and rectangular patch with corner cuts. The presented antenna structure is simulated and optimized using CST microwave studio software. The antenna resonates at 2.16 GHz and 2.79 GHz. The bandwidth of the proposed antenna is 2.08 GHz-2.25 GHz at center frequency 2.16 GHz and is 2.7 GHz-2.87 GHz at center frequency 2.79 GHz. The optimized antenna model is fabricated and measured. The measured and simulated results are presented and discussed. The proposed antenna provides a maximum gain of 4.463 dB and the maximum directivity of 5.846 dBi. The maximum radiation efficiency and total efficiency of the antenna are 79.85% and 69.01%, respectively. The proposed antenna is suitable for dual-band wireless applications.

Concept of a Wearable Temperature Sensor for Intelligent Textile

Abstract: This paper proposes the intelligent textile for human body temperature measurement. The main concept of the textile is based on two commercially available LM35DM temperature sensors, knitted electrically conductivity yarns and five areas for press-stud. Three different measurements methods were used in our experiment. Three measurements were performed in the first step, digital thermometer in person’s armpit, contact-less thermometer in the places close to our LM35DM sensors and contact-less thermometer on the forehead. Next, two LM35DM in SOIC8 package were used for temperature measurement within intelligent textile. Finally, the first step was repeated. All measurements were done on ten subjects with their permission to collect body temperature data for scientific purposes. The results show that the average measurement error for first sensor is 0.844°C and 0.278°C for second sensor.

Optimal Power Delivery from Hybrid Micro-grid to Provide Frequency Regulation

Abstract: This paper presents an optimal power delivery system for a hybrid microgrid consisting of a solar PV, wind farm, battery storage and diesel generator. The fuzzy logicbased control approach is used to match the generation and consumers’ demand. The load demand is assumed to vary hourly in a day and season to season in a year. The frequency of AC (alternating current) bus may deviate from standard frequency due to supplydemand imbalance and thus make the system unstable. To overcome the situation, the controller actuates a control signal to maintain system frequency. Frequency deviation is an indication of the load change and battery voltage (state of charge) is taken as input. In case of excessive generation, the extra power will be absorbed in the dump load for an isolated system. Whereas, for a grid-connected system, the surplus generation is fed into the grid. This cost-effective control method ensures the balance between generation and load demand at all times.

User Power Behavior Similarity Clustering Based on Unsupervised Extreme Learning Machine Algorithm

Abstract: Advanced Metering Infrastructure (AMI) for the smart grid is growing rapidly which results in the exponential growth of data collected and transmitted in the device. By clustering this data, it can give the electricity company a better understanding of the personalized and differentiated needs of the user. The existing clustering algorithms for processing data generally have some problems, such as insufficient data utilization, high computational complexity and low accuracy of behavior recognition. In order to improve the clustering accuracy, this paper proposes a new clustering method based on the electrical behavior of the user. Starting with the analysis of user load characteristics, the user electricity data samples were constructed. The daily load characteristic curve was extracted through improved extreme learning machine clustering algorithm and effective index criteria. Moreover, clustering analysis was carried out for different users from industrial areas, commercial areas and residential areas. The improved extreme learning machine algorithm, also called Unsupervised Extreme Learning Machine (US-ELM), is an extension and improvement of the original Extreme Learning Machine (ELM), which realizes the unsupervised clustering task on the basis of the original ELM. Four different data sets have been experimented and compared with other commonly used clustering algorithms by MATLAB programming. The experimental results show that the US-ELM algorithm has higher accuracy in processing power data. The unsupervised ELM algorithm can greatly reduce the time consumption and improve the effectiveness of clustering.

