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Yalin Wang

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35 papers

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EAAI Journal 2026 Journal Article

Concurrent historical data clustering and common feature learning for new-mode zero-shot industrial anomaly detection

Kai Wang
Xinlong Yuan
Xun Lang
Xiaofeng Yuan
Jie Han
Yalin Wang

Due to insufficient understanding of the new operating conditions and lack of operational experience, the new operating conditions in industrial processes are more prone to failures. Rapidly Rapid indication of anomalies in the early stage of faults is very important to production safety. However, no samples are collected for the new modes when the operation just starts. Based on the rationale that not all process correlations change from mode to mode and there exist stable similar features across different modes, we seek to mine common knowledge in multi-modal historical data and leverage it for zero-shot, zero-start industrial condition monitoring. Since no clear mode labels are available in practice, an unsupervised multi-manifold clustering and shared principal component extraction method is proposed. The class indicator matrix and the projection direction of feature subspace are simultaneously learned. A trace-ratio iterative optimization algorithm, together with a parameter initialization strategy, is proposed to accelerate convergence. A numerical example and a real multiple effect evaporator are used to verify the advantages of the proposed method.

YNIMG Journal 2026 Journal Article

Quantification of planar cortical magnification with optimal transport and topological smoothing

Yujian Xiong
Negar Jalili Mallak
Yanshuai Tu
Zhong-Lin Lu
Yalin Wang

The human visual system exhibits non-uniform spatial resolution across the visual field, which is characterized by the cortical magnification factor (CMF) that reflects its anatomical basis. However, current approaches for quantifying CMF using retinotopic maps derived from BOLD functional magnetic resonance imaging (fMRI) are limited by the inherent low signal-to-noise ratio of fMRI data and inaccuracies in the topological relationships of the retinotopic maps. In this study, we introduced a new pipeline to quantify planar CMF from retinotopic maps generated from the population receptive field (pRF) model. The pipeline projected the 3D pRF solutions onto a 2D planar disk, using optimal transport (OT) to preserve local cortical surface areas, and applied topological smoothing to ensure that the resulting retinotopic maps maintain their topology. We then estimated 2D CMF maps from the projected retinotopic maps on the planar disk using the 1-ring patch method. Applying this pipeline to the Human Connectome Project (HCP) 7T dataset, we revealed previously unobserved CMF patterns across the visual field and demonstrated individual differences among the 181 subjects. The pipeline was further validated on the New York University (NYU) 3T dataset, showing reliable and repeatable results. Our study provided new analytical methods and offered novel insights into visual processing.

EAAI Journal 2025 Journal Article

A sampling interval-adaptive transformer for industrial time sequence modeling with heterogeneou s sampling rates in quality prediction

Zijian Xu
Nuo Xu
Kai Wang
Xiaofeng Yuan
Yalin Wang
Chunhua Yang
Weihua Gui
Shuqiao Cheng

The industrial data sequences frequently exhibit irregular sampling frequencies, which pose a number of difficulties for data analysis and modeling. The traditional dynamic models like Recurrent Neural Network (RNN) and Transformer are difficult to model such data sequences. The main reason is that these models assume that data sampling frequency should be constant. To this end, a Sampling Interval-Adaptive Transformer (SIA-Trans) is proposed in this paper to adaptively model the temporal information for heterogeneous sampling sequences in industrial processes. The SIA-Trans uses the sampling interval and position embedding block to address the problem of unequal time intervals and rectify the temporal correlations in time series. Then, the interval-aware self-attention net is designed for dynamic data relationship modeling, taking the processed data through the self-attention mechanism. Finally, the predicted output is obtained after the point-wise feed-forward layer. The proposed SIA-Trans is validated on a real-world hydrocracking process to predict the content of hydrocarbon mixture with five carbon atoms (C5) hydrocarbons in light naphtha, as well as the final boiling point of jet fuel.

EAAI Journal 2025 Journal Article

Multi-step difference-driven domain adversarial network for few-sample fault detection in dynamic industrial systems

Ruiyi Fang
Kai Wang
Xiaofeng Yuan
Zeyu Yang
Yalin Wang
Chunhua Yang

The escalating production demands for manufacture result in heightened complexity in industrial processes, which leads to frequent changes in operating conditions, thus making few-sample scenarios commonplace. Although many deep learning methods achieve good performance in fault detection tasks, they mostly rely on sufficient data. Therefore, the lack of adequate data presents challenges for accurately representing the process. Moreover, the inherent interplay in processes and among equipment often manifests in data with dynamic characteristic. To address these challenges, we propose a dynamic domain adversarial network (DDAN) for dynamic few-sample fault detection. DDAN based on knowledge transfer, aiming to facilitate modeling data-poor domain with cross-domain information from the data-rich domain. It consists of three main components, a feature extractor, a data reconstructor, and a domain discriminator. To effectively extract features from dynamic samples in few-sample scenarios, a multi-step difference method is introduced. Combined with self-attention, the feature extractor highlights the most significant difference block in the dynamic representations. The output of data reconstructor is utilized for fault detection tasks, while the domain discriminator is applied for domain adaptation with a rebalancing loss. The proposed method is validated on a numerical case and a real-world alumina evaporation process. The experimental results demonstrate an average improvement in the fault detection rate of 2. 9%, with an improvement exceeding 6% for latent variable faults.

