Computer Engineering Commons^™

Evaluating The Role Of Carbon Quantum Dots Covered Silica Nanofillers On The Partial Discharge Performance Of Transformer Insulation, Kasi Viswanathan Palanisamy, Chandrasekar Subramaniam, Balaji Sakthivel

Turkish Journal of Electrical Engineering and Computer Sciences

The article presents the experimental results on the role of carbon quantum dots (CQD) covered silica nanofillers on the partial discharge (PD) properties of transformer oil insulation. The improvement in PD performance of nanofiller blend oil is tested with increased voltage gradient and nanofiller concentration. PD of nanoblend oils for various concentrations of modified silica ranging from 0 to 0.1%wt was measured. PD activity of the test samples is simulated in the laboratory with needle, rod and plane electrode geometry combinations. The facets of PD signals such as PD magnitude, PD inception and time duration of PD extracted from phase-resolved …

A Novel Crimping Technique Approach For High Power White Good Plugs, Ömer Ci̇han Kivanç, Okan Özgönenel, Ömer Bostan, Şahi̇n Güzel, Mert Demi̇rsoy

Turkish Journal of Electrical Engineering and Computer Sciences

The crimping process is essential to human health and the durability of devices, especially in domestic appliances. Moreover, terminal crimping is critical to the safe transmission of electricity; incorrect crimping leads to problems including overheating of the plug, power loss, arc, and failure of the mechanical connection. In recent years, analysis has been performed by the finite element method (FEM) to prevent the incorrect design of crimping and to develop higher performance crimping techniques. A novel crimping technique for domestic appliances requiring high powered plugs is proposed in this study. After defining the crimp parameters and the materials that are …

Lightweight Distributed Computing Framework For Orchestrating High Performance Computing And Big Data, Muhammed Numan İnce, Meli̇h Günay, Joseph Ledet

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, the need for the ability to work remotely and subsequently the need for the availability of remote computer-based systems has increased substantially. This trend has seen a dramatic increase with the onset of the 2020 pandemic. Often local data is produced, stored, and processed in the cloud to remedy this flood of computation and storage needs. Historically, HPC (high performance computing) and the concept of big data have been utilized for the storage and processing of large data. However, both HPC and Hadoop can be utilized as solutions for analytical work, though the differences between these may …

A Novel Fault Detection Approach Based On Multilinear Sparse Pca: Application Onthe Semiconductor Manufacturing Processes, Riadh Toumi, Yahia Kourd, Dimitri Lefebvre

Turkish Journal of Electrical Engineering and Computer Sciences

Batch processes are extremely important to researchers since they are widely used in many fields such as biochemistry, pharmacy, and semiconductors. The powerful batch detection method is critical to increase the performance of the overall equipment and to reduce the use of check wafers. Many techniques have been used in batch process monitoring. Among them, the multivariate statistical process control (MSPC) is very useful in batch process monitoring because of the large number of records data. Therefore, batch processes have certain characteristics, such as multimodal batch nonlinearity trajectories, which were challenged by these MSPCs. In this paper, a novel process …

Strategic Integration Of Battery Energy Storage And Photovoltaic At Low Voltage Level Considering Multiobjective Cost-Benefit, Samarjit Patnaik, Manas Nayak, Meera Viswavandya

Turkish Journal of Electrical Engineering and Computer Sciences

Renewable energy sources, such as solar photovoltaic (PV) systems and battery energy storage systems (BESS), help reduce greenhouse gas emissions while fulfilling the world?s growing energy demand. The inclusion of BESS reduces the peak hour demand, and control of charging and discharging of BESS can be economical for distributors facing time-based energy pricing. This paper discusses a novel multiobjective Horse herd optimisation algorithm (MOHHOA) approach, which is inspired by the social behaviour of horses in herds for PV and BESS optimal allocation in the radial distribution system. The proposed algorithm combines multiple benefits like benefits from economic gain per day, …

Two Person Interaction Recognition Based On A Dual-Coded Modified Metacognitive (Dcmmc) Extreme Learning Machine, Saman Nikzad, Afshin Ebrahimi

Turkish Journal of Electrical Engineering and Computer Sciences

Human action recognition has been an active research area for over three decades. However, state-of-the-art proposed algorithms are still far from developing error-free and fully-generalized systems to perform accurate interaction recognition. This work proposes a new method for two-person interaction recognition from videos, based on well-known cognitive theories. The main idea is to perform classification based on a theory of cognition known as dual coding theory. The theory states that human brain processes and represents two types of information to learn/classify data named analogue and symbolic codes, i.e. (verbal as analogue and visual as symbolic). To implement such a theory …

