Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Isa Hierarchy Generation for User-Centric Semantic Web

In ontological theories, is-a hierarchy must represent the essential property of things and hence should be single-inheritance, since the essential property of things cannot exist in multiple. However, we cannot avoid multiperspective issues when we build an ontology because users often want to understand things from their own viewpoints. Especially, in the Semantic Web, the variety of user issues in capturing target domains. In order to tackle this multi-perspective issue, we should adopt an approach of dynamically generating is-a hierarchies according to the viewpoints of users from an ontology using single-inheritance. This article discusses a framework for dynamic is-a hierarchy generation and its implementation as an extended function of Hozo. Through the function, users can understand an ontology from their own viewpoints.
The Prediction of Cellphones’ Fault Rates with Grey Models

The prediction of the faulty rate of a cellphone is essential for the supply chain management system of spare parts. However, the fault rate of the mobile is affected by many factors that makes it difficult to predict. In this work, some new concepts for prediction of faulty rate based on grey model theory such as grey fault rate and grey model fault count are proposed. It is found that the grey fault rate is consistent with the bathtub curve that widely applied in the reliability engineering. The grey model theory is utilized to solve the problem of random individual fault affecting the prediction negatively. The characteristic value of the grey failure rate is defined to describe the fault rate for certain phones’ models. We develop the method to predict the fault of a new phone model based on the data of certain old phone models and their grey failure rate. The proposed method is applied to fault rate prediction of two cellphone models that results with the prediction deviation about 2% over 3 years.
Hyperspectral Image Classification Based on Long Short Term Memory Network

In the task of hyperspectral image classification, how to learn features of hyperspectral image is the important and difficulty issue which may directly affect the classification results. Inspired by the idea of natural language processing, in this paper, we propose a local space long short-term memory network based hyperspectral image classification, which constructs sequential features in the local area of hyperspectral images. This method is based on the integration features of two traditional low-level features, and from these integration features to extract sequential features of the center sample in the local space, then use the long short-term memory network to learn high-level semantic features, finally use them to classify image. This method can not only obtain more representative and discriminative high-level semantic features, and through constructing the local space sequence to enhance positive impact of the useful pixels, inhibit negative effects of useless pixels, it improves the classification accuracy.
Progressively Balanced Multi-class Neural Trees

Decision trees are discriminative classifiers that hierarchically partition the input space to achieve regions containing instances having uniform class label. Existing works in this area have mostly focused on C4.S trees that learn axis aligned partitions. On the other hand, neural trees learn oblique partitions from data and use lesser number of decision nodes hosting perceptrons. However, these perceptrons are susceptible to data imbalances. This motivated us to propose a progressively balanced neural tree where training dataset are balanced prior to perceptron learning. The second contribution is the optimization of the decision function with respect to entropy impurity based objective functions. This formulation also allows a parent node to have more than two child nodes. The proposed algorithm is benchmarked on ten standard datasets against three baseline multi-class classification algorithms.
Vehicle Detection Under UAV Based on Optimal Dense YOLO Method

In this paper, a deep neural network model based on small target detection under UAV platform is designed. Due to the One-stage detection model like YOLO having novel structure and great industrial application potential, this paper proposes a new model of detection based on YOLOv2 structure. Faced with missed detection problem of small target, a series of improved schemes are proposed, which are suitable for small vehicles’ detection under aerial view angle, and can achieve real-time detection, including dense topology and optimal pooling strategy.
Based on Silicon-Micro-Ring-Resonator and Triple-Ring Cavity for Stable and Tunable Erbium Fiber Laser

