Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Electric Vehicle Demand Response Strategy in Traffic-Grid Coupling Networks Based on Charging State Forecasting Model

As the environmental concerns increase globally, electric vehicles (EVs) play an important role in energy saving and emission reduction perspectives. However, the integration of EV charging loads into power grid brings negative effects such as overload and voltage deviation. In order to solve these problems, an EV demand response (DR) strategy in traffic-grid coupling networks based on the charging state forecasting model is proposed. Firstly, the charging state forecasting model was established considering the traffic condition, which can capture the distribution characteristics of the aggregate EV charging loads. Then a DR strategy considering the power flow constraints was proposed. The DR potential of each coupling node are evaluated and the charging loads are rationally dispatched in the strategy. Finally, simulation results show that the proposed method can effectively alleviate the voltage deviation in the regional power grid.
Scalable Pointer-based Memory Protection for Data-intensive Computing

This study provides an architectural support for fast and efficient memory protection for multithread work- loads in data-intensive computing environments. Boundary information used for preventing illegal pointer manipulation can be effectively managed with a novel architecture that enables fast access to the boundary information for memory-centric, many-core systems. Simulation results show that the proposed architecture reduces the average miss latency of boundary information by 33% compared to the existing hardware scheme.
DRAIN Asynchronization for Cyclic Throughput Fluctuation of CUBIC TCP and TCP BBR

The throughputs obtained by CUBIC TCP and TCP BBR periodically fluctuate if they communicate sharing the bottleneck link. This fluctuation severely declines the user experience of network-connected consumer electronics devices. In this paper, we discuss the relationship between the window size of measuring minimum RTT of TCP BBR and the strength of fluctuation. We then show that this fluctuation was relieved by randomizing this window size.
A Knowledge Graph Embedding Approach for Metaphor Processing

Metaphor is a figure of speech that describes one thing (a target) by mentioning another thing (a source) in a way that is not literally true. Metaphor understanding is an interesting but challenging problem in natural language processing. This paper presents a novel method for metaphor processing based on knowledge graph (KG) embedding. Conceptually, we abstract the structure of a metaphor as an attribute-dependent relation between the target and the source. Each specific metaphor can be represented as a metaphor triple $(target, attribute, source)$. Therefore, we can model metaphor triples just like modeling fact triples in a KG and exploit KG embedding techniques to learn better representations of concepts, attributes and concept relations. In this way, metaphor interpretation and generation could be seen as KG completion, while metaphor detection could be viewed as a representation learning enhanced concept pair classification problem. Technically, we build a Chinese metaphor KG in the form of metaphor triples based on simile recognition, and also extract concept-attribute collocations to help describe concepts and measure concept relations. We extend the translation-based and the rotation-based KG embedding models to jointly optimize metaphor KG embedding and concept-attribute collocation embedding. Experimental results demonstrate the effectiveness of our method. Simile recognition is feasible for building the metaphor triple resource. The proposed models improve the performance on metaphor interpretation and generation, and the learned representations also benefit nominal metaphor detection compared with strong baselines.
Analysis of CNN Based Schemes for LDPC Code Classification Using LUT Based Algorithms

This paper analyzes the performance of an LDPC code classification system that determines membership of code-words among 3 randomly generated binary LDPC codes. These codes all have the same codeword size and coderate. High classification accuracies are obtained with relatively small neural networks. The analysis presented here determines the accuracies of various look up tables and compares them to the performance of the neural networks.
Optimisation of reaction conditions for the synthesis of single‐walled carbon nanotubes using response surface methodology

The effects of three preparation variables, i.e. reaction temperature, reaction time and reaction gas (methane/nitrogen) flow rate, on the ratio of the intensity of the Raman D band to the intensity of the G band (ID/IG), carbon mass and the presence of radial breathing mode (RBM) peaks were investigated by using a central composite design to develop two linear models. The most influential factor in each experimental design-response was identified using the analysis of variance. The predicted ID/IG ratio, carbon mass and presence of RBM peaks determined during the process optimisation were found to agree satisfactorily with the experimental values. The optimum conditions for synthesising single-walled carbon nanotubes were determined to be a reaction temperature of 900°C, a reaction time of 59 min and a reaction gas flow rate of 54 mL/min. © 2011 Canadian Society for Chemical Engineering
One-Class Support Vector Machine for Data Streams

