Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Sentiment Analysis of Review Comments by Unsupervised Clustering of Features Using LSA and LDA

Text documents could be classified using words as features. As the number of words in the vocabulary is large, the dimension of the document space will be very high. In that case, the feature vector for a document is too long, and very sparse, and it makes clustering and classification algorithms fail. There are various ways to reduce this dimension. In this work, we used Latent Semantic Analysis (LSA), which is actuated by Singular Value Decomposition (SVD). After SVD, we have a compact representation of the documents, which are clustered. In a separate experiment, we did topic modeling using Latent Dirichlet Allocation (LDA). In this initial work, our premise is that comments are of two categories, positive and negative. We cluster the document, in the reduced dimensional space, into two, using K-means clustering. After dimension reduction by LSA and LDA, the ground truth for documents in two clusters was verified manually, and the results compared.In this work, we used tourists’ comments as documents. Tourists visit to a place is influenced by comments from previous visitors. Our final goal is to extract factors that lead to positive comments and those leading to negative comments. That would help promoting tourist business by focusing on the factors that really matters for the customers.
Uncertainty Measurement for a Grey Information System

A grey information system (GIS) is a new kind of IS. Uncertainty measurement (UM) can provide a new perspective for data analysis and help to reveal the essential features of data. This article studies UM for a GIS. The main work of this paper includes: first, the similarity degree between two information values of each attribute in a GIS is constructed. And then, the tolerance relation induced by a given subsystem is acquired by the similarity degree. After that, the information structure of this subsystem is brought forward. Additionally, measures of uncertainty for a GIS are explored. Moreover, the optimal selection of information structures based on the proposed uncertainty measures is studied and the application of these measures is displayed. Finally, to verify the validity of these measures, statistical effectiveness analysis is carried out. These results will help us understand the intrinsic properties of uncertainty in a GIS.
Thermal Modeling of a Power Ball Grid Array in System-in-Package Configuration

In power electronic designs, preliminary thermal analysis is essential when choosing packages and possible external heatsinks for energy thermal dissipation. In this paper, we describe a methodology for a simple development of a detailed thermal model for a fine pitch plastic Ball Grid Array (BGA) in System-in-Package configuration including one Bipolar-CMOS-DMOS (BCD) device, through the geometric and thermal properties of the power components and layers of the BGA under test. The validation of the proposed thermal model was carried out through a comparison of the simulation results with the experimental data obtained from the thermal analysis through an InfraRed (IR) camera. The absolute estimation error was less than 2°C for a maximum temperature within 100°C, and less than 4.5°C for temperatures above 100°C, with a temperature difference of the case around 50°C.
LiDAR-Guided Reduction Of Spectral Variability in Hyperspectral Imagery

Hyperspectral unmixing has attained a great importance in recent decades in remote sensing applications. Due to some external effect (illumination conditions) or internal effects (concentration of chlorophyll), spectral variability exists in hyperspectral images. This spectral variability causes significant errors in abundance estimates. In this paper, we propose a new framework that incorporates feature extraction with Digital Surface Model (DSM) clustering informationto suppress the effect of spectral variability in hyperspectral unmixing. In this way, meaningful material abundance estimates are obtained. Experiments are conducted on simulated data. Results show that spectral variability can be reduced with the aid of LiDAR data in hyperspectal unmixing.
A Novel Stacked High Step-down DC-DC Converter

A high efficiency and high power density 400/5V converter is proposed in this paper. Its working principle and designing method are illustrated in detail. The proposed converter contains two stacked LLC resonant converters. One LLC operates at unregulated mode which delivers most of the output power, while another LLC converter operates at regulated mode aiming to achieve constant output voltage. A 500kHz 25W stacked LLC resonant converter using GaN devices and planar matrix transformer are designed. Using the leakage inductance as the resonant inductor to further reduce the system volume and increase the power density The simulation and experimental results demonstrate the feasibility of the proposed converter.
Design and Implementation of a 150kW High Frequency Transformer in High Voltage Large Power Solid State Transformer Consist of Multi-DABs

This paper mainly focus on the design and implementation methods of a 150kW high power high frequency transformer (HPHFT) in high voltage solid state transformer consist of multi-DABs (Dual Active Bridges). Based on theoretical analysis and design experience, the detailed calculation process of winding loss considering skin effect and proximity effect is given and also the loss calculation method of nancrystalline materials. Then some other aspects of the design process, such as winding structure, insulation and so on, are introduced. At last, a prototype of a 150kW high frequency transformer was implemented, experimental results of the proposed transformer coincide with the design.
A Novel Sliding Mode Observer-based Sensorless PMSM Control

