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Student Conference on Research and Development (SCOReD)

作者： Wildan Ardian Alamsyah Hilal Huda Muhammad Ali Qureshi School of Computing Telkom University Bandung Indonesia School of Computing CAATIS Tel-U Bandung Indonesia Dept. of Information & Comm. Engg. The Islamia Univ. of Bahawalpur Pakistan

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

The manual Kanban system at PT XYZ, a subsidiary of the Yazaki Corporation, has encountered ongoing difficulties, such as production delays, workload disparities, and the improper handling of kanban cards. This research tackles these inefficiencies by employing a digital Kanban system in conjunction with the Elastic Load Balancing (ELB) algorithm. ELB dynamically distributes workloads among machines, days, and shifts to enhance resource usage and promote equity. A theoretical analysis of the algorithm demonstrates a time complexity of $O(J+M \cdot D \cdot S)$, ensuring scalability for large datasets. Empirical testing with actual production data yielded a 42.35% decrease in production time and a fairness index approaching 0.999, validating the equitable distribution of workloads. The results underscore the ELB algorithm’s capacity to improve production efficiency and function as a scalable solution for extensive manufacturing settings.

关键词： Heuristic algorithms Scalability Production Manuals Load management approximation algorithms Throughput Delays Indexes Time complexity

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Exposure Conscious Path Planning for Equal Exposure Corridors

Exposure Conscious Path Planning for Equal Exposure Corridor...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Eugene T. Hamzezadeh John G. Rogers Neil T. Dantam Andrew J. Petruska Colorado School of Mines Golden CO USA DEVCOM Army Research Laboratory Adelphi MD USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

While maximizing line-of-sight coverage of specific regions or agents in the environment is a well-explored path planning objective, the converse problem of minimizing exposure to the entire environment during navigation is especially interesting in the context of minimizing detection risk. This work demonstrates that minimizing line-of-sight exposure to the environment is non-Markovian, which cannot be efficiently solved optimally with traditional path planning. The optimality gap of the graph-search algorithm A* and the trade-offs in optimality vs. computation time of several approximating heuristics is explored. Finally, the concept of equal-exposure corridors, which afford polynomial time determination of all paths that do not increase exposure, is presented.

关键词： Measurement Embedded systems Navigation Heuristic algorithms Line-of-sight propagation Focusing approximation algorithms Polynomials Trajectory Robots

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Average Connection Establishment Time in Random Access Channel with Limited Resources

Average Connection Establishment Time in Random Access Chann...

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Informatics, Electronics, and Radio Engineering (PIERE), International Conference Problems of

作者： Artem A. Batygin Artem A. Burkov Andrey M. Turlikov Internet of Things and Cyber-Physical Systems Laboratory National Research University Higher School of Economics St. Petersburg Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

In 4G and 5G cellular networks, a 4-step random access procedure is used. A significant challenge arises when a large number of users attempt to connect within a short period of time. To address this issue, the access class barring mechanism is employed. There are numerous studies that analyze the average connection time for users within the random access framework that includes access class barring. However, these studies primarily offer approximate calculations for the average connection time. In this work, the connection process is modeled using a Markov chain. The problem of calculating the average connection time is reduced to finding the average time to reach an absorbing state. An iterative algorithm is proposed to compute the exact value of this metric both for a system using the access class barring mechanism and without its use. It is demonstrated that, under certain conditions, the approximate calculations presented in other works significantly underestimate the average connection time.

关键词： Measurement Cellular networks Accuracy 5G mobile communication Computational modeling approximation algorithms Delays Complexity theory Iterative methods Informatics

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Variational Bayes Inference for Spatial Error Models with Missing Data

arXiv

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arXiv 2024年

作者： Wijayawardhana, Anjana Gunawan, David Suesse, Thomas School of Mathematics and Applied Statistics University of Wollongong WollongongNSW Australia

The spatial error model (SEM) is a type of simultaneous autoregressive (SAR) model for analysing spatially correlated data. Markov chain Monte Carlo (MCMC) is one of the most widely used Bayesian methods for estimating SEM, but it has significant limitations when it comes to handling missing data in the response variable due to its high computational cost. Variational Bayes (VB) approximation offers an alternative solution to this problem. Two VB-based algorithms employing Gaussian variational approximation with factor covariance structure are presented, joint VB (JVB) and hybrid VB (HVB), suitable for both missing at random and not at random inference. When dealing with many missing values, the JVB is inaccurate, and the standard HVB algorithm struggles to achieve accurate inferences. Our modified versions of HVB enable accurate inference within a reasonable computational time, thus improving its performance. The performance of the VB methods is evaluated using simulated and real datasets. © 2024, CC BY.

