arXiv subjects

Wei-Chang Yeh

Publications and source records attributed to Wei-Chang Yeh.

At least 19 recordsLinked to original sources

arXiv2026

Exact Multistate Reliability and Upgrade Design for Heterogeneous HBM Systems via Threshold-Pruned BAT

High Bandwidth Memory (HBM) systems can exhibit partial service rather than only full service or complete isolation: a controller-visible service unit may deliver full, reduced, or zero bandwidth because of sub-channel isolation, lane remapping, or protection overhead. This paper develops an exact multistate reliability framework in which each service unit carries an arbitrary finite set of bandwidth states and reliability is the probability that aggregate delivered bandwidth meets a demand. The binary k-out-of-n model is recovered as a special case, while closed-form binary-mapping error relations quantify mean-bandwidth distortion and provide a screening test for the simpler abstraction. For exact single-threshold evaluation a threshold-pruned multistate Binary-Addition-Tree (TP-mBAT) algorithm is proposed. It is deliberately regime-specific: fixed-grid dynamic programming is preferable on a compact common grid, where a 16-unit commensurate control required 273 pruned-DP updates versus 362,506 TP-mBAT node visits. On a reproducible 14-unit incommensurate benchmark, TP-mBAT is compared against a dynamic program carrying the same threshold rules, so that no baseline is weakened. Both expand the same state space, 6,862 nodes against 6,861 updates, and the separation lies entirely in retained state: 17 traversal entries against 412,121 probability states at central demand, reducing measured peak storage from 25.02 MB to 1,152 B. An exact probability-transfer sensitivity identifies when moving mass from a degraded state to a higher-bandwidth state changes system success, and a reserved-unit floor model admits a third exact pruning rule that is vacuous without such floors. A latent package-state mixture captures shared stress, where ignoring dependence overstates reliability by 8.73 percentage points.

cs.AR↗

arXiv2026

Exact Anchoring and a Dualization-Based Matheuristic for Bi-Level Dual-Defense Network Interdiction

Bi-level interdiction models are frequently solved by metaheuristics whose solution quality cannot be assessed, because exact optima are unavailable at the scales tested. We supply them for the bi-level dual-defense attacker model (BDAM), which couples node interdiction, edge destruction and capacitated supply support, and which we previously solved by a hybrid metaheuristic. First, BDAM's dominant attacker-path term admits an exact single-level reformulation by lower-level dualization, a reduction available whenever arc lengths are linear in the defender's binary decisions; the resulting mixed-integer program certifies optimality on all eighteen three-row and five-row configurations of our earlier benchmark, seventeen in under ten seconds, with strong uncertified incumbents out to 15*30 grids. Our published averages sit 3.55% below that frontier, and the gap widens with scale. Second, the supply rule, like any rule priced on a single attacker shortest path, is ill-posed under ties, and our threat-corridor formulation is tie-invariant by construction. The tie-break moves the objective by under 10^(-3) but the realized supply cost by up to 0.83 units, so the defect is suppressed by the objective weight rather than absent. Third, MILP-DA pairs the exact anchor with the corridor decode and a feasibility repair; compared against our published figures with no re-implementation on either side, it wins on thirteen of eighteen certified configurations and on all eighteen larger ones, every loss falling on a three-row grid.

math.OC↗

arXiv2025

Binary Weight Allocation for Multi-Objective Path Optimization: Efficient Earliest and Latest Path Discovery in Network Systems

This paper proposes earliest and latest path algorithms based on binary weight allocation, assigning weights of 2(i-1) and 2(m-i) to the i-th arc in a network. While traditional shortest path algorithms optimize only distance, our approach leverages Binary-Addition-Tree ordering to efficiently identify lexicographically smallest and largest paths that establish connectivity. These paths partition the solution space into three regions: guaranteed disconnection, transitional connectivity, and guaranteed no simple paths. Our weight allocation enables implicit encoding of multiple objectives directly in binary representations, maintaining the O((|V|+|E|)log|V|) complexity of Dijkstra's algorithm while allowing simultaneous optimization of competing factors like reliability and cost. Experimental validation demonstrates significant computational time reduction compared to traditional multi-objective methods. Applications span telecommunications, transportation networks, and supply chain management, providing efficient tools for network planning and reliability analysis under multiple constraints.

