Author name cluster

Yin Chen

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9 papers

2 author rows

EAAI Journal 2025 Journal Article

An adaptive integrated learning-based virtual sensing framework for temperature prediction in aircraft brake monitoring engineering

Lin Lin
Yin Chen
Song Fu
Hao Zhang
Jinlei Wu

In commercial aviation, effective deceleration relies on accurate brake temperature trends. Traditional thermocouple sensors exhibit discontinuous responses, while machine learning-based methods often struggle with abnormal fluctuations due to high-dimensional signal overlap during low-speed braking. This study proposes an Adaptive Thermal Damping Integrated (ATDI) virtual sensor based on integrated learning to enhance brake temperature monitoring accuracy and address these challenges. A novel feature selection method is proposed that includes extracting baseline variables from five brake-related physical models and identifying significant latent variables using Shapley values, which are calculated by integrating three correlation indices based on linear, nonlinear, and rank consistency differences from aircraft flight records. The ATDI framework employs a time-lagged loss function and self-attention mechanism for dynamic weight assignment in the feature space, capturing critical temporal correlations to determine the thermal trend. A digital approach, adaptive to aircraft kinetic energy and called virtual thermal damping, is incorporated at the end of the prediction to mitigate fluctuations and ensure compliance with thermal conduction principles in braking systems. Experimental results demonstrate that the ATDI framework outperforms comparison networks in temperature value continuity and anomaly fluctuation suppression, especially with Long Short-Term Memory (LSTM) as the meta-learner, across three evaluation metrics. Additionally, application strategies for the ATDI framework in aircraft operation and maintenance are proposed.

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ECAI Conference 2025 Conference Paper

Enhancing Local Search for MaxSAT with Deep Differentiation Clause Weighting

Menghua Jiang 0001
Haokai Gao
Shuhao Chen
Yin Chen

Partial Maximum Satisfiability (PMS) and Weighted Partial Maximum Satisfiability (WPMS) generalize Maximum Satisfiability (MaxSAT), with broad real-world applications. Recent advances in Stochastic Local Search (SLS) algorithms for solving (W)PMS have mainly focused on designing clause weighting schemes. However, existing methods often fail to adequately distinguish between PMS and WPMS, typically employing uniform update strategies for clause weights and overlooking critical structural differences between the two problem types. In this work, we present a novel clause weighting scheme that, for the first time, updates the clause weights of PMS and WPMS instances according to distinct conditions. This scheme also introduces a new initialization method, which better accommodates the unique characteristics of both instance types. Furthermore, we propose a decimation method that prioritizes satisfying unit and hard clauses, effectively complementing our proposed clause weighting scheme. Building on these methods, we develop a new SLS solver for (W)PMS named DeepDist. Experimental results on benchmarks from the anytime tracks of recent MaxSAT Evaluations show that DeepDist outperforms state-of-the-art SLS solvers. Notably, a hybrid solver combining DeepDist with TT-Open-WBO-Inc surpasses the performance of the MaxSAT Evaluation 2024 winners, SPB-MaxSAT-c-Band and SPB-MaxSAT-c-FPS, highlighting the effectiveness of our approach. The code is available at https: //github. com/jmhmaxsat/DeepDist

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ICRA Conference 2019 Conference Paper

MRS-VPR: a multi-resolution sampling based global visual place recognition method

Peng Yin 0001
Rangaprasad Arun Srivatsan
Yin Chen
Xueqian Li
Hongda Zhang
Lingyun Xu
Lu Li
Zhenzhong Jia

Place recognition and loop closure detection are challenging for long-term visual navigation tasks. SeqSLAM is considered to be one of the most successful approaches to achieve long-term localization under varying environmental conditions and changing viewpoints. SeqSLAM uses a brute-force sequential matching method, which is computationally intensive. In this work, we introduce a multi-resolution sampling-based global visual place recognition method (MRS-VPR), which can significantly improve the matching efficiency and accuracy in sequential matching. The novelty of this method lies in the coarse-to-fine searching pipeline and a particle filter-based global sampling scheme, that can balance the matching efficiency and accuracy in the long-term navigation task. Moreover, our model works much better than SeqSLAM when the testing sequence is over a much smaller time scale than the reference sequence. Our experiments demonstrate that MRSVPR is efficient in locating short temporary trajectories within long-term reference ones without compromising on the accuracy compared to SeqSLAM.

