Miracle-dz/UniFAR-FTUs huggingface dataset:
A Supervised Methodology for Analyzing Dysregulation in Splicing Machinery: An Application in Cancer Diagnosis

Deregulated splicing factors have shown to be associated with the development of several types of cancer and, therefore, the determination of such alterations can help the development of tumor-specific molecular targets for early prognosis and therapy. Determining the relevant splicing factors, however, is not a straightforward task mainly due to the heterogeneity of tumors and the variability across samples. In this work, a methodology based on supervised machine learning methods is proposed, allowing the determination of subsets of relevant factors that best discriminate samples. The methodology comprises three main phases: first, a ranking of splicing factors is determined by means of applying feature weighting algorithms; second, the best subset of factors that allows the induction of an accurate classifier is detected; then the confidence over the induced classifier is assessed by means of explaining the individual predictions. Finally, the utility and benefit of the proposed methodology are illustrated by means of analyzing a small dataset of neuroendocrine lung carcinoids, and the results showed that there exist small subsets of deregulated factors which can effectively distinguish between tumor samples and their respective adjacent non-tumor tissues.
Diagnose Chest Pathology in X-ray Images by Learning Multi-Attention Convolutional Neural Network

Automatically diagnosing diseases from X-ray images has been drawing great attention from the community of computer vision and medical image computing in recent years. Existing works predominantly formulate this task as multi-label image classification by deep neural networks, which have demonstrated the effectiveness for classifying general image categories (e.g., in ImageNet). Although promising results are reported, there are still grand challenges to model large intra-class variances and subtle inter-class distinctions existed in those fine-grained categories (e.g., two similar chest pathologies). In this paper, we propose a novel approach for automatic disease diagnosis by computer vision techniques, in which a multi-attention convolutional neural network is designed to learn a set of discriminative features for each category. In particular, we first extract convolutional feature representations from an X-ray image, and further propose to learn multi-attention maps by category. As each attention map consists of multiple types of discriminative features to each category, which are generated by a group of convolutional channels and are optimized by corresponding category labels, the subtle visual representation for fine-grained categories can be captured. To make the final prediction, we generate a probability score for each category by top-k pooling from attention maps, which ensures the most discriminative features can be preserved. Extensive experiments on chest X-ray dataset (namely ChestX-ray14) the largest ChestX-ray 14 dataset show the superior results of the proposed network against the CheXNet, with 1.8% relative increases in terms of the AUC metric.
FPGA Implementation of Novel Routing Algorithm for Butterfly-Fat-Tree Topology based NoC Design

This paper presents a novel routing algorithm for Butterfly-Fat-Tree (BFT) topology based Network-on-Chip (NoC) design. It proposes a routing algorithm along with router addressing scheme for BFT topology which can be used in any generic NoC router architecture. The proposed algorithm has been implemented in software using C, followed by hardware implementation using Verilog. It has been validated using FPGA based hardware and the results show that proposed routing algorithm routes the data from source to destination seamlessly. This can be incorporated with NoC router architecture to verify several functionalities on a hardware based prototype.
Modeling and MPC-based Method for Planning Transportation of Multiple Oil Products in Pipeline Network

Multiple pipeline networks are planned and under construction for long-distance transportation of some petroleum derivatives through a system of pipes typically towards the market areas for consumption. The main purpose of this paper in the form of planning problem consists of demand regulation and efficiency improvement based on optimization factors such as descend total transportation losses and increase response time to satisfy demands regarding plant specification and systematic constraints. The planning process as a scheme that defines how to transport petroleum products and batch sequences injected consecutively through pipeline polyducts with no separating device is one of the most prominent challenges facing industrial engineering. In this paper, the yearly demands are split into monthly problems so that considering two-month prediction horizons, model predictive control strategy is applied to optimize sequences which are regulating demands in the corresponding time steps to the last day of every month. The hierarchical offline reoptimization for the small-sized problem at the beginning of every month, instead of optimizing annual planning as a high dimensional and constrained problem can lead to overcoming computational complexity that is a remarkable challenge in the planning.
Deep Feature Selection using an Enhanced Sparse Group Lasso Algorithm

