| 1. |
- Abdeljaber, Osama, et al.
(författare)
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1-D CNNs for structural damage detection : verification on a structural health monitoring benchmark data
- 2018
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Ingår i: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 275, s. 1308-1317
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Tidskriftsartikel (refereegranskat)abstract
- Structural damage detection has been an interdisciplinary area of interest for various engineering fields. While the available damage detection methods have been in the process of adapting machine learning concepts, most machine learning based methods extract “hand-crafted” features which are fixed and manually selected in advance. Their performance varies significantly among various patterns of data depending on the particular structure under analysis. Convolutional neural networks (CNNs), on the other hand, can fuse and simultaneously optimize two major sets of an assessment task (feature extraction and classification) into a single learning block during the training phase. This ability not only provides an improved classification performance but also yields a superior computational efficiency. 1D CNNs have recently achieved state-of-the-art performance in vibration-based structural damage detection; however, it has been reported that the training of the CNNs requires significant amount of measurements especially in large structures. In order to overcome this limitation, this paper presents an enhanced CNN-based approach that requires only two measurement sets regardless of the size of the structure. This approach is verified using the experimental data of the Phase II benchmark problem of structural health monitoring which had been introduced by IASC-ASCE Structural Health Monitoring Task Group. As a result, it is shown that the enhanced CNN-based approach successfully estimated the actual amount of damage for the nine damage scenarios of the benchmark study.
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| 2. |
- Abiri, Najmeh, et al.
(författare)
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Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems
- 2019
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Ingår i: Neurocomputing. - Amsterdam : Elsevier BV. - 0925-2312 .- 1872-8286. ; 365, s. 137-146
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Tidskriftsartikel (refereegranskat)abstract
- Dealing with missing data in data analysis is inevitable. Although powerful imputation methods that address this problem exist, there is still much room for improvement. In this study, we examined single imputation based on deep autoencoders, motivated by the apparent success of deep learning to efficiently extract useful dataset features. We have developed a consistent framework for both training and imputation. Moreover, we benchmarked the results against state-of-the-art imputation methods on different data sizes and characteristics. The work was not limited to the one-type variable dataset; we also imputed missing data with multi-type variables, e.g., a combination of binary, categorical, and continuous attributes. To evaluate the imputation methods, we randomly corrupted the complete data, with varying degrees of corruption, and then compared the imputed and original values. In all experiments, the developed autoencoder obtained the smallest error for all ranges of initial data corruption.
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| 3. |
- Berg, Jens, 1982-, et al.
(författare)
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A unified deep artificial neural network approach to partial differential equations in complex geometries
- 2018
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Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 317, s. 28-41
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we use deep feedforward artificial neural networks to approximate solutions to partial differential equations in complex geometries. We show how to modify the backpropagation algorithm to compute the partial derivatives of the network output with respect to the space variables which is needed to approximate the differential operator. The method is based on an ansatz for the solution which requires nothing but feedforward neural networks and an unconstrained gradient based optimization method such as gradient descent or a quasi-Newton method. We show an example where classical mesh based methods cannot be used and neural networks can be seen as an attractive alternative. Finally, we highlight the benefits of deep compared to shallow neural networks and device some other convergence enhancing techniques.
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| 4. |
- Dunin-Keplicz, Barbara, et al.
(författare)
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Paraconsistent semantics of speech acts
- 2015
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Ingår i: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 151:2, s. 943-952
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Tidskriftsartikel (refereegranskat)abstract
- This paper discusses an implementation of four speech acts: assert, concede, request and challenge in a paraconsistent framework. A natural four-valued model of interaction yields multiple new cognitive situations. They are analyzed in the context of communicative relations, which partially replace the concept of trust. These assumptions naturally lead to six types of situations, which often require performing conflict resolution and belief revision. The particular choice of a rule-based, DATALOC. like query language 4QL as a four-valued implementation framework ensures that, in contrast to the standard two-valued approaches, tractability of the model is achieved.
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| 5. |
- Fu, Keren, et al.
