pwc-archive/papers-with-abstracts huggingface dataset:
Personalized graph feature-based multi-omics data integration for cancer subtype identification
Authors: Saiful Islam, Md. Nahid Hasan
Date: 2024-08-16 00:00:00
Tasks: Data Integration

Cancer is a highly heterogeneous disease with significant variability in molecular features and clinical outcomes, making diagnosis and treatment challenging. In recent years, high-throughput omic technologies have facilitated the discovery of mechanisms underlying various cancer subtypes by providing diverse omics data, such as gene expression, DNA methylation, and miRNA expression. However, the complexity and heterogeneity of multi-omics data present significant challenges for their integration in exploring cancer subtypes. Various methods have been proposed to address these challenges. In this paper, we propose a novel and straightforward approach for identifying cancer subtypes by integrating patient-specific subnetworks features from different omics data. We construct patient-specific induced subnetwork using a random walk with restart algorithm from patient similarity networks (PSNs) and compute nine structural properties that capture essential network topology. These features are integrated across the three omic datasets to form comprehensive patient profiles. K-means clustering is then applied for cancer subtype identification. We evaluate our approach on five cancer datasets, including breast invasive carcinoma, colon adenocarcinoma, glioblastoma multiforme, kidney renal clear cell carcinoma, and lung squamous cell carcinoma, for three different omic data types. The evaluation shows that our method produces promising and effective results, demonstrating competitive or superior performance compared to existing methods and underscoring its potential for advancing personalized cancer diagnosis and treatment.
LEAD: Towards Learning-Based Equity-Aware Decarbonization in Ridesharing Platforms
Authors: Mahsa Sahebdel, Ali Zeynali, Noman Bashir, Prashant Shenoy, Mohammad Hajiesmaili
Date: 2024-08-19 00:00:00
Tasks: Fairness

Ridesharing platforms such as Uber, Lyft, and DiDi have grown in popularity due to their on-demand availability, ease of use, and commute cost reductions, among other benefits. However, not all ridesharing promises have panned out. Recent studies demonstrate that the expected drop in traffic congestion and reduction in greenhouse gas (GHG) emissions have not materialized. This is primarily due to the substantial distances traveled by the ridesharing vehicles without passengers between rides, known as deadhead miles. Recent work has focused on reducing the impact of deadhead miles while considering additional metrics such as rider waiting time, GHG emissions from deadhead miles, or driver earnings. However, most prior studies consider these environmental and equity-based metrics individually despite them being interrelated. In this paper, we propose a Learning-based Equity-Aware Decarabonization approach, LEAD, for ridesharing platforms. LEAD targets minimizing emissions while ensuring that the driver's utility, defined as the difference between the trip distance and the deadhead miles, is fairly distributed. LEAD uses reinforcement learning to match riders with drivers based on the expected future utility of drivers and the expected carbon emissions of the platform without increasing the rider waiting times. Extensive experiments based on a real-world ridesharing dataset show that LEAD improves the defined notion of fairness by 150% when compared to emission-aware ride-assignment and reduces emissions by 14.6% while ensuring fairness within 28--52% of the fairness-focused baseline. It also reduces the rider wait time, by at least 32.1%, compared to a fairness-focused baseline.
General Impedance Modeling for Modular Multilevel Converter with Grid-forming and Grid-following Control
Authors: Chu Sun, Fei Zhang, Huafeng Xiao, Na Wang, Jikai Chen
Date: 2024-08-19 00:00:00