Anomaly Detection In Rotating Machinery Using Autoencoders Based On Bidirectional Lstm And Gru Neural Networks, Krishna Patra, Rabi Narayan Sethi, Dhiren Kkumar Behera

Turkish Journal of Electrical Engineering and Computer Sciences

A time series anomaly is a form of anomalous subsequence that indicates future faults will occur. The development of novel techniques for detecting this type of anomaly is significant for real-time system monitoring. Several algorithms have been used to classify anomalies successfully. However, the time series anomaly detection algorithm was not studied well. We use a new bidirectional LSTM and GRU neural networks-based hybrid autoencoder to detect if a machine is operating normally in this research. An autoencoder is trained on a set of 12 features taken from healthy operating data gathered promptly after a planned maintenance period using vibration …

Symbolic Ns-3 For Efficient Exhaustive Testing, Jianfei Shao

School of Computing: Dissertations, Theses, and Student Research

Exhaustive testing is an important type of simulation, where a user exhaustively simulates a protocol for all possible cases with respect to some uncertain factors, such as all possible packet delays or packet headers. It is useful for completely evaluating the protocol performance, finding the worst-case performance, and detecting possible design or implementation bugs of a protocol. It is, however, time consuming to use the brute force method with current NS-3, a widely used network simulator, for exhaustive testing. In this paper, we present our work on Sym-NS-3 for more efficient exhaustive testing, which leverages a powerful program analysis technique …

Optimized Damage Assessment And Recovery Through Data Categorization In Critical Infrastructure System., Shruthi Ramakrishnan

Graduate Theses and Dissertations

Critical infrastructures (CI) play a vital role in majority of the fields and sectors worldwide. It contributes a lot towards the economy of nations and towards the wellbeing of the society. They are highly coupled, interconnected and their interdependencies make them more complex systems. Thus, when a damage occurs in a CI system, its complex interdependencies make it get subjected to cascading effects which propagates faster from one infrastructure to another resulting in wide service degradations which in turn causes economic and societal effects. The propagation of cascading effects of disruptive events could be handled efficiently if the assessment and …

Missing Samples Reconstruction Using An Efficient And Robust Instantaneous Frequency Estimation Algorithm, Sadiq Ali, Nabeel Ali Khan

Turkish Journal of Electrical Engineering and Computer Sciences

In order to recover missing samples in a nonstationary signal, this paper employs a time-signal analysis and filtering method. The instantaneous frequency of a multicomponent signal is first estimated by employing a robust and computationally efficient method. Then the time-frequency filtering is performed using a dechirping operation to recover missing samples. These steps are repeated until convergence. The proposed method achieves better performance than the state of art methods both in terms of the accuracy of the recovered signal and computational efficiency.

Binary Flower Pollination Algorithm Based User Scheduling For Multiuser Mimo Systems, Jyoti Mohanty, Prabina Pattanayak, Arnab Nandi, Krishna Lal Baishnab, Fazal Ahmed Talukdar

Turkish Journal of Electrical Engineering and Computer Sciences

In this article, a multiuser (MU) multiinput multioutput (MIMO) system is considered, which is essential to support a huge number of subscribers without consuming extra bandwidth or power. Dirty paper coding (DPC) for MU MIMO channel achieves the peak sum-rate for the MU multiple antenna system at the cost of high computational complexity. Both user and antenna scheduling with a population based meta-heuristic algorithm, i.e. binary flower pollination algorithm (binary FPA) has been demonstrated in this article to achieve system sum-rate comparable to DPC with very less computational complexity and time complexity. Moreover, binary FPA shows a significant improvement in …

Stochastic Day-Ahead Optimal Scheduling Of Multimicrogrids: An Alternating Direction Method Of Multipliers (Admm) Approach, Amin Safari, Hossein Nasiraghdam

Turkish Journal of Electrical Engineering and Computer Sciences

Multimicrogrid system is a novel notion in modern power systems as a result of developing renewable-based generation units and accordingly microgrids in distribution networks. Their energy management might be challenging due to presence of independent units. Thus, in this paper, a decentralized method for energy management of multimicrogrid systems has been proposed. Decentralized methods can enhance the privacy of users and reduce the burden of calculations. Alternating direction method of multipliers (ADMM) is selected as a decentralized approach which has the capability of breaking problems with complicating constraints in order to facilitate the solving process. Using decentralized approach not only …

