pwc-archive/papers-with-abstracts huggingface dataset:
GGADN: Guided generative adversarial dehazing network
Authors: Jian Zhang1 · Qinqin Dong2 · Wanjuan Song3
Date: 2021-07-13 00:00:00
Proceeding: journal 2021 7
Tasks: Image Dehazing

Image dehazing has always been a challenging topic in image processing. The development of deep learning methods,
especially the generative adversarial networks (GAN), provides a new way for image dehazing. In recent years, many deep
learning methods based on GAN have been applied to image dehazing. However, GAN has two problems in image dehazing. Firstly, For haze image, haze not only reduces the quality of the image but also blurs the details of the image. For GAN network, it is difficult for the generator to restore the details of the whole image while removing the haze. Secondly, GAN model is defined as a minimax problem, which weakens the loss function. It is difficult to distinguish whether GAN is making progress in the training process. Therefore, we propose a guided generative adversarial dehazing network (GGADN). Different from other generation adversarial networks, GGADN adds a guided module on the generator. The guided module verifies the network of each layer of the generator. At the same time, the details of the map generated by each layer are strengthened. Network training is based on the pre-trained VGG feature model and L1-regularized gradient prior which is developed by new loss function parameters. From the dehazing results of synthetic images and real images, the proposed method is better than the state-of-the-art dehazing methods.
Designing biological circuits: from principles to applications
Authors: Debomita Chakraborty, Raghunathan Rengaswamy, Karthik Raman
Date: 2021-11-05 00:00:00
Tasks: Morphological Analysis, Robust Design

Genetic circuit design is a well-studied problem in synthetic biology. Ever since the first genetic circuits -- the repressilator and the toggle switch -- were designed and implemented, many advances have been made in this area of research. The current review systematically organizes a number of key works in this domain by employing the versatile framework of generalized morphological analysis. Literature in the area has been mapped based on (a) the design methodologies used, ranging from brute-force searches to control-theoretic approaches, (b) the modelling techniques employed, (c) various circuit functionalities implemented, (d) key design characteristics, and (e) the strategies used for the robust design of genetic circuits. We conclude our review with an outlook on multiple exciting areas for future research, based on the systematic assessment of key research gaps that have been readily unravelled by our analysis framework.
A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning
Authors: Damien Bouchabou, Sao Mai Nguyen, Christophe Lohr, Benoit Leduc, Ioannis Kanellos
Date: 2021-10-18 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. But new challenges are emerging every day. In this paper, we present recent algorithms, works, challenges and taxonomy of the field of human activity recognition in a smart home through ambient sensors. Moreover, since activity recognition in smart homes is a young field, we raise specific problems, missing and needed contributions. But also propose directions, research opportunities and solutions to accelerate advances in this field.
A hemispheric two-channel code accounts for binaural unmasking in humans
Authors: Jörg Encke, Mathias Dietz
Date: 2021-11-08 00:00:00
Tasks: Vocal Bursts Valence Prediction

Sound in noise is better detected or understood if target and masking sources originate from different locations. Mammalian physiology suggests that the neurocomputational process that underlies this binaural unmasking is based on two hemispheric channels that encode interaural differences in their relative neuronal activity. Here, we introduce a mathematical formulation of the two-channel model - the complex-valued correlation coefficient. We show that this formulation quantifies the amount of temporal fluctuations in interaural differences, which we suggest underlie binaural unmasking. We applied this model to an extensive library of psychoacoustic experiments, accounting for 98% of the variance across eight studies. Combining physiological plausibility with its success in explaining behavioral data, the proposed mechanism is a significant step towards a unified understanding of binaural unmasking and the encoding of interaural differences in general.
Inter-channel Conv-TasNet for multichannel speech enhancement
Authors: Dongheon Lee, Seongrae Kim, Jung-Woo Choi
Date: 2021-11-08 00:00:00
Tasks: Speech Enhancement, Speech Separation

