Author name cluster

Qiang Luo

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers

1 author row

EAAI Journal 2026 Journal Article

A knowledge-based memetic algorithm for integrated scheduling of equipment operation and spare parts manufacturing in distributed assembly flexible job shops

Wenxiang Jiang
Qianwang Deng
Qiang Luo
Jingxing Zhang
Jicong Zhou

Under the development context of Industry 4. 0, researches on the integration of equipment operation and maintenance (O&M) activities with spare parts manufacturing have garnered increasing attention. Meanwhile, new challenges emerge in spare parts manufacturing due to the increasing complexity of equipment. However, existing integrated scheduling studies have been simplified in the spare parts manufacturing, making it difficult to cope with actual spare parts replacement scenarios of complex equipment. To address this gap, this paper investigates an integrated scheduling problem (ISP-PAO) that includes distributed flexible production, flexible assembly, and operational strategies of complex equipment. We formalize the ISP-PAO through a mathematical model with dual objectives of minimizing total energy consumption and maximizing operational utility. Furthermore, several problem-specific knowledge properties are systematically analyzed and proved, and a knowledge-based memetic algorithm (KBMA) is further proposed to solve the problem. To strengthen optimization capability, the algorithm incorporates four initialization strategies, five knowledge-based local search operators and an energy-aware pareto front refinement strategy. Extensive experiments validate the effectiveness of proposed components, and comparative studies comprehensively evaluate the superiority and robustness of the KBMA, demonstrating its exceptional performance in addressing the ISP-PAO.

Details DOI

EAAI Journal 2026 Journal Article

A permutation-coded evolutionary algorithm for optimizing the irregular bin packing layout in industrial manufacturing

Qiang Luo
Zuogan Tang
Chunrong Pan

The two-dimensional irregular bin packing problem is a pervasive challenge in industrial manufacturing, where maximizing material utilization directly impacts cost and environmental sustainability. While existing methods, predominantly based on local search, have achieved notable success, they often struggle with the immense combinatorial complexity of permutation spaces, especially in large-scale scenarios. This paper introduces a permutation-coded evolutionary algorithm that leverages global search mechanisms for comprehensive solution space exploration. The proposed algorithm integrates a direct permutation encoding with a deterministic double-scanline decoder, effectively translating sequences into compact layouts. To overcome premature convergence, the algorithm incorporates probabilistic mutation operator selection with an adaptive inferior-solution acceptance criterion, and an identical-fitness replacement strategy. Extensive tests on standard benchmarks demonstrate that the approach outperforms state-of-the-art methods in 59 out of 69 instances, achieving an average improvement of 4. 37% to 32. 74% across different bin sizes. A detailed analysis reveals that the algorithm consistently generates high-quality, economical layouts across diverse problem types, including real-world instances from apparel production, with the potential to generate substantial cost savings and enhance operational efficiency in diverse manufacturing sectors.

Details DOI

EAAI Journal 2025 Journal Article

Deep reinforcement learning for dynamic scheduling in distributed heterogeneous flexible job shop with integrated inventory allocation and product delivery

Liuran Lu
Qianwang Deng
Yangyang Hu
Qiang Luo
Shuocheng Gao
Jingxing Zhang

Traditional scheduling primarily emphasizes machine flexibility and is conducted in static environments. However, in practical production environments, customer orders can be supplied either through direct production or from warehouse inventory, with order delivery depending on logistics vehicles scheduling. Furthermore, to ensure product quality, inspection procedures must be conducted prior to delivery, and unqualified products must be reworked. Therefore, this study investigates a dynamic distributed heterogeneous flexible job shop scheduling problem (DDHFJSP-IAT) that incorporates inventory allocation, quality inspection, and product delivery. First, a mixed-integer linear programming (MILP) model is formulated to simultaneously minimize total costs and total delay penalty. Second, an Improved Rescheduling Method (IRSM) is proposed to address the challenges associated with product inspection and rework. Third, a dual-population memetic algorithm based on the Double Deep Q-Network (D2QDMA) is proposed to solve the DDHFJSP-IAT. This algorithm incorporates a six-layer chromosome encoding scheme, twelve neighborhood search operators based on the problem characteristics, and a cost-saving strategy. The Double Deep Q-Network (DDQN) is employed to dynamically select the most appropriate neighborhood search operators, and 65 normalized state features are extracted to construct the state space. Finally, extensive experiments are conducted utilizing 46 newly formulated benchmark instances. The results confirm the consistency between the MILP model and the D2QDMA, demonstrating that the D2QDMA outperforms five well-known comparison algorithms over 75 % of the instances regarding diversity, convergence, and uniformity, thereby validating its effectiveness and superiority in solving the DDHFJSP-IAT.

