Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Bearing Fault Diagnosis Framework With Deep Adaptation Networks For Industrial Application

In the past decades, a host of fault diagnosis methodologies have been designed and successfully used for bearings. However, most of them still have two deficiencies. (1) Traditional methods extract and select features manually according to a specific issue, but these features may be not appropriate for other tasks, leading to performance degradation of fault diagnosis. (2) Many studies assume that the dataset for model learning obey the uniform distribution as the testing dataset do, which seldom accords with the practice. To remedy these problems, we devise a novel framework for bearing fault diagnosis. First, the raw condition monitoring data are converted to 2D images with continuous wavelet transform. Then the classification model is learned with these 2D images, during which the transfer learning scheme, deep adaptation networks, is introduced for adapting the deep model trained with source data for use in new but related target domain. The presented approach is demonstrated with bearing condition monitoring information, and the results indicate it can identify bearing faults effectively under different operational conditions and has a higher accuracy than conventional approaches.
An Information-Based Waste Management Approach for Pakistan

The mismanaged Solid Municipal Waste (SMW) is not only considered as a big challenge but it is also regarded as the main cause of environmental pollution in Pakistan. Furthermore, the unplanned urbanization with exponential population growth demands an efficient and intelligent way of managing SMW. We, therefore, proposed a novel and medium cost conceptual architectural model in the realm of the Internet of Things (IoTs) for effective management of SMW. This proposed model may be then easily incorporated in a wider context of Smart District SMW management concept. However, in this study, we only proposed a conceptual model for house level SMW management. The aforementioned model will then act as a base unit. A further extension to district level can be easily made by integrating the house level SMW base unit model. The overall conceptual analysis of the proposed model illustrated that IoT based architecture will reveal better performance if implemented and adopted correctly in Pakistan.
Deep Learning based End-to-End Rolling Bearing Fault Diagnosis

Rolling bearings play an important part in rotating machinery. As they work in complex conditions, faults will occur sometimes. Therefore, it is necessary to detect the faults early. Traditional bearing fault diagnosis methods are often based on mechanism analysis and feature selection, and the process is relatively complicated. Deep learning methods, however, have the ability to extract and select features automatically, which greatly reduces the workload. In recent years, deep learning-based methods have been successfully used in many fields, such as computer vision, voice recognition, medical diagnosis. In this paper, the end-to-end fault methods based on deep learning are proposed. The Long Short-Term Memory (LSTM) network, Gated Recurrent Unit (GRU) network and One-Dimensional Convolutional Neural Network (1D CNN) are used to build the deep learning network architecture respectively. A methodology is proposed for rolling bearing fault diagnosis, including data preprocessing, network modeling, training, validation and testing. Test bench data is used for fault diagnosis and the results show that deep learning based end-to-end methods are effective for the fault diagnosis of rolling bearings and that the model based on 1D CNN has the best performance.
Characteristics Variable Selection of NIR Based on L1/2 Regulation

L<inf>P</inf> norm (P<l) has the feature of sparsity. In view of the sparsity of the characteristic band in the near infrared spectrum, a feature extraction algorithm based on L<inf>1/2</inf> regular is proposed in this paper. According to the nonconvexity of L<inf>1/2</inf> norm, an iterative algorithm is presented. The L<inf>1/2</inf> regular is transformed into a series of L<inf>1</inf> regular for iteration calculation. In this paper, based on the near infrared spectral data, L<inf>1/2</inf> regular, PCR and PLS algorithm are used to detect oil compositions. And then the PLS algorithm and PCR algorithm are compared, the result of which shows that PCR and PLS algorithm have the similar effect and there will be a phenomenon of over fitting, as the number of characteristic band increase. Besides, the modeling sets error decreases and the prediction sets error increases. With L<inf>P</inf> algorithm, both modeling sets and predictive sets error can be small, and extract the characteristic band effectively. The experiments show that the L<inf>1/2</inf> regular has a good application value for the extraction of characteristic band of spectrum.
Assessing Hydration Status and Reported Beverage Intake in the Workplace

The aim was to examine the hydration status of adults working in different jobs at the beginning and end of a shift and their reported water intake. One hundred and fifty-six subjects (89 males, 67 females) were recruited from workplaces within the local area (students, teachers, security, office, firefighters, catering). A urine sample was obtained at the start and end of the shift and was analyzed for osmolality (Uosm), specific gravity (USG), and sodium and potassium concentrations. Euhydration was considered Uosm <700 mOsmol/kg or USG <1.020. At the end of the shift, subjects were asked to report all water intake from beverages during the shift. Females had lower Uosm than males at the start (656 [range, 85-970] vs 738 [range, 164-1090] mOsmol/kg) and end (461 [range, 105-1014] vs 642 [range, 130-1056] mOsmol/kg; P < .05) of their working day. Fifty-two percent of individuals who appeared hypohydrated at the start of the shift were also hypohydrated at the end. Reported water intake from beverages was greater in males compared with females (1.2 [range, 0.0-3.3] vs 0.7 [range, 0.0-2.0] L, respectively; P < .0001). In conclusion, a large proportion of subjects exhibited urine values indicating hypohydration, and many remained in a state of hypohydration at the end of the shift.
Predicting Pathogenic Non-coding Variants on Imbalanced Data Set using Cluster Ensemble Sampling