AAAI Conference 2025 Conference Paper

Multimodal Variational Autoencoder: A Barycentric View

Peijie Qiu
Wenhui Zhu
Sayantan Kumar
Xiwen Chen
Jin Yang
Xiaotong Sun
Abolfazl Razi
Yalin Wang

Multiple signal modalities, such as vision and sounds, are naturally present in real-world phenomena. Recently, there has been growing interest in learning generative models, in particular variational autoencoder (VAE), to for multimodal representation learning especially in the case of missing modalities. The primary goal of these models is to learn a modality-invariant and modality-specific representation that characterizes information across multiple modalities. Previous attempts at multimodal VAEs approach this mainly through the lens of experts, aggregating unimodal inference distributions with a product of experts (PoE), a mixture of experts (MoE), or a combination of both. In this paper, we provide an alternative generic and theoretical formulation of multimodal VAE through the lens of barycenter. We first show that PoE and MoE are specific instances of barycenters, derived by minimizing the asymmetric weighted KL divergence to unimodal inference distributions. Our novel formulation extends these two barycenters to a more flexible choice by considering different types of divergences. In particular, we explore the Wasserstein barycenter defined by the 2-Wasserstein distance, which better preserves the geometry of unimodal distributions by capturing both modality-specific and modality-invariant representations compared to KL divergence. Empirical studies on three multimodal benchmarks demonstrated the effectiveness of the proposed method.

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YNIMG Journal 2025 Journal Article

qPRF: A system to accelerate population receptive field modeling

Sebastian Waz
Yalin Wang
Zhong-Lin Lu

BOLD response can be fitted using the population receptive field (PRF) model to reveal how visual input is represented on the cortex (Dumoulin and Wandell, 2008). Fitting the PRF model costs considerable time, often requiring days to analyze BOLD signals for a small cohort of subjects. We introduce the qPRF (“quick PRF”), a system for accelerated PRF modeling that reduced the computation time by a factor > 1, 000 without losing goodness-of-fit when compared to another widely available PRF modeling package (Kay et al. , 2013) on a benchmark of data from the Human Connectome Project (HCP; Van Essen et al. (2013). The system achieves this level of acceleration by pre-computing a tree-like data structure, which it rapidly searches during the fitting step for an optimal parameter combination. We tested the method on a constrained four-parameter version of the PRF model (Strategy 1 herein) and an unconstrained five-parameter PRF model, which the qPRF fitted at comparable speed (Strategy 2). We show how an additional search step can guarantee optimality of qPRF solutions with little additional time cost (Strategy 3). To assess the quality of qPRF solutions, we compared our Strategy 1 solutions to those provided by Benson et al. (2018) who performed a similar four-parameter fit. Both hemispheres of the 181 subjects in the HCP dataset (a total of 10, 753, 572 vertices, each with a unique BOLD time series of 1800 frames) were analyzed by qPRF in 12. 82 h on an ordinary CPU. The absolute difference in R 2 achieved by the qPRF compared to Benson et al. (2018) was negligible, with a median of 0. 025% ( R 2 units being between 0% and 100%). In general, the qPRF yielded a slightly better fitting solution, achieving a greater R 2 on 70. 2% of vertices. We also assess the qPRF method’s model-recovery ability using a simulated dataset. The qPRF may facilitate the development and use of more elaborate models based on the PRF framework and may pave the way for novel clinical applications.

JBHI Journal 2025 Journal Article

Schizophrenia Detection Based on Morphometry of Hippocampus and Amygdala

Qunxi Dong
Yuhang Sheng
Junru Zhu
Zhigang Li
Weijia Liu
Jingyu Liu
Yalin Wang
Bin Hu

Schizophrenia (SZ) is a severe mental disorder characterized by hallucinations, delusions, cognitive impairments, and social withdrawal. It leads to a series of brain abnormalities, particularly the deformation of the hippocampus and amygdala, which are highly associated with emotion, memory, and motivation. Most previous studies have used the hippocampal and amygdaloid volume, whereas surface-based morphometry reflects nuclear deformation more finely, but it is unclear the hippocampal and amygdaloid morphometry relates to schizophrenic pathology and its potential as a biomarker. In this study, we extracted individual multivariate morphometry statistics (MMS) of hippocampus and amygdala from MRI images and analyzed the morphometric differences between groups. After dictionary learning and max pooling, we obtain reduced dimensional features and use machine learning algorithms for individual diagnosis. The results showed that the hippocampus of the schizophrenia group was significantly atrophied bilaterally and the atrophied areas were symmetrical. Subregions of the amygdala are both atrophied and expanded, and in particular, the right amygdala shows a greater degree and extent of deformation. Using the random forest classifier, the accuracy of classification using hippocampal and amygdaloid morphometric features are 94. 52% and 94. 57%, respectively, and the accuracy of classification combining the two morphometric features reached 96. 57%. Our study demonstrates the efficacy of MMS in identifying morphometric differences of the hippocampus and amygdala between healthy controls and schizophrenic, and these findings emphasize the potential of MMS as a reliable biomarker for the diagnosis of schizophrenia.