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AIJ Journal 2015 Journal Article

Ordered completion for logic programs with aggregates

Vernon Asuncion
Yin Chen
Yan Zhang
Yi Zhou

We consider the problem of translating first-order answer set programs with aggregates into first-order sentences with the same type of aggregates. In particular, we show that, on finite structures, normal logic programs with convex aggregates, which cover both monotone and antimonotone aggregates as well as the aggregates appearing in most benchmark programs, can always be captured in first-order logic with the same type of aggregates by introducing auxiliary predicates. More precisely, we prove that every finite stable model of a normal program with convex aggregates is corresponding to a classical model of its enhanced ordered completion. This translation then suggests an alternative way for computing the stable models of such kind of programs. We report some experimental results, which demonstrate that our solver GROCv2 is comparable to the state-of-the-art answer set solvers. We further show that convex aggregates form a maximal class for this purpose. That is, we can always construct a normal logic program under any given non-convex aggregate context and prove that it can never be translated into first-order sentences with the same type of aggregates unless NP = coNP.

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NeurIPS Conference 2012 Conference Paper

Fused sparsity and robust estimation for linear models with unknown variance

Arnak Dalalyan
Yin Chen

In this paper, we develop a novel approach to the problem of learning sparse representations in the context of fused sparsity and unknown noise level. We propose an algorithm, termed Scaled Fused Dantzig Selector (SFDS), that accomplishes the aforementioned learning task by means of a second-order cone program. A special emphasize is put on the particular instance of fused sparsity corresponding to the learning in presence of outliers. We establish finite sample risk bounds and carry out an experimental evaluation on both synthetic and real data.

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AAMAS Conference 2012 Conference Paper

OrgMAP: An Organization-based Approach for Multi-Agent Programming

Cuiyun Hu
Xinjun Mao
Yin Chen
Huiping Zhou

This paper proposes a new organization-based multi-agent programming (OrgMAP) approach to constructing dynamic and flexible software systems. A computational and programming model named Oragent is defined following software engineering principles such as modularity, reusability and etc. Oragent model not only allows programmers to represent the systems with highlevel abstractions in terms of organizations, rules, protocols and roles, but also provides a number of mechanisms, such as encapsulation, inheritance, enactment and event, to improve the dynamics and flexibility of MAS.

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AIJ Journal 2011 Journal Article

Loop-separable programs and their first-order definability

Yin Chen
Fangzhen Lin
Yan Zhang
Yi Zhou

An answer set program with variables is first-order definable on finite structures if the set of its finite answer sets can be captured by a first-order sentence. Characterizing classes of programs that are first-order definable on finite structures is theoretically challenging and of practical relevance to answer set programming. In this paper, we identify a non-trivial class of answer set programs called loop-separable programs and show that they are first-order definable on finite structures.

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AAAI Conference 2010 Conference Paper

First-Order Indefinability of Answer Set Programs on Finite Structures

Yin Chen
Yan Zhang
Yi Zhou

An answer set program with variables is first-order definable on finite structures if the set of its finite answer sets can be captured by a first-order sentence, otherwise this program is first-order indefinable on finite structures. In this paper, we study the problem of first-order indefinability of answer set programs. We provide an Ehrenfeucht-Fraı̈ssé gametheoretic characterization for the first-order indefinability of answer set programs on finite structures. As an application of this approach, we show that the well-known finding Hamiltonian cycles program is not first-order definable on finite structures. We then define two notions named the 0-1 property and unbounded cycles or paths under the answer set semantics, from which we develop two sufficient conditions that may be effectively used in proving a program’s first-order indefinability on finite structures under certain circumstances.

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KR Conference 2006 Conference Paper

First-Order Loop Formulas for Normal Logic Programs

Fangzhen Lin
Yin Chen
Yisong Wang
Mingyi Zhang

In this paper we extend Lin and Zhao's notions of loops and loop formulas to normal logic programs that may contain variables. Under our definition, a loop formula of such a logic program is a first-order sentence. We show that together with Clark's completion, our notion of first-order loop formulas captures the answer set semantics on the instantiation-basis: for any finite set F of ground facts about the extensional relations of a program P, the answer sets of the ground program obtained by instantiating P using F are exactly the models of the propositional theory obtained by instantiating using F the first order theory consisting of the loop formulas of P and Clark's completion of the union of P and F. We also prove a theorem about how to check whether a normal logic program with variables has only a finite number of non-equivalent first-order loops.