Speech enhancement in multichannel settings has been realized by utilizing the spatial information embedded in multiple microphone signals. Moreover, deep neural networks (DNNs) have been recently advanced in this field; however, studies on the efficient multichannel network structure fully exploiting spatial information and inter-channel relationships is still in its early stages. In this study, we propose an end-to-end time-domain speech enhancement network that can facilitate the use of inter-channel relationships at individual layers of a DNN. The proposed technique is based on a fully convolutional time-domain audio separation network (Conv-TasNet), originally developed for speech separation tasks. We extend Conv-TasNet into several forms that can handle multichannel input signals and learn inter-channel relationships. To this end, we modify the encoder-mask-decoder structures of the network to be compatible with 3-D tensors defined over spatial channels, features, and time dimensions. In particular, we conduct extensive parameter analyses on the convolution structure and propose independent assignment of the depthwise and 1$\times$1 convolution layers to the feature and spatial dimensions, respectively. We demonstrate that the enriched inter-channel information from the proposed network plays a significant role in suppressing noisy signals impinging from various directions. The proposed inter-channel Conv-TasNet outperforms the state-of-the-art multichannel variants of neural networks, even with one-tenth of their parameter size. The performance of the proposed model is evaluated using the CHiME-3 dataset, which exhibits a remarkable improvement in SDR, PESQ, and STOI.
Deep Noise Suppression Maximizing Non-Differentiable PESQ Mediated by a Non-Intrusive PESQNet
Authors: Ziyi Xu, Maximilian Strake, Tim Fingscheidt
Date: 2021-11-06 00:00:00
Tasks: Denoising, Speech Enhancement

Speech enhancement employing deep neural networks (DNNs) for denoising are called deep noise suppression (DNS). During training, DNS methods are typically trained with mean squared error (MSE) type loss functions, which do not guarantee good perceptual quality. Perceptual evaluation of speech quality (PESQ) is a widely used metric for evaluating speech quality. However, the original PESQ algorithm is non-differentiable, and therefore cannot directly be used as optimization criterion for gradient-based learning. In this work, we propose an end-to-end non-intrusive PESQNet DNN to estimate the PESQ scores of the enhanced speech signal. Thus, by providing a reference-free perceptual loss, it serves as a mediator towards the DNS training, allowing to maximize the PESQ score of the enhanced speech signal. We illustrate the potential of our proposed PESQNet-mediated training on the basis of an already strong baseline DNS. As further novelty, we propose to train the DNS and the PESQNet alternatingly to keep the PESQNet up-to-date and perform well specifically for the DNS under training. Our proposed method is compared to the same DNS trained with MSE-based loss for joint denoising and dereverberation, and the Interspeech 2021 DNS Challenge baseline. Detailed analysis shows that the PESQNet mediation can further increase the DNS performance by about 0.1 PESQ points on synthetic test data and by 0.03 DNSMOS points on real test data, compared to training with the MSE-based loss. Our proposed method also outperforms the Challenge baseline by 0.2 PESQ points on synthetic test data and 0.1 DNSMOS points on real test data.
Minimum-lap-time Control Strategies for All-wheel Drive Electric Race Cars via Convex Optimization
Authors: Stan Broere, Jorn van Kampen, Mauro Salazar
Date: 2021-11-08 00:00:00
Tasks: All