EAAI Journal 2025 Journal Article

Semantic segmentation model based on edge information for rock structural surface traces detection

Xiaofeng Yuan
Dun Wu
Yalin Wang
Chunhua Yang
Weihua Gui
Shuqiao Cheng
Lingjian Ye
Feifan Shen

Fast and accurate detection of rock structural surface traces is crucial for geology and engineering fields. In recent years, deep learning techniques like U-Net (UNet) have been applied to rock structural surface traces detection by virtue of its high accuracy and strong robustness. However, the loss of important information during the downsampling process may hinder the model performance for rock structural surface traces detection. To alleviate this problem, this paper proposes a semantic segmentation model based on edge information (Edge-UNet) for rock structural surface traces detection. In Edge-UNet, an edge pooling method is designed, which can retain more trace features rich in edge information in the downsampling process, so as to enhance the learning of the model for traces. Then, an edge semantic enhancement structure based on edge pooling is designed to strengthen the edge information in Edge-UNet's encoder. In addition, a channel space attention gate based on edge information is incorporated in Edge-UNet's decoder, which facilitates the model to capture fine trace features. These designs clarify the retention and utilization of edge information in principle which enhances the interpretability of the model. Finally, Convolutional neural network -based and Transformer-based semantic segmentation models were selected for comparison experiments with Edge-UNet, respectively. From the experimental results, Edge-UNet outperforms the other models in three performance metrics, which verifies the superior performance of Edge-UNet in rock structural surface trace detection task.

AAAI Conference 2025 Conference Paper

Sequence Complementor: Complementing Transformers for Time Series Forecasting with Learnable Sequences

Xiwen Chen
Peijie Qiu
Wenhui Zhu
Huayu Li
Hao Wang
Aristeidis Sotiras
Yalin Wang
Abolfazl Razi

Since its introduction, the transformer has shifted the development trajectory away from traditional models (e.g., RNN, MLP) in time series forecasting, which is attributed to its ability to capture global dependencies within temporal tokens. Follow-up studies have largely involved altering the tokenization and self-attention modules to better adapt Transformers for addressing special challenges like non-stationarity, channel-wise dependency, and variable correlation in time series. However, we found that the expressive capability of sequence representation is a key factor influencing Transformer performance in time forecasting after investigating several representative methods, where there is an almost linear relationship between sequence representation entropy and mean square error, with more diverse representations performing better. In this paper, we propose a novel attention mechanism with Sequence Complementors and prove feasible from an information theory perspective, where these learnable sequences are able to provide complementary information beyond current input to feed attention. We further enhance the Sequence Complementors via a diversification loss that is theoretically covered. The empirical evaluation of both long-term and short-term forecasting has confirmed its superiority over the recent state-of-the-art methods.

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EAAI Journal 2024 Journal Article

A task-oriented deep learning framework based on target-related transformer network for industrial quality prediction applications

Yalin Wang
Rao Dai
Diju Liu
Kai Wang
Xiaofeng Yuan
Chenliang Liu

Executing various production tasks is critical to the safe operation and efficient production of industrial processes. As one of them, the detection task of key quality variables directly affects the operation optimization and decision-making of industrial processes, but it is severely limited by the harsh environment and detection instruments. Therefore, the real-time prediction task of key quality variables becomes the basis for optimal control of industrial processes. To address this issue, this paper proposes a task-oriented deep learning framework based on a target-related transformer (TR-Former) network for industrial quality prediction tasks. Specifically, a new target-related self-attention (TR-SA) mechanism is developed to guide feature learning by adding attention scores between task-related target variables and other variables. As a result, the learned features in this instance will be guaranteed to be relevant to the target variable and useful for the quality prediction task. Moreover, the long-range dynamics of industrial process data can also be captured, which can further improve the prediction performance of the model. Finally, extensive experiments were conducted on two industrial processes to validate the superiority of the proposed method in terms of quality prediction tasks. The experimental results demonstrate that the proposed TR-Former method exhibits an improvement ranging from 3% to 13% in the mean absolute error indicator compared to the traditional transformer and other state-of-the-art methods.

EAAI Journal 2024 Journal Article

Anomaly detection using large-scale multimode industrial data: An integration method of nonstationary kernel and autoencoder

Kai Wang
Caoyin Yan
Yanfang Mo
Yalin Wang
Xiaofeng Yuan
Chenliang Liu

Kernel methods and neural networks (NNs) are two mainstream nonlinear data modeling methods and have been widely applied to industrial process monitoring. However, they both present imperfect properties, so the relevant applications are limited. On the one hand, kernels are not so reconstructable, scalable, and robust to hyperparameters that they suffer performance degradation for large-scale data modeling and monitoring. On the other hand, the high-dimensional parameter space of NNs that is sorted to parameter initialization presents severe anomaly detection performance inconsistency, which makes the industry cautious about using NNs. Motivated by these facts, we propose to integrate kernels and NNs, forming a new model structure that is scalable, reconstructable, and performance-consistent. Specifically, a novel autoencoder-based nonstationary pattern selection kernel (AE-NPSK) is proposed by (1) selecting from the training set the critical edges and interior data as the centers of the radial basis functions in the hidden layers and (2) adaptively adjusting the kernel width in the training procedure. Also, the new NN has strong performance consistency, which facilitates the search for optimal parameters. Finally, we test the performance of the proposed method on the challenging multimode processes. The results validate the efficacy of the proposed method.

