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Efficient Static Compaction of Test Patterns Using Partial Maximum Satisfiability

Tsinghua Science and Technology 2021年第1期26卷 1-8页

作者： Huisi Zhou Dantong Ouyang Liming Zhang the Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and TechnologyJilin UniversityChangchun 130012China

Static compaction methods aim at finding unnecessary test patterns to reduce the size of the test set as a post-process of test *** based on partial maximum satisfiability are often used to track many hard problems in various domains,including artificial intelligence,computational biology,data mining,and machine *** observe that part of the test patterns generated by the commercial Automatic Test Pattern Generation(ATPG)tool is redundant,and the relationship between test patterns and faults,as a significant information,can effectively induce the test patterns reduction *** a test pattern can detect one or more faults,we map the problem of static test compaction to a partial maximum satisfiability *** on ISCAS89,ISCAS85,and ITC99 benchmarks show that this approach can reduce the initial test set size generated by TetraMAX18 while maintaining fault coverage.

关键词： test compaction partial maximum satisfiability Automatic Test Pattern Generation(ATPG)

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Hyponymy Graph Model for Word Semantic Similarity Measurement

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Chinese Journal of Electronics 2015年第1期24卷 96-101页

作者： WANG Junhua ZUO Wanli PENG Tao College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering attached to the Ministry of Education

Measuring word semantic similarity is a generic problem with a broad range of applications such as ontology mapping, computational linguistics and artificial intelligence. Previous approaches to computing word semantic similarity did not consider concept occurrence frequency and word’s sense number. This paper introduced Hyponymy graph, and based on which proposed a novel word semantic similarity model. For two words to be compared, we first retrieve their related concepts; then produce lowest common ancestor matrix and distance matrix between concepts; finally calculate distance-based similarity and information-based similarity, which are integrated to get final semantic similarity. The main contribution of our method is that both concept occurrence frequency and word’s sense number are taken into account. This similarity measurement more closely fits with human rating and effectively simulates human thinking process. Our experimental results on benchmark dataset M&C and R&G with Word Net2.1 as platform demonstrate roughly 0.9%–1.2%improvements over existing best approaches.

关键词： Word semantic similarity Concept semantic similarity Semantic similarity Hyponymy graph model Word Net.

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Dilated Convolutional Pixels Affinity Network for Weakly Supervised Semantic Segmentation

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Chinese Journal of Electronics 2021年第6期30卷 1120-1130页

作者： ZHANG Zhe WANG Bilin YU Zhezhou LI Zhiyuan College of Computer Science and Technology Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry

This paper studies semantic segmentation primarily under image-level weak-supervision. Most stateof-the-art technologies have recently used deep classification networks to create small and sparse discriminatory seed regions of each interest target as pseudo-labels for training segmentation networks, which achieve inferior performance compared with the fully supervised *** propose a Dilated convolutional pixels affinity network(DCPAN) to localize and expand the seed regions of objects to bridge this gap. Although introduced dilated convolutional units enable capture of additional location information of objects, it falsely highlighted true negative regions as dilated rate enlarge. To address this problem,we properly integrate dilated convolutional units with different dilated rates and self-attention mechanisms to obtain pixel affinity measure matrix for promoting classification network to generate high-quality object seed regions as pseudo-labels; thus, the performance of the segmentation network is boosted. Furthermore,although our approach seems simple, our method obtains a competitive performance, and experiments show that the performance of DCPAN outperforms other state-ofart approaches in weakly-supervised settings, which only use image-level labels on the Pascal VOC 2012 dataset.

关键词： weakly supervised semantic segmentation pseudolabels deep classification networks high-quality object seed regions pattern classification image segmentation weakly-supervised settings dilated convolutional pixels affinity network image-level weak-supervision image classification DCPAN self-attention mechanism object recognition feature extraction object detection sparse discriminatory seed regions pixel affinity measure matrix Pascal VOC 2012 dataset

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A Novel Iterative Velocity Control Algorithm and Its FPGA Implementation Based on Trigonometric Function

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Chinese Journal of Electronics 2019年第2期28卷 237-245页

作者： WEN Quangang LIANG Yanchun Adriano Tavares Ricardo Teixeira WU Chunguo Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Department of Industrial Electronics University of Minho Campus de Azurm

The servo motor’s flexible acceleration/deceleration(acc/dec) control is an emerging research topic in the automation field. A velocity control algorithm based on trigonometric function is proposed in this *** required parameters, it transforms trigonometric calculations into elementary mathematical operations and calculates the velocity controlling values iteratively, which could avoid trigonometric calculation and reduce the computation time. Experimental results show that the proposed algorithm is suitable for implementation on field programmable gate arrays and achieves a flexible controlling, enhancing both the equipment’s stability and reliability. It is promising to significantly improve the high-speed computerized numerical control equipment’s controlling accuracy, without a huge hardware resource consumption.

