pwc-archive/papers-with-abstracts huggingface dataset:
Marine IoT Systems with Space-Air-Sea Integrated Networks: Hybrid LEO and UAV Edge Computing
Authors: Sooyeob Jung, Seongah Jeong, Jinkyu Kang, Joonhyuk Kang
Date: 2023-01-10 00:00:00
Tasks: Edge-computing

Marine Internet of Things (IoT) systems have grown substantially with the development of non-terrestrial networks (NTN) via aerial and space vehicles in the upcoming sixth-generation (6G), thereby assisting environment protection, military reconnaissance, and sea transportation. Due to unpredictable climate changes and the extreme channel conditions of maritime networks, however, it is challenging to efficiently and reliably collect and compute a huge amount of maritime data. In this paper, we propose a hybrid low-Earth orbit (LEO) and unmanned aerial vehicle (UAV) edge computing method in space-air-sea integrated networks for marine IoT systems. Specifically, two types of edge servers mounted on UAVs and LEO satellites are endowed with computational capabilities for the real-time utilization of a sizable data collected from ocean IoT sensors. Our system aims at minimizing the total energy consumption of the battery-constrained UAV by jointly optimizing the bit allocation of communication and computation along with the UAV path planning under latency, energy budget and operational constraints. For availability and practicality, the proposed methods were developed for three different cases according to the accessibility of the LEO satellite, ``Always On," ``Always Off" and ``Intermediate Disconnected", by leveraging successive convex approximation (SCA) strategies. Via numerical results, we verify that significant energy savings can be accrued for all cases of LEO accessibility by means of joint optimization of bit allocation and UAV path planning compared to partial optimization schemes that design for only the bit allocation or trajectory of the UAV.
Estimating required flexibility for secure distribution grid operation considering the uncertainties of EV and PV
Authors: Manijeh Alipour, Omid Alizadeh-Mousavi
Date: 2023-01-10 00:00:00

Renewable energy productions and electrification of mobility are promising solutions to reduce greenhouse gas emissions. Their effective integration in a power grid encounters several challenges. The uncertain nature of renewable energy productions as well as stochastic consumptions of electric vehicles introduce remarkable intermittency to a distribution grid and results in bi-uncertain characteristics of both supply and demand sides. One way to verify the secure grid operation within acceptable voltage and loading levels is to assess its required flexibility considering possible boundaries of uncertain variables. In this paper, first a comprehensive linear model of distribution grid considering all pertaining constraints is presented. Then, a flexibility estimation technique is proposed based on the feasibility study of the uncertain space of photovoltaic power productions and load containing electric vehicles. The proposed methodology uses grid monitoring data to determine grid state and to model uncertain parameters. The model is applied on a real low voltage (LV) system equipped with grid monitoring devices.
Total energy-shaping control for mechanical systems via Control-by-Interconnection
Authors: Joel Ferguson
Date: 2023-01-10 00:00:00
Tasks: Acrobot

Application of IDA-PBC to mechanical systems has received much attention in recent decades, but its application is still limited by the solvability of the so-called matching conditions. In this work, it is shown that total energy-shaping control of under-actuated mechanical systems has a control-by-interconnection interpretation. Using this interpretation, alternate matching conditions are formulated that defines constraints on the added energy, rather then the total closed-loop energy. It is additionally shown that, for systems that are under-actuated degree one with the mass matrix depending on a single coordinate, the kinetic energy matching conditions resolve to ODEs which can be evaluated numerically. Using this approach controllers are proposed for the benchmark cart-pole and acrobot systems.
Occupant-Oriented Demand Response with Multi-Zone Thermal Building Control
Authors: Moritz Frahm, Thomas Dengiz, Philipp Zwickel, Heiko Maaß, Jörg Matthes, Veit Hagenmeyer
Date: 2023-01-09 00:00:00
Tasks: Model Predictive Control