This paper proposes a novel and stable sliding mode observer (SMO)-based Permanent Magnet Synchronous Motor (PMSM) sensorless control scheme by adopting a novel sliding mode surface function and control function. The estimated position and speed will be more stable and accurate for the PMSM sensorless system, which will further contribute to suppressing the torque ripple and the steady-state error of the system. For the proposed sliding mode surface function, the changing rate is adjusted in real time as the change of the state error. In addition, considering that back electromotive force is influenced by the velocity of the motor, the boundary layer of the saturation function is modified, and the combinatorial switching function will further improve system stability. Finally, the Lyapunov function is used to verify the stability of the adapted SMO, and the simulation results show the effectiveness of the proposed sensorless control.
High Power Integrated Charging System for Electrical Vehicles Based on Winding Multiplexing of PMSM

The charging power of electrical vehicles (EVs) is usually limited by on-board chargers, while the off-board DC charger is constrained by the investment of charging stations. This paper introduces a kind of high-power integrated charging system for EVs based on PMSM winding multiplexing. The integrated charging system reconstructs the stator’s winding and motor driver circuit as a dual three-phase staggered boost for charging, which realizes the driving-charging function integration and the system cost reduction. The structure and working principles of system are presented, then the current ripple elimination is discussed. PFC function and zero torque output are realized by PR feed forward compensation control of current, and the performance are verified by simulation results.
Neighborhood Adaptive Graph Convolutional Network for Node Classification

Recently, graph convolutional neural network as an efficient and effective method has experienced significant attention and becomes the de facto method for learning node or graph representations. However, existing most methods use a fixed-order neighborhood information when integrating node representations for node classification on the graph. In this paper, we present a neighborhood adaptive graph convolutional network (NAGCN), a novel method to efficiently learn each node’s representations. Particularly, we construct a convolutional kernel abstracted from the diffusion process, named as the neighborhood adaptive kernel to more precisely learn and integrate related neighborhood node information for each node. As a result, our proposed method can learn more useful information across the relevant near and distant neighbors according to the real applications. We also adopt a threshold mechanism on the constructed kernel to better reserve the most impact neighbor vertices for each node on the graph. Besides, one learnable feature refinement process is used in the model to obtain high-level node representations with sufficient expressive power. The model is also theoretically analyzed in terms of spectral convolution and message passing algorithm. Notably, extensive experiments demonstrate that our method can achieve better performance on node classification tasks compared to other related approaches.
Assessment of Dynamic Voltage Support Control Schemes for Photovoltaic Generators Connected to Power Systems

Nowadays, the trend is that modern power systems operate with high penetration of photovoltaic generation, which must provide adequate voltage support in contingency situations. This paper presents an analysis and assessment of the effect on the dynamic performance of the voltage magnitude of two voltage control schemes of photovoltaic generators connected to power systems. Fault-ride through (FRT)-based dynamic voltage support schemes with and without injection of reactive power are evaluated. Study cases in the IEEE 14-bus test system are presented in which the performance of the contingency voltage response is assessed from dynamic voltage indices. Results showed that with a control scheme for photovoltaic systems which injects reactive power into the power system in the transient stage it is possible to avoid problems of fault-induced delayed voltage recovery (FIDVR) and fast voltage collapses.
Research on Bearing Current Detection Method of High-speed Motor Driven by PWM Inverter

With the wide application of PWM inverter, bearing failure caused by bearing current seriously affects the reliability and safety of the driving system. Accurate measurement of bearing current is the key to evaluate the simplified model of bearing current and analyze the influence of bearing current. This paper adapts a method for measuring bearing current. Firstly, this paper analyzes the capacitance model of high-speed motor bearings represented by PMSM. Secondly, this paper analyzes and calculates the stray capacitance, and performs finite element calculation for a certain machine. Comparing the analysis results, a method for detecting the shaft current is described. Finally, experimental tests for stray capacitance will be given, the measured stray capacitance value is substituted into the MATLAB simulation model. Based on the proposed detection method, the shaft current waveform is measured to verify the accuracy.
Approximate Arithmetic Circuit Design Using a Fast and Scalable Method

Approximate computing can be applied to error-tolerant applications, by trading off accuracy for lower power consumption, shorter delay and smaller area. In this paper, we focus on the approximate arithmetic circuit design especially targeting combinational multipliers and adders, which are essential computing components in machine learning such as neural network computation. We propose a novel approach to generate approximate circuits from the given correct circuits. The basic idea is to study the circuit’s characteristics from the small instances of the circuits and then establish a common algorithm for larger circuits with the same architectures. We propose two different methods and apply them to adders and multipliers with different architectures. The experimental results demonstrate that our method outperforms the state-of-art methods in terms of the quality of the circuits with orders of magnitude shorter processing time.
Automated Identification of Over-Privileged SmartThings Apps