In this paper, a stable and wavelength-switchable erbium-doped fiber (EDF) laser by utilizing the silicon-micro-ring-resonator (SMRR) and multiple-fiber-ring (MFR) scheme is demonstrated. Here, three optical couplers (CPs) can construct the passive MFR in the proposed and achieve single-longitudinal-mode (SLM) output simultaneously. The SMRR is used inside a fiber cavity as mode restrict element for wavelength-tuning. By properly adjusting the polarization controllers (PCs), the output wavelength can be tuned in a wavelength range of 1544.35 to 1564.10 nm with a ∼ 2.0 nm tuning step. Hence, the proposed EDF laser can reach the characteristics of SLM output, 35 kHz output linewidth, and optical single-to-noise ratios (OSNRs) of > 54.9 dB. In addition, the output stabilities of the power and wavelength in the proposed EDF laser are also discussed.
Classification of Thai Tweets: Mining Treasures from Tweet Heap

This paper describes how to classify Thai language tweets. In this study, we collected experimental data from twitter data which have a specific hashtag. Even if tweet data which have the same hashtags, there are many kinds of tweets: suggesting solutions, emotional tweets, news report, and other tweets. When someone would like to get an inspiration and advice, he may get some advice from tweets which are described suggesting solutions. We conducted some experiments for tweet classification using five machine learning algorithm. The experimental results showed that we can obtain good results. Moreover, we compared the results using tweets written in Thai language and the results using tweets written in Japanese.
Numerical Study of Stroke Detection Using UWB Radar

The main purpose of this contribution is the design and feasibility study of a microwave imaging system based on the UWB radar working in the frequency band 1–8 GHz. For the simulation the 3D anatomical head phantom was used. Strokes of different sizes and in different positions were tested. The position of the stroke was estimated by processing reflected signals using delay and sum beamforming algorithm. According to the results the estimated position of the stroke was perfectly matched with the original stroke position. The system also responds to size of the stroke.
Fixed PRI Source Geolocation Using TOA of A Single Moving Observer

In this paper, the problem of locating a radiating source on earth’s surface using time of arrival (TOA) measurements is solved. The stationary source radiates signal pulses with unknown but fixed pulse repetition interval (PRI). The observer is assumed to move with a constant speed without acceleration so as to reduce the system complexity. First, the model of the problem is given and the observability analysis is performed to yield the conditions under which the problem is solvable. Due to the highly nonlinear property of the problem, a two step localization method is proposed. In the first step a quantum particle-swarm optimization (QPSO) method is applied to find coarse estimates of source position and the unknown PRI. Then the joint estimates of source location and PRI are achieved using the steepest descent method, which ensures that the localization accuracy can achieve the theoretical Cramer-Rao lower bound (CRLB). Numerical Simulations verify our analysis and the effectiveness of the proposed method.
A Power Mismatch Elimination Strategy for an MMC-based PV System in Unbalanced Grids

Application of modular multilevel converter (MMC) for grid integration of photovoltaic (PV) generators has gained much attention lately. This paper proposes a modified power mismatch elimination strategy for an MMC-based PV system. Although the existing power mismatch elimination strategy based on internal power flow of the MMC is capable of eliminating power mismatches in a perfectly balanced power system, it will fail to do so if the grid voltage is unbalanced at point of common coupling (PCC). The proposed modified power mismatch elimination strategy, utilizes the internal power flow of the MMC-based PV system to ensure that the converter leg connected to grid phase with higher voltage delivers higher power to the grid and the leg connected to the phase with lower voltage delivers less power to the grid. As a result, the currents delivered to the host grid become balanced and symmetrical in spite of existing power mismatches and unbalanced grid voltage. The effectiveness of the proposed modified power mismatch elimination strategy is demonstrated by time-domain simulations conducted on a model of the PV system in PSCAD/EMTDC software environment.
CNN-Based Electronic Camouflage Audio Restoration Mechanism Zhengyu Shi