In future energy systems with high shares of renewable energy sources, the electricity demand of buildings has to react to the fluctuating electricity generation in view of stability. As buildings consume one-third of global energy and almost half of this energy accounts for Heating, Ventilation, and Air Conditioning (HVAC) systems, HVAC are suitable for shifting their electricity consumption in time. To this end, intelligent control strategies are necessary as the conventional control of HVAC is not optimized for the actual demand of occupants and the current situation in the electricity grid. In this paper, we present the novel multi-zone controller Price Storage Control (PSC) that not only considers room-individual Occupants' Thermal Satisfaction (OTS), but also the available energy storage, and energy prices. The main feature of PSC is that it does not need a building model or forecasts of future demands to derive the control actions for multiple rooms in a building. For comparison, we use an ideal, error-free Model Predictive Control (MPC), a simplified variant without storage consideration (PC), and a conventional hysteresis-based two-point control. We evaluate the four controllers in a multi-zone environment for heating a building in winter and consider two different scenarios that differ in how much the permitted temperatures vary. In addition, we compare the impact of model parameters with high and low thermal capacitance. The results show that PSC strongly outperforms the conventional control approach in both scenarios and for both parameters. For high capacitance, it leads to 22 % costs reduction while the ideal MPC achieves cost reductions of more than 39 %. Considering that PSC does not need any building model or forecast, as opposed to MPC, the results support the suitability of our developed control strategy for controlling HVAC systems in future energy systems.
Modeling of Four-Winged Micro Ornithopters Inspired by Dragonflies
Authors: Oussama Sifour, Soulaimane Berkane, Abdelhamid Tayebi
Date: 2023-01-09 00:00:00
Tasks: Motion Generation

In this paper, we present a full dynamical model of a four-winged micro ornithopter inspired by a dragonfly-type insect. The micro ornithopter is modeled as four articulated rigid body components (wings) connected to the main body via spherical joints. The dynamical model is derived using Lagrangian mechanics with intrinsic global coordinates, without relying on the common assumptions that neglect the wings-body interactions. Furthermore, the aerodynamic forces are modeled under the quasi-steady motion assumption without restricting the flapping frequency to be relatively high. This provides a full and elegant four-winged micro ornithopter model that captures the interaction between the body and the wings while avoiding the complexities and singularities associated with other coordinate representations (e.g., Euler angles). Simulation studies of the inertial effects of the relative motion between the different parts of the multibody system show the importance of considering the forces and torques, resulting from the wings-body interaction, in motion generation of these insects.
Comprehensive Mapping of Continuous/Switching Circuits in CCM and DCM to Machine Learning Domain using Homogeneous Graph Neural Networks
Authors: Ahmed K. Khamis, Mohammed Agamy
Date: 2023-01-08 00:00:00

This paper proposes a method of transferring physical continuous and switching/converter circuits working in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) to graph representation, independent of the connection or the number of circuit components, so that machine learning (ML) algorithms and applications can be easily applied. Such methodology is generalized and is applicable to circuits with any number of switches, components, sources and loads, and can be useful in applications such as artificial intelligence (AI) based circuit design automation, layout optimization, circuit synthesis and performance monitoring and control. The proposed circuit representation and feature extraction methodology is applied to seven types of continuous circuits, ranging from second to fourth order and it is also applied to three of the most common converters (Buck, Boost, and Buck-boost) operating in CCM or DCM. A classifier ML task can easily differentiate between circuit types as well as their mode of operation, showing classification accuracy of 97.37% in continuous circuits and 100% in switching circuits
Active Fault Isolation for Discrete Event Systems
Authors: Lin Cao, Shaolong Shu, Feng Lin
Date: 2023-01-07 00:00:00
Tasks: valid