(författare)
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Deepside: A general deep framework for salient object detection
- 2019
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Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 356, s. 69-82
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning-based salient object detection techniques have shown impressive results compared to con- ventional saliency detection by handcrafted features. Integrating hierarchical features of Convolutional Neural Networks (CNN) to achieve fine-grained saliency detection is a current trend, and various deep architectures are proposed by researchers, including “skip-layer” architecture, “top-down” architecture, “short-connection” architecture and so on. While these architectures have achieved progressive improve- ment on detection accuracy, it is still unclear about the underlying distinctions and connections between these schemes. In this paper, we review and draw underlying connections between these architectures, and show that they actually could be unified into a general framework, which simply just has side struc- tures with different depths. Based on the idea of designing deeper side structures for better detection accuracy, we propose a unified framework called Deepside that can be deeply supervised to incorporate hierarchical CNN features. Additionally, to fuse multiple side outputs from the network, we propose a novel fusion technique based on segmentation-based pooling, which severs as a built-in component in the CNN architecture and guarantees more accurate boundary details of detected salient objects. The effectiveness of the proposed Deepside scheme against state-of-the-art models is validated on 8 benchmark datasets.
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| 6. |
- Fu, Keren, 1988, et al.
(författare)
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Robust manifold-preserving diffusion-based saliency detection by adaptive weight construction
- 2016
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Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 175:Part A, s. 336-347
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Tidskriftsartikel (refereegranskat)abstract
- Graph-based diffusion techniques have drawn much interest lately for salient object detection. The diffusion performance is heavily dependent on the edge weights in graph representing the similarity between nodes, and are usually set through manually tuning. To improve the diffusion performance, this paper proposes a robust diffusion scheme, referred to as manifold-preserving diffusion (MPD), that is built jointly on two assumptions for preserving the manifold used in saliency detection. The smoothness assumption reflects the conditional random field (CRF) property and the related penalty term enforces similar saliency on similar graph neighbors. The penalty term related to the local reconstruction assumption enforces a local linear mapping from the feature space to saliency values. Graph edge weights in the above two penalties in the proposed MPD method are determined adaptively by minimizing local reconstruction errors in feature space. This enables a better adaption of diffusion on different images. The final diffusion process is then formulated as a regularized optimization problem, taking into account of initial seeds, manifold smoothness and local reconstruction. Consequently, when applied to saliency diffusion, MPD provides a higher performance upper bound than some existing diffusion methods such as manifold ranking. By utilizing MPD, we further introduce a two-stage saliency detection scheme, referred to as manifold-preserving diffusion-based saliency (MPDS), where boundary prior, Harris convex hull, and foci convex hull are employed for deriving initial seeds and a coarse map for MPD. Experiments were conducted on five benchmark datasets and compared with eight existing methods. Our results show that the proposed method is robust in terms of consistently achieving the highest weighted F-measure and lowest mean absolute error, meanwhile maintaining comparable precision–recall curves. Salient objects in different background can be uniformly highlighted in the output final saliency maps.
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| 7. |
- Fu, Keren, 1988, et al.
(författare)
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Spectral salient object detection
- 2018
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Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 275, s. 788-803
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Tidskriftsartikel (refereegranskat)abstract
- Many salient object detection methods first apply pre-segmentation on image to obtain over-segmented regions to facilitate subsequent saliency computation. However, these pre-segmentation methods often ignore the holistic issue of objects and could degrade object detection performance. This paper proposes a novel method, spectral salient object detection, that aims at maintaining objects holistically during pre-segmentation in order to provide more reliable feature extraction from a complete object region and to facilitate object-level saliency estimation. In the proposed method, a hierarchical spectral partition method based on the normalized graph cut (Ncut) is proposed for image segmentation phase in saliency detection, where a superpixel graph that captures the intrinsic color and edge information of an image is constructed and then hierarchically partitioned. In each hierarchy level, a region constituted by superpixels is evaluated by criteria based on figure-ground principles and statistical prior to obtain a regional saliency score. The coarse salient region is obtained by integrating multiple saliency maps from successive hierarchies. The final saliency map is derived by minimizing the graph-based semi-supervised learning energy function on the synthetic coarse saliency map. Despite the simple intuition of maintaining object holism, experimental results on 5 benchmark datasets including ASD, ECSSD, MSRA, PASCAL-S, DUT-OMRON demonstrate encouraging performance of the proposed method, along with the comparisons to 13 state-of-the-art methods. The proposed method is shown to be effective on emphasizing large/medium-sized salient objects uniformly due to the employment of Ncut. Besides, we conduct thorough analysis and evaluation on parameters and individual modules.