NeurIPS Conference 2023 Conference Paper

Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation

Zhangsihao Yang
Mengwei Ren
Kaize Ding
Guido Gerig
Yalin Wang

Pretraining CNN models (i. e. , UNet) through self-supervision has become a powerful approach to facilitate medical image segmentation under low annotation regimes. Recent contrastive learning methods encourage similar global representations when the same image undergoes different transformations, or enforce invariance across different image/patch features that are intrinsically correlated. However, CNN-extracted global and local features are limited in capturing long-range spatial dependencies that are essential in biological anatomy. To this end, we present a keypoint-augmented fusion layer that extracts representations preserving both short- and long-range self-attention. In particular, we augment the CNN feature map at multiple scales by incorporating an additional input that learns long-range spatial self-attention among localized keypoint features. Further, we introduce both global and local self-supervised pretraining for the framework. At the global scale, we obtain global representations from both the bottleneck of the UNet, and by aggregating multiscale keypoint features. These global features are subsequently regularized through image-level contrastive objectives. At the local scale, we define a distance-based criterion to first establish correspondences among keypoints and encourage similarity between their features. Through extensive experiments on both MRI and CT segmentation tasks, we demonstrate the architectural advantages of our proposed method in comparison to both CNN and Transformer-based UNets, when all architectures are trained with randomly initialized weights. With our proposed pretraining strategy, our method further outperforms existing SSL methods by producing more robust self-attention and achieving state-of-the-art segmentation results. The code is available at https: //github. com/zshyang/kaf. git.

EAAI Journal 2023 Journal Article

Semi-supervised LSTM with historical feature fusion attention for temporal sequence dynamic modeling in industrial processes

Yiyin Tang
Yalin Wang
Chenliang Liu
Xiaofeng Yuan
Kai Wang
Chunhua Yang

In modern industrial processes, the data-driven soft sensor technology has been widely used for the prediction of key quality variables. Due to the important of dynamics and nonlinearity in industrial process data, deep learning models like long short-term memory (LSTM) network are well suited for temporal sequence dynamic modeling due to their excellent long-term memory function and feature extraction capability. Furthermore, industrial processes generate a large amount of process data with irregular sampling frequencies. However, traditional LSTM cannot fully utilize the process data with irregular sampling frequency and the guidance value of historical data samples for feature learning. To address these issues, a novel semi-supervised LSTM with history feature fusion attention (HFFA-SSLSTM) model is proposed in this paper. First, the semi-supervised learning strategy is implemented in LSTM to fully utilize the unlabeled data and mine the temporal sequence features of labeled samples and unlabeled samples with irregular sampling frequencies. Then, a novel historical feature fusion attention (HFFA) mechanism is developed, which utilizes historical hidden features to learn attention scores for obtaining weighted historical information-related features. Finally, the extracted features are combined to form the soft sensor model to perform time series prediction tasks for key quality variables in industrial processes. The experimental results on the actual industrial hydrocracking data set demonstrate the effectiveness of the proposed HFFA-SSLSTM model and its possibility of applicating in real industrial processes.

EAAI Journal 2021 Journal Article

Common and specific deep feature representation for multimode process monitoring using a novel variable-wise weighted parallel network

Kai Wang
Zhiying Guo
Yalin Wang
Xiaofeng Yuan
Chunhua Yang

Multimodal data are common in industrial processes because of switched operating conditions, varying feedstocks and changed product designs and so on. To guarantee process safety and improving process performance, a variable-wise weighted parallel stacked auto-encoder model is proposed for nonlinear multimode process monitoring. Considering the similarity and difference between multiple operating modes with complex process nonlinearities, mode-common and mode-specific deep features are parallelly extracted with the proposed new model. Since each variable distinctly contributes to the mode-common features, variable-wise weights are designed with an optimal transport distance between modes when the mode-common features are learned. Moreover, different from designing a unified monitoring index for all modes, three asymmetric indices are designed to not only trigger an alarm for an anomaly, but also indicate whether the anomaly is caused by mode-common factors, mode-specific factors or others. Thus, the real-time monitoring results, together with some diagnosis information are simultaneously presented. A numerical example and a real industry application are used to validate the monitoring efficacy of the proposed model.

EAAI Journal 2021 Journal Article

Deep learning with nonlocal and local structure preserving stacked autoencoder for soft sensor in industrial processes

Chenliang Liu
Yalin Wang
Kai Wang
Xiaofeng Yuan

Deep learning-based soft sensor has been widely used for quality prediction in modern industry. Traditional deep learning like stacked autoencoder (SAE) only captures the feature representations by minimizing the global reconstruction errors, which causes a loss of the intrinsic geometric structure embedded in the raw data. To address this problem, a nonlocal and local structure preserving stacked autoencoder (NLSP-SAE) is proposed for soft sensor. Different from the original SAE, NLSP-SAE aims to extract the meaningful structure-relevant features by establishing a new objective function with a regularizer of the nonlocal and local data structure information. For local structure preserving, NLSP-SAE enforces two adjacent data points to be near each other in the reconstructed space. While for nonlocal structure preserving, NLSP-SAE constrains two nonadjacent data points to be far apart from each other. The application on an industrial hydrocracking process demonstrates that NLSP-SAE can improve the prediction accuracy for quality variables.

