pwc-archive/papers-with-abstracts huggingface dataset:
No sensitivity to functional forms in the Rosenzweig-MacArthur model with strong environmental stochasticity
Authors: Frédéric Barraquand
Date: 2022-09-02 00:00:00
Tasks: Sensitivity

The classic Rosenzweig-MacArthur predator-prey model has been shown to exhibit, like other coupled nonlinear ordinary differential equations (ODEs) from ecology, worrying sensitivity to model structure. This sensitivity manifests as markedly different community dynamics arising from saturating functional responses with nearly identical shapes but different mathematical expressions. Using a stochastic differential equation (SDE) version of the Rosenzweig-MacArthur model with the three functional responses considered by Fussman & Blasius (2005), I show that such sensitivity seems to be solely a property of ODEs or stochastic systems with weak noise. SDEs with strong environmental noise have by contrast very similar fluctuations patterns, irrespective of the mathematical formula used. Although eigenvalues of linearised predator-prey models have been used as an argument for structural sensitivity, they can also be an argument against structural sensitivity. While the sign of the eigenvalues' real part is sensitive to model structure, its magnitude and the presence of imaginary parts are not, which suggests noise-driven oscillations for a broad range of carrying capacities. I then discuss multiple ways to evaluate structural sensitivity in a stochastic setting, for predator-prey or other ecological systems.
Multilevel Richardson-Romberg and Importance Sampling in Derivative Pricing
Authors: Devang Sinha, Siddhartha P. Chakrabarty
Date: 2022-09-02 00:00:00

In this paper, we propose and analyze a novel combination of multilevel Richardson-Romberg (ML2R) and importance sampling algorithm, with the aim of reducing the overall computational time, while achieving desired root-mean-squared error while pricing. We develop an idea to construct the Monte-Carlo estimator that deals with the parametric change of measure. We rely on the Robbins-Monro algorithm with projection, in order to approximate optimal change of measure parameter, for various levels of resolution in our multilevel algorithm. Furthermore, we propose incorporating discretization schemes with higher-order strong convergence, in order to simulate the underlying stochastic differential equations (SDEs) thereby achieving better accuracy. In order to do so, we study the Central Limit Theorem for the general multilevel algorithm. Further, we study the asymptotic behavior of our estimator, thereby proving the Strong Law of Large Numbers. Finally, we present numerical results to substantiate the efficacy of our developed algorithm.
Event-Driven Tactile Learning with Location Spiking Neurons
Authors: Peng Kang, Srutarshi Banerjee, Henry Chopp, Aggelos Katsaggelos, Oliver Cossairt
Date: 2022-07-23 00:00:00

The sense of touch is essential for a variety of daily tasks. New advances in event-based tactile sensors and Spiking Neural Networks (SNNs) spur the research in event-driven tactile learning. However, SNN-enabled event-driven tactile learning is still in its infancy due to the limited representative abilities of existing spiking neurons and high spatio-temporal complexity in the data. In this paper, to improve the representative capabilities of existing spiking neurons, we propose a novel neuron model called "location spiking neuron", which enables us to extract features of event-based data in a novel way. Moreover, based on the classical Time Spike Response Model (TSRM), we develop a specific location spiking neuron model - Location Spike Response Model (LSRM) that serves as a new building block of SNNs. Furthermore, we propose a hybrid model which combines an SNN with TSRM neurons and an SNN with LSRM neurons to capture the complex spatio-temporal dependencies in the data. Extensive experiments demonstrate the significant improvements of our models over other works on event-driven tactile learning and show the superior energy efficiency of our models and location spiking neurons, which may unlock their potential on neuromorphic hardware.
Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem
Authors: Barzan Hussein Tahir, Tarik A. Rashid, Hafiz Tayyab Rauf, Nebojsa Bacanin, Amit Chhabra, S. Vimal, Zaher Mundher Yaseen
Date: 2022-07-14 00:00:00
Tasks: Decision Making

Economic Load Dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to Economic Load Dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness Dependent Optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness Dependent Optimizer (FDO) examines the search spaces based on the searching approach of Particle Swarm Optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have carried out Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of Fitness Dependent Optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for Fitness Dependent Optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multi-dimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. Empirical results obtained using the enhanced variant of the Fitness Dependent Optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional Fitness Dependent Optimizer. The experimental study obtained 7.94E-12.
Singing Voice Separation with Deep U-Net Convolutional Networks
Authors: Andreas Jansson, Eric Humphrey, Nicola Montecchio, Rachel Bittner, Aparna Kumar, Tillman Weyde
Date: 2017-10-27 00:00:00
Proceeding: International Society for Music Information Retrieval 2017 10
Tasks: Speech Separation, Translation

