Digital condition monitoring for wider blue economy.

In the process of decommissioning energy systems, condition monitoring is crucial. It can make the health status of offshore oil and gas installations, pipelines, wind farms etc. transparent to policymakers and stakeholders, and aid them in creating a better repurposing plan for the assets that will be decommissioned to create a sustainable ocean economy. In most cases, condition monitoring calls for experienced engineers to perform on-site testing, which raises labour costs as well as commuter carbon emissions (M.J. Hasan & Kim, 2019; Rai et al., 2021). A revolution in decarbonised and sustainable decommissioning may result from further digitalisation of condition monitoring to address this problem. We can gather and manipulte enormous amounts of real-time data, and create a simulated representation of physical assets. We can then quickly predict their health conditions by combining artificial intelligence, the Internet of Things, and augmented-, virtual- and mixed reality techniques (M.J. Hasan et al., 2019; Yan et al. 2018, 2020, 2021). Digital condition monitoring has social and economic benefits, including: 1) Delivering a plausible innovation that can be successfully used in other UK industries; 2) Opening a new high-tech talent demand market in the UK; 3) Reducing carbon emissions of decommissioning projects, especially for the marine environment; 4) Reshaping the offshore marine environment to benefit the blue economy; 5) Reducing costs across the decommissioning chain, from design and manufacturing to purchasing and maintenance

Service Failure Complaints Identification in Social Media: A Text Classification Approach

The emergence of social media has brought up plenty of platforms where dissatisfied customers can share their service encounter experiences. Those customers’ feedbacks have been widely recognized as valuable information sources for improving service quality. Due to the sparse distribution of customer complaints and diversity of topics related to non-complaints in social media, manually identifying complaints is time-consuming and inefficient. In this study, a supervised learning approach including samples enlargement and classifiers construct was proposed. Applying small labeled samples as training samples, reliable complaints samples and non-complaints samples were identified from the unlabeled dataset during the sample enlargement process. Combining the enlarged samples and the labeled samples, SVM and KNN algorithms were employed to construct the classifier. Empirical results show that the proposed approach can efficiently distinguish complaints from non-complaints in social media, especially when the number of labeled samples is very small

Nondestructive quantitative measurement for precision quality control in additive manufacturing using hyperspectral imagery and machine learning.

Measuring the purity of the metal powder is essential to maintain the quality of additive manufacturing products. Contamination is a significant concern, leading to cracks and malfunctions in the final products. Conventional assessment methods focus more on physical integrity rather than material composition and can be time-consuming. By capturing spectral data from a wide frequency range along with the spatial information, hyperspectral imaging (HSI) can detect minor differences in terms of temperature, moisture, and chemical composition to tackle this challenge. In this article, we explore the application of HSI in conjunction with machine learning for nondestructive inspection of metal powders. By employing near-infrared and visible HSI cameras, we introduce the utilization of HSI for this purpose. We delve into the technical challenges encountered and present detailed solutions through three case studies, including the establishment of a spectral dictionary, contamination detection, and band selection analysis. Our experimental results demonstrate the immense potential of HSI and its synergy with machine learning for nondestructive testing in powder metallurgy, particularly in meeting the requirements of industrial manufacturing environments

Train the Neural Network by Abstract Images

Like the textbook for students\u27 learning, the training data plays a significant role in the network\u27s training. In most cases, people intend to use big-data to train the network, which leads to two problems. Firstly, the knowledge learned by the network is out of control. Secondly, the space occupation of big-data is huge. In this paper, we use the concepts-based knowledge visualization [33] to visualize the knowledge learned by the model. Based on the observation results and information theory, we make three conjectures about the key information provided by the dataset. Finally, we use experiments to prove that the artificial abstracted data can be used in networks\u27 training, which can solve the problem mentioned above. The experiment is designed based on Mask-RCNN, which is used to detect and classify three typical human poses on the construction site

Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment

Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction. In this paper, we built a system to creat a semantic 3D map by combining 3D point cloud from ORB SLAM with semantic segmentation information from Convolutional Neural Network model PSPNet-101 for large-scale environments. Besides, a new dataset for KITTI sequences has been built, which contains the GPS information and labels of landmarks from Google Map in related streets of the sequences. Moreover, we find a way to associate the real-world landmark with point cloud map and built a topological map based on semantic map.Comment: Accepted by 2019 China Symposium on Cognitive Computing and Hybrid Intelligence(CCHI'19

Crowdsourced quality assessment of enhanced underwater images: a pilot study.

