Scholarly Works, Electrical and Computer Engineering

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  • Securing Your Airspace: Detection of Drones Trespassing Protected Areas
    Famili, Alireza; Stavrou, Angelos; Wang, Haining; Park, Jung-Min (Jerry); Gerdes, Ryan (MDPI, 2024-03-22)
    Unmanned Aerial Vehicle (UAV) deployment has risen rapidly in recent years. They are now used in a wide range of applications, from critical safety-of-life scenarios like nuclear power plant surveillance to entertainment and hobby applications. While the popularity of drones has grown lately, the associated intentional and unintentional security threats require adequate consideration. Thus, there is an urgent need for real-time accurate detection and classification of drones. This article provides an overview of drone detection approaches, highlighting their benefits and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost under different operating conditions. We conclude that multi-sensor detection systems offer more compelling results, but further research is required.
  • A Logical Circuit Optimization in Balancing Delay and Energy Consumption
    Shan, Qihang (ACM, 2023-11-03)
    The fast-developing chip manufacturing technique and scaling of transistors allow us to fit more transistors on a small chip. The scaling down process, however, is facing a challenge. The smaller transistors are, the more influential quantum channeling and silicon atom size limit become. To improve efficiency, the solution of scaling down is no longer an option. Therefore, to further improve the efficiency of a chip without scaling down transistors, this paper presents a combinational circuit and focuses on an optimization approach where energy consumption is reduced in exchange for increasing delay. By adjusting the size of transistors, energy is saved while maintaining delay to an acceptable range. This approach manages to reduce energy consumption by about 56% while increasing delay by 50%. This paper represents one of many possible approaches that researchers had and has been working on and this tradeoff can benefit some circuit designs depending on the circuit’s purpose and hope to bring some insights on further optimization.
  • Application of Distributed Ledger Technology in Distribution Networks
    Zhou, Yue; Manea, Andrei Nicolas; Hua, Weiqi; Wu, Jianzhong; Zhou, Wei; Yu, James; Rahman, Saifur (IEEE, 2022-06-24)
    In the transition to a society with net-zero carbon emissions, high penetration of distributed renewable power generation and large-scale electrification of transportation and heat are driving the conventional distribution network operators (DNOs) to evolve into distribution system operators (DSOs) that manage distribution networks in a more active and flexible way. As a radical decentralized data management technology, distributed ledger technology (DLT) has the potential to support a trustworthy digital infrastructure facilitating the DNO-DSO transition. Based on a comprehensive review of worldwide research and practice, as well as the engagement of relevant industrial experts, the application of DLT in distribution networks is identified and analyzed in this article. The DLT features and DSO needs are first summarized, and the mapping relationship between them is identified. Detailed DSO functions are identified and classified into five categories (i.e., 'planning,' 'operation,' 'market,' 'asset,' and 'connection') with the potential of applying DLT to various DSO functions assessed. Finally, the development of seven key DSO functions with high DLT potential is analyzed and discussed from the technical, legal, and social perspectives, including peer-to-peer energy trading, flexibility market facilitation, electric vehicle charging, network pricing, distributed generation register, data access, and investment planning.
  • Optimal Scheduling of Integrated Energy Systems With Multiple CCHPs for High Efficiency and Low Emissions
    Xie, Haimin; Liu, Hui; Wan, Can; Goh, Hui Hwang; Rahman, Saifur (IEEE, 2023-08-14)
    In order to reach carbon neutrality, there is growing interest in reducing greenhouse gas (GHG) and improving energy efficiency. One way to address this issue is the optimal scheduling of the integrated energy system (IES) with multiple combined cooling heating and power (CCHP) systems as proposed in this article. We model IES as a device with multiple input/output ports by the energy hub (EH) framework and propose a multiobjective optimal model to improve energy efficiency and reduce GHG emissions. The proposed model is constructed as a mixed-integer nonlinear programming (MINLP) due to considering nonlinear couplings of multiple energy flows and the unit commitment of multiple CCHP systems. To improve the computational efficiency, the proposed MINLP model is transformed into a nonlinear programming (NLP) model by a fast unit commitment technique based on the approximation of the aggregated online capacity. Finally, simulation results show the effectiveness of the proposed approach in reducing GHG emissions and improving energy efficiency as well as computational efficiency.