math.CO↗

arXiv2025

Data-Driven Approximation of Binary-State Network Reliability Function: Algorithm Selection and Reliability Thresholds for Large-Scale Systems

Network reliability assessment is pivotal for ensuring the robustness of modern infrastructure systems, from power grids to communication networks. While exact reliability computation for binary-state networks is NP-hard, existing approximation methods face critical tradeoffs between accuracy, scalability, and data efficiency. This study evaluates 20 machine learning methods across three reliability regimes full range (0.0-1.0), high reliability (0.9-1.0), and ultra high reliability (0.99-1.0) to address these gaps. We demonstrate that large-scale networks with arc reliability larger than or equal to 0.9 exhibit near-unity system reliability, enabling computational simplifications. Further, we establish a dataset-scale-driven paradigm for algorithm selection: Artificial Neural Networks (ANN) excel with limited data, while Polynomial Regression (PR) achieves superior accuracy in data-rich environments. Our findings reveal ANN's Test-MSE of 7.24E-05 at 30,000 samples and PR's optimal performance (5.61E-05) at 40,000 samples, outperforming traditional Monte Carlo simulations. These insights provide actionable guidelines for balancing accuracy, interpretability, and computational efficiency in reliability engineering, with implications for infrastructure resilience and system optimization.

cs.LG↗

arXiv2025

A Cut-Based BAT-MCS Approach for Binary-State Network Reliability Assessment

The BAT-MCS is an integrated Monte Carlo simulation method (MCS) that combines a binary adaptation tree algorithm (BAT) with a self-regulating simulation mechanism. The BAT algorithm operates deterministically, while the Monte Carlo simulation method is stochastic. By hybridizing these two approaches, BAT-MCS successfully reduces variance, increases efficiency, and improves the quality of its binary-state network reliability. However, it has two notable weaknesses. First, the selection of the supervectors, sub-vectors that form the core of BAT-MCS, is overly simplistic, potentially affecting overall performance. Second, the calculation of the approximate reliability is complicated, which limits its strength in reducing variance. In this study, a new BAT-MCS called cBAT-MCS is proposed to enhance the performance of the BAT-MCS. The approach reduces the complexity of MCS. Selecting the super-vector based on a novel layer-cut approach can reduce both runtime and variance. Extensive numerical experiments on large-scale binary-state network demonstrate that the proposed new cBAT-MCS outperforms traditional MCS and original BAT-MCS approaches in terms of computational efficiency and accuracy.

cs.CE↗

arXiv2024

Applying Incremental Learning in Binary-Addition-Tree Algorithm for Dynamic Binary-State Network Reliability

This paper presents a novel approach to enhance the Binary-Addition-Tree algorithm (BAT) by integrating incremental learning techniques. BAT, known for its simplicity in development, implementation, and application, is a powerful implicit enumeration method for solving network reliability and optimization problems. However, it traditionally struggles with dynamic and large-scale networks due to its static nature. By introducing incremental learning, we enable the BAT to adapt and improve its performance iteratively as it encounters new data or network changes. This integration allows for more efficient computation, reduced redundancy without searching minimal paths and cuts, and improves overall performance in dynamic environments. Experimental results demonstrate the effectiveness of the proposed method, showing significant improvements in both computational efficiency and solution quality compared to the traditional BAT and indirect algorithms, such as MP-based algorithms and MC-based algorithms.