This paper presents a convex optimization framework to compute the minimum-lap-time control strategies of all-wheel drive (AWD) battery electric race cars, accounting for the grip limitations of the individual tyres. Specifically, we first derive the equations of motion (EOM) of the race car and simplify them to a convex form. Second, we leverage convex models of the electric motors (EMs) and battery, and frame the time-optimal final-drives design and EMs control problem in space domain. The resulting optimization problem is fully convex and can be efficiently solved with global optimality guarantees using second-order conic programming algorithms. Finally, we validate our modeling assumptions via the original non-convex EOM, and simulate our framework on the Formula Student Netherlands endurance race track. Thereby, we compare a torque vectoring with a fixed power split configuration, showing that via torque vectoring we can make a better use of the individual tyre grip, and significantly improve the achievable lap time by more than 4%. Finally, we present a design study investigating the respective impact of the front and rear EM size on lap time, revealing that the rear motor sizing is predominant due to the higher vertical rear tyre load caused by the center of pressure position and rearwards load transfer under acceleration.
Privacy-Preserving Distributed Average Consensus in Finite Time using Random Gossip
Authors: Nicolaos E. Manitara, Apostolos I. Rikos, Christoforos N. Hadjicostis
Date: 2021-11-08 00:00:00
Tasks: Privacy Preserving

In this paper, we develop and analyze a gossip-based average consensus algorithm that enables all of the components of a distributed system, each with some initial value, to reach (approximate) average consensus on their initial values after executing a finite number of iterations, and without having to reveal the specific value they contribute to the average calculation. We consider a fully-connected (undirected) network in which each pair of components (nodes) can be randomly selected to perform pairwise standard gossip averaging of their values, and propose an enhancement that can be followed by each node that does not want to reveal its initial value to other (curious) nodes. We assume that curious nodes try to identify the initial values of other nodes but do not interfere in the computation in any other way; however, as a worst-case assumption, curious nodes are allowed to collaborate arbitrarily and are assumed to know the privacy-preserving strategy (but not the actual parameters chosen by the nodes that want to preserve their privacy). We characterize precisely conditions on the information exchange that guarantee privacy-preservation for a specific node. The protocol also provides a criterion that allows the nodes to determine, in a distributed manner (while running the enhanced gossip protocol), when to terminate their operation because approximate average consensus has been reached, i.e., all nodes have obtained values that are within a small distance from the exact average of their initial values.
Decentralized Coordinated State Estimation in Integrated Transmission and Distribution Systems
Authors: Ying Zhang, Yanbo Chen, Jianhui Wang, Yue Meng, Tianqiao Zhao
Date: 2021-11-08 00:00:00
Tasks: State Estimation

Current transmission and distribution system states are mostly unobservable to each other, and state estimation is separately conducted in the two systems owing to the differences in network structures and analytical models. The large-scale integration of transmission and active distribution systems calls for an effective solution to global state estimation. Unlike existing independent state estimation methods on both levels of these systems, we propose a decentralized coordinated transmission and distribution system state estimation (C-TDSE) method. This method enables accurate monitoring of the integrated systems with a global reference in a decentralized manner and reconciles the mismatches of voltages and powers on the boundaries of the systems. The comparative analysis on the integrated transmission and distribution systems points to improved estimation results relative to the independent state estimation methods.
Robust dynamic self-triggered control for nonlinear systems using hybrid Lyapunov functions
Authors: Michael Hertneck, Frank Allgöwer
Date: 2021-11-08 00:00:00

Self-triggered control (STC) is a resource efficient approach to determine sampling instants for Networked Control Systems. At each sampling instant, an STC mechanism determines not only the control inputs but also the next sampling instant. In this article, an STC framework for perturbed nonlinear systems is proposed. In the framework, a dynamic variable is used in addition to current state information to determine the next sampling instant, rendering the STC mechanism dynamic. Using dynamic variables has proven to be powerful for increasing sampling intervals for the closely related concept of event-triggered control, but has so far not been exploited for STC. Two variants of the dynamic STC framework are presented. The first variant can be used without further knowledge on the disturbance and leads to guarantees on input-to-state stability. The second variant exploits a known disturbance bound to determine sampling instants and guarantees asymptotic stability of a set containing the origin. In both cases, hybrid Lyapunov function techniques are used to derive the respective stability guarantees. Different choices for the dynamics of the dynamic variable, that lead to different particular STC mechanisms, are presented for both variants of the framework. The resulting dynamic STC mechanisms are illustrated with two numerical examples to emphasize their benefits in comparison to existing static STC approaches.
State Drift and Gait Plan in Feedback Linearization Control of A Tilt Vehicle
Authors: Zhe Shen, Takeshi Tsuchiya
Date: 2021-11-08 00:00:00