The permission system in the SmartThings platform governs how apps access devices. The system was designed to protect devices from third-party apps, by forcing apps to access devices through their capabilities. Design flaws in the system result in apps being over-privileged with unauthorized capabilities. This vulnerability represents serious security challenges to this platform and its users. In this paper, we present an automated tool that can identify over-privilege vulnerability in SmartThings apps. We have identified common patterns, and we have used this knowledge to design our automated over-privilege detection tool. We have evaluated the effectiveness of our tool on 222 official and third-party apps, and we have found that approximately 5.5% of defined devices were misused with 76 identified instances of over-privilege.
An Energy-Efficient Computing-in-Memory Neuromorphic System with On-Chip Training

The aim of neuromorphic computing system is to implement the computational power and efficiency of the human brain. Computing-in-memory (CIM) is a promising and energy-efficient way to perform intensive computations, whose structure is similar to human brain synapse. A 8.78TOPS/W biologically-inspired neuromorphic computing system for pattern recognition based on CIM architecture is presented in this work. The proposed system supports on-chip training with energy-efficient bio-plausible spike-timing-dependent plasticity (STDP) rule and performs multiply-and-accumulate (MAC) computations inside SRAM array during inference, which greatly reduces the energy consumption. Simulated in 65-nm technology, the proposed system achieves good performance and energy efficiency for pattern recognition. The total energy consumption of training and classifying per image of the proposed system is 0.20 nJ. And the proposed spiking neural network (SNN) just consumes 0.074mW at 1.0V with the throughput of 2.5M images/s in inference phase.
Implicit Discourse Relation Recognition via a BiLSTM-CNN Architecture With Dynamic Chunk-Based Max Pooling

Implicit discourse relation recognition is a serious challenge in discourse analysis, which aims to understand and annotate the latent relations between two discourse arguments, such as temporal and comparison. Most neural network-based models encode linguistic features (such as syntactic parsing and position information) as embedding vectors, which are prone to error propagation due to unsuitable pre-processing. Other methods apply different attention or memory mechanisms, mainly considering the key points in the discourse, yet ignore some valuable clues. In particular, those using convolution neural networks retain local contexts but lose word order information due to the standard pooling operation. The methods that use bidirectional long short-term memory network consider the word sequence and retain the global information, but cannot capture the context with different range sizes. In this paper, we propose a novel Dynamic Chunk-based Max Pooling BiLSTM-CNN framework (DC-BCNN) to address these issues. First, we exploit BiLSTMs to capture the semantic representations of discourse arguments. Second, we adopt the proposed convolutional layer to automatically extract the “multi-granularity” features (just like n-gram) by setting different convolution filter sizes. Then, we design a dynamic chunk-based max pooling strategy to obtain the important scaled features of different parts in one discourse argument. This strategy can dynamically divide each argument into several segments (called chunks) according to the argument length and the number of current pooling layer in the CNN and then select the maximum value of each chunk to indicate crucial information. We further utilize a fully connected layer with a softmax function to recognize discourse relations. The experimental results on two corpora (i.e., PDTB and HIT-CDTB) show that our proposed model is effective in implicit discourse relation recognition.
Investigation on Thermo-Physical Properties of 500 nm CVD Grown Tungsten Layers using Time Domain Thermoreflectance and Scanning Thermal Microscopy

Two valuable tools for the thermal characterization of thin films are Time Domain Thermoreflectance (TDTR) and Scanning Thermal Microscopy (SThM). TDTR is capable of delivering temperature dependent thermal properties by comparing measured TDTR data to different models of heat transport. In this work different models are applied on a 500 nm thin, CVD grown W-layer on TiN coated Si. SThM is a complementary technique where the thermal conductivity (λ) gets directly measured in a quantitative way using the 3ω method. TDTR and SThM show that room temperature W-samples are altered by heat treatment under argon atmosphere: On the as deposited W-layer, λ is about 154 W/mK. After a short heat treatment (2.5 minutes at 425°C), λ decreases to about 147 W/mK, while along heat treatment (4 cycles of ~18 minutes at 425°C) increases λ to ~155 W/mK.
Electromagnetic Performance Prediction of a Double-Rotor Flux-Switching Motor Based on General Air-Gap Equivalent Algorithms Model

In this paper, a dynamic nonlinear magnetic network model is proposed to predict the electromagnetic performance of a double-rotor flux-switching motor (DRFSM) in which a general air-gap equivalent model considering the irregular field distribution of double air gaps and the dynamic variation of air gaps composition is involved. The double air-gap distribution and inner-outer machine back-EMF of the DRFSM are efficiently predicted by the proposed computation model. The results are validated by the finite element analysis (FEA) and experiments, where good agreements prove the accuracy and effectiveness of the proposed model.
A Framework for Usability Design to Promote Awareness of Information Disseminated via Mobile Government Applications