Priority Enabled Content Based Forwarding In Fog Computing Via Sdn, Yasi̇n İnağ, Metehan Güzel, Feyza Yildirim Okay, Mehmet Demi̇rci̇, Suat Özdemi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

As the number of Internet of Things (IoT) applications increases, an efficient transmitting of the data generated by these applications to a centralized cloud server can be a challenging issue. This paper aims to facilitate transmission by utilizing fog computing (FC) and software defined networking (SDN) technologies. To this end, it proposes two novel content based forwarding (CBF) models for IoT networks. The first model takes advantage of FC to reduce transmission and computational delay. Based on the first model, the second model makes use of the prioritization concept to address the timely delivery of critical data while ensuring the …

Estimation Of Mode Shape In Power Systems Under Ambient Conditions Using Advanced Signal Processing Approach, Rahul S, Sunitha R

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a dynamic approach for the monitoring and estimation of electromechanical oscillatory modes in the power system in real time with less computational burden. Extensive implementation of phasor measurement units (PMU) and the utilization of advanced signal processing techniques help in identifying the dynamic behaviors of oscillatory modes. Conventional nonstationary analysis techniques are computationally weak to handle a larger quantity of data in real-time. This research utilizes the variational mode decomposition (VMD) for signal decomposition, which is highly tolerant to noise and computationally more robust. The predefined parameters of the VMD process are assigned using FFT analysis of …

A Novel Deep Reinforcement Learning Based Stock Price Prediction Using Knowledge Graph And Community Aware Sentiments, Anil Berk Altuner, Zeynep Hi̇lal Ki̇li̇mci̇

Turkish Journal of Electrical Engineering and Computer Sciences

Stock market prediction has been an important topic for investors, researchers, and analysts. Because it is affected by too many factors, stock market prediction is a difficult task to handle. In this study, we propose a novel method that is based on deep reinforcement learning methodologies for the prediction of stock prices using sentiments of community and knowledge graph. For this purpose, we firstly construct a social knowledge graph of users by analyzing relations between connections. After that, time series analysis of related stock and sentiment analysis is blended with deep reinforcement methodology. Turkish version of Bidirectional Encoder Representations from …

Residential Energy Management System Based On Integration Of Fuzzy Logic And Simulated Annealing, Ömer Ci̇han Kivanç, Beki̇r Tevfi̇k Akgün, Semi̇h Bi̇lgen, Sali̇h Bariş Öztürk, Suat Baysan, Ramazan Nejat Tuncay

Turkish Journal of Electrical Engineering and Computer Sciences

With the increase in prosperity level and industrialization, energy need continues to overgrow in many countries. To meet the rapidly increasing energy needs, countries attach great importance to using limited natural resources rationally, diversifying their energy production using novel technologies, improving the efficiency of existing technologies, and implementing policies and strategies toward alternative energy sources. In particular, individual energy prosumers (someone that both produces and consumes energy) head toward smart home energy management systems (SHEMS) that include renewable energy sources in their homes. By integrating PV solar panels into houses, there is a need to optimize home energy production/consumption scenarios …

A New Speed Planning Method Based On Predictive Curvature Calculation For Autonomous Driving, Beki̇r Öztürk, Volkan Sezer

Turkish Journal of Electrical Engineering and Computer Sciences

As the number of vehicles in traffic is increasing day by day, the accident rates and driving effort are significantly raising. For this reason, ensuring safety and driving comfort is becoming more and more important. Driver assistance systems are the most common systems that are adopted for this purpose. With the development of technology, lane tracking support and adaptive cruise control systems are now being sold as standard equipment. More advanced research is being done for fully autonomous driving. One of the most critical parts of autonomous driving is speed profile planning. In this paper, a curvature-based predictive speed planner …

Offline Tuning Mechanism Of Joint Angular Controller For Lower-Limb Exoskeleton With Adaptive Biogeographical-Based Optimization, Mohammad Soleimani Amiri, Rizauddin Ramli

Turkish Journal of Electrical Engineering and Computer Sciences

Designing an accurate controller to overcome the nonlinearity of dynamic systems is a technical matter in control engineering, particularly for tuning the parameters of the controller precisely. In this paper, a tuning mechanism for a proportional-integral-derivative (PID) controller of lower limb exoskeleton (LLE) joints by adaptive biogeographical based-optimization (ABBO) is presented. The tuning of the controller is defined as an optimization problem and solved by ABBO, which is an iterative algorithm inspired by a blending crossover operator (BLX-?). The parameters of the migration change proportionally to the growth of iteration that conveys the error to rapid convergence by narrowing the …

