Miracle-dz/UniFAR-FTUs huggingface dataset:
A Gearbox Fault Feature Extraction Method based on Composite Time-frequency Atomic Dictionary and Fast Kurtogram

The vibration signal of gearbox is characterized by complex components and multiple modes of aliasing. In order to extract the fault feature of gearbox, a new method based on composite time-frequency atomic dictionary and fast kurtogram is proposed. Firstly, according to the characteristics of the gearbox fault vibration signal, a composite time-frequency atomic dictionary composed of steady-state modulation dictionary and impact modulation dictionary is constructed. Then, the vibration signal is denoised by OMP. Moreover, the optimal filter parameters are determined by fast kurtogram to filter the noise reduction signal, and the frequency spectrum analysis of the filtered signal is carried out to obtain the gearbox fault feature. At the same time, the performance of the composite time-frequency atomic dictionary and classical DCT, Fourier and LFMF dictionaries and their robustness under different noises are studied. The result shows that the composite time-frequency atomic dictionary is superior to other dictionaries in reconstruction accuracy and computational efficiency. Finally, the above method is applied to the experimental signal, which can accurately extract the gearbox fault feature.
TL-IDPS: Two Level Intrusion Detection and Prevention System using Probabilistic Optimal Feature Set Estimation

Wireless networks that can exchange any type of data are vulnerable to multiple intrusions and increase potential security risks, so the design of an Intrusion Detection and Prevention System (IDPS) that analyzes the packet features and detects different intruders (i.e., the types of attack) is necessary. Whereas, the existence of redundant and irrelevant features hinders the potential of IDPS. In this paper, we propose TL-IDPS, a Two-Level classification IDPS of wireless network based on optimized features. In the phase of intrusion detection, one-hot method, normalization and correlation estimation are used to mitigate the redundant features. Then, the fuzzy membership function with cuttlefish algorithm maps and consolidates the extracted features and selects optimal features. Based on the optimal features, Di-distance k-nearest neighbor (K-NN) as the first level classify the intruder or non-intruder. Further the type of intruder is identified by deep Q-network. From the result of detected intruders, the further arrival of those intruders is prevented. Experimental results conducted from multiple evaluation metrics using the UNSW-NB15 dataset prove that our proposed TL-IDPS is more effective than existing IDPS methods.
Parameter Optimization of Classification Techniques for PDF based Malware Detection

Embedding malicious codes in Portable Document Format (PDF) files has been one of the prime a venues i n a never-ending cat and mouse game between malware authors and malware analysts in past decade. By planting such malicious files in a victim machine by any means, e.g., email attachments, adversaries pose severe damage from personal assets to large business network infrastructure. To combat this menace, academic and industry based security researchers have been carrying out machine learning based techniques to discover novel types of PDF based malware. In our study, we examine state-of-the-art classification techniques that have been previously experimented in this active area of research to empirically derive the optimal parameter settings of such highly configurable algorithms. We investigate, 20 classifiers derived from 9 machine learning families on a large dataset. To evaluate our models’ performances, we report precision, recall, and F1 scores along with accuracy and provide a comparison chart to show how each family stands up to the expectation. We observe, 16 out of 20 studied classifiers achieve better results compared to its respective default configuration. As parameter optimization problem is very less talked about in this application domain, we are optimistic that our detailed experimental analysis will help select the optimal settings of a classifier from the studied machine learning algorithm suite.
Wide-angle Ultra-Wideband PolSAR Imaging Simulation of Canonical Targets

By adopting an ultra-wide bandwidth and exploiting a wide-angle synthetic aperture, SAR can achieve high spatial resolutions in the range and azimuth directions, respectively. Incorporate with polarimetry diversity, which allows to explore the polarimetric scattering mechanism of the illuminated targets. Integrated these characteristics aforementioned, wide-angle ultra-wideband polarimetric synthetic aperture radar with canonical targets have been investigated numerically and experimentally. To further validate the simulated results, the corresponding experiment with a dihedral was conducted by a GB-SAR system in the field, which exhibit good agreement with the simulated results.
A Near-optimal Protocol for the Subset Selection Problem in RFID Systems

In many real-time RFID-enabled applications (e.g., logistic tracking and warehouse controlling), a subset of wanted tags is often selected from a tag population for monitoring and querying purposes. How this subset of tags is rapidly selected, which is referred to as the subset selection problem, becomes pivotal for boosting the efficiency in RFID systems. Current state-of-the-art schemes result in high communication latencies, which are far from the optimum, and this degrades the system performance. This problem is addressed in this paper by using a simple Bit-Counting Function BCF(), which has also been employed widely by other protocols in RFID systems. In particular, we first propose a near-OPTimal SeLection protocol, denoted by OPTSL, to rapidly solve this problem based on the simple function BCF(). Second, we prove that the communication time of OPTSL is near-optimal with rigorous theoretical analysis. Finally, we conduct extensive simulations to verify that the communication time of the proposed OPT-SL is not only near-optimal but also significantly less than that of benchmark protocols.
A Book Recommendation Algorithm Based on Data Cleaning and Association Rules