In practice, we can not only disable some events, but also enforce the occurrence of some events prior to the occurrence of other events by external control. In this paper, we combine these two control mechanisms to synthesize a more powerful supervisor. Here our control goal is to design an isolation supervisor which ensures in the closed-loop system, faults are isolatable in the sense that after a fault occurs, we can determine which type the fault belongs to by observing the output of the closed-loop system. The isolation supervisor starts to work when the occurrence of faults is detected. We then solve the isolation supervisor synthesis problem as follows. For a given discrete event system, we firstly construct a bipartite transition system which includes all feasible isolation supervisors. An isolation supervisor is feasible if it enforces only events that are physically possible. We then develop an algorithm to check whether the synthesis problem is solvable or not. The algorithm can also be used to find a valid isolation supervisor if the synthesis problem is solvable. The method of combining two control mechanisms can be used to synthesize more powerful supervisors for other supervisory control problems of discrete event systems as well.
On Frequency-Based Optimal Portfolio with Transaction Costs
Authors: Chung-Han Hsieh, Yi-Shan Wong
Date: 2023-01-07 00:00:00

The aim of this paper is to investigate the impact of rebalancing frequency and transaction costs on the log-optimal portfolio, which is a portfolio that maximizes the expected logarithmic growth rate of an investor's wealth. We prove that the frequency-dependent log-optimal portfolio problem with costs is equivalent to a concave program and provide a version of the dominance theorem with costs to determine when an investor should invest all available funds in a particular asset. Then, we show that transaction costs may cause a bankruptcy issue for the frequency-dependent log-optimal portfolio. To address this issue, we approximate the problem to obtain a quadratic concave program and derive necessary and sufficient optimality conditions. Additionally, we prove a version of the two-fund theorem, which states that any convex combination of two optimal weights from the optimality conditions is still optimal. We test our proposed methods using both intraday and daily price data. Finally, we extend our empirical studies to an online trading scenario by implementing a sliding window approach. This approach enables us to solve a sequence of concave programs rather than a potentially computational complex stochastic dynamic programming problem.
Planning and Tracking Control of Full Drive-by-Wire Electric Vehicles in Unstructured Scenario
Authors: Guoying Chen, Min Hua, Wei Liu, Jinhai Wang, Shunhui Song, Changsheng Liu
Date: 2023-01-07 00:00:00
Tasks: Autonomous Driving, Model Predictive Control

Full drive-by-wire electric vehicles (FDWEV) with X-by-wire technology can achieve independent driving, braking, and steering of each wheel, providing a good application platform for autonomous driving technology. Path planning and tracking control, in particular, are critical components of autonomous driving. However, It is challenging to comprehensively design an robust control algorithm by integrating vehicle path planning in a complicated unstructured scenario for FDWEV. To address the above issue, this paper first proposes the artificial potential field (APF) method for path planning in the prescribed park with different static obstacles to generate the reference path information, where speed planning is incorporated considering kinematics and dynamic constraints. Second, two tracking control methods, curvature calculation (CC-based) and model predictive control (MPC-based) methods with the lateral dynamics model, are proposed to track the desired path under different driving conditions, in which a forward-looking behavior model of the driver with variable preview distance is designed based on fuzzy control theory. CarSim-AMESim-Simulink co-simulation is conducted with the existence of obstacles. The simulation results show that the proposed two control approaches are effective for many driving scenarios and the MPC-based path-tracking controller enhances dynamic tracking performance and ensures good maneuverability under high-dynamic driving conditions.
A Deep Reinforcement Learning-Based Controller for Magnetorheological-Damped Vehicle Suspension
Authors: AmirReza BabaAhmadi, Masoud ShariatPanahi, Moosa Ayati
Date: 2023-01-06 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