The decomposition of a music audio signal into its vocal and backing track components is analogous to image-toimage translation, where a mixed spectrogram is transformed into its constituent sources. We propose a novel application of the U-Net architecture — initially developed for medical imaging — for the task of source separation, given its proven capacity for recreating the fine, low-level detail required for high-quality audio reproduction. Through both quantitative evaluation and subjective assessment, experiments demonstrate that the proposed algorithm achieves state-of-the-art performance.
Clinical implementation of a bionic hand controlled with kineticomyographic signals
Authors: Ali Moradi, Hamed Rafiei, Mahla Daliri, Mohammad-R. Akbarzadeh-T., Alireza Akbarzadeh, Amir-M. Naddaf-Sh. & Sadra Naddaf-Sh.
Date: 2022-08-31 00:00:00
Proceeding: Scientific Reports 2022 8

Sensing the proper signal could be a vital piece of the solution to the much evading attributes of prosthetic hands, such as robustness to noise, ease of connectivity, and intuitive movement. Towards this end, magnetics tags have been recently suggested as an alternative sensing mechanism to the more common EMG signals. Such sensing technology, however, is inherently invasive and hence only in simulation stages of magnet localization to date. Here, for the first time, we report on the clinical implementation of implanted magnetic tags for an amputee's prosthetic hand from both the medical and engineering perspectives. Specifically, the proposed approach introduces a flexor–extensor tendon transfer surgical procedure to implant the tags, artificial neural networks to extract human intention directly from the implanted magnet's magnetic fields -in short KineticoMyoGraphy (KMG) signals- rather than localizing them, and a game strategy to examine the proposed algorithms and rehabilitate the patient with his new prosthetic hand. The bionic hand's ability is then tested following the patient's intended gesture type and grade. The statistical results confirm the possible utility of surgically implanted magnetic tags as an accurate sensing interface for recognizing the intended gesture and degree of movement between an amputee and his bionic hand.
Estimating Human Weight from A Single Image
Authors: Zhi Jin, Junjia Huang, Wenjin Wang, Aolin Xiong, Xiaojun Tan
Date: 2022-02-01 00:00:00
Proceeding: IEEE Transactions on Multimedia 2022 2

Body weight as one of the biometric traits has been studied in both the forensic and medical domains. However, estimating weight directly from 2D images is particularly challenging, since the visual inspection is rather sensitive to the distance between the subject and camera, even for the frontal view images. In this case, the widely used Body Mass Index (BMI) which is associated with body height and weight can be employed as a measure of weight to indicate the health conditions. Previous works on the estimation of BMI have predominantly focused on using multiple 2D images, 3D images, or facial images, however, these cues are not always available. To address this issue, we explore the feasibility of obtaining BMI from a single 2D body image with a dual-branch regression framework proposed in this work. More specifically, the framework comprises an anthropometric feature computation branch and a deep learning-based feature extraction branch. One aggregation layer maps all the features to an estimated BMI value. In addition, a new public 2Dimage-to-BMI dataset is collected and released to facilitate the study, which contains 4189 images (1477 males and 2712 females) from around 3000 subjects with the attributes including gender, age, height, and weight. Extensive experiments confirm that the proposed framework combining anthropometric features and deep features outperforms the single-type feature approaches in most cases on BMI estimation.
Solar Irradiance Forecasting with Transformer Model
Authors: Jiří Pospíchal, Martin Kubovčík, Iveta Dirgová Luptáková
Date: 2022-09-02 00:00:00
Proceeding: MDPI Applied Sciences 2022 9
Tasks: model, Prediction, Solar Irradiance Forecasting

Solar energy is one of the most popular sources of renewable energy today. It is therefore essential to be able to predict solar power generation and adapt energy needs to these predictions. This paper uses the Transformer deep neural network model, in which the attention mechanism is typically applied in NLP or vision problems. Here, it is extended by combining features based on their spatiotemporal properties in solar irradiance prediction. The results were predicted for arbitrary long-time horizons since the prediction is always 1 day ahead, which can be included at the end along the timestep axis of the input data and the first timestep representing the oldest timestep removed. A maximum worst-case mean absolute percentage error of 3.45% for the one-day-ahead prediction was obtained, which gave better results than the directly competing methods.
Participation of an energy hub in electricity and heat distribution markets: An mpec approach
Authors: Rui Li, Wei Wei, Shengwei Mei, Qinran Hu, Qiuwei Wu
Date: 2018-05-04 00:00:00
Proceeding: IEEE Transactions on Smart Grid 2018 5