Using data mining technology can extract valuable information from users’ book borrowing data, obtain users’ borrowing behavior, and provide personalized book recommendation service for users. The traditional association rule algorithms don’t carry out data cleaning before use, resulting in a single user’s single lending records becoming outliers in the overall data set, which makes the running time of the Apriori algorithm increase significantly. In this paper, according to the support threshold, confidence threshold and filtering threshold of the data set firstly, then the Apriori algorithm is used to analyze the association rules of the cleaned data set. The experimental results show that in the case of both large and small amount of data, the analysis time of Apriori algorithm with data cleaning is shorter, the strong association rules are stronger, and the effect is remarkable in the field of personalized book recommendation.
Scattering Control of Electromagnetic Waves via Space-Modulation Metasurface

Controlling the scattering of light or electromagnetic waves are conventionally realized based on the different phases profile of metasurfaces elements. However, this approach is limited to the operating frequency bandwidth and cannot achieve such functionality as frequency beam-scanning. Here, we present a concept of space-modulated metasurface in which the structure is making up with the non-resonated elements. We show that the design can achieve both frequency beam-scanning and fixed-frequency beam-scanning. Also, it provides a large operation bandwidth comparing to the conventional metasurface-based resonating structure. Finally, we fabricate the sample, and the measured responses show reasonable agreement with the simulated results.
An Overview of Data Locality Based Scheduler in Hadoop MapReduce Environment

MapReduce is a processing framework used extensively for substantial set of data set. Default MapReduce schedulers are not efficient enough to process data sets of heterogeneous nature. Thereby, it leads to low performance and reduced data locality. Working in a heterogeneous environment require more memory, power and advance architecture and computation. This paper provides the outline on MapReduce scheduling, how it is affected by the data locality and heterogeneous data sets. Examination on data locality based scheduling mechanism is done, that aims in reducing the job’s running time or CPU time and increases the efficiency, bandwidth and throughput of the cluster i.e. enhancing the overall performance of the Hadoop cluster. In addition to these merits, the lack point of this data locality based scheduling approach is also reviewed.
Image Recognition Based on Mixed Attention Mechanism in Smart Home Appliances

Aiming at the problems of complexity of feature extraction and low recognition accuracy in the recognition process of oven food images, this paper proposes a method of oven food image recognition based on mixed attention mechanism. The model classifies and identifies the oven food samples by convolution neural network Xception, and the features can be extracted more quickly and effectively by the depth separable convolution in the network. However, there are some problems in the oven food image, such as small part of the food, complex background, background interference and so on, so the attention mechanism is added to the convolution neural network model. The attention mechanism gives different weights to different feature dimensions of the data, which realizes the enhancement of the food part in the oven food image and inhibits the irrelevant background. The experimental results show that the accuracy of this model for oven food recognition reaches 99 %, compared with the traditional convolution neural network has been greatly improved. The results confirm the effectiveness of the proposed identification method in this paper.
Trajectory Tracking of The Wheeled Robot Based on Pulse Width Modulation Control

To address the problem of expensive continuous actuators in the original trajectory tracking control method of the wheeled robot, a more cost-effective pulse width modulation controller is designed in this paper. The controller determines the duty cycle according to the state difference at the sampling instant, so as to change the power of the actuator to achieve the control purpose. Firstly, the mathematical model of the wheeled robot is established. Then, design the position control law based on error feedback. Through the analysis of the Lyapunov stability criterion, it is proved that the proposed control method can make the tracking error converge to zero asymptotically on the premise that the system reference trajectory is known, so as to realize the trajectory tracking. Finally, the effectiveness of the controller is verified by numerical simulation results.
Research on visualization planning method of distribution network based on graphical model integration

High efficient video coding (HEVC) is a new video coding compression standard. HEVC adopts context-based adaptive binary arithmetic coding (CABAC) as the entropy coding scheme. In this paper, the overall architecture and efficiency of the main frequency are improved by the optimization of the input and output modules and the module optimization of the arithmetic coding CABAC hardware structure. In terms of input module optimization, four-level buffer input and residual coefficient transmission optimization are adopted; in terms of arithmetic coding module optimization, context model index pre-reading, pre-normalization look-up table and in-line serial stream output design are adopted so as to improve the overall efficiency of the architecture and the main frequency, reduce resource consumption, and achieve a high-frequency hardware architecture of the efficient coding pipeline. The combined results show that the pipeline can operate at 370MHz with 43.49K gates aiming at 90nm process. The processing rate and throughput can support real-time encoding of 1080P video under the general test conditions of the HEVC standard of 30 frames per second.
Application of Power Routers in Standalone Power Systems