Details DOI

AAAI Conference 2024 Conference Paper

A Brain-Inspired Way of Reducing the Network Complexity via Concept-Regularized Coding for Emotion Recognition

Han Lu
Xiahai Zhuang
Qiang Luo

The human brain can effortlessly and reliably perceive emotions, whereas existing facial emotion recognition (FER) methods suffer from drawbacks such as complex model structures, high storage requirements, and poor interpretability. Inspired by the role of emotion concepts in visual perception coding within the human brain, we propose a dual-pathway framework emulating the neural computation of emotion recognition. Specifically, these two pathways are designed to model the representation of emotion concepts in the brain and the visual perception process, respectively. For the former, we adopt a disentangled approach to extract emotion concepts from complex facial geometric attributes; for the latter, we employ an emotional confidence evaluation strategy to determine which concept is optimal for regularizing the perceptual coding. The proposed concept-regularized coding strategy endows the framework with flexibility and interpretability as well as good performances on several benchmarking FER datasets.

PDF Details DOI

YNIMG Journal 2022 Journal Article

Dynamic neural reconfiguration for distinct strategies during competitive social interactions

Ruihan Yang
Yina Ma
Bao-Bao Pan
Meghana A. Bhatt
Terry Lohrenz
Hua-Guang Gu
Jonathan W. Kanen
Colin F. Camerer

Information exchange between brain regions is key to understanding information processing for social decision-making, but most analyses ignore its dynamic nature. New insights on this dynamic might help us to uncover the neural correlates of social cognition in the healthy population and also to understand the malfunctioning neural computations underlying dysfunctional social behavior in patients with mental disorders. In this work, we used a multi-round bargaining game to detect switches between distinct bargaining strategies in a cohort of 76 healthy participants. These switches were uncovered by dynamic behavioral modeling using the hidden Markov model. Proposing a novel model of dynamic effective connectivity to estimate the information flow between key brain regions, we found a stronger interaction between the right temporoparietal junction (rTPJ) and the right dorsolateral prefrontal cortex (rDLPFC) for the strategic deception compared with the social heuristic strategies. The level of deception was associated with the information flow from the Brodmann area 10 to the rTPJ, and this association was modulated by the rTPJ-to-rDLPFC information flow. These findings suggest that dynamic bargaining strategy is supported by dynamic reconfiguration of the rDLPFC-and-rTPJ interaction during competitive social interactions.

Details DOI

YNICL Journal 2020 Journal Article

Effective connectivity of the right anterior insula in schizophrenia: The salience network and task-negative to task-positive transition

Qiang Luo
Baobao Pan
Huaguang Gu
Molly Simmonite
Susan Francis
Peter F. Liddle
Lena Palaniyappan

Triple network dysfunction theory of schizophrenia postulates that the interaction between the default-mode and the fronto-parietal executive network is disrupted by aberrant salience signals from the right anterior insula (rAI). To date, it is not clear how the proposed resting-state disruption translates to task-processing inefficiency in subjects with schizophrenia. Using a contiguous resting and 2-back task performance fMRI paradigm, we quantified the change in effective connectivity that accompanies rest-to-task state transition in 29 clinically stable patients with schizophrenia and 31 matched healthy controls. We found an aberrant task-evoked increase in the influence of the rAI to both executive (Cohen’s d = 1. 35, p = 2. 8 × 10−6) and default-mode (Cohen’s d = 1. 22, p = 1. 5 × 10−5) network regions occur in patients when compared to controls. In addition, the effective connectivity from middle occipital gyrus (dorsal visual cortex) to insula is also increased in patients as compared with healthy controls. Aberrant insula to executive network influence is pronounced in patients with more severe negative symptom burden. These findings suggest that control signals from rAI are abnormally elevated and directed towards both task-positive and task-negative brain regions, when task-related demands arise in schizophrenia. This aberrant, undiscriminating surge in salience signalling may disrupt contextually appropriate allocation of resources in the neuronal workspace in patients with schizophrenia.

