pwc-archive/papers-with-abstracts huggingface dataset:
Reconfigurable Intelligent Surface Enhanced Device-to-Device Communications
Authors: Zelin Ji, Zhijin Qin
Date: 2020-06-22 00:00:00
Tasks: Position

Reconfigurable intelligent surface (RIS) technology is a promising method to enhance the device-to-device (D2D) communications. To maximize the sum rate of the cellular and D2D networks, a joint optimization of the position and the phase shift of RIS in D2D communications is considered in this paper. To solve the non-convex sum rate maximum problem, we propose a novel convolutional neural network (CNN) based deep Q-network (DQN) that jointly optimizes the RIS position and its phase shift with lower complexity. Numerical results illustrate that the proposed algorithm can achieve higher sum rate compared to the benchmark algorithms, meanwhile meeting the quality of service (QoS) requirements at D2D receivers and the base station (BS).
Vibration transfer path analysis and path ranking for NVH optimization of a vehicle interior
Authors: Babak Sakhaei, Mohammad Durali, Navid Hashemi
Date: 2020-06-15 00:00:00

By new advancements in vehicle manufacturing; evaluation of vehicle quality assurance has got a more critical issue. Today noise and vibration generated inside and outside the vehicles are more important factors for customers than previous. So far several researchers have focused on interior noise transfer path analysis and the results have been published in related papers but each method has its own limitations. In present work, the vibration transfer path analysis and vibration path ranking of a car interior has been performed. As interior vibration is a source of structural borne noise problem, thus the results of this research can be used to present the structural borne noise state in a vehicle. The method proposed in this paper, in opposite of the earlier methods, do not need to disassemble the power train from the chassis. The procedure shows a good ability of vibration path ranking in a vehicle and is an effective tool to diagnose the vibration problem inside the vehicle. The simulated vibration spectrums in different speeds of the engine have a good compliance with the tested results however some incompatibilities exist and have been discussed in details. The simulated results show the strength of the method in engine mount optimization.
Edge server deployment scheme of blockchain in IoVs
Authors: Liya Xu, Mingzhu Ge, Weili Wu, Member, IEEE
Date: 2020-06-16 00:00:00
Tasks: Edge-computing

With the development of intelligent vehicles, security and reliability communication between vehicles has become a key problem to be solved in Internet of vehicles(IoVs). Blockchain is considered as a feasible solution due to its advantages of decentralization, unforgeability and collective maintenance. However, the computing power of nodes in IoVs is limited, while the consensus mechanism of blockchain requires that the miners in the system have strong computing power for mining calculation. It consequently cannot satisfy the requirements, which is the challenges for the application of blockchain in IoVs. In fact, the application of blockchain in IoVs can be implemented by employing edge computing. The key entity of edge computing is the edge servers(ESs). Roadside nodes(RSUs) can be deployed as ESs of edge computing in IoVs. We have studied the ES deployment scheme for covering more vehicle nodes in IoVs, and propose a randomized algorithm to calculate approximation solutions. Finally, we simulated the performance of the proposed scheme and compared it with other deployment schemes.
Calibration of chemical sensors in mobile wireless networks
Authors: Rakesh Gosangi, Harsha Chenji, Radu Stoleru, Ricardo Gutierrez-Osuna
Date: 2020-06-17 00:00:00

Low-power chemical sensors deployed on mobile platforms make it possible to monitor pollutant concentrations across large urban areas. However, chemical sensors are prone to drift (e.g., aging, damage, poisoning) and have to be calibrated periodically. In this paper, we present an opportunistic calibration approach that relies on encounters between sensors; when in vicinity of each other, sensors exchange measurements and use the accumulated information to re-calibrate. We formulate the calibration process as weighted least-squares, where the most recent measurements are assigned the highest weights. We model the weights with an exponential decay function (in time) and optimize the decay constant using simulated annealing (SA). We validated the proposed method on a simulated sensor network with the sensors' mobility driven by random-waypoint (RWP) models. We present results in terms of average calibration errors for different weight functions, and network sizes.
ETMA: A New Software for Event Tree Analysis with Application to Power Protection
Authors: Mohamed Abdelghany, Waqar Ahmad, Sofiene Tahar, Sowmith Nethula
Date: 2020-06-18 00:00:00
Tasks: Decision Making

