InsightiGen: a versatile tool to generate insight for an academic systematic literature review

A comprehensive literature review has always been an essential first step of every meaningful research. In recent years, however, the availability of a vast amount of information in both open-access and subscription-based literature in every field has made it difficult, if not impossible, to be certain about the comprehensiveness of one's survey. This subsequently can lead to reviewers' questioning of the novelties of the research directions proposed, regardless of the quality of the actual work presented. In this situation, statistics derived from the published literature data can provide valuable quantitative and visual information about research trends, knowledge gaps, and research networks and hubs in different fields. Our tool provides an automatic and rapid way of generating insight for systematic reviews in any research area.Comment: 15 pages, 5 figure

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less than the normal data samples. At the same time, in an industrial condition, accelerometers encounter high levels of disruptive signals and the collected samples turn out to be heavily noisy. As a consequence, many traditional Fault Detection and Diagnosis (FDD) frameworks get poor classification performances when dealing with real-world circumstances. Three main solutions have been proposed in the literature to cope with this problem: (1) the implementation of generative algorithms to increase the amount of under-represented input samples, (2) the employment of a classifier being powerful to learn from imbalanced and noisy data, (3) the development of an efficient data pre-processing including feature extraction and data augmentation. This paper proposes a hybrid framework which uses the three aforementioned components to achieve an effective signal-based FDD system for imbalanced conditions. Specifically, it first extracts the fault features, using Fourier and wavelet transforms to make full use of the signals. Then, it employs Wasserstein Generative Adversarial Networks (WGAN) to generate synthetic samples to populate the rare fault class and enhance the training set. Moreover, to achieve a higher performance a novel combination of Convolutional Long Short-term Memory (CLSTM) and Weighted Extreme Learning Machine (WELM) is proposed. To verify the effectiveness of the developed framework, different datasets settings on different imbalance severities and noise degrees were used. The comparative results demonstrate that in different scenarios GAN-CLSTM-ELM outperforms the other state-of-the-art FDD frameworks.Comment: 23 pages, 11 figure

An Innovative Approach to Intraoperative Quality Assurance for Low Dose Rate Brachytherapy

BrachyView is a novel in-body imaging system, aimed to accurately localise brachytherapy sources using high-resolution pixelated silicon detectors and a pinhole collimator. In the recent years, many research projects have studied the different possibilities for real-time, intra-operative, dynamic dose treatment planning to increase the quality of brachytherapy implants. The capabilities of this pinhole camera were tested through a proof of concept study using four active seeds. A more clinically realistic scenario, using twenty active seeds implanted in a PMMA phantom, was the clear next step. To further imitate a clinical scenario, 20 seeds were implanted and imaged using a single pinhole lead collimator with a diameter of 400 μm. BrachyView was successful at locating the seeds within 1-2 mm of their expected positions which was verified via co-registration with a full clinical post-implant CT scan with 0.8 mm width. The first BrachyView prototype to feature a triple-chip detector embedded within a tungsten collimator with three single cone pinholes was used to localise 30 active seeds embedded within 11 needles, implanted in a soft gel prostate phantom, under ultrasound guidance. For verification, a full post implant CT was also performed. The BrachyView was able to accurately resolve the all the seeds with a maximum discrepancy of 1.78 m

A Conceptual Framework for Localization of Active Sound Sources in Manufacturing Environment Based on Artificial Intelligence

Sound source localization (SSL) is aimed at locating the source of a sound in a space and has been used for decades in many applications, such as robotics, room acoustic analysis, voice communication, and medicine. The main advantages of sound-based methods are their low cost, since they require only a set of microphones, and their high precision in sound source detection due to the possibility of sound penetration through barriers. Although SSL methods have been used in robotics in rescue missions and human-robot interaction, they have not been implemented yet in manufacturing environments, even though the advent of Industry 4.0 and 5.0 manufacturing sectors would benefit greatly from intelligent tools like SSL to make industrial areas smarter. In this paper, a new framework based on SSL is proposed to identify active sound sources like human operators, mobile robots, and machinery in the manufacturing area which can enhance the awareness of a multi-agent system. In our approach, the sound source is estimated through a source region location system based on a Convolutional LSTM method. To make the framework more realistic, a three-stage procedure is proposed, where in the first step only a human and a robot are considered, in the second scenario an asset is added, and in the final stage multiple sound sources are included in the workplace. The proposed framework can improve occupational safety and enhance the cooperation between a human and robot agents in an industrial system