Integration of electricity and heat distribution networks offers extra flexibility to system operation and improves energy efficiency. The energy hub (EH) plays an important role in energy production, conversion and storage in such coupled infrastructures. This paper provides a new outlook and thorough mathematical tool for studying the integrated energy system from a deregulated market perspective. A mathematic program with equilibrium constraints (MPEC) model is proposed to study the strategic behaviors of a profit-driven energy hub in the electricity market and heating market under the background of energy system integration. In the upper level, the EH submits bids of prices and quantities to a distribution power market and a heating market; in the lower level, the two markets are cleared and energy contracts between the EH and two energy markets are determined. Network constraints of physical systems are explicitly represented by an optimal power flow problem and an optimal thermal flow problem. The proposed MPEC formulation is approximated by a mixed-integer linear program via performing integer disjunctions on the complementarity and slackness conditions and binary expansion technique on the bilinear production terms. Case studies demonstrate the effectiveness of the proposed model and method.
Optimal dispatch of zero-carbon-emission micro energy Internet integrated with non-supplementary fired compressed air energy storage system
Authors: Rui Li, Laijun CH, Tiejiang YU, Chunlai LI
Date: 2016-10-01 00:00:00
Proceeding: Journal of Modern Power Systems and Clean Energy 2016 10

To utilize heat and electricity in a clean and integrated manner, a zero-carbon-emission micro Energy Internet (ZCE-MEI) architecture is proposed by incorporating non-supplementary fired compressed air energy storage (NSF-CAES) hub. A typical ZCE-MEI combining power distribution network (PDN) and district heating network (DHN) with NSF-CAES is considered in this paper. NSF-CAES hub is formulated to take the thermal dynamic and pressure behavior into account to enhance dispatch flexibility. A modified DistFlow model is utilized to allow several discrete and continuous reactive power compensators to maintain voltage quality of PDN. Optimal operation of the ZCE-MEI is firstly modeled as a mixed integer nonlinear programming (MINLP). Several transformations and simplifications are taken to convert the problem as a mixed integer linear programming (MILP) which can be effectively solved by CPLEX. A typical test system composed of a NSF-CAES hub, 33-bus PDN, and 8-node DHN is adopted to verify the effectiveness of the proposed ZCE-MEI in terms of reducing operation cost and wind curtailment.
Dispatchable Generation of a Novel Compressed-Air Assisted Wind Turbine and its Operation Mechanism
Authors: J. Cheng, R. Li, F. Choobineh, Q. Hu and S. Mei
Date: 2018-11-23 00:00:00
Proceeding: IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2018 11

The intermittency of the wind resource and limitation of wind turbines (WT) create underutilization of wind power and a significant barrier to the dispatchability of WT. The industrial solutions to mitigate these issues are to improve turbine electronics and to integrate either storage or a gas turbine with a large regulation capacity to a wind farm. This paper proposes a novel configuration, i.e., a compressed-air-assisted wind turbine (CA-WT), to recycle the mechanical spillage and refill the capacity vacancy, which increases the total generation, capacity factor, and dispatchability of WT. The proposed CA-WT integrates an adaptive structure, which is composed of a continuous variable transmission, variable displacement machine, and air tank, to mitigate the deviation between wind energy and load. We develop systematic models and regulation strategies for each component of the dispatchable CA-WT. Case studies are conducted to compare the performance between the CA-WT and WT in the autonomous and grid-connection operation. The results show that the proposed system improves the generation by 39.2$%$, and increases the wind energy penetration rate from 68.57$%$ to 97.08$%$ in the autonomous operation. For the grid-connection operation, the proposed system increase the wind electricity penetration level from 19.12  to 23.57$%$.
Deep Learning Based Channel Estimation Schemes for IEEE 802.11p Standard
Authors: Abdul Karim Gizzini, Marwa Chafii, Ahmad Nimr, Gerhard Fettweis
Date: 2020-06-18 00:00:00
Proceeding: IEEE Access 2020 6
Tasks: Deep Learning

IEEE 802.11p standard is specially developed to define vehicular communications requirements and support cooperative intelligent transport systems. In such environment, reliable channel estimation is considered as a major critical challenge for ensuring the system performance due to the extremely time-varying characteristic of vehicular channels. The channel estimation of IEEE 802.11p is preamble based, which becomes inaccurate in high mobility scenarios. The major challenge is to track the channel variations over the course of packet length while adhering to the standard specifications. The motivation behind this paper is to overcome this issue by proposing a novel deep learning based channel estimation scheme for IEEE 802.11p that optimizes the use of deep neural networks (DNN) to accurately learn the statistics of the spectral temporal averaging (STA) channel estimates and to track their changes over time. Simulation results demonstrate that the proposed channel estimation scheme STA-DNN significantly outperforms classical channel estimators in terms of bit error rate. The proposed STA-DNN architectures also achieve better estimation performance than the recently proposed auto-encoder DNN based channel estimation with at least 55.74% of computational complexity decrease.
Enhancing Least Square Channel Estimation Using Deep Learning
Authors: Abdul Karim Gizzini, Marwa Chafii, Ahmad Nimr, Gerhard Fettweis
Date: 2020-06-30 00:00:00
Proceeding: IEEE 91st Vehicular Technology Conference (VTC2020-Spring) 2020 6
Tasks: Deep Learning