Event Tree (ET) analysis is a widely used forward deductive safety analysis technique for decision-making at a system design stage. Existing ET tools usually provide Graphical Users Interfaces (GUI) for users to manually draw system-level ET diagrams, which consist of nodes and branches, describing all possible success and failure scenarios. However, these tools do not include some important ET analysis steps, e.g., the automatic generation and reduction of a complete system ET diagram. In this paper, we present a new Event Trees Modeling and Analysis (ETMA) tool to facilitate users to conduct a complete ET analysis of a given system. Some key features of ETMA include: (i) automatic construction of a complete ET model of real-world systems; (ii) deletion/reduction of unnecessary ET nodes and branches; (iii) partitioning of ET paths; and (iv) probabilistic analysis of the occurrence of a certain event. For illustration purposes, we utilize our ETMA tool to conduct the ET analysis of a protective fault trip circuit in power grid transmission lines. We also compared the ETMA results with Isograph, which is a well-known commercial tool for ET analysis.
AI-Augmented Multi Function Radar Engineering with Digital Twin: Towards Proactivity
Authors: Mathieu Klein, Thomas Carpentier, Eric Jeanclaude, Rami Kassab, Konstantinos Varelas, Nico de Bruijn, Frédéric Barbaresco, Yann Briheche, Yann Semet, Florence Aligne
Date: 2020-06-18 00:00:00

Thales new generation digital multi-missions radars, fully-digital and software-defined, like the Sea Fire and Ground Fire radars, benefit from a considerable increase of accessible degrees of freedoms to optimally design their operational modes. To effectively leverage these design choices and turn them into operational capabilities, it is necessary to develop new engineering tools, using artificial intelligence. Innovative optimization algorithms in the discrete and continuous domains, coupled with a radar Digital Twins, allowed construction of a generic tool for "search" mode design (beam synthesis, waveform and volume grid) compliant with the available radar time budget. The high computation speeds of these algorithms suggest tool application in a "Proactive Radar" configuration, which would dynamically propose to the operator, operational modes better adapted to environment, threats and the equipment failure conditions.
Evaluating Different Machine Learning Techniques as Surrogate for Low Voltage Grids
Authors: Stephan Balduin, Tom Westermann, Erika Puiutta
Date: 2020-06-19 00:00:00
Tasks: BIG-bench Machine Learning

The transition of the power grid requires new technologies and methodologies, which can only be developed and tested in simulations. Especially larger simulation setups with many levels of detail can become quite slow. Therefore, the number of possible simulation evaluations decreases. One solution to overcome this issue is to use surrogate models, i.e., data-driven approximations of (sub)systems. In a recent work, a surrogate model for a low voltage grid was built using artificial neural networks, which achieved satisfying results. However, there were still open questions regarding the assumptions and simplifications made. In this paper, we present the results of our ongoing research, which answer some of these question. We compare different machine learning algorithms as surrogate models and exchange the grid topology and size. In a set of experiments, we show that algorithms based on linear regression and artificial neural networks yield the best results independent of the grid topology. Furthermore, adding volatile energy generation and a variable phase angle does not decrease the quality of the surrogate models.
Deep Job Understanding at LinkedIn
Authors: Li Shan, Shi Baoxu, Yang Jaewon, Yan Ji, Wang Shuai, Chen Fei, He Qi
Date: 2020-05-29 00:00:00
Tasks: Transfer Learning