This paper proposes a restoration mechanism based on convolution neural network (CNN) for electronic camouflage audio. Since there are certain change rules in the process of converting original audio into electronic camouflage audio and audio is short-time stationary, convolution and nonlinear mapping are performed on the historical sampling acoustic information and restoring factors of the electronic camouflage audio. After companding transformation, the reduction audio is outputted. In the experiment, the voiceprint features comparison, LPC analysis and human ear identity judgement are made between restoring audio and original audio. The results show the validity of the proposed mechanism. It is of great theoretical and practical significance to the restoration of electronic camouflage audio in judicial expertise.
DeepGFL: Deep Feature Learning via Graph for Attack Detection on Flow-Based Network Traffic

The ability to mine structurally complex and latent relationship among network flows has become the focus of many initiatives. Learning graph representation for network attack detection has become a critical issue which is an across-network machine learning task. However, the challenge of effectively representing graph for network traffic is unmet yet, especially for detecting various threat patterns which is modeled as attributed graph. In the same time, existing methods could not capture higher-order subgraph structures. For these reasons, we propose a new way to model network graph called Deep Graph Feature Learning (DeepGFL) for network attack detection to solve this problem. DeepGFL is a framework studying deep features from attributed network flow graph. We automatically generalize higher-order features from raw features obtained from attributed graphs and then implement network attack detection. We evaluate the proposed framework with raw features threat detection on a real world datasets. Experimental results show that DeepGFL is more effective, more accurate and more space efficient for network attack detection.
On-Chip Optical Vector Quadrature De-Multiplexer Proposal for QAM De-Aggregation by Single Bi-Directional SOA-Based Phase-Sensitive Amplifier

In this paper, a simple optical vector quadrature de-multiplexer (QD) scheme is proposed for de-aggregating input 10 Gbaud 16/32/64 quadrature amplitude modulation (QAM) signals into two pulse amplitude modulation (PAM) streams. The proposed QD is based on a single bi-directional degenerate phase-sensitive amplifier (PSA), where the wavelength and the polarization status of the extracted in-phase and quadrature components stay the same as the input signal. Since the proposed QD is realized using PSA based on semiconductor optical amplifier (SOA), it is also possible to realize an on-chip QD system, providing an integrated platform for optical signal processing. Through numerical simulation, the transfer characteristics of the proposed QD system show that the SOA-based PSA has a high phase-sensitive gain extinction ratio of 44.1 dB. The constellations, error vector magnitudes (EVMs), and bit-error rates (BERs) of the data streams after the de-aggregation are numerically investigated to verify the proposed QD scheme. For the 10 Gbaud 16/32/64 QAM signals with an input optical signal-to-noise ratio (OSNR) of 25 dB, the de-aggregated PAM4/PAM6/PAM8 signals show 6.1, 5.6, and 8.2 dB receiver OSNR improvements at the BER of 10−3, respectively. Also, to optimize the performance of the subsystem, the BER performance dependence on the phase difference between two arms in the subsystem is also examined. The simulation results reveal that the proposed QD can accomplish the function of optical vector de-aggregation well for the high-level QAM signals. The proposed QD can be applied to information de-aggregation, format conversion, and direct detection for optical vector signals, which may have great potential values for the flexible optical networks.
Development of Six-Degree-of-Freedom Inertial Sensors With an 8-in Advanced MEMS Fabrication Platform

This paper presents the development of an 8-in six-degree-of-freedom (DOF) inertial sensor based on an advanced capacitive inertial sensor fabrication platform. The platform integrates three-axis gyroscopes and three-axis accelerometers on the same chip. The fabricated sensors are vacuum packaged at the wafer level with a polysilicon-based through-silicon interposer (TSI) using the aluminum–germanium eutectic bonding approach. Wafer-level measurement results indicate that a fabrication yield of greater than 92% and a vacuum level of <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>100 mtorr have been achieved. The fabricated inertial sensors and the customized application-specific integrated circuits are encapsulated in a 5 mm <inline-formula><tex-math notation="LaTeX">$\times$</tex-math></inline-formula> 5 mm <inline-formula><tex-math notation="LaTeX">$\times$</tex-math></inline-formula> 1.3 mm quad-flat no-leads package using the plastic molding technology. The system-level characterization of the developed six-DOF inertial sensors have been implemented. Several reliability tests conducted according to the relevant JEDEC standards prove that the packaged sensors are highly reliable and robust for a wide range of operating environments.
A Novel UWB Quadrifilar Plannar Spiral Antenna