In the past few years, many variants in the non-coding regions of the human genome have been reported by personal whole genome sequencing. It is a challenge to distinguish pathogenic non-coding variants from such a large number of benign non-coding variants. Many machine learning methods for predicting pathogenic non-coding variants have been proposed. However, the precision and recall rates of the currently existing methods decline rapidly when the number of negative samples in the data increases. Both under-and over-sampling techniques have been employed in the field of machine learning to resolve the poor performance of classification methods on imbalanced data. Even though, we observed that a more sophisticated method with better performance is still largely desired for the problem of predicting pathogenic non-coding variants. In this regard, this study aims at presenting a general framework for imbalanced data learning, CE-SMURF, which incorporates both Cluster Ensemble (CE) sampling and hyper-ensemble techniques to further improve the prediction accuracy of detecting pathogenic non-coding variants. The results demonstrate that the final setting of CE-SMURF (f = 0, r = 0.1) is superior in training, and outperforms other existing methods on the testing data, providing a valuable insight to tackle the imbalanced learning issue for many future applications in the field of genomic precision medicine.
A Hybrid Framework Combining Data-level Fusion and Model-based Models for Remaining Useful Life Prediction

In this work, a hybrid prognostic framework is put forward to predict remaining useful life (RUL) through the fusion of multiple degradation-based sensor data. This approach is tested on measurements obtained from an industrial centrifugal pump. The prognostic framework consists of determination of predication start point, sensor selection and fusion, and prognostics steps that lead to accurate RUL prediction. This approach first applies the canonical variate analysis (CVA) method to the pump for determining the prediction start time. We also present an approach for constructing a single-valued prognostic health indicator through the fusion of fault root-case variables. Moreover, the particle filter (PF) algorithm is deployed to improve the metabolism grey forecasting model (MGFM) to realize the prediction of both RUL and uncertainties.
An Empirical Study on Sensor-aware Design of Convolutional Neural Networks for P300 Speller in Brain Computer Interface

A Brain Computer Interface (BCI) character speller allows human-beings to directly spell characters using eye-gazes, thereby building communication between the human brain and a computer. Convolutional Neural Networks (CNNs) have achieved state-of-the-art results on the BCI character spelling accuracy. Unfortunately, to the best of our knowledge, it has not been studied whether the CNN should be designed differently to increase the spelling accuracy when the number of sensors used to acquire EEG signals is different. This paper performs an empirical study to investigate this issue. First, we show a motivational example which motivates us for this investigation. Then, we propose a method to design CNNs according to the number of sensors used in the BCI character speller. This method automatically configures a parametric CNN we have devised according to the given number of sensors. Experimental results on six datasets show that we need to design different CNNs when different number of sensors are used for the acquisition of EEG signals. Experimental results also show that our designed sensor-aware CNNs outperform other CNNs in terms of spelling accuracy in most cases. Our CNNs can increase the spelling accuracy achieved by other CNNs with up to 34%.
Complex Networks of Characters in Fictional Novels

Using the complex network theory to analyze the character social network in literatures provides a perspective in literary analysis. This paper proves that the character social networks of Harry Potter and The Lord of the Rings are small-world and scale-free and not only preferential attachment but also some specific plots affect the development of The Lord of the Rings trilogy. That means the social networks from two literatures obey the complex network theories that the social networks in real word follow. Hence, these above properties make the two fictional novels’ networks more like networks in the real world and make people accept them well.
ADC Maps Texture Analysis for the Evaluation of Kidney Function: A Preliminary Study*

Assessment of renal microstructure and function non-invasively, has an important role in monitoring and predicting chronic kidney disease (CKD). The goal of this study is to differentiate healthy and CKD patients using texture analysis. Apparent diffusion coefficient (ADC) maps were generated from diffusion weighted magnetic resonance images (DWI), from which statistical and wavelet-transform based texture parameters were extracted. The results of this preliminary study indicated that chronic kidney disease affects texture parameters. The correlation and the energy of wavelet coefficient in “low-high” energy components in the third wavelet decomposition level were found to be the best textural predictors of kidney dysfunction.
Influence of Motion Speed on the Perception of Latency in Avatar Control

