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A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring Article

Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui

Engineering 2021, Volume 7, Issue 9,   Pages 1262-1273 doi: 10.1016/j.eng.2020.08.028

Abstract:

Data-driven process-monitoring methods have been the mainstream for complex industrial systems due to their universality and the reduced need for reaction mechanisms and first-principles knowledge. However, most data-driven process-monitoring methods assume that historical training data and online testing data follow the same distribution. In fact, due to the harsh environment of industrial systems, the
collected data from real industrial processes are always affected by many factors, such as the changeable operating environment, variation in the raw materials, and production indexes. These factors often cause the distributions of online monitoring data and historical training data to differ, which induces a model mismatch in the process-monitoring task. Thus, it is difficult to achieve accurate process monitoring when a model learned from training data is applied to actual online monitoring. In order to resolve the problem of the distribution divergence between historical training data and online testing data that is induced by changeable operation environments, a robust transfer dictionary learning (RTDL) algorithm is proposed in this paper for industrial process monitoring. The RTDL is a synergy of representative learning and domain adaptive transfer learning. The proposed method regards historical training data and online testing data as the source domain and the target domain, respectively, in the transfer learning problem. Maximum mean discrepancy regularization and linear discriminant analysis-like regularization are then incorporated into the dictionary learning framework, which can reduce the distribution divergence between the source domain and target domain. In this way, a robust dictionary can be learned even if the characteristics of the source domain and target domain are evidently different under the interference of a realistic and changeable operation environment. Such a dictionary can effectively improve the performance of process monitoring and mode classification. Extensive experiments including a numerical simulation and two industrial systems are conducted to verify the efficiency and superiority of the proposed method.

Keywords： Process monitoring     Multimode process     Dictionary learning     Transfer learning    

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Incentive-Aware Blockchain-Assisted Intelligent Edge Caching and Computation Offloading for IoT Article

Qian Wang, Siguang Chen, Meng Wu

Engineering 2023, Volume 31, Issue 12,   Pages 127-138 doi: 10.1016/j.eng.2022.10.014

Abstract:

The rapid development of artificial intelligence has pushed the Internet of Things (IoT) into a new stage. Facing with the explosive growth of data and the higher quality of service required by users, edge computing and caching are regarded as promising solutions. However, the resources in edge nodes (ENs) are not inexhaustible. In this paper, we propose an incentive-aware blockchain-assisted intelligent edge caching and computation offloading scheme for IoT, which is dedicated to providing a secure and intelligent solution for collaborative ENs in resource optimization and controls. Specifically, we jointly optimize offloading and caching decisions as well as computing and communication resources allocation to minimize the total cost for tasks completion in the EN. Furthermore, a blockchain incentive and contribution co-aware federated deep reinforcement learning algorithm is designed to solve this optimization problem. In this algorithm, we construct an incentive-aware blockchain-assisted collaboration mechanism which operates during local training, with the aim to strengthen the willingness of ENs to participate in collaboration with security guarantee. Meanwhile, a contribution-based federated aggregation method is developed, in which the aggregation weights of EN gradients are based on their contributions, thereby improving the training effect. Finally, compared with other baseline schemes, the numerical results prove that our scheme has an efficient optimization utility of resources with significant advantages in total cost reduction and caching performance.

Keywords： Computation offloading     Caching     Incentive     Blockchain     Federated deep reinforcement learning    

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Ensemble-transfer-learning-based channel parameter prediction in asymmetric massive MIMO systems Research Article

Zunwen HE, Yue LI, Yan ZHANG, Wancheng ZHANG, Kaien ZHANG, Liu GUO, Haiming WANG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 2,   Pages 275-288 doi: 10.1631/FITEE.2200169

Abstract: s have been proposed to reduce the burden of data processing and hardware cost in sixth-generation mobile networks (6G). However, in the asymmetric massive MIMO system, reciprocity between the uplink (UL) and downlink (DL) wireless channels is not valid. As a result, pilots are required to be sent by both the base station (BS) and user equipment (UE) to predict double-directional channels, which consumes more transmission and computational resources. In this paper we propose an ensemble-transfer-learning-based channel method for asymmetric massive MIMO systems. It can predict multiple DL channel parameters including path loss (PL), multipath number, delay spread (DS), and angular spread. Both the UL channel parameters and environment features are chosen to predict the DL parameters. Also, we propose a two-step feature selection algorithm based on the SHapley Additive exPlanations (SHAP) value and the minimum description length (MDL) criterion to reduce the computation complexity and negative impact on model accuracy caused by weakly correlated or uncorrelated features. In addition, the method is introduced to support the prediction model in new propagation conditions, where it is difficult to collect enough training data in a short time. Simulation results show that the proposed method is more accurate than the back propagation neural network (BPNN) and the 3GPP TR 38.901 . Additionally, the proposed instance-transfer-based method outperforms the method without transfer learning in predicting DL parameters when the beamwidth or the communication sector changes.