关键词： approximation algorithms

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Enhancing RIS-Aided OTFS-NOMA Communications through Phase Shift Optimization

Enhancing RIS-Aided OTFS-NOMA Communications through Phase S...

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Communications and Networking (MECOM), IEEE Middle East Conference on

作者： Aoying Li Wei Liang Lixin Li Wensheng Lin Xu Li Baoguo Wei School of Electronic Information Collage Northwestern Polytechnical University China Research & Development Institute of Northwestern Polytechnical University in Shenzhen China

ISBN: (数字)9798350376715

ISBN: (纸本)9798350376722

Non-orthogonal multiple access (NOMA) and orthogonal time frequency space (OTFS) are two important technologies of the sixth generation mobile communication (6G) networks for the sake of enhancing spectral efficiency, and a much more reliable and low-latency integration system is developed, namely OTFS-NOMA networks. In this paper, the reconfigurable intelligent surfaces (RIS) aided OTFS-NOMA networks are concived, and the comperhensive collbrative optimization problem of the transmission rate and phase configurations is established. In order to resolve this objective function, a heuristic phase reconstruction arrangment (HPRA) algorithm is proposed, aim to maximize the overall system’s transmission rate while satisfy the minimum rate requirement as well as phase *** addition to phase optimization, we have integrated a power optimization approach into the HPRA algorithm, which further optimizes system performance by optimizing the power coefficient first and then putting the resulting coefficient into the HPRA algorithm. The HPRA algorithm utilizes penalty function techniques to effectively handle minimum rate constraints. Through an iterative search, the algorithm determines the approximate optimal phase configuration that maximizes the transmission *** on the simulation experiments, the effectiveness of the proposed algorithm is verified. Furthermore, by comparing with the conventional methods, our HPRA algorithm could significantly improve the overall transmission rate while ensuring the constraints.

关键词： NOMA Time-frequency analysis Spectral efficiency System performance Heuristic algorithms Simulation Reconfigurable intelligent surfaces approximation algorithms Reliability Optimization

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Computational Moment Control of Ensemble Systems

Computational Moment Control of Ensemble Systems

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IEEE Conference on Decision and Control

作者： Yuan-Hung Kuan Wei Zhang Jr-Shin Li Department of Electrical and Systems Engineering Washington University in St. Louis St. Louis MO

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Finely manipulation of a large population of structurally identical dynamical systems exhibiting different dynamics, referred to as an ensemble system, is a crucial task arising from various emerging applications across diverse disciplines. A significant challenge in controlling this class of systems is the inherent scalability issue, involving computational complexity and efficiency, due to the massive size. To overcome this bottleneck, in this paper, we introduce a moment transform that maps ensemble systems defined on the space of continuous functions to their associated moment systems defined on the space of moment sequences. This transformation enables the approximation of the dynamics of an ensemble system in terms of a finite-dimensional truncated moment system. We leverage this reduction to facilitate control design for ensemble systems by developing an iterative computational optimal control algorithm with convergence guarantees. The efficiency and performance of the proposed algorithm are further demonstrated through its application to practical ensemble control problems encountered in physics and robotics.

关键词： Heuristic algorithms Scalability Optimal control Transforms Aerospace electronics approximation algorithms Sampling methods Robots Physics Convergence

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Deep Reinforcement Learning-Based (DRLB) Optimization for Autonomous Driving Vehicle Path Planning

Deep Reinforcement Learning-Based (DRLB) Optimization for Au...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Hulin Jin Zhiran Jin Yong-Guk Kim School of Computer Science and Technology Anhui University Hefei China Foothill Preparatory School Temple City California USA Department of Computer Engineering Sejong University seoul South Korea

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This comprehensive study presents a novel DRLB assisted path planning (PP) algorithm for the autonomous driving (AD) vehicles with the novel designed deep reinforcement learning (DRL). The proposed algorithm addresses the challenges faced by traditional path planning methods, such as large Q-table sizes, high computational complexity, and convergence problems. Unlike conventional approaches that rely on predefined environment maps or potential field methods, the proposed algorithm uses DRL to learn optimal paths in dynamic and complex environments. The algorithm uses a grid map representation for environmental modeling, where obstacles are enclosed in rectangular frames to ensure path accuracy and safety. Additionally, integrating an artificial potential field approach (APFA) helps guide the vehicle to the target while avoiding obstacles. The algorithm uses a novel Q-learning algorithm to approximate the value function between the vehicle state and action, which enables efficient path planning. The algorithm is visually evaluated through extensive simulation tests, demonstrating its effectiveness and superiority over traditional methods from the perspectives of the accuracy and stability.