The contemporary power generation is characterized by changing the technological platform while performing a transition to smart grids technologies whose key elements are distributed generation (DG) plants and electrical energy storage units. Pilot projects of such grids can be implemented in standalone power systems while using DG plants operating on renewable energy sources. The prospective lines, ensuring efficient use of the DG plants in standalone energy systems, is formation of energy microgrids that allow combining the DG and energy storage units based on special interfaces. Power routers can be used as such interfaces which are built based on high-frequency solid state transformers with two active semiconductor bridges.The article presents power router models, DG plants based on renewable energy sources and electrical energy storage units, based on which the standalone power system microgrid is formed. The research was carried out with application of simulation modeling packages Simulink and SimPowerSystems of MATLAB system. The modeling results allow to formulate conclusions that the power router designed based on a solid state transformer with active double bridge can be used to generate standalone power system energy microgrid. When main power is interrupted and is transferred to a standby power supply due to the use of power router, DC and AC consumers operate steadily due to the absence of significant voltage dips.
Multilayer Flexible Substrate Antenna Sensor for PT Measurement From Blood Plasma to Avoid Turbidity and Reagent Sensitivity Variations Through Regression Modelling

Passive antenna is applied in medical applications such as sensor, radiometer, rectenna, and implantable device for accurate measurement and analysis. Moreover, passive antennas can replace the optical sensors in the pathological instruments to overcome the drawbacks such as optical interference, offset voltage, time consumption and dark current. Furthermore, prothrombin time (PT) measurement through optical and mechanical instruments requires repeated testing for accurate results due to turbidity (lipemia) in blood Plasma and reagent sensitivity during coagulation. Plasma samples in instruments with more K vitamin influences the accuracy of PT measurements. In this paper, we propose a method to measure the PT from blood plasma through passive Ultra-Wideband sensor and overcomes the above problem. The proposed multilayer flexible substrate ultra-wide band (MFS-UWB) passive antenna absorbs the electromagnetic radiations from the concentration of plasma during clotting. The Concentration of plasma is proportional to electromagnetic energy density during clotting. The clotting duration time is acquired based on voltage. During clotting, observed electromagnetic emitted voltage is converted into frequency for measuring the clot concentration and the observed radiation from human blood clotting frequency ranges from 2GHz to 3GHz and voltage from 0.55mV to 0.85mV. The acquired blood clotting concentration voltage measures the prothrombin time using regression modelling and validated through laboratory values.
Determination of Initial Projectile Velocity in the Presence of Static Fields Using Deep Actor Critic Method

The movement of a projectile, which can be a mass or a charge, through a static field, can be important in various fields of physics ranging from particle physics to rocket science. In many cases, the movement of the projectile can be solely dependent on the initial velocity provided to the projectile. The determination of this initial velocity can be very complicated if the existing static field is too complex. In such cases, neural networks can be the key to modelling the environment and accurately estimate the initial velocity. This AI-driven velocity estimation can be extremely difficult using supervised learning due to the lack of seen examples of the state (initial position) to action (velocity) relation, which is also known as the policy. In this work, this problem is treated as a continuous-action-space contextual bandit problem and a neural network-based actor critic method is used to model the environment and determine the action policy. This method is tested on two different static field configurations using a Python-based GUI named Pygame. After training, the success rates of the projectile hitting the targeted destinations are 94.8% and 77.9% respectively, which implies that the proposed method can successfully model the action policy with high accuracy.
Weighted Local Outlier Factor for Detecting Anomaly on In-Vehicle Network

Modern vehicles are generally equipped with dozens of (or even hundreds of) electronic and intelligent devices and bloom into more involved information hub in enabling V2X networking. Protecting this increasingly complex vehicle ecosystem can be an arduous task, especially as the proliferation of data across distinct connected devices makes them more vulnerable than ever before. Intrusion detection systems (IDSs) have been found extremely rewarding in monitoring in-vehicle network traffic and detecting potential intrusions. The paper presents WLOF-InV, a novel unsupervised method based on local density for IDS on in-vehicle network. Given historical in-vehicle data of message identifiers, WLOF-InV first segments the traffic into a slice of (e.g., m) sliding windows. For each sliding window, WLOF-InV exerts information gain to select features for dimensionality reduction and squeezes out n features which are then bundled together to form a row vector and eventually gets an $m\times n$ matrix. WLOF-InV then adaptively determines the hyperparameters for local outlier factor (LOF) model (optimizing the scores for ranking the training data and the cutoff position for anomalies). In online detection, WLOF-InV determines the features by the information gain and invokes abnormal score weighting mode (which weights the LOF value of each dimension data by entropy method) to obtain the complete LOF score (of the overall traffic), and thereby grabs the anomaly traffic by resorting to the adjusted model. WLOF-InV is validated on the real data of three attack types (DoS, fuzzy, and impersonation). Experimental results demonstrate that WLOF-InV contrives next to optimal performance.
Shape-Recognizable Origami Sheet Device With Single Walled Carbon Nanotube Strain Sensor