A Blockchain Based Buyer-seller Watermark Protocol with Trustless Third Party

Abstract: Background: With the development and innovation of digital information technologies and new-generation Internet information platforms, new types of information exchange methods have been spawned It has broken the restriction of the traditional internet boundary, and integrated all round connections between people and objects Methods: Based on the above progresses, digital multimedia contents distributed or published much more convenient on the internet than before and most of them without any copyright protection The dishonest owner can easily copy and distribute the digital multimedia content without reducing any perceptual quality According to the relative concerns, watermark protocol networks play a very important role on usage tracking and copyrights infringement authentication etc However, most of the watermark protocols always require a “fully trusted third party”, which has a potential risk to suffer conspiracy attack Results: Therefore, in this paper, we focus on designing a watermark protocol with trustless third party via blockchain for protecting copyrights of owners that they want to publish or distribute on the internet The proposed watermark protocol includes three sub-protocols which covers the negotiation process, transaction process and identification processes Conclusion: In addition, this paper also provides a fully detail analysis that describes the benefits and weaknesses of current solution

Maximizing Electrical Power Saving Using Capacitors Optimal Placement

Abstract: Low power factor is regarded as one of the most dedicated issues in large scale inductive power networks, because of the lost energy in term of a reactive power. Accordingly, installing capacitors in the network improves the power factor and hence decreases the reactive power. This paper presents an approach to maximize the saving in terms of financial costs, energy resources, environmental protection, and also enhance the power system efficiency. Moreover, the proposed technique tends to avoid the penalties imposed over the electricity bill (in the case of the power factor drops below the permissible limit), by applying a proposed method that consists of two stages. The first stage determines the optimal amount of compensating capacitors by using a suggested analytical method. The second stage employs a statistical approach to assess the reduction in energy losses resulting from the capacitors placement in each of the network nodes. Accordingly, the expected beneficiaries from improving the power factor are mainly large inductive networks such as large scale factories and industrial field. A numerical example is explained in useful detail to show the effectiveness and simplicity of the proposed approach and how it works. The proposed technique tends to minimize the energy losses resulted from the reactive power compensation, release the penalties imposed on electricity bills due to the low power factor. The numerical examples show that the saved cost resulted from improving the power factor, and energy loss reduction is around 10.94 % per month from the total electricity bill. The proposed technique to install capacitors has significant benefits and effective power consumption improvement when the cost of the imposed penalty is regarded as high. The tradeoff in this technique is between the cost of the installed capacitors and the saving gained from the compensation.

Adaptive Neural Dynamic Surface Control for the Chaotic PMSM System with External Disturbances and Constrained Output

Abstract: This article studies the issue of adaptive neural dynamic surface control for the chaotic permanent magnet synchronous motor system with constrained output, external disturbances and parameter perturbations. Firstly, a virtual controller and two practical controllers are created based on the backstepping framework. In the process of creating controllers, adaptive technique and radial basis function neural networks are used to handle unknown parameters and nonlinearities, respectively. The nonlinear damping items are applied to overcome external disturbances. The barrier Lyapunov function is used to prevent the violation of system output constraint. Meanwhile, the first-order filter to eliminate the “explosion of complexity” of traditional back stepping has been introduced. Then, it is proved that all the closed-loop signals are uniform ultimate asymptotic stability and the tracking error converges to a small set of origin. The effectiveness and robustness of the developed approach are illustrated by numerical simulations. The raised control scheme is a useful tool for enhancing the performance of the chaotic PMSM system with external disturbances, constrained output and parameter perturbations.

Fuzzy Clustering Algorithm with Histogram Based Initialization for Remotely Sensed Imagery

Abstract: The paper presents histogram-based initialzation of Fuzzy C Means (FCM) clustering algorithm for remote sensing image analysis. The drawback of well known FCM clustering is sensitive to the choice of initial cluster centers. In order to overcome this drawback, the proposed algorithm, first, determines the optimal initial cluster centers by maximizing the histogram-based weight function. By using these initial cluster centers, the given image is segmented using fuzzy clustering. The major contribution of the proposed method is the automatic initialization of the cluster centers and hence, the clustering performance is enhanced. Also, it is empirically free of experimentally set parameters. Experiments are performed on remote sensing images and cluster validity indices Davies-Bouldin, Partition index, Xie-Beni, Partition Coefficient and Partition Entropy are computed and compared with prominent methods such as FCM, K-Means, and automatic histogram based FCM. The experimental outcomes show that the proposed method is competent for remote sensing image segmentation.