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

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

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

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

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

Artificial-Noise-Aided Secure Transmission with Directional Modulation based on Random Frequency Diverse Arrays

In this paper, a random frequency diverse array based directional modulation with artificial noise (RFDA-DMAN) scheme is proposed to enhance physical layer security of wireless communications. Specifically, we first design the RFDADM- AN scheme by randomly allocating frequencies to transmitantennas, thereby achieving two-dimensionally (i.e., angle and range) secure transmissions, and outperforming the state-of-theart one-dimensional (i.e., angle) phase array (PA) based DM scheme. Then we derive the closed-form expression of a lower bound on the ergodic secrecy capacity (ESC) of our RFDA-DMAN scheme. Based on the theoretical lower bound derived, we further optimize the transmission power allocation between the useful signal and artificial noise (AN) in order to improve the ESC. Simulation results show that 1) our RFDA-DM-AN scheme achieves a higher secrecy capacity than that of the PA based DM scheme, 2) the lower bound derived is shown to approach the ESC as the number of transmit antennas N increases and precisely matches the ESC when N is sufficiently large, and 3) the proposed optimum power allocation achieves the highest ESC of all power allocations schemes in the RFDA-DM-AN