cs.LG↗

arXiv2023

Building Reliable Budget-Based Binary-State Networks

Everyday life is driven by various network, such as supply chains for distributing raw materials, semi-finished product goods, and final products; Internet of Things (IoT) for connecting and exchanging data; utility networks for transmitting fuel, power, water, electricity, and 4G/5G; and social networks for sharing information and connections. The binary-state network is a basic network, where the state of each component is either success or failure, i.e., the binary-state. Network reliability plays an important role in evaluating the performance of network planning, design, and management. Because more networks are being set up in the real world currently, there is a need for their reliability. It is necessary to build a reliable network within a limited budget. However, existing studies are focused on the budget limit for each minimal path (MP) in networks without considering the total budget of the entire network. We propose a novel concept to consider how to build a more reliable binary-state network under the budget limit. In addition, we propose an algorithm based on the binary-addition-tree algorithm (BAT) and stepwise vectors to solve the problem efficiently.

cs.NI↗

arXiv2022

Novel Node-Based BAT for Finding All Minimal Cuts

The binary-state network, a basic network, and its components are either working or failed. It is fundamental to all types of current networks, such as utility networks (gas, water, electricity, and 4G/5G), the Internet of Things (IoT), social networks, and supply chains. Network reliability is an important index in the planning, design, evaluation, and control of network systems, and the minimal path (MC) is the basis of an MC-based algorithm for calculating reliability. A new BAT called recursive node-based BAT is proposed and implemented in a recursive manner such that the jth vector in the ith iteration is equal to its parent, which is the jth vector, except that its ith coordinate value is one. Based on time complexity analysis and experiments on 20 benchmark binary-state networks, the proposed recursive node-based BAT is more efficient than the best-known node-based algorithm in finding MCs when combined with powerful rules to discard these infeasible vectors and all their offspring.

eess.SY↗

arXiv2022

Development of a Parallel BAT and Its Applications in Binary-state Network Reliability Problems

Various networks are broadly and deeply applied in real-life applications. Reliability is the most important index for measuring the performance of all network types. Among the various algorithms, only implicit enumeration algorithms, such as depth-first-search, breadth-search-first, universal generating function methodology, binary-decision diagram, and binary-addition-tree algorithm (BAT), can be used to calculate the exact network reliability. However, implicit enumeration algorithms can only be used to solve small-scale network reliability problems. The BAT was recently proposed as a simple, fast, easy-to-code, and flexible make-to-fit exact-solution algorithm. Based on the experimental results, the BAT and its variants outperformed other implicit enumeration algorithms. Hence, to overcome the above-mentioned obstacle as a result of the size problem, a new parallel BAT (PBAT) was proposed to improve the BAT based on compute multithread architecture to calculate the binary-state network reliability problem, which is fundamental for all types of network reliability problems. From the analysis of the time complexity and experiments conducted on 20 benchmarks of binary-state network reliability problems, PBAT was able to efficiently solve medium-scale network reliability problems.

cs.DC↗

arXiv2022

Newly Developed Flexible Grid Trading Model Combined ANN and SSO algorithm

In modern society, the trading methods and strategies used in financial market have gradually changed from traditional on-site trading to electronic remote trading, and even online automatic trading performed by a pre-programmed computer programs because the continuous development of network and computer computing technology. The quantitative trading, which the main purpose is to automatically formulate people's investment decisions into a fixed and quantifiable operation logic that eliminates all emotional interference and the influence of subjective thoughts and applies this logic to financial market activities in order to obtain excess profits above average returns, has led a lot of attentions in financial market. The development of self-adjustment programming algorithms for automatically trading in financial market has transformed a top priority for academic research and financial practice. Thus, a new flexible grid trading model combined with the Simplified Swarm Optimization (SSO) algorithm for optimizing parameters for various market situations as input values and the fully connected neural network (FNN) and Long Short-Term Memory (LSTM) model for training a quantitative trading model to automatically calculate and adjust the optimal trading parameters for trading after inputting the existing market situation is developed and studied in this work. The proposed model provides a self-adjust model to reduce investors' effort in the trading market, obtains outperformed investment return rate and model robustness, and can properly control the balance between risk and return.

q-fin.TR↗

arXiv2022

Novel Recursive Inclusion-Exclusion Technology Based on BAT and MPs for Heterogeneous-Arc Binary-State Network Reliability Problems