YNICL Journal 2020 Journal Article

Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline

Qunxi Dong
Wen Zhang
Cynthia M. Stonnington
Jianfeng Wu
Boris A. Gutman
Kewei Chen
Yi Su
Leslie C. Baxter

Ventricular volume (VV) is a widely used structural magnetic resonance imaging (MRI) biomarker in Alzheimer's disease (AD) research. Abnormal enlargements of VV can be detected before clinically significant memory decline. However, VV does not pinpoint the details of subregional ventricular expansions. Here we introduce a ventricular morphometry analysis system (VMAS) that generates a whole connected 3D ventricular shape model and encodes a great deal of ventricular surface deformation information that is inaccessible by VV. VMAS contains an automated segmentation approach and surface-based multivariate morphometry statistics. We applied VMAS to two independent datasets of cognitively unimpaired (CU) groups. To our knowledge, it is the first work to detect ventricular abnormalities that distinguish normal aging subjects from those who imminently progress to clinically significant memory decline. Significant bilateral ventricular morphometric differences were first shown in 38 members of the Arizona APOE cohort, which included 18 CU participants subsequently progressing to the clinically significant memory decline within 2 years after baseline visits (progressors), and 20 matched CU participants with at least 4 years of post-baseline cognitive stability (non-progressors). VMAS also detected significant differences in bilateral ventricular morphometry in 44 Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects (18 CU progressors vs. 26 CU non-progressors) with the same inclusion criterion. Experimental results demonstrated that the ventricular anterior horn regions were affected bilaterally in CU progressors, and more so on the left. VMAS may track disease progression at subregional levels and measure the effects of pharmacological intervention at a preclinical stage.

AAAI Conference 2020 Conference Paper

Regularized Wasserstein Means for Aligning Distributional Data

Liang Mi
Wen Zhang
Yalin Wang

We propose to align distributional data from the perspective of Wasserstein means. We raise the problem of regularizing Wasserstein means and propose several terms tailored to tackle different problems. Our formulation is based on the variational transportation to distribute a sparse discrete measure into the target domain. The resulting sparse representation well captures the desired property of the domain while reducing the mapping cost. We demonstrate the scalability and robustness of our method with examples in domain adaptation, point set registration, and skeleton layout.

YNICL Journal 2019 Journal Article

Applying surface-based hippocampal morphometry to study APOE-E4 allele dose effects in cognitively unimpaired subjects

Qunxi Dong
Wen Zhang
Jianfeng Wu
Bolun Li
Emily H. Schron
Travis McMahon
Jie Shi
Boris A. Gutman

Apolipoprotein E (APOE) e4 is the major genetic risk factor for late-onset Alzheimer's disease (AD). The dose-dependent impact of this allele on hippocampal volumes has been documented, but its influence on general hippocampal morphology in cognitively unimpaired individuals is still elusive. Capitalizing on the study of a large number of cognitively unimpaired late middle aged and older adults with two, one and no APOE-e4 alleles, the current study aims to characterize the ability of our automated surface-based hippocampal morphometry algorithm to distinguish between these three levels of genetic risk for AD and demonstrate its superiority to a commonly used hippocampal volume measurement. We examined the APOE-e4 dose effect on cross-sectional hippocampal morphology analysis in a magnetic resonance imaging (MRI) database of 117 cognitively unimpaired subjects aged between 50 and 85 years (mean = 57.4, SD = 6.3), including 36 heterozygotes (e3/e4), 37 homozygotes (e4/e4) and 44 non-carriers (e3/e3). The proposed automated framework includes hippocampal surface segmentation and reconstruction, higher-order hippocampal surface correspondence computation, and hippocampal surface deformation analysis with multivariate statistics. In our experiments, the surface-based method identified APOE-e4 dose effects on the left hippocampal morphology. Compared to the widely-used hippocampal volume measure, our hippocampal morphometry statistics showed greater statistical power by distinguishing cognitively unimpaired subjects with two, one, and no APOE-e4 alleles. Our findings mirrored previous studies showing that APOE-e4 has a dose effect on the acceleration of brain structure deformities. The results indicated that the proposed surface-based hippocampal morphometry measure is a potential preclinical AD imaging biomarker for cognitively unimpaired individuals.

IJCAI Conference 2018 Conference Paper

Dynamically Hierarchy Revolution: DirNet for Compressing Recurrent Neural Network on Mobile Devices

Jie Zhang
Xiaolong Wang
Dawei Li
Yalin Wang

Recurrent neural networks (RNNs) achieve cutting-edge performance on a variety of problems. However, due to their high computational and memory demands, deploying RNNs on resource constrained mobile devices is a challenging task. To guarantee minimum accuracy loss with higher compression rate and driven by the mobile resource requirement, we introduce a novel model compression approach DirNet based on an optimized fast dictionary learning algorithm, which 1) dynamically mines the dictionary atoms of the projection dictionary matrix within layer to adjust the compression rate 2) adaptively changes the sparsity of sparse codes cross the hierarchical layers. Experimental results on language model and an ASR model trained with a 1000h speech dataset demonstrate that our method significantly outperforms prior approaches. Evaluated on off-the-shelf mobile devices, we are able to reduce the size of original model by eight times with real-time model inference and negligible accuracy loss.