With the vast spread of the RFID applications in most field of life's, most researchers pays more attention to the most cheap but challenging technology in the RFID which is the chipless RFID. The challenges in such a technology stems from the need for a reader antenna that achieve an UWB operation, circular polarization, flat gain and a compact size. This is suitable for tags of either linear or circular orientations. This paper discusses the design, the simulation and the measurement of a quadrifilar spiral antenna with four-arms and six-turns that suits the RFID reader specifications. The antenna is fabricated over FR4 material of thickness 1.6 mm using the photolithographic process. The antenna arms are printed on one side of the substrate while the feeding coaxial connector is mounted on the other side without any ground planes. The radiation pattern and the polarization are controlled by adjusting the phase shift between the four arms. The four arms allow a total length less than the longest wavelength, so the antenna size is miniaturized to large extent. The fabricated antenna has a compact size, a high gain and an ultra-wide band (UWB) radiation as well as circularly polarized (CP) radiation. The antenna has wide bandwidth compared to the antennas in literatures. The antenna operates in the range from 1.2 GHz to 12.4 GHz. By comparing the simulated and the experimental results a very promising coincidence is noticed.
High Efficiency Electrical Power Supply System for Satellite

The temperature & insolation level is inversely proportional with photovoltaic (PV) voltage and directly with PV open circuit voltage. Cell material and manufacturing determines the voltage factor which is usually fixed for temperature & insolation. To achieve temperature and insolation independent maximum power point tracker (MPPT) of satellite's solar cells with a simple and reliable technique this remarkable property is used. Maximum power operating point of the system can be calculated & open circuit voltage can be measured continuously using microcontroller. The MPPT is designed and constructed with the solar array as input and the buck/boost converter. The battery weight and solar array is downsized as the efficiency of the power supply is increased which is an important designing factor for satellite. The control schemes are integrated and the duty of the system is decide using the microcontroller. The efficiency of the system is calculated across the different circuits of the overall system. In hardware open loop H-bridge inverter based on PIC16F877A is implemented and output results are showed in the CRO/DSO. The prototype of the above system is proposed.
A Novel Images Enhancement strategy Based on Dynamic Range Compression and Details Enhancement for MRI images

Magnetic resonance imaging (MRI) plays a significant role in medical detection technologies but is difficult to avoid suffering from un-uniformity, low brightness and noise pollution. These factors might affect the doctor‘s diagnosis and can lead to misdiagnosis in practical applications. In this paper, a new MRI images enhancement strategy is proposed which consists of a dynamic range compression (DRC) operation and a details enhancement (DE) operation. The DRC can efficiently compress the high dynamic range to a suitable range and protect the difference between the pixels, and DE adopts an improved bilateral filtering combined with Laplacian pyramid to remove the noise and enhance the details. A series of experiments results of real MRI images demonstrate that the method is more effective than other methods.
A Switched-Capacitor DC–DC Converter With Variable Number of Voltage Gains and Fault-Tolerant Operation

This paper presents research results of voltage gain number control methods of a power electronic resonant zero-current switching dc–dc switched-capacitor (SC) voltage multiplier (SCVM) in a topology with fault tolerance capability (FSCVM). The converters operated under the basic switching patterns are constant-voltage-gain dc–dc series-parallel resonant SC converters. However, this paper presents a method for the output voltage regulation by a special switching strategy. A variable voltage gain is made possible by selecting the number of active switching cells, using an appropriate control method. This creates a set of voltage ratio ranges. In addition, the proposed topology and method of control enable a fault-tolerant operation of the FSCVM under various failures of the device. The paper presents an analysis of the concept, simulation results and an experimental verification in a three-cell FSCVM dc–dc resonant boost converter operating at a 200-watt charge. Issues regarding the selection of components related to the problem of inrush currents that can occur during the voltage ratio increase are also analyzed in this paper, and the design for low inrush currents is demonstrated.
Multi-Fault Rapid Diagnosis for Wind Turbine Gearbox Using Sparse Bayesian Extreme Learning Machine