In various information systems, application learning algorithms have to act in a dynamic environment where the acquired data is in data streams. In contrast to static data mining, processing streams introduce an array of computational and algorithmic stipulations. With the continuous input of data in data streams, one would like a mechanism that automatically identifies unusual events in the time series. The topic has been in the limelight as it has huge potential for real-time activities. To show the algorithm’s robustness, we have trained the classifier to multiple activities and its success in identifying each activity. The paper explores the possibility of using the One-Class Support Vector Machine (OCSVM) for novelty detection in data streams.
A Framework Classification of Heart Sound Signals in PhysioNet Challenge 2016 Using High Order Statistics and Adaptive Neuro-Fuzzy Inference System

To investigate the performance of Adaptive Neuro-Fuzzy Inference System (ANFIS), activated by spectral analysis features, for detection of abnormal cardiac valves sound signals. A dataset of 1837 heart sound signals were acquired from international PhysioNet Challenge 2016 databases (classes A, B and E). This included 1369 normal and 468 abnormal signals. The signals were de-noised using Notch and Butterworth filtering, fed to Discrete Fourier Transform, and 5 features using High Order Spectral (HOS) analysis were extracted from the third Cumulant. Later, the ANFIS neural network was trained and tested to discern abnormal signals. The results showed that the selected features were statistically significant ( $\text{p} < 0.05$ ). The proposed method was tested and achieved classification of 63-89% accuracy, 63-100% sensitivity, and 62-100% specificity, respectively. The results were compared with reports utilizing different neural network techniques, indicating competitive performance. The HOS spectral features can be reliable to participate in neural network systems to sort heart sound (HS) signals as normal or abnormal. The bispectral matrix is a new presentation of attributes describing signals. The ANFIS is a suggestive successful tool, which has not been attempted in Physio-net challenge 2016. The HOS attributes and ANFIS can participate successfully in PhysioNet Challenge 2016.
TAS Scheduling for Real-Time Forwarding of Emergency Event Traffic in TSN

IEEE 802.1 Time-Sensitive Networking (TSN) Task Group defines the IEEE TSN standards for time-sensitive networks where time-critical traffic with various latency requirements coexist. However, low-latency and low-jitter are guaranteed only for traffic streams whose transmission intervals and sizes are known and have been scheduled in advance. In an actual network environment, emergency event traffic with the highest priority may occur sporadically without prior notice, and this may deteriorate the performance of both scheduled and emergency event traffic. To address this issue, we propose an enhancement to the TAS scheduling algorithm by introducing a protection band that allows rapid transmission of the emergency event traffic with minimal influence to the scheduled traffic, and evaluate the effectiveness of our proposed scheme using OMNeT++ simulator.
Combination of Pole Number of Motor and Suspension Windings and the Influence on Suspension Force in Bearingless Motors

In high speed drives of the permanent magnet (PM) synchronous bearingless motors, the eddy current is generated in the PMs on the rotor in accordance with the same principle as induction motors. As a result, Lorenz force is generated in the rotor PMs. It is concerned that suspension force will not be generated to follow the command due to Lorentz force. In this paper, the bearingless motors with 4- pole motor and 2-pole suspension windings (4t2sBelm) and 2- pole motor and 4-pole suspension windings (2t4sBelm) are focused and the influence of Lorenz force on magnetic levitation performance is conducted. Furthermore, the behavior of both types is compared considering Maxwell Force and it is found which type is suitable in high speed drives. Finally the motor efficiencies of both types are derived using a simulation software by Finite Element Method (FEM). The results are considered and it is discussed in detail why the difference between them is caused.
Weighted LDA Using Metadata for Extracting Topics Needed by Teachers

This paper proposes a method for weighted latent Dirichlet allocation (LDA) using metadata for extracting topics with latent needs by teachers. The proposed method enables us to identify and display contents needed by teachers. Specifically, by improving the original LDA through weighting of word frequencies using metadata that reflects teachers’ needs (e.g. Likes), the proposed method enables extraction of topics that are not only common to many contents but also needed by teachers. Experiments using contents on Forestanet, i.e., the largest Web service for teachers in Japan, verify the effectiveness of the proposed method.
PrTaurus: An Availability-Enhanced EMR Service on Preemptible Cloud Instances

EMR (Elastic Map Reduce) is a service provided by mainstream cloud vendors for data processing users to directly obtain well-managed Hadoop YARN clusters on the cloud. Preemptible instance is a kind of cloud server that is cheap but is likely to be reclaimed by cloud vendors suddenly. Running EMR clusters on preemptible instances relies on YARN's own fault-tolerance, which is limited. In this paper, we present PrTaurus as an availability-enhanced EMR service on preemptible instances. PrTaurus integrates a system-level checkpoint capability based on Docker into YARN to further improve its fault-tolerance. In addition, PrTaurus's scheduling strategy takes advantage of Alibaba Cloud's one-hour protection policy. Furthermore, a new method that comprehensively considers cost-efficiency, preemption risk and overhead is proposed to select cluster instances. We evaluated PrTaurus through simulations on real-world workload and instance price traces. Experimental results show that compared with the existing EMR clusters running on preemptible instances, PrTaurus significantly reduces cost (13.0%-74.6%), instance preemptions (60.3%-88.9%), and task preemptions (86.0 % – 98.6 %).
Influence of Variation of Recoil Permeability of Bonded Magnets on Demagnetization Characteristics of Permanent Magnet Assisted Synchronous Reluctance Motor