  • A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System
    Roy, Rajib Baran; Rokonuzzaman, Md; Amin, Nowshad; Mishu, Mahmuda Khatun; Alahakoon, Sanath; Rahman, Saifur; Mithulananthan, Nadarajah; Rahman, Kazi Sajedur; Shakeri, Mohammad; Pasupuleti, Jagadeesh (IEEE, 2021-07-13)
    In this paper, artificial neural network (ANN) based Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms are deployed in maximum power point tracking (MPPT) energy harvesting in solar photovoltaic (PV) system to forge a comparative performance analysis of the three different algorithms. A comparative analysis among the algorithms in terms of the performance of handling the trained dataset is presented. The MATLAB/Simulink environment is used to design the maximum power point tracking energy harvesting system and the artificial neural network toolbox is utilized to analyze the developed model. The proposed model is trained with 1000 dataset of solar irradiance, temperature, and voltages. Seventy percent data is used for training, while 15% data is employed for validation, and 15% data is utilized for testing. The trained datasets error histogram represents zero error in the training, validation, and test phase of data matching. The best validation performance is attained at 1000 epochs with nearly zero mean squared error where the trained data set is converged to the best training results. According to the results, the regression and gradient are 1, 1, 0.99 and 0.000078, 0.0000015739 and 0.26139 for Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient algorithms, respectively. The momentum parameters are 0.0000001 and 50000 for Levenberg-Marquardt and Bayesian Regularization algorithms, respectively, while the Scaled Conjugate Gradient algorithm does not have any momentum parameter. The Scaled Conjugate Gradient algorithm exhibit better performance compared to Levenberg-Marquardt and Bayesian Regularization algorithms. However, considering the dataset training, the correlation between input-output and error, the Levenberg-Marquardt algorithm performs better.
  • An Assessment of Multistage Reward Function Design for Deep Reinforcement Learning-Based Microgrid Energy Management
    Goh, Hui Hwang; Huang, Yifeng; Lim, Chee Shen; Zhang, Dongdong; Liu, Hui; Dai, Wei; Kurniawan, Tonni Agustiono; Rahman, Saifur (IEEE, 2022-06-01)
    Reinforcement learning based energy management strategy has been an active research subject in the past few years. Different from the baseline reward function (BRF), the work proposes and investigates a multi-stage reward mechanism (MSRM) that scores the agent's step and final performance during training and returns it to the agent in real time as a reward. MSRM will also improve the agent's training through expert intervention which aims to prevent the agent from being trapped in sub-optimal strategies. The energy management performance considered by MSRM-based algorithm includes the energy balance, economic cost, and reliability. The reward function is assessed in conjunction with two deep reinforcement learning algorithms: double deep Q-learning network (DDQN) and policy gradient (PG). Upon benchmarking with BRF, the numerical simulation shows that MSRM tends to improve the convergence characteristic, reduce the explained variance, and reduce the tendency of the agent being trapped in suboptimal strategies. In addition, the methods have been assessed with MPC-based energy management strategies in terms of relative cost, self-balancing rate, and computational time. The assessment concludes that, in the given context, PG-MSRM has the best overall performance.
  • OPTILOD: Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones
    Famili, Alireza; Stavrou, Angelos; Wang, Haining; Park, Jung-Min (Jerry) (MDPI, 2024-03-14)
    For many applications, drones are required to operate entirely or partially autonomously. In order to fly completely or partially on their own, drones need to access location services for navigation commands. While using the Global Positioning System (GPS) is an obvious choice, GPS is not always available, can be spoofed or jammed, and is highly error-prone for indoor and underground environments. The ranging method using beacons is one of the most popular methods for localization, especially for indoor environments. In general, the localization error in this class is due to two factors: the ranging error, and the error induced by the relative geometry between the beacons and the target object to be localized. This paper proposes OPTILOD (Optimal Beacon Placement for High-Accuracy Indoor Localization of Drones), an optimization algorithm for the optimal placement of beacons deployed in three-dimensional indoor environments. OPTILOD leverages advances in evolutionary algorithms to compute the minimum number of beacons and their optimal placement, thereby minimizing the localization error. These problems belong to the Mixed Integer Programming (MIP) class and are both considered NP-hard. Despite this, OPTILOD can provide multiple optimal beacon configurations that minimize the localization error and the number of deployed beacons concurrently and efficiently.
  • Optimizing dynamic electric ferry loads with intelligent power management
    Roy, Rajib Baran; Alahakoon, Sanath; Arachchillag, Shantha Jayasinghe; Rahman, Saifur (Elsevier, 2023-12)
    In recent years, there has been an increasing shift towards using environmentally friendly renewable resources in marine vessels, replacing traditional diesel generators. However, one of the main challenges faced in renewable energy-driven marine vessels is dynamic load management. The feasibility of a renewable-powered electric marine vessel largely depends on the optimal utilization of renewable resources, and storage is an essential component of the marine electric vessel. This paper proposes a two-stage power management system (PMS) for an electric ferry powered by the fuel cell and battery energy storage systems (BESS). The primary objective of the proposed PMS is to ensure a balance between the generated power and the ferry load by minimizing the consumption of hydrogen (H2) fuel. The first stage of the PMS employs particle swarm optimization (PSO), bacterial foraging optimization (BFO), and a hybrid PSO-BFO algorithm to optimize the fuel cell and battery capacity. This is done so that the generated power can follow the load demand. The second stage of the PMS utilizes the Mamdani rule-based fuzzy logic system (FLS) to match the load demand with the generated power. The hybrid PSO-BFO algorithm optimizes the fuzzy control parameters to meet the dynamic load by ensuring optimal H2 fuel consumption and battery state of charge (SOC). To obtain optimal values, the load profile of a conventional ferry is used for the proposed PMS. Based on the optimization results, the optimal capacities are found to be 318 kWh and 317.64 kWh for the fuel cell and BESS, respectively, which are obtained using the hybrid PSO-BFO algorithm. The optimal value of H2 fuel consumption during cruising is found to be 18 kg. A simulated model-based approach validates the operation of the proposed PMS. The proposed PMS ensures optimal H2 fuel consumption and battery SOC while meeting the dynamic load demands of the ferry. The results obtained demonstrate the effectiveness of the proposed PMS in optimizing the renewable energy-driven marine vessel power system.