Modular multilevel converter (MMC) has complex topology, control architecture and broadband harmonic spectrum. For this, linear-time-periodic (LTP) theory, covering multi-harmonic coupling relations, has been adopted for MMC impedance modeling recently. However, the existing MMC impedance models usually lack explicit expressions and general modeling procedure for different control strategies. To this end, this paper proposes a general impedance modeling procedure applicable to various power converters with grid-forming and grid-following control strategies. The modeling is based on a unified representation of MMC circuit as the input and output relation between the voltage or current on the AC side and the exerted modulation index, while the control part vice versa, thereby interconnected as closed-loop feedback. With each part expressed as transfer functions, the final impedance model keeps the explicit form of harmonic transfer function matrix, making it convenient to directly observe and analyze the influence of each part individually. Thereby the submodule capacitance is found as the main cause of difference between MMC impedance compared to two-level converter, which will get closer as the capacitance increases. Effectiveness and generality of the impedance modeling method is demonstrated through comprehensive comparison with impedance scanning using electromagnetic transient simulation.
Adaptive BESS and Grid Setpoints Optimization: A Model-Free Framework for Efficient Battery Management under Dynamic Tariff Pricing
Authors: Alaa Selim, Huadong Mo, Hemanshu Pota, Daoyi Dong
Date: 2024-08-19 00:00:00
Tasks: Deep Reinforcement Learning, Management

This paper introduces an enhanced framework for managing Battery Energy Storage Systems (BESS) in residential communities. The non-convex BESS control problem is first addressed using a gradient-based optimizer, providing a benchmark solution. Subsequently, the problem is tackled using multiple Deep Reinforcement Learning (DRL) agents, with a specific emphasis on the off-policy Soft Actor-Critic (SAC) algorithm. This version of SAC incorporates reward refinement based on this non-convex problem, applying logarithmic scaling to enhance convergence rates. Additionally, a safety mechanism selects only feasible actions from the action space, aimed at improving the learning curve, accelerating convergence, and reducing computation times. Moreover, the state representation of this DRL approach now includes uncertainties quantified in the entropy term, enhancing the model's adaptability across various entropy types. This developed system adheres to strict limits on the battery's State of Charge (SOC), thus preventing breaches of SOC boundaries and extending the battery lifespan. The robustness of the model is validated across several Australian states' districts, each characterized by unique uncertainty distributions. By implementing the refined SAC, the SOC consistently surpasses 50 percent by the end of each day, enabling the BESS control to start smoothly for the next day with some reserve. Finally, this proposed DRL method achieves a mean reduction in optimization time by 50 percent and an average cost saving of 40 percent compared to the gradient-based optimization benchmark.
Minimal Sensor Placement for Generic State and Unknown Input Observability
Authors: Ranbo Cheng, Yuan Zhang, Amin MD Al, Yuanqing Xia
Date: 2024-08-19 00:00:00

This paper addresses the problem of selecting the minimum number of dedicated sensors to achieve observability in the presence of unknown inputs, namely, the state and input observability, for linear time-invariant systems. We assume that the only available information is the zero-nonzero structure of system matrices, and approach this problem within a structured system model. We revisit the concept of state and input observability for structured systems, providing refined necessary and sufficient conditions for placing dedicated sensors via the Dulmage-Mendelsohn decomposition. Based on these conditions, we prove that determining the minimum number of dedicated sensors to achieve generic state and input observability is NP-hard, which contrasts sharply with the polynomial-time complexity of the corresponding problem with known inputs. We also demonstrate that this problem is hard to approximate within a factor of $(1-o(1)){\rm{log}}(n)$, where $n$ is the state dimension. Notwithstanding, we propose nontrivial upper and lower bounds that can be computed in polynomial time, which confine the optimal value of this problem to an interval with length being the number of inputs. We further present a special case for which the exact optimal value can be determined in polynomial time. Additionally, we propose a two-stage algorithm to solve this problem approximately. Each stage of the algorithm is either optimal or suboptimal and can be completed in polynomial time.
Prescribed-time Convergent Distributed Multiobjective Optimization with Dynamic Event-triggered Communication
Authors: Tengyang Gong, Zhongguo Li, Yiqiao Xu, Zhengtao Ding
Date: 2024-08-18 00:00:00
Tasks: Decision Making, Distributed Optimization, Multiobjective Optimization