To stabilize a conventional quadrotor, simplified equivalent vehicles, e.g., autonomous car, are developed to test the designed controller. Based on that, various controllers based on feedback linearization have been developed. With the recently developed concept of tilt-rotor, there lacks the simplified or equivalent model, however. Indeed, the tilt structure is relatively unusual in vehicles. In this research, we put forward a unique fictional vehicle with tilt structure, which is to help evaluate the property of the tilt-structure-aimed controllers. One phenomenon, state drift, in controlling an over-actuated tilt structure by feedback linearization is presented subsequently. State drift can be easily neglected and is not paid attention to in the current researches in tilt-rotor controller design so far. We report this phenomenon and provide a potential approach to avoid this behavior.
Quantitative Resilience of Generalized Integrators
Authors: Jean-Baptiste Bouvier, Kathleen Xu, Melkior Ornik
Date: 2021-11-07 00:00:00

To design critical systems engineers must be able to prove that their system can continue with its mission even after losing control authority over some of its actuators. Such a malfunction results in actuators producing possibly undesirable inputs over which the controller has real-time readings but no control. By definition, a system is resilient if it can still reach a target after a partial loss of control authority. However, after such a malfunction, a resilient system might be significantly slower to reach a target compared to its initial capabilities. To quantify this loss of performance we introduce the notion of quantitative resilience as the maximal ratio of the minimal times required to reach any target for the initial and malfunctioning systems. Naive computation of quantitative resilience directly from the definition is a complex task as it requires solving four nested, possibly nonlinear, optimization problems. The main technical contribution of this work is to provide an efficient method to compute quantitative resilience of control systems with multiple integrators and nonsymmetric input sets. Relying on control theory and on two novel geometric results we reduce the computation of quantitative resilience to a linear optimization problem. We illustrate our method on two numerical examples: a trajectory controller for low-thrust spacecrafts and a UAV with eight propellers.
A matrix theoretic characterization of the strongly reachable subspace
Authors: Imrul Qais, Chayan Bhawal, Debasattam Pal
Date: 2021-11-07 00:00:00

In this paper, we provide novel characterizations of the weakly unobservable and the strongly reachable subspaces corresponding to a given state-space system. These characterizations provide closed-form representations for the said subspaces. In this process, we establish that the strongly reachable subspace is intimately related to the space of admissible impulsive inputs. We also show how to calculate the dimensions of these subspaces from the transfer matrix of the system.
Planning for net zero by 2050, what HVAC system interventions will today's code minimum commercial buildings require?
Authors: Patrick Pease, Jayati Chhabra, Zahra Zolfaghari
Date: 2021-11-06 00:00:00

Heating, Ventilation and Air conditioning (HVAC) systems account for approximately 40% of the total energy used by buildings in the USA. To reduce this consumption States enforce minimum energy codes that currently range from strict (ASHRAE 90.1-2016) to relaxed (ASHRAE 90.1-2007) with some states following no particular standard. To reach as close as possible to net zero carbon and energy, each statewide energy code requires different levels of interventions for each code minimum building. This paper presents a collection of potential HVAC retrofits to transition each State's current code minimum buildings towards the goal of net zero to achieve a carbon free future by 2050. The study shall use a large array of code minimum criteria and climate zones covering the 48 contiguous United States to determine the most successful interventions at reducing the energy use of buildings meeting today's energy codes. Office use type has been selected for the study as they account for 18% of total buildings and close to 19% of the total commercial floorspace. A number of interventions will be applied to this use type that will vary based on current code and climate zone; however, a common theme will be electrification through the use of heat pump technology. Each intervention will be scored based on energy and carbon savings, along with level of difficulty and cost. The study not only provides a comprehensive transition guideline toward the net zero energy and carbon but also predicts the future project opportunities.
Design, Modelling, and Simulation analysis of a Single Axis MEMS-based Capacitive Accelerometer
Authors: Veena. S, Newton Rai, H. L. Suresh, Veda Sandeep Nagaraja
Date: 2021-11-06 00:00:00
Tasks: Sensitivity