Keywords： Asymmetric massive multiple-input multiple-output (MIMO) system     Channel model     Ensemble learning     Instance transfer     Parameter prediction    

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Two-level hierarchical feature learning for image classification Article

Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 9,   Pages 897-906 doi: 10.1631/FITEE.1500346

Abstract: In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network (CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count (CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods.

Keywords： Transfer learning     Feature learning     Deep convolutional neural network     Hierarchical classification     Spectral clustering    

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A software defect prediction method with metric compensation based on feature selection and transfer learning Research Article

Jinfu CHEN, Xiaoli WANG, Saihua CAI, Jiaping XU, Jingyi CHEN, Haibo CHEN,caisaih@ujs.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 5,   Pages 715-731 doi: 10.1631/FITEE.2100468

Abstract: Cross-project software solves the problem of insufficient training data for traditional , and overcomes the challenge of applying models learned from multiple different source projects to target project. At the same time, two new problems emerge: (1) too many irrelevant and redundant features in the model training process will affect the training efficiency and thus decrease the prediction accuracy of the model; (2) the distribution of metric values will vary greatly from project to project due to the development environment and other factors, resulting in lower prediction accuracy when the model achieves cross-project prediction. In the proposed method, the Pearson method is introduced to address data redundancy, and the based technique is used to address the problem of large differences in data distribution between the source project and target project. In this paper, we propose a software method with based on and . The experimental results show that the model constructed with this method achieves better results on area under the receiver operating characteristic curve (AUC) value and F1-measure metric.

Keywords： Defect prediction     Feature selection     Transfer learning     Metric compensation    

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Multi-View Point-Based Registration for Native Knee Kinematics Measurement with Feature Transfer Learning Article

Cong Wang, Shuaining Xie, Kang Li, Chongyang Wang, Xudong Liu, Liang Zhao, Tsung-Yuan Tsai

Engineering 2021, Volume 7, Issue 6,   Pages 881-888 doi: 10.1016/j.eng.2020.03.016

Abstract:

Deep-learning methods provide a promising approach for measuring in-vivo knee joint motion from fast registration of two-dimensional (2D) to three-dimensional (3D) data with a broad range of capture. However, if there are insufficient data for training, the data-driven approach will fail. We propose a feature-based transfer-learning method to extract features from fluoroscopic images. With three subjects and fewer than 100 pairs of real fluoroscopic images, we achieved a mean registration success rate of up to 40%. The proposed method provides a promising solution, using a learning-based registration method when only a limited number of real fluoroscopic images is available.

Keywords： 2D–3D registration     Machine learning     Domain adaption     Point correspondence    

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Experimental Study on Relationship Between Mass Loss Rate and Smoke Transportation to the Distant Location in Fires

Feng Wenxing,Yang Lizhong,Fang Tingyong,Huang Rui,Fan Weicheng

Strategic Study of CAE 2005, Volume 7, Issue 1,   Pages 81-85

Abstract:

The various materials as fuel are burnt in an experimental device of Room-Corridor structure, the mass loss rate of which, and the relationship of the mass loss rate with the smoke transportation velocity and CO concentration at a distant location are studied in detail in this paper. It describes the characteristics of mass loss rate of various materials and indicates that the smoke transportation velocity is a linear function of mass loss rate, and is sensitive to the variation of the mass loss rate. It takes a relatively long time for the peak of the toxic species concentration to transport to the distant location.