This paper addresses distributed constrained multiobjective resource allocation problems (DCMRAPs) within multi-agent networks, where each agent has multiple, potentially conflicting local objectives, constrained by both local and global constraints. By reformulating the DCMRAP as a single-objective weighted $L_p$ problem, a distributed solution is enabled, which eliminates the need for predetermined weighting factors or centralized decision-making in traditional methods. Leveraging prescribed-time control and dynamic event-triggered mechanisms (ETMs), novel distributed algorithms are proposed to achieve Pareto optimality within a prescribed settling time through sampled communication. Using generalized time-based generators (TBGs), these algorithms provide more flexibility in optimizing accuracy and control smoothness without the constraints of initial conditions. Novel dynamic ETMs are designed to work with generalized TBGs to promote communication efficiency, which adjusts to both local error metrics and network-based disagreements. The Zeno behavior is excluded. Validated by Lyapunov analysis and simulations, our method demonstrates superior control performance and efficiency compared to existing methods, advancing distributed optimization in complex environments.
Safety Critical Control for Nonlinear Systems with Complex Input Constraints
Authors: Yaosheng Deng, Yang Bai, Yujie Wang, Masaki Ogura, Mir Feroskhan
Date: 2024-08-18 00:00:00

In this paper, we propose a novel Control Barrier Function (CBF) based controller for nonlinear systems with complex, time-varying input constraints. To deal with these constraints, we introduce an auxiliary control input to transform the original system into an augmented one, thus reformulating the constrained-input problem into a constrained-output one. This transformation simplifies the Quadratic Programming (QP) formulation and enhances compatibility with the CBF framework. As a result, the proposed method can systematically address the complex, time-varying, and state-dependent input constraints. The efficacy of the proposed approach is validated using numerical examples.
Accelerating Chance-constrained SCED via Scenario Compression
Authors: Qian Zhang, Le Xie
Date: 2024-08-17 00:00:00

This paper studies some compression methods to accelerate the scenario-based chance-constrained security-constrained economic dispatch (SCED) problem. In particular, we show that by exclusively employing the vertices after convex hull compression, an equivalent solution can be obtained compared to utilizing the entire scenario set. For other compression methods that might relax the original solution, such as box compression, this paper presents the compression risk validation scheme to assess the risk arising from the sample space. By quantifying the risk associated with compression, decision-makers are empowered to select either solution risk or compression risk as the risk metric, depending on the complexity of specific problems. Numerical examples based on the 118-bus system and synthetic Texas grids compare these two risk metrics. The results also demonstrate the efficiency of compression methods in both problem formulation and solving processes.
Reinforcement learning-based adaptive speed controllers in mixed autonomy condition
Authors: Han Wang, Hossein Nick Zinat Matin, Maria Laura Delle Monache
Date: 2024-08-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

The integration of Automated Vehicles (AVs) into traffic flow holds the potential to significantly improve traffic congestion by enabling AVs to function as actuators within the flow. This paper introduces an adaptive speed controller tailored for scenarios of mixed autonomy, where AVs interact with human-driven vehicles. We model the traffic dynamics using a system of strongly coupled Partial and Ordinary Differential Equations (PDE-ODE), with the PDE capturing the general flow of human-driven traffic and the ODE characterizing the trajectory of the AVs. A speed policy for AVs is derived using a Reinforcement Learning (RL) algorithm structured within an Actor-Critic (AC) framework. This algorithm interacts with the PDE-ODE model to optimize the AV control policy. Numerical simulations are presented to demonstrate the controller's impact on traffic patterns, showing the potential of AVs to improve traffic flow and reduce congestion.
Highly Sensitive and Compact Quad-Band Ambient RF Energy Harvester
Authors: Rasool Keshavarz, Negin Shariati
Date: 2024-08-17 00:00:00