For fullbody interaction, avatar simulation and control involves several steps that leads to delay (or latency) between the users' and their avatar's motion. Previous works have shown an impact of this delay on the perception-action loop, with possible impact on Presence and embodiment. In this paper we explore how the speed of the motion can impact the way people perceive and react to such a delay. We conducted an experiment where users were asked to follow a moving object with their finger, while embodied in a realistic avatar. We artificially increased the latency (up to 300ms) and measured their performance in the mentioned task.Our results show that motion speed influenced the perception of latency: we found critical latencies of 80ms for medium and fast motion speeds, while the critical latency reached 120ms for a slow motion speed. We also found that performance was affected by latency before the critical latency for medium and fast speeds, but not for a slower speed.
Using Electronic Patient Reported Outcomes to Foster Palliative Cancer Care: The MyPal Approach

Palliative care is offered along with primary treatment to improve the quality of life of the patient by relieving the symptoms and stress of a serious illness such as cancer. As per modern definitions, palliative care is appropriate at any age and at any stage of the illness, regardless of the eventual outcome. Patient-reported outcomes (PRO), i.e., health status measurements reported directly by the patients or their proxies, and especially their availability in electronic form (ePROs), are gradually gaining popularity as building blocks of innovative palliative care interventions. This paper presents MyPal, an EC-funded collaborative research project that aims to exploit advanced eHealth technologies to develop and evaluate two novel ePRO-based general palliative care interventions for cancer patients. In particular, the paper presents: (1) a short overview of MyPal; (2) the target populations, i.e., adults suffering from chronic lymphocytic leukemia (CLL) or myelodysplastic syndromes (MDS), and children with solid or hematologic malignancies; (3) the ePRO-based interventions being designed for the target populations, (4) the eHealth platform for delivering the interventions under development, and (5) the international, multi-center clinical studies to be conducted for assessing these interventions, i.e., a randomized controlled trial (RCT) and an observational study for adults and children, respectively.
Cost-effective Network Planning for Rural Communities

Broadband Internet access is central to the regeneration of remote communities and reducing the digital divide between rural and urban regions. This paper focuses on rural communities with limited financial resources, environmental issues including long reach from conurbations, and mountainous or otherwise adverse terrain, typically with limited access to a wired power supply. As such, regular access technologies based on cable or fibre optics are not financially viable. To overcome this challenge, we consider the deployment of a Free-Space Optical (FSO) based relay network as primary technology, using diversity to provide resilience to atmospheric effects. More precisely, this paper describes a novel network planning tool based on a Multi-Objective Evolutionary Algorithm (MOEA), that determines the suitable location of FSO relay nodes, taking into account end-to-end link speed and the degree of path diversity. This MOEA approach can account for Line-of-Sight occlusions and allows various compromises to be selected from a Pareto front to suit individual needs. We provide suitable results to show the satisfactory operation of the tool and outline avenues for future development.
Clustering and Classification of Human Microbiome Data: Evaluating the Impact of Different Settings in Bioinformatics Workflows

Microbiome studies are attracting increasing interest, especially in human health applications, where their use for disease prognostics, diagnostics and treatment can have immense effects on life quality. The settings in the microbiome data preprocessing stage can lead to the great variability of the generated operational taxonomic unit (OTU) tables, reflected in the size and sparseness of this data matrix. As there are still no solid guidelines on the best practices, it is valuable to assess which machine learning algorithms provide higher stability of results under variable preprocessing settings. In this study, we have generated OTU tables using data from the Moving pictures of human microbiome study using two different reference databases (Greengenes and Silva) and four levels of the similarity threshold (ranging from 90 to 99%), processed in the QIIME bioinformatics package. The results of the two best-performing classification and clustering algorithms are presented in detail: Random Forest classifier (RF) and Spectral clustering (SC). The random forest classifier has outperformed spectral clustering in terms of accuracy. As the rate of data generation increases, while the cost of labeling remains high, further improvement of clustering performance and ensemble approaches should be explored.
Effective Connectivity in the Primary Somatosensory Network using Combined EEG and MEG

The primary somatosensory cortex remains one of the most investigated brain areas. However, there is still an absence of an integrated methodology to describe the early temporal alterations in the primary somatosensory network. Source analysis based on combined Electro-(EEG) and Magneto-(MEG) Encephalography (EMEG) has been recently shown to outperform the one's based on single modality EEG or MEG. The study and potential of combined EMEG form the goal of the current study, which investigates the time-variant connectivity of the primary somatosensory network. A subject-individualized pipeline combines a functional source separation approach with the effective connectivity analysis of different spatiotemporal source patterns using a realistic and skull-conductivity calibrated head model. Three-time windows are chosen for each modality EEG, MEG, and EMEG to highlight the thalamocortical and corticocortical interactions. The results show that EMEG is promising in suppressing a so-called connectivity 'leakage' effect when later components seem to influence earlier components, just due to too similar leadfields. Our current results support the notion that EMEG is superior in suppressing the spurious flows within a network of very rapid alterations.
Multi-Task Learning and Attention Mechanism Based Long Short-Term Memory for Temperature Prediction of EMU Bearing