  • Hybrid Modular Multilevel Converters for High-AC/Low-DC Medium-Voltage Applications
    Motwani, Jayesh Kumar; Liu, Jian; Boroyevich, Dushan; Burgos, Rolando; Zhou, Zhi; Dong, Dong (IEEE, 2024-02-12)
    With ever-increasing power-density requirements, technologies such as energy storage systems and electric-vehicles can benefit greatly from interfacing medium-voltage (MV)-AC grid like 13.8kV or 30kV using high-AC/low-DC voltage converter. Using modular high-AC/low-DC voltage converter can help increase power-density and efficiency, while reducing total conversion steps and providing flexibility. Full-bridge modular multilevel converters (FB-MMC) and solid-state transformers are existing solutions for such operations, but suffer from limitations of high semiconductor requirements, large submodule capacitors and/or many high-frequency transformers. Three new hybrid-MMC (HMMC) topologies are proposed in this paper as alternative solutions for such high-AC/low-DC voltage operations. Each of the three developed HMMCs utilizes a unique combination of low-frequency high-voltage switches and fast-switching lowvoltage switch based submodules to generate multilevel-AC voltage. HMMCs are compared extensively to state-of-the-art FB-MMC and are shown to have semiconductor savings of over 27%, 38% lower submodule capacitor size, and 53% lower losses for 13.8-kV-AC/6-kV-DC operation. Due to these benefits like higher efficiency, significantly smaller submodule capacitance requirements, and fewer semiconductors, HMMCs can be an excellent option for high-AC/low-DC applications. Practical considerations like snubber and DC split-capacitor requirement are also elaborated for developing and commercializing HMMCs. Comparison results are verified using a 17.5 kW three-phase MV laboratory prototype.
  • On the Characterization of the Performance-Productivity Gap for FPGA
    Gondhalekar, Atharva; Twomey, Thomas; Feng, Wu-chun (IEEE, 2022)
    Today, FPGA vendors provide a C++/C-based programming environment to enhance programmer productivity over using a hardware-description language at the register-transfer level. The common perception is that this enhanced pro-ductivity comes at the expense of significantly less performance, e.g., as much an order of magnitude worse. To characterize this performance-productivity tradeoff, we propose a new composite metric, II, that quantitatively captures the perceived discrepancy between the performance and productivity of any two given FPGA programming languages, e.g., Verilog vs. OpenCL. We then present the implications of our metric via a case study on the design of a Sobel filter (i.e., edge detector) using three different programming models - Verilog, OpenCL, oneAPI - on an Intel Arria 10 GX FPGA accelerator. Relative to performance, our results show that an optimized OpenCL kernel achieves 84% of the performance of an optimized Verilog version of the code on a 7680×4320 (8K) image. Conversely, relative to productivity, OpenCL offers a 6.1 x improvement in productivity over Verilog, while oneAPI improves the productivity by an additional factor of 1.25 x over OpenCL.
  • Edge-Connected Jaccard Similarity for Graph Link Prediction on FPGA
    Sathre, Paul; Gondhalekar, Atharva; Feng, Wu-chun (IEEE, 2022-01-01)
    Graph analysis is a critical task in many fields, such as social networking, epidemiology, bioinformatics, and fraud de-tection. In particular, understanding and inferring relationships between graph elements lies at the core of many graph-based workloads. Real-world graph workloads and their associated data structures create irregular computational patterns that compli-cate the realization of high-performance kernels. Given these complications, there does not exist a de facto 'best' architecture, language, or algorithmic approach that simultaneously balances performance, energy efficiency, portability, and productivity. In this paper, we realize different algorithms of edge-connected Jaccard similarity for graph link prediction and characterize their performance across a broad spectrum of graphs on an Intel Stratix 10 FPGA. By utilizing a high-level synthesis (HLS)-driven, high-productivity approach (via the C++-based SYCL language) we rapidly prototype two implementations - a from-scratch edge-centric version and a faithfully-ported commodity GPU implementation - which would have been intractable via a hardware description language. With these implementations, we further consider the benefit and necessity of four HLS-enabled optimizations, both in isolation and in concert - totaling seven distinct synthesized hardware pipelines. Leveraging real-world graphs of up to 516 million edges, we show empirically-measured speedups of up to 9.5 x over the initial HLS implementations when all optimizations work in concert.
