Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Electromechanical Transient-Electromagnetic Transient Hybrid Simulation Algorithm for Power System

With the increasing scale of power grids, traditional electromechanical transient or electromagnetic transient simulation software has begun to reveal its own limitations when analyzing the operating characteristics of the power grid. Realizing the hybrid simulation of electromagnetic transients and electromechanical transients in the power system has expanded the scale of electromagnetic transient simulation. On the other hand, it also provides the necessary system background for the electromagnetic transient network simulation analysis. This paper uses electromagnetic transient-electromechanical transient hybrid simulation technology for engineering analysis and application, which can avoid the system equivalent workload due to the limitation of electromagnetic transient simulation scale, and can greatly improve the accuracy of system analysis and research. For the study of AC and DC systems, DC multi-infeed systems and high-power electronic equipment, the dynamic impact of system operations will effectively play a role, so the electromagnetic transient -electromechanical transient hybrid simulation has become hot spot in the field of power system of simulation.
Improved Deep Belief Network to Feature Extraction in Chinese Text Classification

In the field of text classification, the classical text categorization method uses bag-of-words (BOW)model as a classification feature. The BOW model only focuses on the word frequency, but ignores the connection between words, so the BOW model has defects of insufficient expression of the text. The word embedding model maps each individual word to the new vector space, which makes up for the defects of isolated vocabulary and insufficient text expression. As a result, using the word embedding can improve the accuracy of the text classification. In order to avoid the dimension explosion, this paper uses keyword-based word embedding to represent Chinese text. In the feature extraction process, we use the combination of deep belief network and deep Boltzmann machine to extract word vector features of the text. This improved deep belief network feature extraction method further improves the accuracy of text classification.
Leucocyte Trafficking via the Lymphatic Vasculature— Mechanisms and Consequences

The lymphatics fulfill a vital physiological function as the conduits through which leucocytes traffic between the tissues and draining lymph nodes for the initiation and modulation of immune responses. However, until recently many of the molecular mechanisms controlling such migration have been unclear. As a result of careful research, it is now apparent that the process is regulated at multiple stages from initial leucocyte entry and intraluminal crawling in peripheral tissue lymphatics, through to leucocyte exit in draining lymph nodes where the migrating cells either participate in immune responses or return to the circulation via efferent lymph. Furthermore, it is increasingly evident that most if not all leucocyte populations migrate in lymph and that such migration is not only important for immune modulation, but also for the timely repair and resolution of tissue inflammation. In this article, I review the latest research findings in these areas, arising from new insights into the distinctive ultrastructure of lymphatic capillaries and lymph node sinuses. Accordingly, I highlight the emerging importance of the leucocyte glycocalyx and its novel interactions with the endothelial receptor LYVE-1, the intricacies of endothelial chemokine secretion and sequestration that direct leucocyte trafficking and the significance of the process for normal immune function and pathology.
Switching Transient Analysis for Normally-off GaN Transistor With p-GaN Gate in a Phase-Leg Circuit

Commercially available normally-off GaN power high-electron-mobility transistor (HEMT) devices have typically adopted a p-GaN gate structure. In the gate region, there exist a Schottky junction (between gate electrode and the p-GaN layer) and a p-GaN/AlGaN/GaN heterojunction. As the p-GaN layer is not directly shorted to the gate electrode and conducting channel, it can be considered as electrically “floating. ” With drain voltage changing during a switching process, the variation of net charge in the floating p-GaN layer would cause instability in threshold voltage. Besides, because of the distinctive features of the p-GaN gate HEMT structure under certain gate/drain bias, gate-related junction capacitances would exhibit behavior different from those of a Si mosfet. Consequently, the switching transient performance of a GaN transistor with a p-GaN gate could be significantly influenced by the aforementioned factors. In this work, the threshold voltage instability associated with the drain-to-gate voltage stress is first analyzed. Despite the difficulty in directly performing capacitance measurements inside the device structure, a hybrid physical-behavior modeling method is proposed. The model is capable of extracting the capacitance bias relationships with regard to the gate region from static terminal measurements. In previous works, the advanced analytical model would make modest change on a Si mosfet's model. As a result, without fully considering the specific features of a GaN transistor with the p-GaN gate, the simulated waveforms would exhibit 20–50% discrepancy from the experiment. In contrast, the proposed switching transient analytical approach would exhibit improved accuracy (<10% disparity). Consequently, the switching transient performance of the GaN transistor with the p-GaN gate could be more accurately evaluated.
Sarcasm Detection Using Recurrent Neural Network