YNICL Journal 2017 Journal Article

A T1 and DTI fused 3D corpus callosum analysis in MCI subjects with high and low cardiovascular risk profile

Yi Lao
Binh Nguyen
Sinchai Tsao
Niharika Gajawelli
Meng Law
Helena Chui
Michael Weiner
Yalin Wang

Understanding the extent to which vascular disease and its risk factors are associated with prodromal dementia, notably Alzheimer's disease (AD), may enhance predictive accuracy as well as guide early interventions. One promising avenue to determine this relationship consists of looking for reliable and sensitive in-vivo imaging methods capable of characterizing the subtle brain alterations before the clinical manifestations. However, little is known from the imaging perspective about how risk factors such as vascular disease influence AD progression. Here, for the first time, we apply an innovative T1 and DTI fusion analysis of 3D corpus callosum (CC) on mild cognitive impairment (MCI) populations with different levels of vascular profile, aiming to de-couple the vascular factor in the prodromal AD stage. Our new fusion method successfully increases the detection power for differentiating MCI subjects with high from low vascular risk profiles, as well as from healthy controls. MCI subjects with high and low vascular risk profiles showed differed alteration patterns in the anterior CC, which may help to elucidate the inter-wired relationship between MCI and vascular risk factors.

YNIMG Journal 2017 Journal Article

ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide

Paul M. Thompson
Ole A. Andreassen
Alejandro Arias-Vasquez
Carrie E. Bearden
Premika S. Boedhoe
Rachel M. Brouwer
Randy L. Buckner
Jan K. Buitelaar

In this review, we discuss recent work by the ENIGMA Consortium (http: //enigma. ini. usc. edu) – a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date – of schizophrenia and major depression – ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others 1 1 Abbreviations: ADNI, Alzheimer's Disease Neuroimaging Initiative (http: //www. adni-info. org); CHARGE, the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (http: //www. chargeconsortium. com); IMAGEN, IMAging GENetics Consortium (http: //www. imagen-europe. com). , ENIGMA's genomic screens – now numbering over 30, 000 MRI scans – have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants – and genetic variants in general – may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures – from tens of thousands of people – that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.

YNIMG Journal 2017 Journal Article

FGWAS: Functional genome wide association analysis

Chao Huang
Paul Thompson
Yalin Wang
Yang Yu
Jingwen Zhang
Dehan Kong
Rivka R. Colen
Rebecca C. Knickmeyer

Functional phenotypes (e. g. , subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e. g. , functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30, 000 vertices on the left and right hippocampal surfaces, and 501, 584 SNPs.

YNIMG Journal 2017 Journal Article

Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures

Gang Wang
Yalin Wang

In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i. e. , mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis.

YNIMG Journal 2015 Journal Article

Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry

Jie Shi
Cynthia M. Stonnington
Paul M. Thompson
Kewei Chen
Boris Gutman
Cole Reschke
Leslie C. Baxter
Eric M. Reiman

Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.

YNIMG Journal 2014 Journal Article

Analysis of sampling techniques for imbalanced data: An n = 648 ADNI study

Rashmi Dubey
Jiayu Zhou
Yalin Wang
Paul M. Thompson
Jieping Ye

Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the mild cognitive impairment (MCI) cases eligible for the study are nearly two times the Alzheimer's disease (AD) patients for structural magnetic resonance imaging (MRI) modality and six times the control cases for proteomics modality. Constructing an accurate classifier from imbalanced data is a challenging task. Traditional classifiers that aim to maximize the overall prediction accuracy tend to classify all data into the majority class. In this paper, we study an ensemble system of feature selection and data sampling for the class imbalance problem. We systematically analyze various sampling techniques by examining the efficacy of different rates and types of undersampling, oversampling, and a combination of over and undersampling approaches. We thoroughly examine six widely used feature selection algorithms to identify significant biomarkers and thereby reduce the complexity of the data. The efficacy of the ensemble techniques is evaluated using two different classifiers including Random Forest and Support Vector Machines based on classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity measures. Our extensive experimental results show that for various problem settings in ADNI, (1) a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach; and (2) sparse logistic regression with stability selection achieves competitive performance among various feature selection algorithms. Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results.

YNIMG Journal 2014 Journal Article

Bi-level multi-source learning for heterogeneous block-wise missing data

Shuo Xiang
Lei Yuan
Wei Fan
Yalin Wang
Paul M. Thompson
Jieping Ye

Bio-imaging technologies allow scientists to collect large amounts of high-dimensional data from multiple heterogeneous sources for many biomedical applications. In the study of Alzheimer's Disease (AD), neuroimaging data, gene/protein expression data, etc. , are often analyzed together to improve predictive power. Joint learning from multiple complementary data sources is advantageous, but feature-pruning and data source selection are critical to learn interpretable models from high-dimensional data. Often, the data collected has block-wise missing entries. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), most subjects have MRI and genetic information, but only half have cerebrospinal fluid (CSF) measures, a different half has FDG-PET; only some have proteomic data. Here we propose how to effectively integrate information from multiple heterogeneous data sources when data is block-wise missing. We present a unified “bi-level” learning model for complete multi-source data, and extend it to incomplete data. Our major contributions are: (1) our proposed models unify feature-level and source-level analysis, including several existing feature learning approaches as special cases; (2) the model for incomplete data avoids imputing missing data and offers superior performance; it generalizes to other applications with block-wise missing data sources; (3) we present efficient optimization algorithms for modeling complete and incomplete data. We comprehensively evaluate the proposed models including all ADNI subjects with at least one of four data types at baseline: MRI, FDG-PET, CSF and proteomics. Our proposed models compare favorably with existing approaches.