This paper proposes a novel approach to controller design for MR-damped vehicle suspension system. This approach is predicated on the premise that the optimal control strategy can be learned through real-world or simulated experiments utilizing a reinforcement learning algorithm with continuous states/actions. The sensor data is fed into a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which generates the actuation voltage required for the MR damper. The resulting suspension space (displacement), sprung mass acceleration, and dynamic tire load are calculated using a quarter vehicle model incorporating the modified Bouc-Wen MR damper model. Deep RL's reward function is based on sprung mass acceleration. The proposed approach outperforms traditional suspension control strategies regarding ride comfort and stability, as demonstrated by multiple simulated experiments
Technology Report : Smartphone-Based Pedestrian Dead Reckoning Integrated with Data-Fusion-Adopted Visible Light Positioning
Authors: ShangSheng Wen, Ziyang Ge, Danlan Yuan, Yingcong Chen, Xuecong Fang
Date: 2023-01-06 00:00:00
Tasks: Indoor Localization

Pedestrian dead-reckoning (PDR) is a potential indoor localization technology that obtains location estimation with the inertial measurement unit (IMU). However, one of its most significant drawbacks is the accumulation of its measurement error. This paper proposes a visible light positioning (VLP)-integrated PDR system, which could achieve real-time and accurate indoor positioning using IMU and the camera sensor of our smartphone. A multi-frame fusion method is proposed in the encoding and decoding process of the system, reaching 98.5% decoding accuracy with a 20-bit-long ID at the height of 2.1 m, which allows the variation in the shutter speeds of cameras and heights of the LED. Meanwhile, absolute locations and step length could be calibrated with the help of a single light-emitting diode (LED), promising average accuracy within 0.5 meters in a 108-meter walk.
Non-intrusive Water Usage Classification Considering Limited Training Data
Authors: Pavlos Pavlou, Stelios Vrachimis, Demetrios G. Eliades, Marios M. Polycarpou
Date: 2023-01-02 00:00:00
Tasks: Classification, Time Series Analysis

Smart metering of domestic water consumption to continuously monitor the usage of different appliances has been shown to have an impact on people's behavior towards water conservation. However, the installation of multiple sensors to monitor each appliance currently has a high initial cost and as a result, monitoring consumption from different appliances using sensors is not cost-effective. To address this challenge, studies have focused on analyzing measurements of the total domestic consumption using Machine Learning (ML) methods, to disaggregate water usage into each appliance. Identifying which appliances are in use through ML is challenging since their operation may be overlapping, while specific appliances may operate with intermittent flow, making individual consumption events hard to distinguish. Moreover, ML approaches require large amounts of labeled input data to train their models, which are typically not available for a single household, while usage characteristics may vary in different regions. In this work, we initially propose a data model that generates synthetic time series based on regional water usage characteristics and resolution to overcome the need for a large training dataset with real labeled data. The method requires a small number of real labeled data from the studied region. Following this, we propose a new algorithm for classifying single and overlapping household water usage events, using the total domestic consumption measurements.
Evaluation of image registration for measuring deformation fields in soft tissue mechanics
Authors: Ondřej Lisický, Stéphane Avril, Bastien Eydan, Baptiste Pierrat, Jiří Burša
Date: 2023-01-09 00:00:00
Tasks: Image Registration

High-fidelity biomechanical models usually involve the mechanical characterization of biological tissues using experimental methods based on optical measurements. In most experiments, strains are evaluated based on displacements of a few markers and represents an average within the region of interest (ROI). Full-field measurements may improve description of non-homogeneous materials such as soft tissues. The approach based on non-rigid Image Registration is proposed and compared with standard Digital Image Correlation (DIC) on a set of samples, including (i) complex heterogeneous deformations with sub-pixel displacement, (ii) a typical uniaxial tension test of aorta, and (iii) an indentation test on skin. The possibility to extend the ROI to the whole sample and the exploitation of a natural tissue pattern represents the main assets of the proposed method whereas the results show similar accuracy as standard DIC when analysing sub-pixel deformations. Therefore, displacement and strain fields measurement based on Image Registration is very promising to characterize heterogeneous specimens with irregular shapes and/or small dimensions, which are typical features of soft biological tissues.
The value of internal memory for population growth in varying environments
Authors: Leo Law, BingKan Xue
Date: 2023-01-09 00:00:00