A highly efficient and compact quad band energy harvester (QBEH) circuit based on the extended composite right and left handed transmission lines (ECRLHTLs) technique is presented.The design procedure based on ECRLHTLs at four desired frequency bands is introduced to realize a quad band matching network (QBMN).The proposed QBEH operates at four frequency bands f1=0.75 GHz,f2=1.8 GHz,f3=2.4 GHz and f4=5.8 GHz. The simulations and experimental results of the proposed QBEH exhibit overall (end to end) efficiency of 55percent and 70percent while excited at four frequency bands simultaneously with negative 20dBm (10 microWatt) and negative 10dBm (100 microWatt) input power, respectively.Due to applying multi band excitation technique and radio frequency (RF) combining method in the QBEH circuit, the sensitivity is improved, and sufficient power is generated to realize a self sustainable sensor (S3) using ambient low level RF signals.A favorable impedance matching over a broad low input power range of negative 50 to negative 10 dBm (0.01 to 100 microWatt) is achieved, enabling the proposed QBEH to harvest ambient RF energy in urban environments. Moreover, an accurate theoretical analyses based on the Volterra series and Laplace transformation are presented to maximize the output DC current of the rectifier over a wide input power range.Theoretical, simulation and measurement results are in excellent agreement, which validate the design accuracy for the proposed quad band structure.The proposed new energy harvesting technique has the potential to practically realize a green energy harvesting solution to generate a viable energy source for low powered sensors and IoT devices, anytime, anywhere.
Dual-Band, Slant-Polarized MIMO Antenna Set for Vehicular Communication
Authors: Rasool Keshavarz, Dan Winson, Justin Lipman, Mehran Abolhasan, Negin Shariati
Date: 2024-08-17 00:00:00

Slant-polarized Multi Input Multi Output (MIMO) antennas are able to improve the performance of mobile communication systems in terms of channel capacity. Especially, the implementation of MIMO configurations for automotive applications requires to consider high gain, wideband, low-profile and affordable antennas in the communication link. In this work design, simulation and measurement of a new dual-band slant-polarized MIMO antenna with HPBW (Half Power Beam Width) of around 900 are presented. Then, four replicas of the proposed antenna set are placed at four different poles (North, South, West and East) to cover 3600 around the vehicle as an omni-directional pattern. In the real world scenario, the proper antenna set is selected to communicate with the intended user. Each slant MIMO antenna set consists of two inclined (450) low band (LB: 700 to 900 MHz) and two inclined high band (HB: 1.7 to 2.7 GHz) log-periodic antennas. The measured gain of LB and HB antennas are 7 dBi and 8 dBi, respectively. Great agreement between simulation and measurement results confirms the accuracy of the design and simulation procedures of antenna system using optimization algorithm (Genetic method). The proposed antenna is also measured in the field for industrial applications.
Gaussian Processes with Noisy Regression Inputs for Dynamical Systems
Authors: Tobias M. Wolff, Victor G. Lopez, Matthias A. Müller
Date: 2024-08-16 00:00:00
Tasks: Gaussian Processes, regression

This paper is centered around the approximation of dynamical systems by means of Gaussian processes. To this end, trajectories of such systems must be collected to be used as training data. The measurements of these trajectories are typically noisy, which implies that both the regression inputs and outputs are corrupted by noise. However, most of the literature considers only noise in the regression outputs. In this paper, we show how to account for the noise in the regression inputs in an extended Gaussian process framework to approximate scalar and multidimensional systems. We demonstrate the potential of our framework by comparing it to different state-of-the-art methods in several simulation examples.
Study on Human-Variability-Respecting Optimal Control Affecting Human Interaction Experience
Authors: Sean Kille, Balint Varga, Sören Hohmann
Date: 2024-08-16 00:00:00

Broad application of human-machine interaction (HMI) demands advanced and human-centered control designs for the machine's automation. Human natural motor action shows stochastic behavior, which has so far not been respected in HMI control designs. Using a previously presented novel human-variability-respecting optimal controller we present a study design which allows the investigation of respecting human natural variability and its effect on human interaction experience. Our approach is tested in simulation based on an identified real human subject and presents a promising approach to be used for a larger subject study.
A New Control Law for TS Fuzzy Models: Less Conservative LMI Conditions by Using Membership Functions Derivative
Authors: Leonardo Amaral Mozelli, Victor Costa da Silva Campos
Date: 2024-08-16 00:00:00