Sarcasm is a sharp and often ironic utterance that is meant to convey contempt or mock. In today's world, one of the challenging problems for opinion mining task is sarcasm detection. Many researchers are exploring the properties of sarcasm like semantic properties, syntactical properties, lexical feature etc, to design algorithms for sarcasm detection. We aim at using a recurrent neural network (RNN) model for sarcasm detection because it automatically extracts features required for machine learning approaches. Along with the recurrent neural network, this model also uses long short-term memory (LSTM) cells on tensorflow to capture syntactic and semantic information over Twitter tweets to detect sarcasm. Finally, we present the result of this model and a statistical overview of the dataset.
Cool Interconnect: A 1024-bit Wide Bus for Chip-to-Chip Communications in 3-D Integrated Circuits

In this paper, we present “Cool Interconnect,” a 1024-bit wide bus that we have developed to provide a standardized method of interconnecting chips in 3-D integrated circuits (3DICs). This wide bus chip-to-chip interconnect can be used to realize low-power high-performance multicore systems to meet the increasing demand for advanced electronics to enable autonomous vehicles and Internet of Things devices. The bus has been implemented using a $40\times 40$ fine-pitch array of through-silicon vias and bump joints. In addition, we have developed a testing methodology based on the boundary scan method to confirm the electrical performance of wide bus chip-to-chip interconnect in 3DICs. Cool Interconnect test chips were designed, fabricated, and flip-chip stacked. Joint Test Action Group (JTAG) and electrical connection tests were used to confirm the successful operation of the test chips, including the chip-to-chip interconnect.
Load Control for the DC Electrical Power Distribution System of the More Electric Aircraft

The more electric aircraft concept aims at increasing the amount and the power of the electrical subsystems on the next generation of aircraft. One of the major challenges in this framework is that the electric power is generated by means of electrical generators connected to the jet turbine shaft. In order to satisfy the peak power demand and overload conditions, the generators are oversized, increasing weight and total cost of operation. Possible solutions to this problem is to decrease the size of the generator and to provide the peak power with storage systems. As an alternative, load disconnection during peak demand (load-shedding) can be enforced with solid-state switches. This paper presents a different approach, i.e., by modifying the dc voltage of the distribution, the load can be controlled. In order to achieve this objective, a precise on-line identification algorithm is proposed. Theoretical analysis is performed, and simulation results on a study-case distribution system are reported, highlighting how the proposed load identification and control method effectively manages a power curtailment condition without load-shedding or storage systems. Power hardware in the loop simulations on a dc microgrid with a multiport power converter confirm the simulation analysis.
Model Development of Marble Quality Identification Using Thresholding, Sobel Edge Detection and Gabor Filter in a Mobile Platform

Organizations in the marble industry use machines to identify the marble slab’s quality which is costly for developing countries like the Philippines. Marble is classified by expert human visually and classification is prone to error. This paper presents a study on marble classification using image processing based on color and texture. Features extraction using Thresholding, Gabor Filtering, Sobel Edge and Local Binary Pattern (LBP). Three supervised learning was used which includes Support Vector Machine, Decision Tree, and Random Forest. 120 marble images were trained and 75 images were used for testing. LBP is consistent with 86.67% accuracy, 0.800 of Kappa and execution-time of 2 seconds using the Decision tree. The Model was applied to the prototype with 82% accuracy to 100 unlabeled images tested with the expert. In conclusion, the developed model can classify the marble quality which is higher than the accuracy from the previous research work applied to the industrial machines.
Auto-estimation of Relative Orientation Between a Roof-Mounted Spherical Camera and a Vehicle Based on Spherical SLAM

While humans can drive a vehicle using visual information, we investigate the possibility of driving assistance by using a single spherical camera. As the first step, this paper proposes a method of auto-estimation of the relative orientation between a roof-mounted spherical camera and a vehicle. The location of the spherical camera is estimated by a full-view SLAM (Simultaneously Localization and mapping) method [18]. If the trajectory of the camera is on a plane, the normal vector of the plane can be computed by fitting the plane. By combining the heading direction of the vehicle with the computed normal vector, the coordinate system of the vehicle can be determined. Experimental results are given to show the effectiveness of the proposed method.
Posture Correction of Quadruped Robot for Adaptive Slope Walking