This article describes a shape-recognizable sheet device using origami structures with single-walled carbon nanotubes (SWCNTs) strain sensors. Recently, a sheet-like measurement system that is directly attached to an object has been attracting attention for obtaining the shape, motion, and deformation of the object as alternative systems with external cameras. However, previous sheet-like measurement systems are difficult to distinguish between the stretch and the bend of the object appropriately. Here we propose a sheet-like measurement system that can distinguish the stretch and the bend individually by using origami structures. We formed a strain sensor using SWCNTs that was deposited on a substrate to form a 5–7 nm thin film. By patterning the SWCNTs strain sensors, origami-based sheet devices were fabricated. We confirmed that our origami sheet device obtained the bending and stretching information separately by attaching the device to cylinders. We believe that the origami system could contribute to the observation of complex deformations such as artificial skin, human interface, and biomedical applications. [2020-0324]
Design and Implementation of Trust-based Access Control Model for Cloud Computing

In recent years, cloud services are frequently attacked. Cloud computing access control has attracted high attention in recent research field. The dynamic nature of cloud computing users and resource allocation determine that traditional access control is no longer applicable. This paper introduces the concept of trust in role-based access control, and designs a Trust-based Access Control Model. The introduction of trust is contribute to identify malicious users. Dynamic access control is well adapted to cloud computing. Through the above mechanisms this model guarantees the security of cloud computing and data.
Experimental evaluation of series resonance scheme for 2 × 2 MIMO IPT

In an inductive power transfer (IPT) consisting of one transmitter and one receiver, series resonant capacitors are inserted on both sides to achieve high power transfer efficiency and load-independent output current. However, when IPT is carried from multiple transmitters to multiple receivers, the load currents vary with the load resistances due to impact of cross talk among the coils. As an initial attempt to address this problem, we proposed 2 × 2 series resonant scheme to mitigate cross talk and achieve constant load currents for IPT system with two transmitters and two receivers. Effectiveness of our proposal is confirmed by experiments.
Deadlock-Guided Testing

Static deadlock analyses might be able to verify the absence of deadlock. However, they are usually not able to detect its presence. Moreover, when a potential deadlock is detected, they provide little (and often no) information that can help the user in finding the source of the anomalous behaviour. This paper proposes a testing methodology that combines static analysis and symbolic execution for effective deadlock detection in asynchronous programs. When the program features a deadlock, our testing methodology provides an effective technique to catch deadlock traces. While if the program does not have deadlock, but the static deadlock analysis inaccurately spotted it, our approach is able to prove deadlock freedom (up to the limit of the performed symbolic exploration).
A Fixed-Latency Architecture to Secure GOOSE and Sampled Value Messages in Substation Systems

International Electrotechnical Commission (IEC) 62351-6 standard specifies the security mechanisms to protect real-time communications based on IEC 61850. Generic Object Oriented Substation Events (GOOSE) and Sampled Value (SV) messages must be generated, transmitted and processed in less than 3 ms, which challenges the introduction of IEC 62351-6. After evaluating the security threats to IEC 61850 communications and the state of the art in GOOSE and SV security, this work presents a novel architecture based on wire-speed processing able to provide message authentication and confidentiality. This architecture has been implemented and tested to evaluate its performance, resource usage, and the latency introduced. Other proposals in the scientific literature do not support real-time traffic, so they are not suitable for GOOSE and SV messages. Whereas the others exceed the target latency of 3 ms or do not comply with the standards, our design authenticates and encrypts real-time IEC 61850 data in less than 7 $\mu$ s—predictable latency—, and complies with IEC 62351:2020.
Experimental Study on 2-Element Rectenna Array Using Yagi-Uda Antenna

This paper proposes a rectenna array employing planar Yagi-Uda antennas. The planar Yagi-Uda antenna has endfire radiation and rectenna arrays can be realized by simply stacking the proposed rectennas. Therefore, the proposed rectenna array effectively increases the output DC power by arraying multiple rectennas. In this paper, the performance of a 2-element rectenna array is experimentally evaluated. The maximum output power is obtained when the array spacing is 0. 7λ and the optimum load resistance of each single rectenna is almost constant regardless of the array spacing.