关键词： Accuracy Q-learning Heuristic algorithms approximation algorithms Path planning Stability analysis Robustness Safety Vehicle dynamics Autonomous vehicles

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Unifying Sum Rate Analysis and Optimization of RIS-Assisted MU-MIMO Networks: An Operator-Valued Free Probability Approach

Unifying Sum Rate Analysis and Optimization of RIS-Assisted ...

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IEEE International Conference on Communications in China (ICCC)

作者： Siqiang Wang Zhong Zheng Jing Guo Zesong Fei School of Information and Electronics Beijing Institute of Technology Beijing China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

The fundamental performance limit of wireless systems is closely related to the transmission strategy of the transceivers as well as the wireless channels. As the channels are determined by the intrinsic characteristics of the environments, traditional system design is transceiver-centric, which adapts the transmit signals to the environments. Sharply different from the transceiver-centric design, by introducing the reconfigurable intelligent surface (RIS) into wireless systems, both the transceivers and the channels can be designed by properly adjusting the deployment of RIS panels as well as the phase-change and amplification of the RIS elements. However, as the RIS induces structural changes to the wireless channels, it is difficult to analyze and optimize the RIS-assisted systems in a unified framework. In this paper, we adopt the operatorvalued free probability theory to analyze the ergodic sum rate of an uplink MU-MIMO communication systems assisted by multiple cascaded RIS panels, where the sum rate is derived for rather generic system configurations, allowing arbitrary number of transceivers’ antennas, number of RIS panels, and a generic fading model. The obtained sum rate expression also enables a unified optimization framework that jointly designs the multi-antenna transceivers and the RIS configurations. Finally, numerical results are provided to verify the accuracy of the derived ergodic sum rate, and to show the performance of the joint optimization algorithms.

关键词： Wireless communication Fading channels Accuracy Reconfigurable intelligent surfaces approximation algorithms Transceivers Numerical models

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Bilinear Hybrid Expectation Maximization and Expectation Propagation for Semi-Blind Channel Estimation

Bilinear Hybrid Expectation Maximization and Expectation Pro...

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European Signal Processing Conference (EUSIPCO)

作者： Zilu Zhao Dirk Slock Communication Systems Department EURECOM France

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

This paper discusses channel estimation during uplink transmission in Cell-Free (CF) Massive MIMO (MaMIMO) systems. We model the problem as a semi-blind estimation problem with independent and identically distributed (i.i.d.) Gaussian input. Two hybrid Expectation Maximization (EM) and Expectation Propagation (EP) algorithms are proposed to improve convergence behavior. The first algorithm, EM-EP, adopts a vector-level EP approach by treating the per-user channel coefficients and data sequence as EP variables. To make the algorithm tractable, we use the central limit theorem (CLT) to approximate the interference terms and employ EM to construct a majorizer function for the likelihood of the received data, leading to majorization minimization. To further enhance convergence behavior, we propose a matrix-level loop-free EM-EP algorithm. In this algorithm, we treat the channel coefficients and data sequences corresponding to users using the same pilot as EP variables. This method is an alternating minimization algorithm, ensuring convergence. Our simulations verify the effectiveness of the two proposed algorithms.

关键词： Simulation Signal processing algorithms Channel estimation Massive MIMO Interference Signal processing approximation algorithms Minimization Uplink Convergence

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Some Notes on the Sample Complexity of Approximate Channel Simulation

Some Notes on the Sample Complexity of Approximate Channel S...

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Information Theory Workshops (ISIT-W), IEEE International Symposium on

作者： Gergely Flamich Lennie Wells University of Cambridge Cambridge UK

ISBN: (数字)9798350348446

ISBN: (纸本)9798350348453

Channel simulation algorithms can efficiently encode random samples from a prescribed target distribution $Q$ and find applications in machine learning-based lossy data compression. However, algorithms that encode exact samples usually have random runtime, limiting their applicability when a consistent encoding time is desirable. Thus, this paper considers approximate schemes with a fixed runtime instead. First, we strengthen a result of Agustsson and Theis [1] and show that there is a class of pairs of target distribution $Q$ and coding distribution $P$ , for which the runtime of any approximate scheme scales at least super-polynomially in $D_{\infty}[Q\Vert P]$ . We then show, by contrast, that if we have access to an unnormalised Radon-Nikodym derivative $r\propto dQ/dP$ and knowledge of $D_{\text{KL}}[Q\Vert P]$ , we can exploit global-bound, depth-limited A* coding [2] to ensure $D_{TV}[Q\Vert P]\leq\epsilon$ and maintain optimal coding performance with a sample complexity of only $\exp_{2}((D_{\text{KL}}[Q\Vert P]+o(1))/\epsilon)$ .

关键词： Runtime Machine learning algorithms Limiting Conferences Data compression approximation algorithms Encoding

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