In order to reduce operation and maintenance costs, reliability, and quick response capability of multi-fault intelligent diagnosis for the wind turbine system are becoming more important. This paper proposes a rapid data-driven fault diagnostic method, which integrates data pre-processing and machine learning techniques. In terms of data pre-processing, fault features are extracted by using the proposed modified Hilbert–Huang transforms (HHT) and correlation techniques. Then, time domain analysis is conducted to make the feature more concise. A dimension vector will then be constructed by including the intrinsic mode function energy, time domain statistical features, and the maximum value of the HHT marginal spectrum. On the other hand, as the architecture and the learning algorithm of pairwise-coupled sparse Bayesian extreme learning machine (PC-SBELM) are more concise and effective, it could identify the single- and simultaneous-fault more quickly and precisely when compared with traditional identification techniques such as pairwise-coupled probabilistic neural networks (PC-PNN) and pairwise-coupled relevance vector machine (PC-RVM). In this case study, PC-SBELM is applied to build a real-time multi-fault diagnostic system. To verify the effectiveness of the proposed fault diagnostic framework, it is carried out on a real wind turbine gearbox system. The evaluation results show that the proposed framework can detect multi-fault in wind turbine gearbox much faster and more accurately than traditional identification techniques.
Frequency Stability of 3D Encapsulated VHF MEMS Resonator

Frequency stability of 3D encapsulated VHF MEMS resonators with Q-factor of 10000 are systematically studied. A negating capacitive compensation technique was developed to eliminating the parasitic effect caused by the PCB circuits. The long-term frequency stability results of the resonance frequency variation and the noise floor of Allan Deviation were $\pm 1$ ppm and 26 ppb, respectively, which were comparable to these of the typical quartz resonator. The thermal cycling test between −40 °C and 85 °C for both the long-term operation and the temperature cycling were measured and the results show that the resonant frequency drifts were less than $\pm$ 1.5 ppm, indicating the high frequency stability of the encapsulated disk resonator.
Time Sensitive Data Access Control in Cloud Using Time and Attribute Factors

The novel form utilized for secure information put away in the cloud is an issue. From one perspective, it liberates information proprietors from the particular association, and is less asking for information proprietors to share their information to expected clients. Then again, it postures new difficulties on security and security insistence. To ensure information assurance against the true inquisitive cloud network, diverse works have been proposed to help fine-grained information get the chance to control. In any case, till now, no plans can bolster both fine-grained get the chance to control and time-delicate information appropriating. In this paper, by acquiring CP-ABE (Cipher text-Policy Attribute-based Encryption) and furthermore by consolidating the time based factor and here by presenting the Trapdoor based component where the clients scan for the specific information, the data will be appeared yet the client won't have the capacity to unscramble without utilizing the mystery enter in the given schedule opening and a break glass rule operation has been performed where in absence of the data owner, an emergency account can provide access to the data users.
Compact Over-Mode Waveguide for Power Combining in Sub-Millimeter Wave

Compact over-mode waveguide for massive power combining is proposed. Spatial power combining technique using waveguide structure can be a good candidate to increase combining efficiency. The combiner expand single mode size input opening to 20 times larger output. Additionally, by using specially designed 90° bended path, the size of whole structure and path length of waveguide can be reduced. As results of the simulation, the reflection loss for the combiner lower than −10 dB and the insertion loss is less than −0.5 dB in the frequency range from 90 to 100 GHz showing high combining efficiency.