Keywords： fire     mass loss rate     distant location     transportation velocity     smoke toxicity    

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The Stable Movement of Salty Water Soil and Pattern Researchof Removing the Salt

Zhou Heping,Peng Lixin,Xu Xiaobo

Strategic Study of CAE 2007, Volume 9, Issue 11,   Pages 120-126

Abstract:

Through the research of the water-salt movement under different ground condition for the water saving irrigation in dry area, it has been found that there is the symptom of redistribution of water and salt in certain area:when the evolvement of water and salt movement occurs in vertical and horizontal direction, there is also the important symptom of sideward movement and has certain domino effect. This paper analyses the technology for treatment of the salt soil at home and abroad, and discusses the new mode of salt drainage under both the condition of water-salt directional movement and the condition of salt movement to the earth's surface. The study has the practical meaning for research and discussion on the new method of salt soil amending in dry agricultural area in China.

Keywords： water-salt directional movement     new mode of salt dranage     research and discussion    

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Texture branch network for chronic kidney disease screening based on ultrasound images Research Articles

Peng-yi Hao, Zhen-yu Xu, Shu-yuan Tian, Fu-li Wu, Wei Chen, Jian Wu, Xiao-nan Luo,fuliwu@zjut.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 8,   Pages 1119-1266 doi: 10.1631/FITEE.1900210

Abstract: (CKD) is a widespread renal disease throughout the world. Once it develops to the advanced stage, serious complications and high risk of death will follow. Hence, early screening is crucial for the treatment of CKD. Since ultrasonography has no side effects and enables radiologists to dynamically observe the morphology and pathological features of the kidney, it is commonly used for kidney examination. In this study, we propose a novel convolutional neural network (CNN) framework named the to screen CKD based on images. This introduces a texture branch into a typical CNN to extract and optimize texture features. The model can automatically generate texture features and deep features from input images, and use the fused information as the basis of classification. Furthermore, we train the base part of the network by means of , and conduct experiments on a dataset with 226 images. Experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy of 96.01% and a sensitivity of 99.44%.

Keywords： 慢性肾脏病；超声；纹理分支网络；迁移学习    

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Research Progress and Prospects of Geohazard Mechanism and Risk Prevention Related to Seabed Fluid Migration

Tian Zhaoyang, Jia Yonggang, Zhu Chaoqi, Lu Longyu, Guo Xu, Feng Xuezhi, Wang Hui, Wang Hongwei, He Manchao, Peng Jianbing

Strategic Study of CAE 2023, Volume 25, Issue 3,   Pages 131-140 doi: 10.15302/J-SSCAE-2023.03.012

Abstract:

Seabed fluid migration is a critical process that involves the transport and movement of liquids, gases, and seawater within and outside the seabed, which has significant impacts on the genesis, development, and evolution of seabed geological disasters. Notably, typical disasters such as submarine landslides in the sea area of China demonstrate a strong relevance with seabed fluid migration phenomena. In this paper, we analyze the distribution characteristics of typical fluid migration system types and geological disaster causes taking the northern South China Sea as an example, and we summarize the observation and investigation methods of seabed fluid migration. Furthermore, we propose the primary issues and content that must be addressed in the study of disasters induced by seabed fluid migration and their prevention and control. Specifically, we suggest that research should focus on the three phases, namely disaster genesis induced by deep high-pressure fluid migration, disaster development caused by gas hydrate decomposition and fluid migration, and disaster triggering resulting from ocean water movement. Based on breakthroughs in technological bottlenecks such as multi-system integration, multi-scale cooperation, and multi-dimensional information processing in deep-sea exploration, we must conduct in-depth research on the evolution mechanisms of seabed disaster genesis, development, and triggering under the influence of seabed fluid migration. Additionally, we must develop theoretical  methods for seabed disaster risk prevention and control under the coupled effects of seabed fluid migration, geological environment, and human activities.

Keywords： seabed fluid migration     marine geologic hazards     risk prevention and control     northern South China Sea    

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Federated unsupervised representation learning Research Article

Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 8,   Pages 1181-1193 doi: 10.1631/FITEE.2200268

Abstract: To leverage the enormous amount of unlabeled data on distributed edge devices, we formulate a new problem in called federated unsupervised (FURL) to learn a common representation model without supervision while preserving data privacy. FURL poses two new challenges: (1) data distribution shift (non-independent and identically distributed, non-IID) among clients would make local models focus on different categories, leading to the inconsistency of representation spaces; (2) without unified information among the clients in FURL, the representations across clients would be misaligned. To address these challenges, we propose the federated contrastive averaging with dictionary and alignment (FedCA) algorithm. FedCA is composed of two key modules: a dictionary module to aggregate the representations of samples from each client which can be shared with all clients for consistency of representation space and an alignment module to align the representation of each client on a base model trained on public data. We adopt the contrastive approach for local model training. Through extensive experiments with three evaluation protocols in IID and non-IID settings, we demonstrate that FedCA outperforms all baselines with significant margins.