As the world's largest professional network, LinkedIn wants to create
economic opportunity for everyone in the global workforce. One of its most
critical missions is matching jobs with processionals. Improving job targeting
accuracy and hire efficiency align with LinkedIn's Member First Motto. To
achieve those goals, we need to understand unstructured job postings with noisy
information. We applied deep transfer learning to create domain-specific job
understanding models. After this, jobs are represented by professional
entities, including titles, skills, companies, and assessment questions. To
continuously improve LinkedIn's job understanding ability, we designed an
expert feedback loop where we integrated job understanding models into
LinkedIn's products to collect job posters' feedback. In this demonstration, we
present LinkedIn's job posting flow and demonstrate how the integrated deep job
understanding work improves job posters' satisfaction and provides significant
metric lifts in LinkedIn's job recommendation system.
Locally trimmed least squares: conventional inference in possibly nonstationary models
Date: 2020-06-22 00:00:00
Tasks: Unity

A novel IV estimation method, that we term Locally Trimmed LS (LTLS), is
developed which yields estimators with (mixed) Gaussian limit distributions in
situations where the data may be weakly or strongly persistent. In particular,
we allow for nonlinear predictive type of regressions where the regressor can
be stationary short/long memory as well as nonstationary long memory process or
a nearly integrated array. The resultant t-tests have conventional limit
distributions (i.e. N(0; 1)) free of (near to unity and long memory) nuisance
parameters. In the case where the regressor is a fractional process, no
preliminary estimator for the memory parameter is required. Therefore, the
practitioner can conduct inference while being agnostic about the exact
dependence structure in the data. The LTLS estimator is obtained by applying
certain chronological trimming to the OLS instrument via the utilisation of
appropriate kernel functions of time trend variables. The finite sample
performance of LTLS based t-tests is investigated with the aid of a simulation
experiment. An empirical application to the predictability of stock returns is
also provided.
Mathematical modeling and prediction of COVID-19 in Moscow city and Novosibirsk region
Date: 2020-06-18 00:00:00

The paper formulates and solves the problem of identification of unknown
parameters of mathematical models of the spread of COVID-19 coronavirus
infection, based on SEIR type models, based on additional information about the
number of detected cases, mortality, self-isolation coefficient and tests
performed for the Moscow city and the Novosibirsk Region from 03.23.2020.
Within the framework of the models used, the population is divided into seven
(SEIR-HCD) and five (SEIR-D) groups with similar characteristics with
transition probabilities between groups depending on a specific region.
Identifiability analysis of the SEIR-HCD mathematical model was carried out,
which revealed the least sensitive unknown parameters to additional
measurements. The tasks of refining the parameters are reduced to minimizing
the corresponding target functionals, which were solved using stochastic
methods (simulating annealing, differential evolution, genetic algorithm,
etc.). For a different amount of tested data, a prognostic scenario for the
development of the disease in the city of Moscow and the Novosibirsk region was
developed, the peak is predicted the development of the epidemic in Moscow with
an error of 2 days and 174 detected cases, and an analysis of the applicability
of the developed models was carried out.
Intelligent Reflecting Surfaces with Spatial Modulation: An Electromagnetic Perspective
Authors: Okan Yurduseven, Stylianos D. Assimonis, Michail Matthaiou
Date: 2020-06-22 00:00:00
Tasks: TAG

Electromagnetic wave control using the concept of a reflecting surface is first studied as a near-field and a far-field problem. Using a secondary source present in a wireless communication environment, such as a backscatter tag, it is possible to leverage the incoming radiation from the source as a reference-wave to synthesize the desired wavefront across the reflecting surface, radiating a field of interest. In this geometry, the phase grating, which is synthesized using an array of sub-wavelength unit cells, is calculated by interacting the incident reference-wave and the desired wavefront, similar to a hologram. When illuminated by the reference-wave, the reflected wavefront from the calculated phase grating is guaranteed to constructively add in the direction of the desired radiation (beam-steering in the far-field) and also focus at the intended depth (beam-focusing in the radiative near-field). Leveraging a dynamic modulation mechanism in the context of an intelligent reflective surface (IRS) illuminated by a backscatter tag, later, we present that one can selectively focus and defocus at an arbitrarily positioned receiver within the 3D field of view of the reflecting surface. This enables the control of the amplitude of the radiated electric field at the receiver location, paving the way for a spatial modulation mechanism by means of reconfiguring the reflecting surface in a backscattered wireless communication environment. In addition to the phase modification approach on a unit cell level to reconfigure the aperture radiated wavefronts, we finally present a time varying IRS concept making use of a time-delay based approach relying on a delay adjustment between the reflection coefficients of the IRS' unit cell lattices.
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control
Authors: Renke Huang, Yujiao Chen, Tianzhixi Yin, Xinya Li, Ang Li, Jie Tan, Wenhao Yu, YuAn Liu, Qiuhua Huang
Date: 2020-06-22 00:00:00
Tasks: Computational Efficiency, Deep Reinforcement Learning, Model Predictive Control, reinforcement-learning, Reinforcement Learning (RL)

Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability. With increased uncertainties and rapidly changing operational conditions in power systems, existing methods have outstanding issues in terms of either speed, adaptiveness, or scalability. Deep reinforcement learning (DRL) was regarded and adopted as a promising approach for fast and adaptive grid stability control in recent years. However, existing DRL algorithms show two outstanding issues when being applied to power system control problems: 1) computational inefficiency that requires extensive training and tuning time; and 2) poor scalability making it difficult to scale to high dimensional control problems. To overcome these issues, an accelerated DRL algorithm named PARS was developed and tailored for power system voltage stability control via load shedding. PARS features high scalability and is easy to tune with only five main hyperparameters. The method was tested on both the IEEE 39-bus and IEEE 300-bus systems, and the latter is by far the largest scale for such a study. Test results show that, compared to other methods including model-predictive control (MPC) and proximal policy optimization(PPO) methods, PARS shows better computational efficiency (faster convergence), more robustness in learning, excellent scalability and generalization capability.
Signal Shaping for Non-Uniform Beamspace Modulated mmWave Hybrid MIMO Communications
Authors: Shuaishuai Guo, Haixia Zhang, Peng Zhang, Shuping Zhang, Chengcheng Xu, Mohamed-Slim Alouini
Date: 2020-06-23 00:00:00

This paper investigates adaptive signal shaping methods for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications based on the maximizing the minimum Euclidean distance (MMED) criterion. In this work, we utilize the indices of analog precoders to carry information and optimize the symbol vector sets used for each analog precoder activation state. Specifically, we firstly propose a joint optimization based signal shaping (JOSS) approach, in which the symbol vector sets used for all analog precoder activation states are jointly optimized by solving a series of quadratically constrained quadratic programming (QCQP) problems. JOSS exhibits good performance, however, with a high computational complexity. To reduce the computational complexity, we then propose a full precoding based signal shaping (FPSS) method and a diagonal precoding based signal shaping (DPSS) method, where the full or diagonal digital precoders for all analog precoder activation states are optimized by solving two small-scale QCQP problems. Simulation results show that the proposed signal shaping methods can provide considerable performance gain in reliability in comparison with existing mmWave transmission solutions.
Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration
Date: 2020-06-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Dynamic distribution network reconfiguration (DNR) algorithms perform hourly
status changes of remotely controllable switches to improve distribution system
performance. The problem is typically solved by physical model-based control
algorithms, which not only rely on accurate network parameters but also lack
scalability. To address these limitations, this paper develops a data-driven
batch-constrained reinforcement learning (RL) algorithm for the dynamic DNR
problem. The proposed RL algorithm learns the network reconfiguration control
policy from a finite historical operational dataset without interacting with
the distribution network. The numerical study results on three distribution
networks show that the proposed algorithm not only outperforms state-of-the-art
RL algorithms but also improves the behavior control policy, which generated
the historical operational data. The proposed algorithm is also very scalable
and can find a desirable network reconfiguration solution in real-time.
Achieving Performance and Safety in Large Scale Systems with Saturation using a Nonlinear System Level Synthesis Approach
Date: 2020-06-23 00:00:00