Demagnetization analysis is complicated in the case of motors with bonded magnets, which have unique B-H curves and do not exhibit clear knee points. We have proposed a permanent magnet assisted synchronous motor (PMASynRM) containing bonded magnets, and examined its demagnetization characteristics in the case where the recoil relative permeability (µrec) was assumed to be constant. However, µrec in fact depends on the reverse magnetic field from the stator and the temperature. This study therefore investigates the irreversible demagnetization characteristics of a PMASynRM taking the variation of µrec for the bonded magnets into account. The analysis results are compared to those when µrec is assumed to be constant. The results indicate that there is no problem with assuming a constant µrec of 1.15 and an analysis model having mainly the reluctance torque.
A Smart Location-Aware Hand Sanitizer Dispenser System

The design, development and results from the deployment of a hand hygiene monitoring and reminder system is presented. The system is based on a dispenser equipped with low-energy Bluetooth technology which when coupled with a mobile application provides the basic location-awareness necessary to track the proximity of health-care worker to the dispenser. The system architecture provides the unique feature of selectively enabling the dispenser action in the smart dispenser only when the intended user is in its proximity. This feature is critical for deployment of hand sanitizer dispensers in hospital wards where there is apprehension in patients ingesting the sanitizer persists. The mobile application maintains an online database of the hand hygiene event records. The records are presented to administrators through a web-based dashboard for assessment and improvement of organizational hand-hygiene compliance levels. Preliminary quantitative results as well as qualitative results are presented and demonstrate the effectiveness of the system to serve as form of motivation for health care workers to improve their hand hygiene compliance.
Non Invasive Continuous Detection of Mental Stress via Readily Available Mobile-Based Help Parameters

Mental stress is a universal condition experienced by all humans alike at least once in their lifespan. Stress can vary from person to person depending on their age, gender, socioeconomic background and lifestyle. Although some amount of stress act as a beneficial factor, accumulated stress levels over a long period could lead to many other health problems. Hence, early detection and diagnosis is the pre-eminent method in which this damaging phenomenon can be managed. Vocal indices and facial expressions of an individual disclose surfeit amounts of information including emotions, and in turn stress. In this research two noninvasive and dynamic mechanisms, in the form of speech emotion analysis and facial expression analysis, are used in detecting stress, through emotion analysis, of an individual in a mobile and real-life environment as opposed to utilizing only one mechanism to detect stress in a controlled environment. This study proposes a holistic approach in detecting mental stress, through the categorization and identification of fear/anxiety, sadness, anger and disgust as stress emotions via extracted vocal and facial features. A finalized product is proposed to recognize stress, averaged biased on the prediction probabilities of the two detection mechanisms which then can be used to individually and independently monitor stress in order to maintain it without relying on physical medical checkups.
Age-dependent degradation estimation focusing on dynamic features of head motion in daily conversation

We are developing a system that can estimate the degrees of decline for the elderly from daily conversation and inform them when the estimated result indicates a decline. In the paper, we discuss the effectiveness of the dynamic features of gestures during conversations to identify age-dependent degradation. By focusing on the head motion when a speaker responds to a partner while talking, we compare the optical flow values of images between independent elderly speakers and facility elderly speakers. The results of identification experiments using Support Vector Machine (SVM) indicate the average of correct rate is 83.3%. This result suggests that the dynamic feature of head motion has a tendency to be effective for age-dependent degradation estimation.
Detection of Long Narrow Landing Features for Autonomous UAV Perching

It is highly advantageous for Unmanned Aerial Vehicles (UAVs) to be able to perch on natural features or human-made structures in order to conserve power, maintain a fixed position for data collection, or for performing manipulator tasks. Long narrow landing features can commonly be found on industrial structures such as power transmission towers and are convenient for UAVs to perch on due to being easy to grasp and being generally unobstructed by the presence of other nearby objects. A landing feature detection algorithm was developed and implemented using a Microsoft Kinect to provide 3D point cloud data. The algorithm was tested on a scene composed of two step-ladders and several rods joining the two together. The algorithm detects line segments in the point cloud data using Random Sample Consensus (RANSAC), and then evaluates the suitability of the detected lines for landing. The algorithm successfully detected rods which were at a suitable orientation, the correct diameter and length, and which were unobstructed by other objects.
A Carrier Frequency Offset based Key Masking Scheme for Physical-Layer Authentication in Spatially Correlated Channels