Feature selection is an important method of data dimensionality reduction widely used in machine learning. In this framework, the sparse representation based feature selection methods are very attractive. This is because of the nature of these methods which try to represent a data with as less as possible non-zero coefficients. In deep neural networks, a very high dimensional feature space is usually existed. In such a situation, one can take advantages of the feature selection approaches into account. In this paper, first, three sparse feature selection methods are compared. The Sparse Group Lasso (SGL) algorithm is one of the adopted approaches. This method is theoretically very well-organized and leads to good results for man-made features. The most important property of this method is that it highly induces the sparsity to the data. A main step of the SGL method is the features grouping step. In this paper, a k-means clustering based method is applied for grouping of the features. Our experimental results show that this sparse representation based method leads to very successful results in deep neural networks.
Multi-View Learning for Vehicle Re-Identification

Vehicle re-identification (ReID) aims to identify a target vehicle in different cameras with non-overlapping views, and it plays an important role when the car licence plate recognition is unavailable or unreliable. Compared with face recognition and person ReID tasks, it is difficult to train an effective vehicle ReID model due to two reasons: the different views greatly affect the visual appearance of a vehicle, and different vehicles may exhibit fairly similar visual appearance when their images are captured from one unified single view. To handle these training difficulties, we introduce several latent groups to represent multiple views. Then, the vehicle ReID problem is modeled as two sub tasks, including matching vehicles in a same view and across different views. A fine-grain ranking loss and a relative coarse-grain ranking loss are proposed to each task respectively. Extensive experimental analyses and evaluations on two benchmarks demonstrate the proposed method can achieve state-of-the-art performance.
Partial Derivate Contribution Plot Based on KPLS-KSER for Nonlinear Process Fault Diagnosis

In the process monitoring of nonlinear systems, kernel function is the main means to solve the nonlinear data by mapping low-dimensional nonlinear data to high-dimensional linear data. However, the use of kernel function has two disadvantages: 1. Kernel function needs a lot of calculation, especially under the condition of large number of training samples, 2. Kernel function leads to the inability to obtain the relationship between input variables and statistics. So that the identification of fault variables is difficult. In this paper, Taylor series expansion is used to remove the high order infinitesimal term, so that the Gaussian kernel function is replaced by the input matrix, which greatly reduces the amount of computation required for fault detection and diagnosis. The replacement method is introduced into the KPLS model, and the input matrix is successfully decomposed into quality-related and unrelated parts by using SVD decomposition. Based on the detection model, through the gradient theory, the partial derivate is used to calculate the gradient of each variable in the statistics to isolate the fault variables. In order to verify the effectiveness of the algorithm, this paper uses the TEP model to carry on the simulation experiment, has obtained the very good process monitoring effect, at the same time has greatly reduced the simulation experiment time.
Virtual Flux Droop Control with Constant Switching Frequency for Power Sharing Between Parallel Inverters in Islanded Microgrids

Droop control method is a popular method for power sharing in islanded microgrids with parallel-connected inverters. Virtual flux droop control is a simple strategy based on the direct flux control (DFC) and hysteresis controllers, in which the active and the reactive powers are proportional to the phase angle and the amplitude of the virtual flux (integral of the output voltage of the inverter), respectively. However, because of the hysteresis controllers, it suffers from a drawback that is the variable switching frequency, which leads to the voltage and current ripples. In this paper, novel phase angle and virtual flux controllers with simple structure have been proposed which lead to a fixed switching frequency, while there is no need for the space vector modulation. Therefore, the ripples of voltage and current have been reduced. In order to evaluate the effectiveness of the proposed method, simulation studies have been carried out in Matlab/Simulink. The results show that the proposed controllers can reduce the voltage and power ripples, while the switching frequency has been fixed.
Application of Improved Clustering Algorithm in Commodity Diversity Recommendation

The increasing development of online retail creates a problem of how to meet consumer's new personalized needs that consider not only the accuracy, but also the diversity of the recommended commodities. The traditional commodity diversity recommendation algorithm always focuses on the combination of different prices or categories, ignoring the mapping between demands of consumers and commodities. This paper improves the application of the clustering algorithm model, and proposes a method to recommend groups of commodities with scene characteristics to consumer according to their common interest. By extracting the association between the consumer interest features and commodities, groups with scene characteristics is formed based on the commodity tags combination. Based on the analysis of the key nodes in the consumer and commodities relationship network, this study improves the algorithm model in the application, introduces the category factor into the process tag filtering and clustering, and balances the weight of the minority classes. Through experimental verification, the method proposed in this paper provides a better effect for providing consumers with a diversified recommendation to a certain extent.
Indirect Adaptive Taylor Aeries Control of Differential Drive Wheeled Mobile Robots