The ability to maintain balance and stability is a major challenge for quadruped robots in locomotion. For example, when walking in the unstructured environment, the robot not only needs to adjust its gait according to the terrain information, but also needs to tune the body posture based on the real-time body posture acquired from inertial measurement unit (IMU) to maintain stability. In this paper, we present a method that focuses on the design of the posture correction controller based on the IMU feedback for adaptive slope walking for a quadruped robot. To this end, the posture correction controller is integrated into the foot trajectory generator to maintain balance. We experimentally validated our approach both on simulation and real quadruped robot. Compared with the one only with the foot trajectory generator, the quadruped robot can walk more stable with the posture correction controller integrated into the foot trajectory generator and has a better locomotion ability. Our method increases locomotion stability when walking on the slopes.
A Framework for Hybrid Location Prediction via Decision Tree Classification

Location prediction has attracted massive attention from various robotics research fields such as human-robot collaboration, trajectory planning in human-robot interaction and etc. By far many location predictors are proposed while none of them is capable of stably outperforming others when facing different mobility patterns. In this paper, a framework for hybrid location prediction is proposed with the idea that different location predictor is selected intelligently by analysing personalized mobility patterns. Experiments conducted on realworld datasets show the effectiveness of the proposed framework compared with single predictor. Furthermore, evaluation and discussion are offered to obtain more insights into this topic. Finally, the conclusion is drawn to summarize the entire work.
Computational Framework for Machine Fault Diagnosis with Autoencoder Variants

Fault diagnosis is an integral component of Condition Monitoring systems used in industries. Deep learning techniques have recently been proven to be very effective in handling heterogeneous real time monitoring data. This paper presents a computational framework for machine fault diagnosis based on deep learning approaches and autoencoder variants. The framework includes methods for extraction of time domain and frequency domain features from raw data, training of autoencoders, and finally, detecting the presence of fault(s) using classifiers. A total of four different variants of autoencoders have been tested and analyzed for fault diagnosis framework; additionally, the option of reducing the learning complexity in autoencoder with layer wise feature selection strategy with ITER ranking has also been applied. For validation purpose, four standard fault diagnosis datasets were used. To obtain inferences, statistical tests was performed across all tested feature extraction techniques, autoencoder variants, and classifiers. The analysis suggests that stacked denoising sparse encoder in conjunction with frequency domain features and support vector classifiers gives consistent classification accuracy thus builds effective fault diagnosis system.
Convolutional Neural Network Architecture for Semaphore Recognition

This paper proposes convolutional neural networks SRNet and Tiny-SRNet for human semaphore action recognition. SRNet is composed of 5 layers of convolution and 3 layers of fully connected layers. In addition to the first convolution layer. , a batch normalization layer is added before each convolution layer. In order to enable deep learning algorithms to be applied to both mobile and embedded platforms. , Tiny-SRNet removes the full connected layers in SRNet and replaces them with a convolutional layer and a global average pooling layer. The experimental results show that compared with the mainstream classification models AlexNet. , GoogleNet and VGGI6. , SRNet achieves the highest recognition rate of 98.9% on the semaphore dataset. , and Tiny-SRNet compresses its model size to 1/24 of SRNet with a reduction of 1.7% accuracy.
Kubebench: A Benchmarking Platform for ML Workloads

Machine Learning (ML) workloads are becoming mainstream in the enterprise but the plethora of choices around ML toolkits and multi-cloud infrastructure make it difficult to compare their performance and costs. In this paper, we motivate the need for benchmarking ML systems in a consistent way, discuss the requirements of an ML benchmarking platform, and propose a design that satisfies the requirements. We present Kubebench, an example open-source implementation of an ML benchmarking platform based on Kubeflow, itself an open-source project for managing any ML stack on Kubernetes, a widely used container management platform.
Research on Image Target Detection and Recognition Based on Deep Learning

Image target detection and recognition is an important research area in image understanding and computer vision, and has attracted extensive attention of scholars for its wide application. In recent years, with the enlightenment of human vision system, deep learning technology has been put forward, and has become a hot spot. The deep learning model has a multi-layer network structure, which can overcome the shortcomings of the traditional image target recognition methods. In the detection and recognition of image targets, deep learning methods are more and more widely used. This is because the deep learning multilayer network structure can express complex functions in a more concise way than the shallow network, and can learn the deep feature representation. Therefore, this paper uses the method based on deep learning, researches on image target detection and recognition, mainly introduces the region proposal based on the depth of learning based target recognition method of support vector machine algorithm and target detection depth. In this paper, deep learning is applied to image detection and recognition, further to improve the accuracy of image detection and recognition. It has important guiding significance in deep learning and image research.
Mark Carney: The turn of the year