In this paper, we propose a key masking scheme using carrier frequency offsets for physical-layer challenge-response authentication mechanism (C-PHY-CRAM) which prevents eavesdropper from exploiting a secret key with correlated channels in multicarrier systems. In the conventional PHY-CRAM method, if an eavesdropper is close to a legitimate user, a secret key can be exposed to the eavesdropper since two channels to mask the secret key can be strongly correlated. To avoid the potential leakage of the key, CFO induced by the local oscillator difference between transceivers is employed as an extra attribute to encapsulate a secret key in the proposed C-PHY-CRAM. Through channel simulations, we demonstrate that the proposed scheme outperforms conventional PHY-CRAM and its alternative for correlated channel environments.
OTA-C Filter Based on the Low Noise and HD3 Transconductance for ECG Detection $\sim$

This paper presents a 5-order low-pass filter (LPF) structure based on the operational transconductance amplifier(OTA) structure that is designed with low noise and HD3. To ensure that the filter has a lower power dissipation, noise, and third harmonic distortion(HD3), the OTA operates in weak inversion region and uses technology including current division, current cancellation, source degeneration. The LPF is simulated by the 0.18 µm CMOS process, and the results show that the LPF locates within 250Hz and consumes 65.49 µW, the ripple is less than 0.2dB, and the HD3 is less than -70.21dB (@50mV). The maximum input reference noise (IRN) is $71.86\ \mu V/\sqrt{Hz}$ The proposed LPF is suitable for Electrocardiograph (ECG) signal detection analog front circuit.
Fast mask near-field calculation using fully convolution network

Near-field calculation for thick mask is a fundamental task in lithography simulations. This paper proposes a fully convolution network (FCN) method to improve the efficiency of three-dimensional (3D) mask near-field calculation compared to the rigorous electromagnetic field (EMF) simulation methods. Taking into account the 3D mask effects, the network is trained based on a set of mask samples and the corresponding near-field data obtained by the EMF simulator. During the testing stage, the trained FCN is used to rapidly predict the diffraction near-field of the testing mask patterns. The performance of the proposed FCN approach is evaluated by simulations based on EUV lithography.
Paving the Path for Two-Sided Energy Markets: An Overview of Different Approaches

The transition in the energy sector, initiated by increasing uptake of distributed energy resources (DER) and grid digitization, provides opportunities to improve energy efficiency through electricity market reformation. The development of a two-sided market allows delivering benefits of improved efficiency to energy customers. This paper discusses the opportunities and challenges related to transitioning to a two-sided energy market. The drivers to move toward a two-sided market, the benefits that this market can provide for different stakeholders, and enabling technologies for this transition are studied. An overview of different approaches that lay the groundwork for two-sided markets, including demand response (DR), virtual power plants (VPPs), peer-to-peer (P2P) trading, and transactive energy (TE) is provided. A classification of different approaches, along with examples of academic and industrial works, are presented to give some insights on the way each approach can pave the path to the market reformation. Finally, different approaches are compared and some of the challenges that need to be addressed in future works are identified.
Evaluation of Equivalent Core Length in Synchronous Machines with Radial Ventilation Ducts for Industrial Applications

Radial ventilation ducts are applied to stator or rotor core inside synchronous machines about 1MW and over in industrial applications. The radial ducts have a positive effect on reducing temperature rise in windings. As some part of main magnetic flux flows the radial ducts, called fringing effect, main design variables such as terminal voltage, synchronous reactance, and equivalent core length can be changed significantly.In this paper, the equivalent core length using several calculation methods is calculated and applied to a 2-D finite element analysis. A 3-D finite element analysis is performed considering actual core length. The appropriate method is verified that the voltage and core loss between results of the analysis and the factory test are compared.
Machine Learning Meets Communication Networks: Current Trends and Future Challenges

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction.
Application and Assessment of Click Modular Firewall vs POX Firewall in SDN/NFV Framework

The evolution of Software Defined Networks (SDN) and Network Function Virtualization (NFV) introduced a revolutionary development in network architecture. SDN together with NFV provides users a platform to design flexible virtual networks (VNs) on a shared computer infrastructure. However, network administrators have specific requirements to secure this network. There are new security demands for VN such as flexible network function migrations and user-focused security system, which may not be supported by traditional firewalls. In our work we have implemented SDN and NFV based firewalls on an open source platform mininet. POX module and Click Modular Router are used to develop our firewall modules. Then we evaluated the performance of both firewalls with packet loss and throughput measurement.