This note proposes a new type of Parallel Distributed Controller (PDC) for Takagi-Sugeno (TS) fuzzy models. Our idea consists of using two control terms based on state feedback, one composed of a convex combination of linear gains weighted by the normalized membership grade, as in traditional PDC, and the other composed of linear gains weighted by the time-derivatives of the membership functions. We present the design conditions as Linear Matrix Inequalities, solvable through numerical optimization tools. Numerical examples are given to illustrate the advantages of the proposed approach, which contains the the traditional PDC as a special case.
A review of the calculation methods of optimal power flow in integrated energy systems
Authors: Xialiang Huangpu
Date: 2024-08-15 00:00:00

The analysis of Integrated Energy Systems (IES) is crucial for enhancing the comprehensive and complementary utilization of clean energy across China, significantly impacting the effective planning, operational coordination, and security control of the IES network. This paper presents a systematic review of the current research on optimal power flow (OPF) within IES, addressing the spatiotemporal interrelationships and coupled co-supply among primary energy processes such as electricity, gas, and heat (cooling). It highlights the challenges and future directions in this field, underscoring the lack of comprehensive studies on coupled power flow modeling for electricity-heat-gas systems and the need for more robust modeling approaches that align with practical engineering applications. Furthermore, the paper discusses the potential of multi-target energy storage systems to enhance energy consumption efficiency and flexibility in energy resource management. The existing models and algorithms for target power flow and optimal power flow are critiqued for their lack of flexibility and comprehensiveness, particularly in handling multi-objective flows and ensuring system safety and reliability. The study emphasizes the necessity for further development of safety and reliability assessment frameworks to support the evolving demands of integrated energy systems.
Artificial Intelligence in Power System Security and Stability Analysis: A Comprehensive Review
Authors: Runhao Zhang
Date: 2024-08-14 00:00:00

This review comprehensively examines the integration of artificial intelligence (AI) in enhancing the dynamic security assessments of modern power systems. It highlights the pivotal role of AI in facilitating scenario generation, incident prediction, risk assessment, and severity grading, thereby addressing the complexities introduced by renewable energy integration and advancements in digital grid technologies. The paper delves into data-driven techniques, with a particular focus on decision trees that effectively bridge operational characteristics with security metrics. These methodologies enable real-time, accurate predictions of system behaviors under varied operational conditions and support the optimization of control strategies. Through detailed analysis, we demonstrate how AI applications can transform traditional security assessment protocols, enhancing both the efficacy and efficiency of power system operations. The findings advocate for the potential of AI to significantly enhance the reliability and resilience of electrical grids, marking a paradigm shift towards more adaptive and intelligent power infrastructure.
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-Series
Authors: Rithwik Gupta, Daniel Muthukrishna, Michelle Lochner
Date: 2024-08-05 00:00:00
Tasks: Anomaly Detection, Astronomy, Multi-class Anomaly Detection, Representation Learning, Time Series

Automating anomaly detection is an open problem in many scientific fields, particularly in time-domain astronomy, where modern telescopes generate millions of alerts per night. Currently, most anomaly detection algorithms for astronomical time-series rely either on hand-crafted features or on features generated through unsupervised representation learning, coupled with standard anomaly detection algorithms. In this work, we introduce a novel approach that leverages the latent space of a neural network classifier for anomaly detection. We then propose a new method called Multi-Class Isolation Forests (MCIF), which trains separate isolation forests for each class to derive an anomaly score for an object based on its latent space representation. This approach significantly outperforms a standard isolation forest when distinct clusters exist in the latent space. Using a simulated dataset emulating the Zwicky Transient Facility (54 anomalies and 12,040 common), our anomaly detection pipeline discovered $46\pm3$ anomalies ($\sim 85\%$ recall) after following up the top 2,000 ($\sim 15\%$) ranked objects. Furthermore, our classifier-based approach outperforms or approaches the performance of other state-of-the-art anomaly detection pipelines. Our novel method demonstrates that existing and new classifiers can be effectively repurposed for real-time anomaly detection. The code used in this work, including a Python package, is publicly available, https://github.com/Rithwik-G/AstroMCAD.