We present a novel class of nonlinear controllers that interpolates among
differently behaving linear controllers as a case study for recently proposed
Linear and Nonlinear System Level Synthesis framework. The structure of the
nonlinear controller allows for simultaneously satisfying performance and
safety objectives defined for small- and large-disturbance regimes. The
proposed controller is distributed, handles delays, sparse actuation, and
localizes disturbances. We show our nonlinear controller always outperforms its
linear counterpart for constrained LQR problems. We further demonstrate the
anti-windup property of an augmented control strategy based on the proposed
controller for saturated systems via simulation.
Subcompartmentalization of polyampholyte species in organelle-like condensates is promoted by charge pattern mismatch and strong excluded-volume interaction
Authors: Tanmoy Pal, Jonas Wessén, Suman Das, Hue Sun Chan
Date: 2020-06-23 00:00:00

Polyampholyte field theory and explicit-chain molecular dynamics models of sequence-specific phase separation of a system with two intrinsically disordered protein (IDP) species indicate consistently that a substantial polymer excluded volume and a significant mismatch of the IDP sequence charge patterns can act in concert, but not in isolation, to demix the two IDP species upon condensation. This finding reveals an energetic-geometric interplay in a stochastic, "fuzzy" molecular recognition mechanism that may facilitate subcompartmentalization of membraneless organelles.
Real-time Control of Battery Energy Storage Systems to Provide Ancillary Services Considering Voltage-Dependent Capability of DC-AC Converters
Authors: Zhao Yuan, Antonio Zecchino, Rachid Cherkaoui, Mario Paolone
Date: 2020-06-23 00:00:00

Frequency response and voltage support are vital ancillary services for power grids. In this paper, we design and experimentally validate a real-time control framework for battery energy storage systems (BESSs) to provide ancillary services to power grids. The objective of the control system is to utilize the full capability of the BESSs to provide ancillary services. We take the voltage-dependent capability curve of the DC-AC converter and the security requirements of BESSs as constraints of the control system. The initial power set-points are obtained based on the droop control approach. To guarantee the feasibility of the power set-points with respect to both the converter capability and BESS security constraints, the final power set-points calculation is formulated as a nonconvex optimization problem. A convex and computationally efficient reformulation of the original control problem is then proposed. We prove that the proposed convex optimization gives the global optimal solution to the original nonconvex problem. We improve the computational performance of this algorithm by discretizing the feasible region of the optimization model. We achieve a 100 ms update time of the controller setpoint computation in the experimental validation of the utility-scale 720 kVA / 560 kWh BESS on the EPFL campus.
A mathematical model of cell fate selection on a dynamic tissue
Date: 2020-06-23 00:00:00

Multicellular tissues are the building blocks of many biological systems and organs. These tissues are not static, but dynamically change over time. Even if the overall structure remains the same there is a turnover of cells within the tissue. This dynamic homeostasis is maintained by numerous governing mechanisms which are finely tuned in such a way that the tissue remains in a homeostatic state, even across large timescales. Some of these governing mechanisms include cell motion, and cell fate selection through inter cellular signalling. However, it is not yet clear how to link these two processes, or how they may affect one another across the tissue. In this paper, we present a multicellular, multiscale model, which brings together the two phenomena of cell motility, and inter cellular signalling, to describe cell fate selection on a dynamic tissue. We find that the affinity for cellular signalling to occur greatly influences a cells ability to differentiate. We also find that our results support claims that cell differentiation is a finely tuned process within dynamic tissues at homeostasis, with excessive cell turnover rates leading to unhealthy (undifferentiated and unpatterned) tissues.
The unbearable lightness of equilibria in a low interest rate environment
Authors: Guido Ascari, Sophocles Mavroeidis
Date: 2020-06-20 00:00:00

Structural models with no solution are incoherent, and those with multiple solutions are incomplete. We show that models with occasionally binding constraints are not generically coherent. Coherency requires restrictions on the parameters or on the support of the distribution of the shocks. In presence of multiple shocks, the support restrictions cannot be independent from each other, so the assumption of orthogonality of structural shocks is incompatible with coherency. Models whose coherency is based on support restrictions are generically incomplete, admitting a very large number of minimum state variable solutions.