Least square (LS) channel estimation employed in various communications systems suffers from performance degradation especially in low signal-to-noise ratio (SNR) regions. This is due to the noise enhancement in the LS estimation process. Minimum mean square error (MMSE) takes into consideration the noise effect and achieves better performance than LS with higher complexity. This paper proposes to correct the LS estimation error using deep learning (DL). Simulation results show that the proposed DL-based schemes perform better than both LS and MMSE channel estimation scheme, with less complexity than accurate MMSE.
Joint TRFI and Deep Learning for Vehicular Channel Estimation
Authors: Abdul Karim Gizzini, Marwa Chafii, Ahmad Nimr, Gerhard Fettweis
Date: 2020-12-07 00:00:00
Proceeding: IEEE Globecom Workshops (GC Wkshps) 2020 12
Tasks: Deep Learning

IEEE 802.11p standard enables the wireless technology that defines vehicular communications. However, IEEE 802.11p frame structure employing low pilot density is not enough to track the channel variations in high mobility scenarios, leading to significant performance degradation. Therefore, ensuring communication reliability in vehicular environments is considered as a major challenge. In this work, this challenge is tackled by employing deep learning into conventional channel estimation through utilizing deep neural networks (DNN) as an additional non-linear processing unit to correct the interpolation error of the time domain reliable test frequency domain interpolation (TRFI) channel estimates, besides learning higher order statistics of the estimated channel, resulting in a better channel tracking over time. Simulation results demonstrate the performance superiority of the proposed TRFI-DNN scheme over conventional schemes and the recently proposed DNN estimators with a significant computational complexity decrease, especially in high mobility vehicular scenarios.
Low Complex Methods for Robust Channel Estimation in Doubly Dispersive Environments
Authors: Abdul Karim Gizzini, Marwa Chafii
Date: 2022-03-28 00:00:00
Proceeding: IEEE Access 2022 3

Wireless communications play a significant role in facilitating several mobile applications like unmanned aerial vehicles, high-speed railway, and vehicular communications. Particularly, the concept of connected vehicles brings a new level of connectivity to vehicles. Along with novel on- board computing and sensing technologies, vehicular networks serve as a key enabler of intelligent transportation systems and smart cities. However, in such environments, the propagation medium between the network nodes is highly time-varying leading to considerable reliability challenges. Ensuring communication reliability by the means of accurate channel estimation in such environments is very important. Initially, vehicular communications standards apply the basic least square (LS) estimation that is not enough for the dynamic vehicular environment. Moreover, the frame structure has low pilot density, making channel tracking a difficult task to achieve, especially in high mobility scenarios. Conventional estimators either employ data subcarriers besides pilots in the estimation process, or the estimated channel and noise statistics. Therefore they suffer from significant performance degradation due to high error probability resulting from hard symbol demapping and the sensitivity against the change in the employed channel statistics. The motivation behind this paper is to overcome this challenge by proposing a low complex and robust channel estimation scheme based on truncated discrete Fourier transform (T-DFT) that updates the channel estimates using DFT interpolation without the need for data subcarriers decisions and the estimated channel statistics. Moreover, further performance improvement can be achieved by considering temporal averaging on top of T-DFT estimation. Analytical and simulation results carried out using different vehicular channel models reveal the performance superiority of the proposed schemes compared to conventional estimators while recording a significant decrease in computational complexity and execution time.
Adaptive Channel Estimation based on Deep Learning
Authors: Abdul Karim Gizzini, Marwa Chafii, Ahmad Nimr, Gerhard Fettweis
Date: 2020-11-18 00:00:00
Proceeding: IEEE 92nd Vehicular Technology Conference (VTC2020-Fall) 2020 11
Tasks: Deep Learning, Indoor Localization

Channel state information is very critical in various applications such as physical layer security, indoor localization, and channel equalization. In this paper, we propose an adaptive channel estimation based on deep learning that assumes the signal-to-noise power ratio (SNR) knowledge at the receiver, and we show that the proposed scheme highly outperforms linear minimum mean square error based channel estimation in terms of normalized minimum square error, with similar order of online computational complexity. The proposed channel estimation scheme is also evaluated for an imperfect estimation of the SNR and showed to be robust for a high SNR estimation error.