Using Taylor series system as a universal approximator, which benefits a simpler structure in comparison with common approximators, this paper suggests an indirect adaptive controller equipped with uncertainty estimator for electrically-driven wheeled mobile robots. The proposed control scheme compensates both parametric and nonparametric uncertainties and guarantees the Ultimately Uniformly Boundedness (UUB) stability. Also, this research takes actuators' dynamics into account, and considers unknown dynamic external disturbances to the mathematical model of wheeled mobile robot in order to show the efficiency of the proposed controller by simulation results.
Research and Application of Artificial Technology for Substation Environment Surveillance System

In order to satisfy the requirements of power operation scene safety monitoring and lessen the human accidents, a novel framework of video surveillance in the substation based on artificial technology was presented. Firstly, a multi-class detector based on region-based fully convolutional networks (R-FCN) was proposed to realize the detection and localization of multiple targets. Then types of violation and defect were discriminated by secondary analysis for the workers or equipments based on detection results. In order to implement auto-alarm of flames and smoke, an algorithm for automatic smoke and flame detection is presented, using different methods to extract candidate regions and a unified CNN to recognition. In addition, a virtual electronic fence was designed to prevent workers from entering dangerous areas and other no-working areas. The experimental results demonstrate that the approach is effective for detecting illegal operations of works automatically and substation security monitoring, and it will improve the automation and intelligentization level of unmanned substation.
Induction Motor Analysis Using Magnetostatic Finite Element Simulations Considering Skewing

A model for the induction motor analysis using magnetostatic finite element simulations is presented. The aim of the model is the step-by-step motor analysis with a defined voltage source. In order to be general the skewing effect is taken into account. The analysis method is based on the 2D multi-slice technique already known for asynchronous machines, in which the field problem is solved by integrating, step-by-step non-linear time-dependent equations. The approach proposed here is based on magnetostatic simulations of each slice, since the rotor current is computed outside the electromagnetic formulation, using a coupled analytical-finite element model of the machine. Analyzing a grid connected induction motor, it is necessary to impose the proper voltage during the simulation. The step-by-step analysis is carried out solving the circuital equations outside the finite element problem, to impose the correct behavior of the voltage. Finally measurements comparison is done to verify the analysis results.
A comparison of conventional and segmental rotor 12/10 switched reluctance motors

Segmental rotor Switched Reluctance Machines (SRMs) have been demonstrated to produce more torque than toothed rotor SRMs because each coil is able to link more magnetic flux. This paper considers a six-phase segmental rotor SRM and compares it to a conventional SRM with the same lamination length and outer dimensions. Both fully-pitched (FP) and single-tooth wound (STW) motor configurations of the segmental rotor SRM are investigated. The FP winding machine produces higher torque than the single tooth winding machine when using the same phase conduction angles but has unacceptably long end windings. Optimized conduction widths and current shapes are subsequently applied to the single tooth winding segmental rotor SRM. The segmental SRM is shown to produce about 10% higher rated torque, with less core loss and copper loss than the conventional SRM throughout the whole speed range.
Boundary Control for a Flexible String System with Prescribed Bound

This paper focuses on the prescribed performance constraint control issue of vibrating flexible string systems affected by external disturbances. To stabilize the vibration, tackle the external disturbance, and achieve the pre-set performance, a smooth performance function and an error transformation are introduced to construct the boundary constrained controller and disturbance observer. The suggested control can ensure the boundary output constraint convergent to an arbitrarily small residual set with a larger convergence rate, and achieve the asymptotic stability of the controlled system. By selecting appropriate design parameters, the control performance is validated via the obtained simulation results.
The Research and Design of the Proxy for Certificate Validation Based on Distributed Cross-Certification

This paper chooses a typical model (PKI hybrid trust model) as the paper's research sample which has more applicability and practicability. This paper proposes a scheme of constructing a trusted proxy for certificate validation and gives out a proxy's framework model. By transferring the work of certification path building and validation to the proxy, this scheme solves the "fat" PKI client problem. The paper designs the message data structures in SCVP request and response, and let PKI clients using these messages to interact with the proxy. The paper also gives out the details of the path-building module. It proposes an algorithm to build the certification paths to the PKI trust model chosen by this paper. The paper illustrates the detailed processing in path building. Besides the paper proposes a scheme that optimizes the path building algorithm by completing some work of path validation during the process of path building. The optimized scheme could eliminate some invalid intermediate certificates, and give intermediate certificates the weight which is used to sort, so that the paths built can be more possibly valid and the proxy can be more efficient.
Extracting Activity Patterns from Altering Biological Networks: A Sparse Autoencoder Approach