Mobile application have become the first choice for disseminating information in several situations. Many government agencies developed m-government applications, mainly for information dissemination and electronic services delivery. Information has great effects to citizen’s life in several ways. Warning messages are an obvious example. They are usually provided to people in affected or risk areas. Effective information dissemination of such information requires more than assembling them on the user interface. It requires a careful design. Designing how they are presented is not an easy task. Moreover, effective and efficient presentation can be difficult. Appropriate presentation aids in understanding of information and gives the correct clue on what to do next. This paper presents the analysis of usability factors to support design and development of m-government applications which main task is information dissemination. Existing usability models were analyzed. Hundreds of publications in related areas were reviewed. Then, usability factors to m-government applications were collected and clustered. The usability design framework is proposed to promote effective use of the usability factors in the development process. The framework is designed to support incorporation of usability factors that lead to usable interface. The factors included in the framework are Learnability Satisfaction, Memorability, Simplicity, Privacy, and Security.
Real-Time Forecasting and Classification of Trunk Muscle Fatigue Using Surface Electromyography

Low Back Pain (LBP) affects the vast majority of the population at some point in their lives. People with LBP show altered trunk muscle activity and enhanced fatigability of trunk muscles is associated with the development and future risk of LBP. Therefore, a system that can forecast trunk muscle activity and detect fatigue can help subjects, practitioners and physiotherapists in the diagnosis, monitoring and recovery of LBP. In this paper, we present a novel approach in order to determine whether subjects are fatigued, or transitioning to fatigue, 25 seconds ahead of time using surface Electromyography (sEMG) from 14 trunk muscles. This is achieved using a three-step approach: A) extracting features related to fatigue from sEMG, B) forecasting the features using a real-time adaptive filter and C) performing dimensionality reduction (from 70 to 2 features) and then classifying subjects using a supervised machine learning algorithm. The forecasting classification accuracy across 13 patients is 99.1% ± 0.004 and the area under the micro and macro ROC curve is 0.935 ± 0.036 and 0.940 ±0.034 as determined by 10-fold cross validation. The proposed approach enables a computationally efficient solution which could be implemented in a wearable device for preventing muscle injury.
A Novel On-line IGBT Junction Temperature Measurement Method Based on On-state Voltage Drop

Insulated gate bipolar transistor (IGBT) module is the most widely used power electronic device in converters. The junction temperature monitoring of IGBT is critical for avoiding sudden failures and health management of converters. However, the existing monitoring methods have some disadvantages including low feasibility of on-line measurement, intrusiveness and slow response. A novel on-line IGBT junction temperature measurement method based on on-state voltage drop is proposed in this paper. IGBT junction temperature has a strong influence on IGBT on-state voltage drop. This paper considered the influence of IGBT current and measurement circuit temperature. The method proposed can realize on-line monitoring of IGBT junction temperature with low cost and simple circuit structure. The experimental results validate the feasibility of the proposed technique.
IR-UWB Study for Intra-Satellite Wireless Communication

This paper reports the prototype evaluation results of the Impulse Radio UWB (IR-UWB) module developed for an intra-satellite wireless communication system. The PN-Pulse Density Modulation (PN-PDM) scheme and the Pulse Position Modulation (PPM) scheme are adopted as our unique functions for resistance in a multipath environment, and a thermal structure model of a small satellite was used to evaluate the performance of both schemes. We have confirmed the robustness of communication by the PN-PDM scheme, realized error-free communication of 1.3 Mbps data rate, and confirmed the effectiveness of the reflection wave cancelling function.
Discriminative Object Discovery Toward Personalized Sightseeing Spot Recommendation

Discovering discriminative objects representing the local characteristics is an important task for sightseeing spot discovery, assessment, and personalized recommendation. Some patch-based methods have recently been proposed to discover discriminative objects from geo-social images on social network sites. Conventional methods discover discriminative objects by comparing two or more cities during the early steps and require the entire process to be re-run when the comparative cities change. This approach makes conventional methods inflexible for personalized sightseeing recommendations in which different users may have different backgrounds and the comparative targets may differ. In this paper, we propose a flexible framework for discriminative object discovery toward a personalized sightseeing recommendation. Our framework detects common patches/objects in advance and leaves the comparison process of discovering discriminative objects at the later steps. Our framework is more efficient than previous approaches, and the experiment results on real-world datasets also show that it maintains a good performance in discriminative object discovery.