The Iwar Range + 21 Years: Cyber Defense Education In 2022, Joseph H. Schafer, Chris Morrell, Ray Blaine

Military Cyber Affairs

Twenty-one years ago, The IWAR Range paper published by CCSC described nascent information assurance (now cybersecurity[1]) education programs and the inspiration and details for constructing cyber ranges and facilitating cyber exercises. This paper updates the previously published work by highlighting the dramatic evolution of the cyber curricula, exercise networks and ranges, influences, and environments over the past twenty years.

[1] In 2014, DoD adopted “cybersecurity” instead of “information assurance.” [34:1]

Radiomic Features To Predict Overall Survival Time For Patients With Glioblastoma Brain Tumors Based On Machine Learning And Deep Learning Methods, Lina Chato

UNLV Theses, Dissertations, Professional Papers, and Capstones

Machine Learning (ML) methods including Deep Learning (DL) Methods have been employed in the medical field to improve diagnosis process and patient’s prognosis outcomes. Glioblastoma multiforme is an extremely aggressive Glioma brain tumor that has a poor survival rate. Understanding the behavior of the Glioblastoma brain tumor is still uncertain and some factors are still unrecognized. In fact, the tumor behavior is important to decide a proper treatment plan and to improve a patient’s health. The aim of this dissertation is to develop a Computer-Aided-Diagnosis system (CADiag) based on ML/DL methods to automatically estimate the Overall Survival Time (OST) for …

A Novel Data Lineage Model For Critical Infrastructure And A Solution To A Special Case Of The Temporal Graph Reachability Problem, Ian Moncur

Graduate Theses and Dissertations

Rapid and accurate damage assessment is crucial to minimize downtime in critical infrastructure. Dependency on modern technology requires fast and consistent techniques to prevent damage from spreading while also minimizing the impact of damage on system users. One technique to assist in assessment is data lineage, which involves tracing a history of dependencies for data items. The goal of this thesis is to present one novel model and an algorithm that uses data lineage with the goal of being fast and accurate. In function this model operates as a directed graph, with the vertices being data items and edges representing …

Message-Locked Searchable Encryption: A New Versatile Tool For Secure Cloud Storage, Xueqiao Liu, Guomin Yang, Willy Susilo, Joseph Tonien, Rongmao Chen, Xixiang Lv

Research Collection School Of Computing and Information Systems

Message-Locked Encryption (MLE) is a useful tool to enable deduplication over encrypted data in cloud storage. It can significantly improve the cloud service quality by eliminating redundancy to save storage resources, and hence user cost, and also providing defense against different types of attacks, such as duplicate faking attack and brute-force attack. A typical MLE scheme only focuses on deduplication. On the other hand, supporting search operations on stored content is another essential requirement for cloud storage. In this article, we present a message-locked searchable encryption (MLSE) scheme in a dual-server setting, which achieves simultaneously the desirable features of supporting …

Benchmarking Library Recognition In Tweets, Ting Zhang, Divya Prabha Chandrasekaran, Ferdian Thung, David Lo

Research Collection School Of Computing and Information Systems

Software developers often use social media (such as Twitter) to shareprogramming knowledge such as new tools, sample code snippets,and tips on programming. One of the topics they talk about is thesoftware library. The tweets may contain useful information abouta library. A good understanding of this information, e.g., on thedeveloper’s views regarding a library can be beneficial to weigh thepros and cons of using the library as well as the general sentimentstowards the library. However, it is not trivial to recognize whethera word actually refers to a library or other meanings. For example,a tweet mentioning the word “pandas" may refer to …

Machine Learning-Based Device Type Classification For Iot Device Re- And Continuous Authentication, Kaustubh Gupta

School of Computing: Dissertations, Theses, and Student Research

Today, the use of Internet of Things (IoT) devices is higher than ever and it is growing rapidly. Many IoT devices are usually manufactured by home appliance manufacturers where security and privacy are not the foremost concern. When an IoT device is connected to a network, currently there does not exist a strict authentication method that verifies the identity of the device, allowing any rogue IoT device to authenticate to an access point. This thesis addresses the issue by introducing methods for continuous and re-authentication of static and dynamic IoT devices, respectively. We introduce mechanisms and protocols for authenticating a …