Underwater image enhancement (UIE) is essential for a high-quality underwater optical imaging system. While a number of UIE algorithms have been proposed in recent years, there is little study on image quality assessment (IQA) of enhanced underwater images. In this paper, we conduct the first crowdsourced subjective IQA study on enhanced underwater images. We chose ten state-of-the-art UIE algorithms and applied them to yield enhanced images from an underwater image benchmark. Their latent quality scales were reconstructed from pair comparison. We demonstrate that the existing IQA metrics are not suitable for assessing the perceived quality of enhanced underwater images. In addition, the overall performance of 10 UIE algorithms on the benchmark is ranked by the newly proposed simulated pair comparison of the methods

Fusion of dominant colour and spatial layout features for effective image retrieval of coloured logos and trademarks

Due to its uniqueness and high value in commercial side, logos and trademarks play a key role in e-business based global marketing. Detecting misused and faked logos need designated and accurate image processing and retrieval techniques. However, existing colour and shape based retrieval techniques, which are mainly designed for natural images, cannot provide effective retrieval of logo images. In this paper, an effective approach is proposed for content-based image retrieval of coloured logos and trademarks. By extracting the dominant colour from colour quantization and measuring the spatial similarity, fusion of colour and spatial layout features is achieved. The proposed approach has been tested on a database containing over 250 logo images. Experimental results show that the proposed methodology yields more accurate results in retrieving relevant images than conventional approaches even with added Gaussian and Salt&pepper noise

Printed texture guided color feature fusion for impressionism style rendering of oil paintings.

As a major branch of Non-Photorealistic Rendering (NPR), image stylization mainly uses computer algorithms to render a photo into an artistic painting. Recent work has shown that the ex-traction of style information such as stroke texture and color of the target style image is the key to image stylization. Given its stroke texture and color characteristics, a new stroke rendering method is proposed. By fully considering the tonal characteristics and the representative color of the original oil painting, it can fit the tone of the original oil painting image into a stylized image whilst keeping the artist's creative effect. The experiments have validated the efficacy of the proposed model in comparison to three state-of-the-arts. This method would be more suitable for the works of pointillism painters with a relatively uniform style, especially for natural scenes, otherwise, the results can be less satisfactory

A music cognition-guided framework for multi-pitch estimation.

As one of the most important subtasks of automatic music transcription (AMT), multi-pitch estimation (MPE) has been studied extensively for predicting the fundamental frequencies in the frames of audio recordings during the past decade. However, how to use music perception and cognition for MPE has not yet been thoroughly investigated. Motivated by this, this demonstrates how to effectively detect the fundamental frequency and the harmonic structure of polyphonic music using a cognitive framework. Inspired by cognitive neuroscience, an integration of the constant Q transform and a state-of-the-art matrix factorization method called shift-invariant probabilistic latent component analysis (SI-PLCA) are proposed to resolve the polyphonic short-time magnitude log-spectra for multiple pitch estimation and source-specific feature extraction. The cognitions of rhythm, harmonic periodicity and instrument timbre are used to guide the analysis of characterizing contiguous notes and the relationship between fundamental frequency and harmonic frequencies for detecting the pitches from the outcomes of SI-PLCA. In the experiment, we compare the performance of proposed MPE system to a number of existing state-of-the-art approaches (seven weak learning methods and four deep learning methods) on three widely used datasets (i.e. MAPS, BACH10 and TRIOS) in terms of F-measure (F1) values. The experimental results show that the proposed MPE method provides the best overall performance against other existing methods

SSA-LHCD: a singular spectrum analysis-driven lightweight network with 2-D self-attention for hyperspectral change detection.

As an emerging research hotspot in contemporary remote sensing, hyperspectral change detection (HCD) has attracted increasing attention in remote sensing Earth observation, covering land mapping changes and anomaly detection. This is primarily attributable to the unique capacity of hyperspectral imagery (HSI) to amalgamate both the spectral and spatial information in the scene, facilitating a more exhaustive analysis and change detection on the Earth's surface, proving to be successful across diverse domains, such as disaster monitoring and geological surveys. Although numerous HCD algorithms have been developed, most of them face three major challenges: (i) susceptibility to inherent data noise, (ii) inconsistent accuracy of detection, especially when dealing with multi-scale changes, and (iii) extensive hyperparameters and high computational costs. As such, we propose a singular spectrum analysis-driven-lightweight network for HCD, where three crucial components are incorporated to tackle these challenges. Firstly, singular spectrum analysis (SSA) is applied to alleviate the effect of noise. Next, a 2-D self-attention-based spatial–spectral feature-extraction module is employed to effectively handle multi-scale changes. Finally, a residual block-based module is designed to effectively extract the spectral features for efficiency. Comprehensive experiments on three publicly available datasets have fully validated the superiority of the proposed SSA-LHCD model over eight state-of-the-art HCD approaches, including four deep learning models