The traction motor is one of the key components that plays an important role in ensuring the safety and stability of the running EMU (Electric Multiple Units). The running state of the traction motor can be determined through monitoring and predicting the change of EMU bearing temperature. In this paper, we propose a Long Short-Term Memory Neural Network based on Multi-task Learning and Attention Mechanism for the bearing temperature prediction in view of the complex influencing factors of bearing temperature in train operation. The model learns the characteristics of temperature sensors in different positions jointly through multi-task learning. And the Long Short-Term Memory Neural Network based on Attention Mechanism is used to consider the influence of current operating conditions and previous train records on bearing temperature in different degrees. So the model takes various influencing factors and spatial-temporal correlation into consideration. The experimental results with actual EMU datasets show that our method outperforms the baseline approaches.
A Temporal Convolution Network Solution for EEG Motor Imagery Classification

EEG motor imagery recognition based brain computer interface has been an import scheme to construct an alternative pathway of the brain to the outside world. EEG signal is usually buried in noise and has very low signal to noise ratio (SNR), which has presented great challenge for efficient motor imagery classification. In addition, the large intra-subject and inter-subject signal variance toward one specific motor imagery also brings difficulty for accurate classification. In recent years, some deep learning solutions based on AutoEncoder, Restricted Boltzmann Machine, CNN and RNN have been proposed for EEG motor imagery classification which have well improved the motor imagery classification accuracy. However, the multi-subject and multi-task motor imagery classification problem remains a challenge. The high computational cost of the existing deep learning solutions is another serious issue to be addressed. In this paper, a new motor imagery classification solution based on Temporal Convolutional Network (TCN) is developed. The dilated causal convolution within TCN could well incorporate the temporal information in a parallel way with much higher computational efficiency than the traditional RNNs. Time stacked spatial EEG signal has been employed as the input to the TCN. Based on which, both the spatial distribution information and temporal variation of the brain signal have been considered. Extensive experiments have shown that the proposed TCN solution has obtained state of the art performance on multi-subject and multi-task motor imagery classification. A high classification accuracy as 97.89% on 20 subjects and 5 tasks has been reached.
Validation of MR-Based Attenuation Correction of a Newly Released Whole-Body Simultaneous PET/MR System

The aim of this study was to validate quantitative performance of a newly released simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) scanner, by using MR-based attenuation correction (MRAC), both in phantom study and in patient study. PET/MRI image uniformities of a phantom under different hardware configurations were tested and compared. Thirty patients were examined with 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) PET/computed tomography (CT) and subsequent PET/MRI. PET images from PET/MRI were corrected with MRAC (PETMR), CT-based attenuation maps (μ-maps, PETCT), and segmented CT μ-maps (PETCTSeg) derived from PET/CT. Standardized uptake values (SUVs) were compared among the 3 sets of PET in main organs (bone, liver and lung) and in 52 FDG-avid lesions, including soft-tissue lesions and bone lesions. The result showed that PET imaging uniformities of PET/MRI under different configurations were good (<8.8%). The SUV differences among the 3 sets of PET varied with organs and lesion types. In detail, the mean relative differences of SUV between PETMR and PETCT were as follows: −18.8%, bone (SUVmean); −8.0%, liver (SUVmean); −12.2%, lung (SUVmean); −18.1%, bone lesions (SUVmean); −13.3%, bone lesions (SUVmax); −8.2%, soft-tissue lesions (SUVmean); and −7.3%, soft-tissue lesions (SUVmax). The mean relative differences between PETMR and PETCTSeg were as follows: −19.0%, bone (SUVmean); −3.5%, liver (SUVmean); −3.3%, lung (SUVmean); −19.3%, bone lesions (SUVmean); −17.5%, bone lesions (SUVmax); −5.5%, soft-tissue lesions (SUVmean); and −4.4%, soft-tissue lesions (SUVmax). The differences of SUV between PETMR and PETCT were larger than those between PETMR and PETCTSeg, in both soft tissue and soft-tissue lesions (P < 0.001), but not in bone or bone lesions. In conclusion, MRAC in the newly released PET/MR system is accurate in most tissues, with SUV deviations being generally less than 10%, compared to PET/CT. In bone, however, underestimations can be substantial, which may be partially attributed to segmentation of the MR-based μ-maps.