Keywords： Federated learning     Unsupervised learning     Representation learning     Contrastive learning    

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Driftor: mitigating cloud-based side-channel attacks by switching and migrating multi-executor virtual machines Regular Papers

Chao YANG, Yun-fei GUO, Hong-chao HU, Ya-wen WANG, Qing TONG, Ling-shu LI

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 5,   Pages 731-748 doi: 10.1631/FITEE.1800526

Abstract:

Co-residency of different tenants’ virtual machines (VMs) in cloud provides a good chance for side-channel attacks, which results in information leakage. However, most of current defense suffers from the generality or compatibility problem, thus failing in immediate real-world deployment. VM migration, an inherit mechanism of cloud systems, envisions a promising countermeasure, which limits co-residency by moving VMs between servers. Therefore, we first set up a unified practical adversary model, where the attacker focuses on effective side channels. Then we propose Driftor, a new cloud system that contains VMs of a multi-executor structure where only one executor is active to provide service through a proxy, thus reducing possible information leakage. Active state is periodically switched between executors to simulate defensive effect of VM migration. To enhance the defense, real VM migration is enabled at the same time. Instead of solving the migration satisfiability problem with intractable CIRCUIT-SAT, a greedy-like heuristic algorithm is proposed to search for a viable solution by gradually expanding an initial has-to-migrate set of VMs. Experimental results show that Driftor can not only defend against practical fast side-channel attack, but also bring about reasonable impacts on real-world cloud applications.

Keywords： Cloud computing     Side-channel attack     Information leakage     Multi-executor structure     Virtual machine switch     Virtual machine migration    

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Learning to select pseudo labels: a semi-supervised method for named entity recognition Research Articles

Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6,   Pages 809-962 doi: 10.1631/FITEE.1800743

Abstract: models have achieved state-of-the-art performance in (NER); the good performance, however, relies heavily on substantial amounts of labeled data. In some specific areas such as medical, financial, and military domains, labeled data is very scarce, while is readily available. Previous studies have used to enrich word representations, but a large amount of entity information in is neglected, which may be beneficial to the NER task. In this study, we propose a for NER tasks, which learns to create high-quality labeled data by applying a pre-trained module to filter out erroneous pseudo labels. Pseudo labels are automatically generated for and used as if they were true labels. Our semi-supervised framework includes three steps: constructing an optimal single neural model for a specific NER task, learning a module that evaluates pseudo labels, and creating new labeled data and improving the NER model iteratively. Experimental results on two English NER tasks and one Chinese clinical NER task demonstrate that our method further improves the performance of the best single neural model. Even when we use only pre-trained static word embeddings and do not rely on any external knowledge, our method achieves comparable performance to those state-of-the-art models on the CoNLL-2003 and OntoNotes 5.0 English NER tasks.

Keywords： 命名实体识别；无标注数据；深度学习；半监督学习方法    

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New directions for artificial intelligence: human, machine, biological, and quantum intelligence Comment

Li WEIGANG,Liriam Michi ENAMOTO,Denise Leyi LI,Geraldo Pereira ROCHA FILHO

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 6,   Pages 984-990 doi: 10.1631/FITEE.2100227

Abstract:

This comment reviews the “once learning” mechanism (OLM) that was proposed byWeigang (1998), the subsequent success of “one-shot learning” in object categories (Li FF et al., 2003), and “you only look once” (YOLO) in objective detection (Redmon et al., 2016). Upon analyzing the current state of research in artificial intelligence (AI), we propose to divide AI into the following basic theory categories: artificial human intelligence (AHI), artificial machine intelligence (AMI), artificial biological intelligence (ABI), and artificial quantum intelligence (AQI). These can also be considered as the main directions of research and development (R&D) within AI, and distinguished by the following classification standards and methods: (1) human-, machine-, biological-, and quantum-oriented AI R&D; (2) information input processed by dimensionality increase or reduction; (3) the use of one/a few or a large number of samples for knowledge learning.