Current network applications, such as utility networks (gas, water, electricity, and 4G/5G), the Internet of Things (IoT), social networks, and supply chains, are all based on binary state networks. Reliability is one of the most commonly used tools for evaluating network performance, and the minimal path (MP) is a basic algorithm for calculating reliability. However, almost all existing algorithms assume that all undirected arcs are homogeneous; that is, the probability of an arc from nodes a to b is equal to that from nodes b to a. Therefore, based on MPs, the binary-addition-tree algorithm (BAT), and the inclusion-exclusion technique (IET), a novel recursive inclusion-exclusion technology algorithm known as recursive BAT-based IET (RIE) is proposed to solve the heterogeneous-arc binary-state network reliability problem to overcome the above obstacles in applications. The computational complexity of the proposed RIE is analyzed using an illustrative example. Finally, 11 benchmark problems are used to verify the performance of RIE.

cs.NI↗

arXiv2022

QB-II for Evaluating the Reliability of Binary-State Networks

Current real-life applications of various networks such as utility (gas, water, electric, 4G/5G) networks, the Internet of Things, social networks, and supply chains. Reliability is one of the most popular tools for evaluating network performance. The fundamental structure of these networks is a binary state network. Distinctive methods have been proposed to efficiently assess binary-state network reliability. A new algorithm called QB-II (quick binary-addition tree algorithm II) is proposed to improve the efficiency of quick BAT, which is based on BAT and outperforms many algorithms. The proposed QB-II implements the shortest minimum cuts (MCs) to separate the entire BAT into main-BAT and sub-BATs, and the source-target matrix convolution products to connect these subgraphs intelligently to improve the efficiency. Twenty benchmark problems were used to validate the performance of the QB-II.

cs.DS↗

arXiv2022

A BAT-based Exact-Solution Algorithm for the Series-Parallel Redundancy Allocation Problem with Mixed Components

The series-parallel (active) redundancy allocation problem with mixed components (RAP) involves setting reliable objectives for components or subsystems to meet the resource consumption constraint, e.g., the total cost. RAP has been an active research area for the past four decades. The NP-hard difficulties confronted by RAP are maintaining feasibility with respect to two constraints: cost and weight. A novel algorithm called the bound-rule-BAT (BRB) based on the binary-addition-tree algorithm (BAT), the dominance rule, and dynamic bounds are proposed to solve the exact solutions of the most famous RAP benchmark problems called the (33-variation) Fyffe RAP. From the experiments, the proposed BRB can solve the Fyffe RAP correctly under the assumption that the maximal number of components of each subsystem is eight, and this is the first exact-solution algorithm that can solve the Fyffe RAP within 8 seconds and 60 seconds if no reliability lower bound is used.

cs.DM↗

arXiv2022

Application of Long Short-Term Memory Recurrent Neural Networks Based on the BAT-MCS for Binary-State Network Approximated Time-Dependent Reliability Problems

Reliability is an important tool for evaluating the performance of modern networks. Currently, it is NP-hard and #P-hard to calculate the exact reliability of a binary-state network when the reliability of each component is assumed to be fixed. However, this assumption is unrealistic because the reliability of each component always varies with time. To meet this practical requirement, we propose a new algorithm called the LSTM-BAT-MCS, based on long short-term memory (LSTM), the Monte Carlo simulation (MCS), and the binary-adaption-tree algorithm (BAT). The superiority of the proposed LSTM-BAT-MCS was demonstrated by experimental results of three benchmark networks with at most 10-4 mean square error.

eess.SY↗

arXiv2022

Self-Adaptive Binary-Addition-Tree Algorithm-Based Novel Monte Carlo Simulation for Binary-State Network Reliability Approximation