Characterizing And Predicting Human Visual Perception Of Unmanned Aerial Vehicle Gestures, Paul Fletcher

School of Computing: Dissertations, Theses, and Student Research

Unmanned Aerial Vehicles (UAVs) are being used in public domains and hazardous environments where effective communication strategies are critical. UAV gesture techniques have been shown to communicate meaning to human observers and may be ideal in contexts that require lightweight systems such as unmanned aerial flight, however, this work may be limited to an idealized range of viewer perspectives. As gesture is a visual communication technique it is necessary to consider how the perception of a robot gesture may suffer from obfuscation or self-occlusion from some viewpoints. This thesis presents the results of three online user-studies that examine participants’ ability …

Identification Of Return-Oriented Programming Attacks Using Risc-V Instruction Trace Data, Daniel F. Koranek, Scott R. Graham, Brett J. Borghetti, Wayne C. Henry

Faculty Publications

An increasing number of embedded systems include dedicated neural hardware. To benefit from this specialized hardware, deep learning techniques to discover malware on embedded systems are needed. This effort evaluated candidate machine learning detection techniques for distinguishing exploited from non-exploited RISC-V program behavior using execution traces. We first developed a dataset of execution traces containing Return Oriented Programming (ROP) exploitation on the RISC-V Instruction Set Architecture (ISA) and then developed several deep learning bidirectional Long Short-Term Memory (LSTM) models capable of distinguishing exploited traces from non-exploited traces, each using subsets of features from the execution traces. An objective of this …

Study On Near-Body Pressure Characteristics Of Bionic Robotic Fish Undulating In Near Wall Region, Ou Xie, Aiguo Song, Qixin Zhu

Journal of System Simulation

Abstract: To avoid unbalanced workload assignment, we studied the vehicle routing problem with refined oil secondary distribution considering workload balance. A bi-objectivemixed integer programming model was built to minimize the total distribution cost and the maximum difference in vehicle route length. A heuristic variable neighborhood tabu search algorithm was designed. An improved Solomon_I1 insertion algorithm was developed to generate afeasible initial solution such that the total distribution cost was as small as possible. Then, the variable neighborhood tabu search algorithm was used to improve the initial solution and thereby obtain the approximate optimal solution. The simulation results show that in …

Multi-Modality Affective Computing Model Based On Personality And Memory Mechanism, Sijin Zhou, Dicheng Chen, Geng Tu, Dazhi Jiang

Journal of System Simulation

Abstract: With the development of affective computing, the correlation of memory, individuation and emotion is more and more important. Focus on the machine emotion shortcomings in the perception, understanding and expression, an emotion computing model integrating the emotion perception, understanding and expression is proposed. The model is a memory-oriented deep network perception model that accepts multiple modal inputs (visual, auditory, lexical) and applies a fuzzy emotion integration decision to realize the understanding of uncertain emotions. The simulation experiments prove that the model has a good performance in all kinds of multimodal affective computing.

Virtual Scene Stereoscopic Panorama Generation And Viewport Rendering Algorithm, Haoxiang Li, Chunyi Chen, Xiaojuan Hu, Yunbiao Liu, Qiwei Xing

Journal of System Simulation

Abstract: Aiming at the nonuniform sampling in map projection and the redundancy in aspheric projection, spherical Fibonacci lattices is used to sample the visible spherical area to generate spherical Fibonacci lattice panorama with low-redundancy and high-quality. On the basis of panoramic stereo imaging model, the binocular ray direction generation algorithm for spherical stereo panorama is proposed. With Fibonacci grid, an adaptive filtering method of light-visibility map for panorama is designed to generate spherical panorama with approximate soft shadow. The nearest neighbor interpolation is used to render the viewport of panorama. Extensive experiments show that the frame frequency of the viewport …

Simulation On Cold Chain Distribution Path Of Fresh Agricultural Products Under Low-Carbon Constraints, Tao Ning, Tao Gou, Xiangdong Liu

Journal of System Simulation

Abstract: The freshness distribution requirements of fresh agricultural products may increase the carbon emissions of the cold chain distribution process. A cold chain distribution scheduling strategy and simulation method for the fresh agricultural products under low-carbon constraints is proposed. Based on the quantitative analysis of the carbon tax mechanism, a mathematical model of minimizing the carbon emissions and minimizing the overall cost of distribution is established. Comprehensively analyzing the conventional factors such as the product delivery volume, delivery time and loading and unloading time in logistics distribution, an improved quantum ant colony algorithm based on adaptive rotation angle …