  • Scaling out a combinatorial algorithm for discovering carcinogenic gene combinations to thousands of GPUs
    Dash, Sajal; Al-Hajri, Qais; Feng, Wu-chun; Garner, Harold R.; Anandakrishnan, Ramu (IEEE, 2021-05-01)
    Cancer is a leading cause of death in the US, second only to heart disease. It is primarily a result of a combination of an estimated two-nine genetic mutations (multi-hit combinations). Although a body of research has identified hundreds of cancer-causing genetic mutations, we don't know the specific combination of mutations responsible for specific instances of cancer for most cancer types. An approximate algorithm for solving the weighted set cover problem was previously adapted to identify combinations of genes with mutations that may be responsible for individual instances of cancer. However, the algorithm's computational requirement scales exponentially with the number of genes, making it impractical for identifying more than three-hit combinations, even after the algorithm was parallelized and scaled up to a V100 GPU. Since most cancers have been estimated to require more than three hits, we scaled out the algorithm to identify combinations of four or more hits using 1000 nodes (6000 V100 GPUs with ≈ 48× 106 processing cores) on the Summit supercomputer at Oak Ridge National Laboratory. Efficiently scaling out the algorithm required a series of algorithmic innovations and optimizations for balancing an exponentially divergent workload across processors and for minimizing memory latency and inter-node communication. We achieved an average strong scaling efficiency of 90.14% (80.96%-97.96% for 200 to 1000 nodes), compared to a 100 node run, with 84.18% scaling efficiency for 1000 nodes. With experimental validation, the multi-hit combinations identified here could provide further insight into the etiology of different cancer subtypes and provide a rational basis for targeted combination therapy.
  • Electrically Small Antennas' Design Criteria and Measurement Challenges
    Manteghi, Majid (2023-05-24)
    The contrast between the design criteria for electrically small transmit and receive antennas is studied in this work. On the transmit side, radiation efficiency (ohmic loss plus return loss) and data throughput are critical. However, a higher impedance mismatch on the receiving front end may reduce ohmic loss and expand the frequency bandwidth. So, a calculated mismatch can be added to improve the performance of the receiving ESA by lowering the overall noise figure and widening the frequency bandwidth. These contradictory design criteria suggest utilizing separate transmit and receive antennas to improve the transmitter and receiver performances.
  • On the Role of Uncertainty in Poisson Target Models Used for Placement of Spatial Sensors
    Kim, Mingyu; Yetkin, Harun; Stilwell, Daniel J.; Jimenez, Jorge (SPIE, 2023-01-01)
    This paper addresses the role of uncertainty in spatial point-process models, such as those that might arise in modelling ship traffic. We consider a doubly stochastic Poisson point process where the intensity function is uncertain. To assess the role of uncertainty, we conduct a large set of numerical trials where we estimate a doubly stochastic Poisson point-process model from historical target data, and the evaluate the model by assessing the target detection performance of a set of sensors whose locations are selected using the model. Our work is motivated by seabed sensors that detect ship traffic, and we conduct numerical trials using historical ship traffic data near the mouth of the Chesapeake Bay, Virginia, USA, that was recorded by the Automated Identification System.
  • Single-Image 3D Human Digitization with Shape-guided Diffusion
    Albahar, Badour; Saito, Shunsuke; Tseng, Hung-Yu; Kim, Changil; Kopf, Johannes; Huang, Jia-Bin (ACM, 2023-12-10)
    We present an approach to generate a 360-degree view of a person with a consistent, high-resolution appearance from a single input image. NeRF and its variants typically require videos or images from different viewpoints. Most existing approaches taking monocular input either rely on ground-truth 3D scans for supervision or lack 3D consistency. While recent 3D generative models show promise of 3D consistent human digitization, these approaches do not generalize well to diverse clothing appearances, and the results lack photorealism. Unlike existing work, we utilize high-capacity 2D diffusion models pretrained for general image synthesis tasks as an appearance prior of clothed humans. To achieve better 3D consistency while retaining the input identity, we progressively synthesize multiple views of the human in the input image by inpainting missing regions with shape-guided diffusion conditioned on silhouette and surface normal. We then fuse these synthesized multi-view images via inverse rendering to obtain a fully textured high-resolution 3D mesh of the given person. Experiments show that our approach outperforms prior methods and achieves photorealistic 360-degree synthesis of a wide range of clothed humans with complex textures from a single image.