The Monte Carlo simulation (MCS) is a statistical methodology used in a large number of applications. It uses repeated random sampling to solve problems with a probability interpretation to obtain high-quality numerical results. The MCS is simple and easy to develop, implement, and apply. However, its computational cost and total runtime can be quite high as it requires many samples to obtain an accurate approximation with low variance. In this paper, a novel MCS, called the self-adaptive BAT-MCS, based on the binary-adaption-tree algorithm (BAT) and our proposed self-adaptive simulation-number algorithm is proposed to simply and effectively reduce the run time and variance of the MCS. The proposed self-adaptive BAT-MCS was applied to a simple benchmark problem to demonstrate its application in network reliability. The statistical characteristics, including the expectation, variance, and simulation number, and the time complexity of the proposed self-adaptive BAT-MCS are discussed. Furthermore, its performance is compared to that of the traditional MCS extensively on a large-scale problem.

cs.DM↗

arXiv2021

PAM: Pose Attention Module for Pose-Invariant Face Recognition

Pose variation is one of the key challenges in face recognition. Conventional techniques mainly focus on face frontalization or face augmentation in image space. However, transforming face images in image space is not guaranteed to preserve the lossless identity features of the original image. Moreover, these methods suffer from more computational costs and memory requirements due to the additional models. We argue that it is more desirable to perform feature transformation in hierarchical feature space rather than image space, which can take advantage of different feature levels and benefit from joint learning with representation learning. To this end, we propose a lightweight and easy-to-implement attention block, named Pose Attention Module (PAM), for pose-invariant face recognition. Specifically, PAM performs frontal-profile feature transformation in hierarchical feature space by learning residuals between pose variations with a soft gate mechanism. We validated the effectiveness of PAM block design through extensive ablation studies and verified the performance on several popular benchmarks, including LFW, CFP-FP, AgeDB-30, CPLFW, and CALFW. Experimental results show that our method not only outperforms state-of-the-art methods but also effectively reduces memory requirements by more than 75 times. It is noteworthy that our method is not limited to face recognition with large pose variations. By adjusting the soft gate mechanism of PAM to a specific coefficient, such semantic attention block can easily extend to address other intra-class imbalance problems in face recognition, including large variations in age, illumination, expression, etc.

cs.CV↗

arXiv2021

New Binary-Addition Tree Algorithm for the All-Multiterminal Binary-State Network Reliability Problem

Various real-life applications, for example, Internet of Things, wireless sensor networks, smart grids, transportation networks, communication networks, social networks, and computer grid systems, are always modeled as network structures. The binary-state network composed of binary-state (e.g., functioning or failed) components (arcs and/or nodes) is one of the most popular network structures. The two-terminal network reliability is a success probability that the network is still functioning and can be calculated by verifying the connectivity between two specific nodes, and is an effective and popular technique for evaluating the performance of all types of networks. To obtain complete information for a making better decisions, a multi-terminal network reliability extends the two specific nodes to a specific node subset in which all nodes are connected. In this study, a new algorithm called the all-multiterminal BAT is proposed by revising the binary-addition-tree algorithm (BAT) and the layered-search algorithm (LSA) to calculate all multi-terminal reliabilities. The efficiency and effectiveness of the proposed all-multiterminal BAT are analyzed from the perspective of time complexity and explained via numerical experiments to solve the all-multiterminal network reliability problems.

cs.NI↗

arXiv2021

Novel Binary Addition Tree Algorithm (BAT) for Calculating the Direct Lower-Bound of the Highly Reliable Binary-State Network Reliability

Real-world applications such as the internet of things, wireless sensor networks, smart grids, transportation networks, communication networks, social networks, and computer grid systems are typically modeled as network structures. Network reliability represents the success probability of a network and it is an effective and popular metric for evaluating the performance of all types of networks. Binary-state networks composed of binary-state (e.g., working or failed) components (arcs and/or nodes) are some of the most popular network structures. The scale of networks has grown dramatically in recent years. For example, social networks have more than a billion users. Additionally, the reliability of components has increased as a result of both mature and emergent technology. For highly reliable networks, it is more practical to calculate approximated reliability, rather than exact reliability, which is an NP-hard problem. Therefore, we propose a novel direct reliability lower bound based on the binary addition tree algorithm to calculate approximate reliability. The efficiency and effectiveness of the proposed reliability bound are analyzed based on time complexity and validated through numerical experiments.

cs.DM↗