CoV-ABM: A stochastic discrete-event agent-based framework to simulate spatiotemporal dynamics of COVID-19

The paper develops a stochastic Agent-Based Model (ABM) mimicking the spread of infectious diseases in geographical domains. The model is designed to simulate the spatiotemporal spread of SARS-CoV2 disease, known as COVID-19. Our SARS-CoV2-based ABM framework (CoV-ABM) simulates the spread at any geographical scale, ranging from a village to a country and considers unique characteristics of SARS-CoV2 viruses such as its persistence in the environment. Therefore, unlike other simulators, CoV-ABM computes the density of active viruses inside each location space to get the virus transmission probability for each agent. It also uses the local census and health data to create health and risk factor profiles for each individual. The proposed model relies on a flexible timestamp scale to optimize the computational speed and the level of detail. In our framework each agent represents a person interacting with the surrounding space and other adjacent agents inside the same space. Moreover, families stochastic daily tasks are formulated to get tracked by the corresponding family members. The model also formulates the possibility of meetings for each subset of friendships and relatives. The main aim of the proposed framework is threefold: to illustrate the dynamics of SARS-CoV diseases, to identify places which have a higher probability to become infection hubs and to provide a decision-support system to design efficient interventions in order to fight against pandemics. The framework employs SEIHRD dynamics of viral diseases with different intervention scenarios. The paper simulates the spread of COVID-19 in the State of Delaware, United States, with near one million stochastic agents. The results achieved over a period of 15 weeks with a timestamp of 1 hour show which places become the hubs of infection. The paper also illustrates how hospitals get overwhelmed as the outbreak reaches its pick

Synthesizing Rolling Bearing Fault Samples in New Conditions: A Framework Based on a Modified CGAN

Bearings are vital components of rotating machines that are prone to unexpected faults. Therefore, bearing fault diagnosis and condition monitoring are essential for reducing operational costs and downtime in numerous industries. In various production conditions, bearings can be operated under a range of loads and speeds, which causes different vibration patterns associated with each fault type. Normal data are ample as systems usually work in desired conditions. On the other hand, fault data are rare, and in many conditions, there are no data recorded for the fault classes. Accessing fault data is crucial for developing data-driven fault diagnosis tools that can improve both the performance and safety of operations. To this end, a novel algorithm based on conditional generative adversarial networks (CGANs) was introduced. Trained on the normal and fault data on actual fault conditions, this algorithm generates fault data from normal data of target conditions. The proposed method was validated on a real-world bearing dataset, and fault data were generated for different conditions. Several state-of-the-art classifiers and visualization models were implemented to evaluate the quality of the synthesized data. The results demonstrate the efficacy of the proposed algorithm

BrachyView: multiple seed position reconstruction and comparison with CT post-implant dosimetry

BrachyView is a novel in-body imaging system utilising high-resolution pixelated silicon detectors (Timepix) and a pinhole collimator for brachytherapy source localisation. Recent studies have investigated various options for real-time intraoperative dynamic dose treatment planning to increase the quality of implants. In a previous proof-of-concept study, the justification of the pinhole concept was shown, allowing for the next step whereby multiple active seeds are implanted into a PMMA phantom to simulate a more realistic clinical scenario. In this study, 20 seeds were implanted and imaged using a lead pinhole of 400 μ m diameter. BrachyView was able to resolve the seed positions within 1–2 mm of expected positions, which was verified by co-registering with a full clinical post-implant CT scan