In this paper we address the problem of extracting activity patterns from biological networks that cannot be characterized in the form of a static graph, such as gene regulatory networks wherein the co-expression patterns of different genes may vary over time. A simple autoencoder architecture is applied in order to detect activity patterns appearing in several observational samples of the data. We apply Kullback-Leibler divergence based sparsity constraint on the hidden layer neurons, such a way the activity of a hidden layer unit can be associated to a particular activity pattern which can be extracted using the trained decoder function.
High Speed Synchronous Reluctance Motors for Electric Vehicles: a Focus on Rotor Mechanical Design

This paper deals with the design of high-speed Synchronous Reluctance motors for electric vehicle applications. The need to enhance power density and lowering costs leads to the design of high speed motors with a reduced amount of rare earth. Pure synchronous reluctance motors potentially operate at high speed and exhibit a low cost rotor compared to PM and induction motors. Nevertheless, they present reduced performances in deep flux weakening operations in particular when the so-called radial ribs are introduced to increase the mechanical robustness of the rotor. In this paper the adoption of the radial ribs in the related design challenges are investigated and discussed. The adoption of a suitable commercial optimization tool able to optimize the placement and the sizing of the radial ribs is presented. The approach leads to an original positioning of the radial ribs able to preserve the performances of the motor at high operating speed enhancing the mechanical integrity of the rotor.
QoE-Driven Mobile Streaming: A Location-Aware Approach

In this paper, we maximize the quality of experience (QoE) for mobile video streaming. QoE is modeled to capture user's video quality assessment as well as the freezing and bitrate variation during playback. Based on an observation that network bandwidth is correlated with location, we predict the future locations and accordingly the bandwidth along a trip. Then, the predicted information is utilized to dynamically adapt the video version and bitrate with maximized QoE. We show that our proposed solution well approximates the offline optimal performance by almost 98% on average. The proposed solution is also competitive compared to several popular streaming algorithms.
Low-Cost EDA Device for Screening Diabetic Neuropathy

Purpose: Poor electrodermal activity (EDA) may be related to diabetic neuropathy. The aims of this study were to investigate the characteristics of EDA, to design a portable electronic device capable of acquiring it and to formulate a hypothesis regarding the relationship between EDA and diabetic foot. Materials and methods: This study investigated the relationship between EDA signal and diabetic neuropathy using a self-designed device, and tested it with a procedure defined for diabetic foot screening. After that, mean skin conductance level (M-SCL), mean skin conductance response (M-SCR) to stimuli, and difference in mean SCL between feet (DBF) were obtained. Participants were assessed based on whether they had diabetes or not and on their clinical history and care habits. Afterwards, 3 groups were selected, namely reference, people with diabetes out of risk, and people with diabetes at risk of diabetic foot. Results: 22 patients were included in the final test. Several types of stimuli were applied through a test, and responses were recorded and analyzed. Diabetic patients reflected lower M-SCL and M-SCR values. Statistical difference was found for those being at risk regarding M-SCR (P<0.01). Conclusion: A test protocol was defined, based on prior tests and measures, to acquire proper and reliable data. Lower SCL and SCR were found according to the diabetes progress. In particular, significant difference was found for M-SCR. No significant difference in DBF values for those at risk was found. These results have the potential to be used as part of a diagnostic tool, which may include other parameters and questionnaires, for diabetic foot screening.
Design of compact SIW-based multi-aperture coupler for Ku-band applications

In this paper, two compact multi-aperture substrate integrated waveguide (SIW) couplers are proposed for Ku-band applications. These couplers are designed using ray-tracing method (RTM). RTM is a simple method that can be used for initial design of SIW multi-aperture couplers (MACs). In order to reduce the size of the MAC, two rows of vias are inserted at two sides of SIWs, along the coupling apertures. Then, two structures are proposed and analyzed using RTM. The length of the coupling aperture is reduced by 15% and 26% for the first and second designs, respectively. The proposed structures are simulated and optimized using HFSS software. The results show that the couplers have good return loss, high isolation between output ports, and a broadband frequency bandwidth.