Anniversaries provide the opportunity to look back and plan ahead. When doing so, it is sometimes helpful to recall Shimon Peres’ definition of a young person as someone whose future ambitions exceed their past accomplishments. So while Queen Mary can take justified pride in its long-standing commitment to community engagement and widening local participation, what is most striking are your plans for the future, including to push forward translational research in population-scale genomics, with the potential to bring health benefits to people across the UK and beyond.
Intelligent Detection for Artificial Lateral Line of Bio-Inspired Robotic Fish Using EMD and SVMs

In this paper, in order to solve the problem of accurately detecting the underwater moving target for the bioinspired robotic fish in the complicated natural interferences, where the acoustic perception in information interaction and perception system is vulnerable to multipath effect, and the optical sensing is easily affected by water turbidity, a novel intelligent detection method of artificial lateral line (ALL) is proposed. Firstly, these different intrinsic mode functions (IMFs) are decomposed via empirical mode decomposition (EMD) from original signals received by the ALL system, to separate the target signal from the interferences. Secondly, the characteristic frequency of the vibrating target representing the different fish is obtained from IMFs to detect the underwater moving target. Finally, these power spectrums of IMFs are input into the support vector machines (SVMs) to recognize the orientation of underwater moving target intelligently. Through tank tests, this method is applied to the ALL detection, and these testing results show that the proposed method has better detection performance than the traditional method, such as FFT and neural network, which provides a new way for the collaboration robot fish school.
A Double-Sided Stator Type Axial Bearingless Motor Development for Total Artificial Heart

This paper proposed a bearingless motor that has tilt control function and clarified its fundamental characteristics as a magnetically suspended motor and magnetically suspended pump. The rotor has eight permanent magnets (PMs) on each side (16 PMs in total). The two stators have 12 poles each with two types of concentrated windings. One is for motoring and axial control and the other is for tilt control. This bearingless motor has three degrees of freedom active positioning control. The theoretical analysis clarified operating principle and showed that the proposed motor could control translational motion, inclinational, and rotational movement independently. An experimental setup was fabricated, and the control performance of the self-bearing motor was also investigated. The fabricated prototype achieved a stable magnetically levitated rotation. In addition, the proposed axial self-bearing motor fabricated into a prototype of the magnetically suspended pump to investigate pump characteristics for a total artificial heart applications.
Public String Based Threshold Cryptography (PSTC) for Mobile Ad Hoc Networks (MANET)

Communication is an essential part of everyday life, both as a social interaction and collaboration to achieve goals. Wireless technology has effectively release the users to roam more freely to achieving collaboration and communication. The principle attraction of mobile ad hoc networks (MANET) are their set-up less and decentralized action. However, mobile ad hoc networks are seen as relatively easy targets for attackers. Security in mobile ad hoc network is provided by encrypting the data when exchanging messages and key management. Cryptography is therefore vital to ensure privacy of message and robustness against disruption. The proposed scheme public string based threshold cryptography (PSTC) describes the new scheme based on threshold cryptography that provides reasonably secure and robust cryptography scheme for mobile ad hoc networks. The scheme is implemented and simulated in ns-2. The scheme is based on trust value and analyze against Denial of Service attack as node found the attacker, the node reject all packet from that attacker. In proposed scheme whole network is compromised only when all nodes of network is compromised because threshold nodes only sharing public string not the master private key. The scheme provides confidentiality and integrity. The default threshold value selected is 2 according to time and space analysis.
Association Clustering and Time Series Based Data Mining in Continuous Data for Diabetes Prediction

Large amount of health related data is being produced in various levels of health system. Due to the size of the data it will be difficult to process the data and then extract the analysis. But with the use of different machine learning based approaches data can be processed. Machine learning based approach will provide the efficient data for curing of the patients. Even helps in forecast the future disease. Patients past history for different parameters can contribute to his probability of various health related issues. But using Association clustering and Time Series based data mining in Continuous data early warning system can be developed. This prediction based system can define the disease while analyzing his existing parameters. Some level of care can protect the patient from disease.
On the Power of Vector Linear Network Coding

This paper presents yet another instance of the power of vector linear network coding over scalar linear network coding. Previous works have established that the size of the finite field required to achieve a vector linear solution may be smaller than that size of the finite field required to achieve a scalar linear solution. It has been also shown there exist networks which do not have a scalar linear solution but have a vector linear solution. In this paper we show that the set of characteristics over which a network has a vector linear solution may be larger than the set of characteristics over which it has a scalar linear solution. We prove this result by showing a network which has a scalar linear solution if and only if the characteristic of the finite field is 2, but has a 2-dimensional vector linear solution over every finite fields.