关键词： Acceleration Velocity control Algorithm design and analysis Field programmable gate arrays Servomotors

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Deep-Learning-Based Integration of Sequence and Structure Information for Efficiently Predicting miRNA-Drug Associations

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Journal of Chemical Information and Modeling 2025年 2025 May 17页

作者： Sheng, Nan Liu, Yunzhi Gao, Ling Wang, Lei Si, Chenxu Huang, Lan Wang, Yan Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University ChangChun 130012 China

Extensive research has shown that microRNAs (miRNAs) play a crucial role in cancer progression, treatment, and drug resistance. They have been recognized as promising potential therapeutic targets for overcoming drug resistance in cancer treatment. However, limited attention has been paid to predicting the association between miRNAs and drugs by computational methods. Existing approaches typically focus on constructing miRNA-drug interaction graphs, which may result in their performance being limited by interaction density. In this work, we propose a novel deep learning method that integrates sequence and structural information to infer miRNA-drug associations (MDAs), called DLST-MDA. This approach innovates by utilizing attribute information on miRNAs and drugs instead of relying on the commonly used interaction graph information. Specifically, considering the sequence lengths of miRNAs and drugs, DLST-MDA employs multiscale convolutional neural network (CNN) to learn sequence embeddings at different granularity levels from miRNA and drug sequences. Additionally, it leverages the power of graph neural networks to capture structural information from drug molecular graphs, providing a more representational analysis of the drug features. To evaluate DLST-MDA’s effectiveness, we manually constructed a benchmark data set for various experiments based on the latest databases. Results indicate that DLST-MDA performs better than other state-of-the-art methods. Furthermore, case studies of three common anticancer drugs can evidence their usefulness in discovering novel MDAs. The data and source code are released at https://***/sheng-n/DLST-MDA. © 2025 American Chemical Society.

关键词： Drug interactions

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Intelligent CPSS and its application to health care computing

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Science China(Information Sciences) 2016年第5期59卷 10-12页

作者： Dayou LIU Bo YANG Shang GAO Yungang ZHU Yong LAI College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University

The rapid development of cyber-physical systems(CPS)had a tremendous impact on human behavior and *** human involvement,CPS has naturally evolved into cyberphysical social systems(CPSS)[1].The top five technology brea... 详细信息

关键词： CPSS net

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DeepHBSP:A Deep Learning Framework for Predicting Human Blood-Secretory Proteins Using Transfer Learning

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Journal of Computer Science & Technology 2021年第2期36卷 234-247页

作者： Wei Du Yu Sun Hui-Min Bao Liang Chen Ying Li Yan-Chun Liang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and TechnologyJilin UniversityChangchun 130012China Department of Computer Science College of EngineeringShantou UniversityShantou 515063China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Zhuhai 519041China

The identification of blood-secretory proteins and the detection of protein biomarkers in the blood have an important clinical application *** methods for predicting blood-secretory proteins are mainly based on traditional machine learning algorithms,and heavily rely on annotated protein *** traditional machine learning algorithms,deep learning algorithms can automatically learn better feature representations from raw data,and are expected to be more promising to predict blood-secretory *** present a novel deep learning model(DeepHBSP)combined with transfer learning by integrating a binary classification network and a ranking network to identify blood-secretory proteins from the amino acid sequence information *** loss function of DeepHBSP in the training step is designed to apply descriptive loss and compactness loss to the binary classification network and the ranking network,*** feature extraction subnetwork of DeepHBSP is composed of a multi-lane capsule ***,transfer learning is used to train a highly accurate generalized model with small samples of blood-secretory *** main contributions of this study are as follows:1)a novel deep learning architecture by integrating a binary classification network and a ranking network is proposed,superior to existing traditional machine learning algorithms and other state-of-the-art deep learning architectures for biological sequence analysis;2)the proposed model for blood-secretory protein prediction uses only amino acid sequences,overcoming the heavy dependence of existing methods on annotated protein features;3)the blood-secretory proteins predicted by our model are statistically significant compared with existing blood-based biomarkers of cancer.