Keywords： 人工智能；机器学习；一次性学习；一瞥学习；量子计算    

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Engineering philosophical thinking of the old mining area transformation development

Jin Zhixin

Strategic Study of CAE 2014, Volume 16, Issue 10,   Pages 64-70

Abstract:

The transformation development of the old mining area is a complex system engineering, it needs to command the overall situation by standing on the height of system engineering and needs to follow the harmonization in engineering, nature, science, technology, industry, economy and society. Engineering innovation is the main part of the technology innovations, it pushes the development of scientific, technical, industrial, economic and social, also the engineering innovation is relied on scientific and technological progress. Ecological protection is the premise of engineering development, and the ecological engineering construction is promoted by economic development. The protecting and mining of rare coking coal resources is a unity of oppisites. The companies of producting coking coal must pursue both immediate and long-term interests, especially the old mining, so the company should resolve the conflict between the scarcity of resources and the growth of demands by implementing limited exploitation, increasing resource recovery and efficiently recycling of coking coal resources to build the big-vertical-deep industry group. The conflict between frequent accidents and high input of existing security control promotes the development of security theory and technology, the transformation of security theory and technology from system security to structural security is a inevitable process of development.

Keywords： engineering philosophical     asymmetric development     green migration     big-vertical-deep industry group     protective mining     security structure theory    

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Title Author Date Type Operation

A Robust Transfer Dictionary Learning Algorithm for Industrial Process Monitoring

Chunhua Yang, Huiping Liang, Keke Huang, Yonggang Li, Weihua Gui

Journal Article

Incentive-Aware Blockchain-Assisted Intelligent Edge Caching and Computation Offloading for IoT

Qian Wang, Siguang Chen, Meng Wu

Journal Article

Ensemble-transfer-learning-based channel parameter prediction in asymmetric massive MIMO systems

Zunwen HE, Yue LI, Yan ZHANG, Wancheng ZHANG, Kaien ZHANG, Liu GUO, Haiming WANG

Journal Article

Two-level hierarchical feature learning for image classification

Guang-hui SONG,Xiao-gang JIN,Gen-lang CHEN,Yan NIE

Journal Article

A software defect prediction method with metric compensation based on feature selection and transfer learning

Jinfu CHEN, Xiaoli WANG, Saihua CAI, Jiaping XU, Jingyi CHEN, Haibo CHEN,caisaih@ujs.edu.cn

Journal Article

Multi-View Point-Based Registration for Native Knee Kinematics Measurement with Feature Transfer Learning

Cong Wang, Shuaining Xie, Kang Li, Chongyang Wang, Xudong Liu, Liang Zhao, Tsung-Yuan Tsai

Journal Article

Experimental Study on Relationship Between Mass Loss Rate and Smoke Transportation to the Distant Location in Fires

Feng Wenxing,Yang Lizhong,Fang Tingyong,Huang Rui,Fan Weicheng

Journal Article

The Stable Movement of Salty Water Soil and Pattern Researchof Removing the Salt

Zhou Heping,Peng Lixin,Xu Xiaobo

Journal Article

Texture branch network for chronic kidney disease screening based on ultrasound images

Peng-yi Hao, Zhen-yu Xu, Shu-yuan Tian, Fu-li Wu, Wei Chen, Jian Wu, Xiao-nan Luo,fuliwu@zjut.edu.cn

Journal Article

Research Progress and Prospects of Geohazard Mechanism and Risk Prevention Related to Seabed Fluid Migration

Tian Zhaoyang, Jia Yonggang, Zhu Chaoqi, Lu Longyu, Guo Xu, Feng Xuezhi, Wang Hui, Wang Hongwei, He Manchao, Peng Jianbing

Journal Article

Federated unsupervised representation learning

Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI,fdzhang@zju.edu.cn,kunkuang@zju.edu.cn

Journal Article

Driftor: mitigating cloud-based side-channel attacks by switching and migrating multi-executor virtual machines

Chao YANG, Yun-fei GUO, Hong-chao HU, Ya-wen WANG, Qing TONG, Ling-shu LI

Journal Article

Learning to select pseudo labels: a semi-supervised method for named entity recognition

Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn

Journal Article

New directions for artificial intelligence: human, machine, biological, and quantum intelligence

Li WEIGANG,Liriam Michi ENAMOTO,Denise Leyi LI,Geraldo Pereira ROCHA FILHO

Journal Article

Engineering philosophical thinking of the old mining area transformation development

Jin Zhixin

Journal Article