YNIMG Journal 2013 Journal Article

Applying tensor-based morphometry to parametric surfaces can improve MRI-based disease diagnosis

Yalin Wang
Lei Yuan
Jie Shi
Alexander Greve
Jieping Ye
Arthur W. Toga
Allan L. Reiss
Paul M. Thompson

Many methods have been proposed for computer-assisted diagnostic classification. Full tensor information and machine learning with 3D maps derived from brain images may help detect subtle differences or classify subjects into different groups. Here we develop a new approach to apply tensor-based morphometry to parametric surface models for diagnostic classification. We use this approach to identify cortical surface features for use in diagnostic classifiers. First, with holomorphic 1-forms, we compute an efficient and accurate conformal mapping from a multiply connected mesh to the so-called slit domain. Next, the surface parameterization approach provides a natural way to register anatomical surfaces across subjects using a constrained harmonic map. To analyze anatomical differences, we then analyze the full Riemannian surface metric tensors, which retain multivariate information on local surface geometry. As the number of voxels in a 3D image is large, sparse learning is a promising method to select a subset of imaging features and to improve classification accuracy. Focusing on vertices with greatest effect sizes, we train a diagnostic classifier using the surface features selected by an L1-norm based sparse learning method. Stability selection is applied to validate the selected feature sets. We tested the algorithm on MRI-derived cortical surfaces from 42 subjects with genetically confirmed Williams syndrome and 40 age-matched controls, multivariate statistics on the local tensors gave greater effect sizes for detecting group differences relative to other TBM-based statistics including analysis of the Jacobian determinant and the largest eigenvalue of the surface metric. Our method also gave reasonable classification results relative to the Jacobian determinant, the pair of eigenvalues of the Jacobian matrix and volume features. This analysis pipeline may boost the power of morphometry studies, and may assist with image-based classification.

YNIMG Journal 2013 Journal Article

Maximizing power to track Alzheimer's disease and MCI progression by LDA-based weighting of longitudinal ventricular surface features

Boris A. Gutman
Xue Hua
Priya Rajagopalan
Yi-Yu Chou
Yalin Wang
Igor Yanovsky
Arthur W. Toga
Clifford R. Jack

We propose a new method to maximize biomarker efficiency for detecting anatomical change over time in serial MRI. Drug trials using neuroimaging become prohibitively costly if vast numbers of subjects must be assessed, so it is vital to develop efficient measures of brain change. A popular measure of efficiency is the minimal sample size (n80) needed to detect 25% change in a biomarker, with 95% confidence and 80% power. For multivariate measures of brain change, we can directly optimize n80 based on a Linear Discriminant Analysis (LDA). Here we use a supervised learning framework to optimize n80, offering two alternative solutions. With a new medial surface modeling method, we track 3D dynamic changes in the lateral ventricles in 2065 ADNI scans. We apply our LDA-based weighting to the results. Our best average n80—in two-fold nested cross-validation—is 104 MCI subjects (95% CI: [94, 139]) for a 1-year drug trial, and 75AD subjects [64, 102]. This compares favorably with other MRI analysis methods. The standard “statistical ROI” approach applied to the same ventricular surfaces requires 165 MCI or 94AD subjects. At 2years, the best LDA measure needs only 67 MCI and 52AD subjects, versus 119 MCI and 80AD subjects for the stat-ROI method. Our surface-based measures are unbiased: they give no artifactual additive atrophy over three time points. Our results suggest that statistical weighting may boost efficiency of drug trials that use brain maps.

YNIMG Journal 2013 Journal Article

Surface fluid registration of conformal representation: Application to detect disease burden and genetic influence on hippocampus

Jie Shi
Paul M. Thompson
Boris Gutman
Yalin Wang

In this paper, we develop a new automated surface registration system based on surface conformal parameterization by holomorphic 1-forms, inverse consistent surface fluid registration, and multivariate tensor-based morphometry (mTBM). First, we conformally map a surface onto a planar rectangle space with holomorphic 1-forms. Second, we compute surface conformal representation by combining its local conformal factor and mean curvature and linearly scale the dynamic range of the conformal representation to form the feature image of the surface. Third, we align the feature image with a chosen template image via the fluid image registration algorithm, which has been extended into the curvilinear coordinates to adjust for the distortion introduced by surface parameterization. The inverse consistent image registration algorithm is also incorporated in the system to jointly estimate the forward and inverse transformations between the study and template images. This alignment induces a corresponding deformation on the surface. We tested the system on Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset to study AD symptoms on hippocampus. In our system, by modeling a hippocampus as a 3D parametric surface, we nonlinearly registered each surface with a selected template surface. Then we used mTBM to analyze the morphometry difference between diagnostic groups. Experimental results show that the new system has better performance than two publicly available subcortical surface registration tools: FIRST and SPHARM. We also analyzed the genetic influence of the Apolipoprotein E∈4 allele (ApoE4), which is considered as the most prevalent risk factor for AD. Our work successfully detected statistically significant difference between ApoE4 carriers and non-carriers in both patients of mild cognitive impairment (MCI) and healthy control subjects. The results show evidence that the ApoE genotype may be associated with accelerated brain atrophy so that our work provides a new MRI analysis tool that may help presymptomatic AD research.