Details DOI

YNICL Journal 2019 Journal Article

Adolescent binge drinking disrupts normal trajectories of brain functional organization and personality maturation

Hongtao Ruan
Yunyi Zhou
Qiang Luo
Gabriel H. Robert
Sylvane Desrivières
Erin Burke Quinlan
ZhaoWen Liu
Tobias Banaschewski

Adolescent binge drinking has been associated with higher risks for the development of many health problems throughout the lifespan. Adolescents undergo multiple changes that involve the co-development processes of brain, personality and behavior; therefore, certain behavior, such as alcohol consumption, can have disruptive effects on both brain development and personality maturation. However, these effects remain unclear due to the scarcity of longitudinal studies. In the current study, we used multivariate approaches to explore discriminative features in brain functional architecture, personality traits, and genetic variants in 19-year-old individuals (n = 212). Taking advantage of a longitudinal design, we selected features that were more drastically altered in drinkers with an earlier onset of binge drinking. With the selected features, we trained a hierarchical model of support vector machines using a training sample (n = 139). Using an independent sample (n = 73), we tested the model and achieved a classification accuracy of 71.2%. We demonstrated longitudinally that after the onset of binge drinking the developmental trajectory of improvement in impulsivity slowed down. This study identified the disrupting effects of adolescent binge drinking on the developmental trajectories of both brain and personality.

Details DOI

YNIMG Journal 2017 Journal Article

Functional connectivity decreases in autism in emotion, self, and face circuits identified by Knowledge-based Enrichment Analysis

Wei Cheng
Edmund T. Rolls
Jie Zhang
Wenbo Sheng
Liang Ma
Lin Wan
Qiang Luo
Jianfeng Feng

A powerful new method is described called Knowledge based functional connectivity Enrichment Analysis (KEA) for interpreting resting state functional connectivity, using circuits that are functionally identified using search terms with the Neurosynth database. The method derives its power by focusing on neural circuits, sets of brain regions that share a common biological function, instead of trying to interpret single functional connectivity links. This provides a novel way of investigating how task- or function-related networks have resting state functional connectivity differences in different psychiatric states, provides a new way to bridge the gap between task and resting-state functional networks, and potentially helps to identify brain networks that might be treated. The method was applied to interpreting functional connectivity differences in autism. Functional connectivity decreases at the network circuit level in 394 patients with autism compared with 473 controls were found in networks involving the orbitofrontal cortex, anterior cingulate cortex, middle temporal gyrus cortex, and the precuneus, in networks that are implicated in the sense of self, face processing, and theory of mind. The decreases were correlated with symptom severity.

Details DOI

YNIMG Journal 2016 Journal Article

Using real-time fMRI to influence effective connectivity in the developing emotion regulation network

Kathrin Cohen Kadosh
Qiang Luo
Calem de Burca
Moses O. Sokunbi
Jianfeng Feng
David E.J. Linden
Jennifer Y.F. Lau

For most people, adolescence is synonymous with emotional turmoil and it has been shown that early difficulties with emotion regulation can lead to persistent problems for some people. This suggests that intervention during development might reduce long-term negative consequences for those individuals. Recent research has highlighted the suitability of real-time fMRI-based neurofeedback (NF) in training emotion regulation (ER) networks in adults. However, its usefulness in directly influencing plasticity in the maturing ER networks remains unclear. Here, we used NF to teach a group of 17 7–16 year-olds to up-regulate the bilateral insula, a key ER region. We found that all participants learned to increase activation during the up-regulation trials in comparison to the down-regulation trials. Importantly, a subsequent Granger causality analysis of Granger information flow within the wider ER network found that during up-regulation trials, bottom-up driven Granger information flow increased from the amygdala to the bilateral insula and from the left insula to the mid-cingulate cortex, supplementary motor area and the inferior parietal lobe. This was reversed during the down-regulation trials, where we observed an increase in top-down driven Granger information flow to the bilateral insula from mid-cingulate cortex, pre-central gyrus and inferior parietal lobule. This suggests that: 1) NF training had a differential effect on up-regulation vs down-regulation network connections, and that 2) our training was not only superficially concentrated on surface effects but also relevant with regards to the underlying neurocognitive bases. Together these findings highlight the feasibility of using NF in children and adolescents and its possible use for shaping key social cognitive networks during development.

Details DOI

YNIMG Journal 2013 Journal Article

Spatio-temporal Granger causality: A new framework

Qiang Luo
Wenlian Lu
Wei Cheng
Pedro A. Valdes-Sosa
Xiaotong Wen
Mingzhou Ding
Jianfeng Feng

That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data.

Details DOI

YNIMG Journal 2011 Journal Article

Granger causality with signal-dependent noise

Qiang Luo
Tian Ge
Jianfeng Feng

It is generally believed that the noise variance in in vivo neuronal data exhibits time-varying volatility, particularly signal-dependent noise. Despite a widely used and powerful tool to detect causal influences in various data sources, Granger causality has not been well tailored for time-varying volatility models. In this technical note, a unified treatment of the causal influences in both mean and variance is naturally proposed on models with signal-dependent noise in both time and frequency domains. The approach is first systematically validated on toy models, and then applied to the physiological data collected from Parkinson patients, where a clear advantage over the classical Granger causality is demonstrated.

Details DOI