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| 8. |
- Ge, Chenjie, 1991, et al.
(författare)
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Multi-Stream Multi-Scale Deep Convolutional Networks for Alzheimer's Disease Detection using MR Images
- 2019
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Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 350, s. 60-69
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses the issue of Alzheimer's disease (AD) detection from Magnetic Resonance Images (MRIs). Existing AD detection methods rely on global feature learning from the whole brain scans, while depending on the tissue types, AD related features in dierent tissue regions, e.g. grey matter (GM), white matter (WM), and cerebrospinal uid (CSF), show different characteristics. In this paper, we propose a deep learning method for multi-scale feature learning based on segmented tissue areas. A novel deep 3D multi-scale convolutional network scheme is proposed to generate multi-resolution features for AD detection. The proposed scheme employs several parallel 3D multi-scale convolutional networks, each applying to individual tissue regions (GM, WM and CSF) followed by feature fusions. The proposed fusion is applied in two separate levels: the rst level fusion is applied on different scales within the same tissue region, and the second level is on dierent tissue regions. To further reduce the dimensions of features and mitigate overtting, a feature boosting and dimension reduction method, XGBoost, is utilized before the classication. The proposed deep learning scheme has been tested on a moderate open dataset of ADNI (1198 scans from 337 subjects), with excellent test performance on randomly partitioned datasets (best 99.67%, average 98.29%), and good test performance on subject-separated partitioned datasets (best 94.74%, average 89.51%). Comparisons with state-of-the-art methods are also included.
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| 9. |
- Huang, Jin, et al.
(författare)
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State of the art on road traffic sensing and learning based on mobile user network log data
- 2018
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Ingår i: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 278, s. 110-118
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Tidskriftsartikel (refereegranskat)abstract
- With the improvement of the storage and big data processing technology, mobile operators are able to extract and store a large amount of mobile network generated user behavior data, in order to develop various intelligent applications. One interesting application based on these data is traffic sensing, which uses techniques of learning human mobility patterns from updated location information in network interaction log data. Mobile networks, under which a huge amount of frequently updated location information of mobile users are tracked, can provide complete coverage to estimate traffic condition on roads and highways. This paper studies potential challenges and opportunities in intelligent traffic sensing from the data science point of view with mobile network generated data. Firstly, we classify the data resources available in the commercial radio network according to different taxonomy criteria. Then we outline the broken-down problems that fit in the framework of traffic sensing based on mobile user network log data. We study the existing data processing and learning algorithms on extracting traffic condition information from a large amount of mobile network log data. Finally we make suggestion on potential future work for traffic sensing on data from mobile networks. We believe the techniques and insights provided here will inspire the research community in data science to develop the machine learning models of traffic sensing on the widely collected mobile user behavior data.
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| 10. |
- Lin, Di, et al.
(författare)
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Neural Networks for Computer-Aided Diagnosis in Medicine : a review
- 2016
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Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 216, s. 700-708
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Tidskriftsartikel (refereegranskat)abstract
- This survey makes an overview of the most recent applications on the neural networks for the computer-aided medical diagnosis (CAMD) over the past decade. CAMD can facilitate the automation of decision making, extraction and visualization of complex characteristics for clinical diagnosis purposes. Over the past decade, neural networks have attained considerable research interest and are widely employed to complex CAMD systems in diverse clinical application domains, such as detecting diseases, classification of diseases, testing the compatibility of new drugs, etc. Overall, this paper reviews the state-of-the-art of neural networks for CAMD. It helps the readers understand the topic of neural networks for CAMD by summarizing the findings addressed in recent academic papers as well as presenting a few open issues of developing the research on this topic.
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