YNIMG Journal 2012 Journal Article

Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data

Lei Yuan
Yalin Wang
Paul M. Thompson
Vaibhav A. Narayan
Jieping Ye

Analysis of incomplete data is a big challenge when integrating large-scale brain imaging datasets from different imaging modalities. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), for example, over half of the subjects lack cerebrospinal fluid (CSF) measurements; an independent half of the subjects do not have fluorodeoxyglucose positron emission tomography (FDG-PET) scans; many lack proteomics measurements. Traditionally, subjects with missing measures are discarded, resulting in a severe loss of available information. In this paper, we address this problem by proposing an incomplete Multi-Source Feature (iMSF) learning method where all the samples (with at least one available data source) can be used. To illustrate the proposed approach, we classify patients from the ADNI study into groups with Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal controls, based on the multi-modality data. At baseline, ADNI's 780 participants (172AD, 397 MCI, 211 NC), have at least one of four data types: magnetic resonance imaging (MRI), FDG-PET, CSF and proteomics. These data are used to test our algorithm. Depending on the problem being solved, we divide our samples according to the availability of data sources, and we learn shared sets of features with state-of-the-art sparse learning methods. To build a practical and robust system, we construct a classifier ensemble by combining our method with four other methods for missing value estimation. Comprehensive experiments with various parameters show that our proposed iMSF method and the ensemble model yield stable and promising results.

YNIMG Journal 2011 Journal Article

Surface-based TBM boosts power to detect disease effects on the brain: An N=804 ADNI study

Yalin Wang
Yang Song
Priya Rajagopalan
Tuo An
Krystal Liu
Yi-Yu Chou
Boris Gutman
Arthur W. Toga

Computational anatomy methods are now widely used in clinical neuroimaging to map the profile of disease effects on the brain and its clinical correlates. In Alzheimer's disease (AD), many research groups have modeled localized changes in hippocampal and lateral ventricular surfaces, to provide candidate biomarkers of disease progression for drug trials. We combined the power of parametric surface modeling and tensor-based morphometry to study hippocampal differences associated with AD and mild cognitive impairment (MCI) in 490 subjects (97 AD, 245 MCI, 148 controls) and ventricular differences in 804 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI; 184 AD, 391 MCI, 229 controls). We aimed to show that a new multivariate surface statistic based on multivariate tensor-based morphometry (mTBM) and radial distance provides a more powerful way to detect localized anatomical differences than conventional surface-based analysis. In our experiments, we studied correlations between hippocampal atrophy and ventricular enlargement and clinical measures and cerebrospinal fluid biomarkers. The new multivariate statistics gave better effect sizes for detecting morphometric differences, relative to other statistics including radial distance, analysis of the surface tensor and the Jacobian determinant. In empirical tests using false discovery rate curves, smaller sample sizes were needed to detect associations with diagnosis. The analysis pipeline is generic and automated. It may be applied to analyze other brain subcortical structures including the caudate nucleus and putamen. This publically available software may boost power for morphometric studies of subcortical structures in the brain.

YNIMG Journal 2010 Journal Article

Multivariate tensor-based morphometry on surfaces: Application to mapping ventricular abnormalities in HIV/AIDS

Yalin Wang
Jie Zhang
Boris Gutman
Tony F. Chan
James T. Becker
Howard J. Aizenstein
Oscar L. Lopez
Robert J. Tamburo

Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics—these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain.

EAAI Journal 2009 Journal Article

A two-stage intelligent optimization system for the raw slurry preparing process of alumina sintering production

Chunhua Yang
Weihua Gui
Lingshuang Kong
Yalin Wang

The raw slurry preparing is a key process to guarantee product for alumina sintering production. To obtain the qualified raw slurry in the presence of uncertainty, a two-stage intelligent optimization system, which weakens uncertainty effects through optimization of raw material proportioning and re-mixing operation, is developed. At the first stage, an integrated model combining the first principle with neural networks is built to predict the raw slurry quality, and a multi-objective hierarchical expert reasoning strategy is proposed to determine an optimal set point of raw slurry proportioning. At the second stage, an optimal scheduling model with uncertainty is built to provide an optimal combination of selected tanks for the mixing of raw slurry in full-filled tanks. The practical running results show that the eligibility rate of raw slurry is effectively improved, and the raw slurry preparing process is successfully simplified and the energy consumption is also obviously reduced.

YNIMG Journal 2004 Journal Article

Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia

Paul M. Thompson
Kiralee M. Hayashi
Elizabeth R. Sowell
Nitin Gogtay
Jay N. Giedd
Judith L. Rapoport
Greig I. de Zubicaray
Andrew L. Janke

This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature–nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhood- and adult-onset schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages.

EAAI Journal 2004 Journal Article

Multi-step optimal control of complex process: a genetic programming strategy and its application

Xiaofang Chen
Weihua Gui
Yalin Wang
Lihui Cen

In many industrial processes, especially chemistry and metallurgy industry, the plant is slow for feedback and data test because of complex and varying factors. Considering the multi-objective feature and the complex problem of production stability in optimal control, this paper proposed an optimal control strategy based on genetic programming (GP), used as a multi-step state transferring procedure. The fitness function is computed by multi-step comprehensive evaluation algorithm, which provides a synthetic evaluation of multi-objective in process state based on single objective models. The punishment to process state variance is also introduced for the balance between optimal performance and stability of production. The individuals in GP are constructed as a chain linked by a few relation operators of time sequence for a facilitated evolution in GP with compact individuals. The optimal solution gained by evolution is a multi-step command program of process control, which not only ensures the optimization tendency but also avoids violent process variation by adjusting control parameters step by step. An optimal control system for operation direction is developed based on this strategy for imperial smelting process in Shaoguan. The simulation and application results showed its effectiveness for production objects optimization in complex process control.