This paper presents the design, simulation, and analytical modeling of the single proposed axis MEMSbased capacitive accelerometer. Analytical modeling has been done for frequency and displacement sensitivity. The performance of the accelerometer was tested for both static and dynamic conditions, and the corresponding static capacitance value was calculated and was found to be C0=0.730455pF, a response time of 95.17{\mu}s, and settling time of 7.261ms and the displacement sensitivity Sd= 3.5362* m/g. It was observed that the sensitivity of the accelerometer depends on its design parameters like beam length, overlap area of comb, sensing mass, and the number of interdigital fingers. A novel capacitive accelerometer has been designed for an operating frequency of 2.1kHz The accelerometer was designed using COMSOL Multiphysics and analyzed using the MATLAB simulator tool. The single proposed axis MEMS-based capacitive accelerometer is suitable for automobile applications such as airbag deployment and navigation.
Compensation of Reactive Power in Grid-Connected Solar PV Array System Using STATCOM and Fixed Capacitor Bank
Authors: CH Venkata Ramesh, A Manjunatha
Date: 2021-11-06 00:00:00

In this article, we propose reactive compensation for the PV integrated grid system using a STATCOM and a fixed capacitor bank. This paper presents a design calculation for a PV integrated grid system with a fixed capacitor and STATCOM. The proposed system is simulated and tested using the MATLAB Simulink software package. The suggested system has been evaluated under a variety of operating circumstances, including changing solar PV array irradiance and changing reactive load power. Detailed simulation and comparisons between the fixed capacitor and STATCOM represented.
Spatiotemporal Impact Assessment of Hurricanes on Electric Power Systems
Authors: Abodh Poudyal, Vishnu Iyengar, Diego Garcia-Camargo, Anamika Dubey
Date: 2021-11-05 00:00:00

Almost 90% of the major power outages in the US are caused due to hurricanes. Due to the highly uncertain nature of hurricanes in both spatial and temporal dimensions, it is essential to quantify the effect of such hurricanes on a power grid. In this paper, we provide a Monte-Carlo-based framework in which several hurricane scenarios and their impact on a power grid are analyzed in spatiotemporal dimensions. The hurricane simulations are performed using samples from previously occurred hurricanes in the US whereas probabilistic assessment of the transmission lines is performed through line fragility model. Finally, a loss metric based on the amount of load disconnected due to hurricanes traveling inland is calculated for each time step. The simulation is performed on ACTIVSg2000: 2000-bus synthetic Texas grid while mapping the transmission lines of the test case on the geographical footprint of Texas. The simulation results show that the loss increases significantly for a few time steps when the wind field of a hurricane is intense and almost saturates when the intensity of the hurricane decays while traversing further. The proposed analysis can provide some insights for proactive planning strategies on improving the resilience of the power grid.
Dual Band GNSS Antenna Phase Center Characterization for Automotive Applications
Authors: Ran Liu, Daniel N. Aloi
Date: 2021-11-05 00:00:00
Tasks: Position

In this paper, a low-cost small size dual-band ceramic GNSS patch antenna is presented from design to real sample. A further study of this patch antenna illustrates the absolute phase center variation measured in an indoor range to achieve a received signal phase error correction. In addition, this low-cost antenna solution is investigated when integrated into a standard multi-band automotive antenna product. This product is evaluated both on its own in an indoor range and on a typical vehicle roof at an outdoor range. By using this evaluation file to estimate the receiver position could achieve phase motion error-free result.