关键词： blood-secretory protein deep learning capsule network transfer learning

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A Survey on Performance Optimization of High-Level Synthesis Tools

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Journal of Computer Science & Technology 2020年第3期35卷 697-720页

作者： Lan Huang Da-Lin Li Kang-Ping Wang Teng Gao Adriano Tavares College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Zhuhai 519041China Algorithm Center University of MinhoGuimaraes 4800058Portugal

Field-programmable gate arrays(FPGAs)have recently evolved as a valuable component of the heterogeneous *** register transfer level(RTL)design flows demand the designers to be experienced in hardware,resulting in a possible failure of ***-level synthesis(HLS)permits designers to work at a higher level of abstraction through synthesizing high-level language programs to RTL *** provides a promising approach to solve these ***,the performance of HLS tools still has *** example,designers remain exposed to various aspects of hardware design,development cycles are still time consuming,and the quality of results(QoR)of HLS tools is far behind that of RTL *** this paper,we survey the literature published since 2014 focusing on the performance optimization of HLS *** with previous work,we extend the scope of the performance of HLS tools,and present a set of three-level evaluation criteria,covering from ease of use of the HLS tools to promotion on specific metrics of *** also propose performance evaluation equations for describing the relation between the performance optimization and the *** find that it needs more efforts on the ease of use for efficient HLS *** suggest that it is better to draw an analogy between the HLS development process and the embedded system design process,and to provide more elastic HLS methodology which integrates FPGAs virtual machines.

关键词： evaluation criterion field-programmable gate array(FPGA) high-level synthesis(HLS) performance optimization quality of results(QoR)

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Attention Guided Enhancement Network for Weakly Supervised Semantic Segmentation

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Chinese Journal of Electronics 2023年第4期32卷 896-907页

作者： ZHANG Zhe WANG Bilin YU Zhezhou ZHAO Fengzhi College of Computer Science and Technology Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry

Weakly supervised semantic segmentation using only image-level labels is critical since it alleviates the need for expensive pixel-level labels. Most cuttingedge methods adopt two-step solutions that learn to produce pseudo-ground-truth using only image-level labels and then train off-the-shelf fully supervised semantic segmentation network with these pseudo labels. Although these methods have made significant progress, they also increase the complexity of the model and training. In this paper, we propose a one-step approach for weakly supervised image semantic segmentation—attention guided enhancement network(AGEN), which produces pseudopixel-level labels under the supervision of image-level labels and trains the network to generate segmentation masks in an end-to-end manner. Particularly, we employ class activation maps(CAM) produced by different layers of the classification branch to guide the segmentation branch to learn spatial and semantic ***, the CAM produced by the lower layer can capture the complete object region but with many ***, the self-attention module is proposed to enhance object regions adaptively and suppress irrelevant object regions, further boosting the segmentation *** on the Pascal VOC 2012 dataset demonstrate that AGEN outperforms alternative state-of-the-art weakly supervised semantic segmentation methods exclusively relying on image-level labels.

关键词： Training Annotations Semantic segmentation Scalability Semantics Benchmark testing Boosting

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A Novel Locomotion Rule Rmbedding Long Short-Term Memory Network with Attention for Human Locomotor Intent Classification Using Multi-Sensors Signals

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Computers, Materials & Continua 2024年第6期79卷 4349-4370页

作者： Jiajie Shen Yan Wang Dongxu Zhang Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of EducationColleague of Computer Science and TechnologyJilin UniversityChangchun130012China College of Software and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun130012China

Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable *** work have achieved impressive performance in classifying steady locomotion ***,it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion *** to the similarities between the information of the transitions and their adjacent steady ***,most of these methods rely solely on data and overlook the objective laws between physical activities,resulting in lower accuracy,particularly when encountering complex locomotion modes such as *** address the existing deficiencies,we propose the locomotion rule embedding long short-term memory(LSTM)network with Attention(LREAL)for human locomotor intent classification,with a particular focus on transitions,using data from fewer sensors(two inertial measurement units and four goniometers).The LREAL network consists of two levels:One responsible for distinguishing between steady states and transitions,and the other for the accurate identification of locomotor *** classifier in these levels is composed of multiple-LSTM layers and an attention *** introduce real-world motion rules and apply constraints to the network,a prior knowledge was added to the network via a rule-modulating *** method was tested on the ENABL3S dataset,which contains continuous locomotion date for seven steady and twelve transitions *** results showed that the LREAL network could recognize locomotor intents with an average accuracy of 99.03%and 96.52%for the steady and transitions states,*** is worth noting that the LREAL network accuracy for transition-state recognition improved by 0.18%compared to other state-of-the-art network,while using data from fewer sensors.

关键词： Lower-limb prosthetics deep neural networks motion classification

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