In varying environments it is beneficial for organisms to utilize available cues to infer the conditions they may encounter and express potentially favorable traits. However, external cues can be unreliable or too costly to use. We consider an alternative strategy where organisms exploit internal sources of information. Even without sensing environmental cues, their internal states may become correlated with the environment as a result of selection, which then form a memory that helps predict future conditions. To demonstrate the adaptive value of such internal memory in varying environments, we revisit the classic example of seed dormancy in annual plants. Previous studies have considered the germination fraction of seeds and its dependence on environmental cues. In contrast, we consider a model of germination fraction that depends on the seed age, which is an internal state that can serve as a memory. We show that, if the environmental variation has temporal structure, then age-dependent germination fractions will allow the population to have an increased long-term growth rate. The more organisms can remember through their internal states, the higher growth rate a population can potentially achieve. Our results suggest experimental ways to infer internal memory and its benefit for adaptation in varying environments.
Measuring and simulating the biophysical basis of the acoustic contrast factor of biological cells
Authors: Cooper Lars Harshbarger, Alen Pavlic, Davide Cesare Bernardoni, Amelie Viol, Jess Gerrit Snedeker, Jürg Dual, Unai Silván
Date: 2023-01-10 00:00:00

The acoustic contrast factor (ACF) is calculated from the relative density and compressibility differences between a fluid and an object in the fluid. To name but one application, this acoustic contrast can be exploited using acoustophoretic systems to isolate cancer cells from a liquid biopsy, such as a blood sample. Knowing the ACF of a cancer cell represents a crucial step in the design of acoustophoretic systems for this purpose, potentially allowing the isolation of circulating cancer cells without labels or contact. For biological cells the static compressibility is different from the high frequency counterpart relevant for the ACF. In this study, we started by characterizing the ACF of low vs. high metastatic cell lines with known associated differences in phenotypic static E-modulus. The change in the static E-modulus, however, was not reflected in a change of the ACF, prompting a more in depth analysis of the influences on the ACF. We demonstrate that static E-modulus increased biological cells through formaldehyde fixation have an increased ACF. Conversely static E-modulus decreased biological cells treated with actin polymerization inhibitor cytochalasin D have a decreased ACF. Complementing these mechanical tests, a numerical COMSOL model was implemented and used to parametrically explore the effects of cell density, cell density ratios, dynamic compressibility and therefore the dynamic bulk modulus. Collectively the combined laboratory and numerical experiments reveal that a change in the static E-modulus alone might, but does not automatically lead to a change of the dynamic ACF for biological cells. This highlights the need for a multiparametic view of the biophysical basis of the cellular ACF, as well as the challenges in harnessing acoustophoretic systems to isolate circulating cells based on their mechanical properties alone.
Evaluating Evasion Strategies in Zebrafish Larvae
Authors: Yusheng Jiao, Brendan Colvert, Yi Man, Matthew J. McHenry, Eva Kanso
Date: 2023-01-05 00:00:00
Tasks: Diversity

An effective evasion strategy allows prey to survive encounters with predators. Prey are generally thought to escape in a direction that is either random or serves to maximize the minimum distance from the predator. Here we introduce a comprehensive approach to determine the most likely evasion strategy among multiple hypotheses and the role of biomechanical constraints on the escape response of prey fish. Through a consideration of six strategies with sensorimotor noise and previous kinematic measurements, our analysis shows that zebrafish larvae generally escape in a direction orthogonal to the predator's heading. By sensing only the predator's heading, this orthogonal strategy maximizes the distance from fast-moving predators, and, when operating within the biomechanical constraints of the escape response, it provides the best predictions of prey behavior among all alternatives. This work demonstrates a framework for resolving the strategic basis of evastion in predator-prey interactions, which could be applied to a broad diversity of animals.