Miracle-dz/UniFAR-FTUs huggingface dataset:
The Protection of LAN Using Semantic Firewalls

The amount and diversity of malware keeps growing while the same basic attack techniques are being used. A firewall is a network security key component that filters inbound and outbound network packets as per predefined security rules. Even though firewalls are an effective defense against some attacks, they have security flaws that can be leveraged in other circumstances. In the present work, it is claimed that an ontology-based semantic firewall and machine learning algorithms can effectively enhance the firewall and protect the LAN. This paper proposes an ontology-based model for the semantic firewall as an effort to explore its effectiveness. The method used in this paper is based on Description Logic (DL) Reasoners, Ontology APIs, and Semantic Web Languages (OWL and SWRL). The proposed semantic firewall takes its decisions of anomalies detection based on a set of protection rules of the ontology-based model. As a result, the proposed approach achieves a detection accuracy of 93%. The conclusion is drawn that the presented ontology classifier gives an understandable model of a semantic firewall (SWF) that offers candid and human-interpretable decision rules, as with other machine learning models.
Thunderstorm Prediction Method Based on CNN-BiLSTM Using BEADS*

Atmospheric electric field signal (AEFS) usually superimposes low-frequency noise, which has a negative effect on thunderstorm monitoring. A thunderstorm prediction method based on Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory (BiLSTM) is proposed. Firstly, AEFS is divided into useful, baseline and noise components by BEADS. After getting the estimated value of the useful signal, the denoised signal is obtained. Then, the AEFS prediction model is built based on BiLSTM. After inputting the AEFS spatial features extracted by CNN into the model, a CNN-BiLSTM hybrid model for thunderstorm prediction is formed. After analyzing the performance of the method, we carried out the experiment in thunderstorm weather. Results show that the SNR of AEFS processed by BEADS is improved effectively. It's worth noting that the determining coefficients before and after BEADS are all above 94.12%, showing a good effect. Finally, the effectiveness of the method is proved again by the coincidence between the predicted results and the radar chart.
An Improved Interleaved Buck/Boost Integrated CLLLC Three-Port DC-DC Converter for Wide Input Range

Power electronic converters are widely used in renewable energy generation scenarios. However, the power fluctuations of renewable energy will make DC microgrid bus voltage perturbed. As a result, the input voltage for the converter is not constant. This paper proposes an improved three-port converter with the advantages of wide input voltage and high integration levels. The interleaved Buck/Boost integrated CLLLC (IBBI-CLLLC) converter can balance power difference by the energy storage device, which expands the operation range. Then, an improved PWM+PFM modulation method is proposed in this paper. By satisfying a specific relationship between duty cycle and switching frequency, the CLLLC converter has no power return phenomenon when the secondary switches are in the synchronous rectification state, which improves the efficiency of the converter. Moreover, the converter has a wide range of DC gain and can realize zero voltage switching (ZVS) in a wide input voltage range. Finally, the correctness of the theoretical analysis is verified by simulation.
Glaucoma Detection using Mask Region-Based Convolutional Neural Networks

Glaucoma is a disease that impairs the vision of the human eyes, and it has grown increasingly common in recent years due to increased pressure in the eyes. It is the result of this disease, which is fatal once it has shown itself, that vision loss occurs. The identification of glaucoma disease has already been achieved via several deep learning (DL) algorithms, as previously stated. The findings of our research on the detection of glaucoma sickness in this paper, in which model used a deep learning technique known as a Convolutional neural network to detect the illness (CNN). The Mask Region-Based convolutional neural network (Mask-RCNN) presents a unique pattern for glaucoma and non-glaucoma affected eyes, which work may utilize to detect glaucoma using machine learning techniques. In this study, the usage of Mask-RCNN is used to create a hierarchical structure for differentiating between images of glaucoma affected and non-affected eyes, allowing for a more accurate categorization of glaucoma patients. With our proposed method may evaluate at six different degrees of complexity and 33 convolutional layers. When detecting glaucoma disease, the proposed research uses the dropout mechanism to enhance the overall efficacy of the performance, as described above. The SCES and ORIGA datasets were used in this research to analyze the intended work, and the results were presented in this paper. The Accuracy of RCNN on the ORIGA dataset was 92.32 percent, Faster-RCNN was 93.89 percent, and Mask RCNN was 95.72 percent, according to the values obtained. When applied to the SCES dataset, the results showed that the Accuracy of the RCNN was 91.56 percent, that of the Faster-RCNN was 94.32 percent, and that of Mask RCNN was 97.56 percent. The mAP of RCNN was 89 percent, Faster-RCNN was 92 percent, and Mask RCNN was 90 percent. mAP of RCNN is 90 percent, Faster-RCNN is 91 percent, and Mask RCNN is 93 percent, according to the results.
Deep Learning Model For IDS In the Internet of Things

Emerging technology makes one's life more comfortable; however, in the Internet of Things, there are a lot of weaknesses like infrastructure, connectivity, network, etc, due to the presence of millions of networked devices that make it difficult to implement safety on each device. Security threats are one of the most important issues recently gaining popularity in IoT, attacks that can cause major disruptions and loss of information within the IoT network. Intrusion Detection System (IDS) has a substantial role in protecting and securing an IoT network through detecting and preventing malicious activities. To develop IDS for timely detection and categorization of cyber threats at the network level, classical machine learning techniques are commonly utilized. However, because malicious attacks are continuously evolving and occurring at extremely large sizes, various problems arise, necessitating a scalable solution. In this paper, a convolutional neural network (CNN) approach, which is a kind of deep learning model for IDS discovery, is developed that is flexible and efficient for detecting and classifying cyber-attacks in IoT networks. The well-applied CNN model on the UNSW-NB15 dataset obtained 100% precision results.
A Novel Low Training Cost SSVEP Detector Design

Over the past years, steady-state visual evoked potentials (SSVEP) has been widely used in brain-computer interfaces (BCIs) due to its high efficiency, robustness and noninvasive. The supervised SSVEP detector attracted the attention of researchers for its high information transfer rate (ITR). However, the tedious training process restricted its extensive application. This study proposed a low-training cost SSVEP detector with a dynamic window strategy. By this method, the users only need to collect 40-s training data and can achieve a high performance. A 40-target data set was employed to evaluate the performance of the proposed method. The results indicated that the proposed method can achieve 177.5 bits/min ITR, which is 11.2% higher than filter bank canonical correlation analysis with dynamic window (FBCCA-DW).
Prevention of Rushing Attack in AOMDV using Random Route Selection Technique in Mobile Ad-hoc Network

Ad Hoc Network is wireless networks that get more attention from past to present. Mobile ad hoc network (MANET) is one of the types of ad hoc networks, it deployed rapidly because it infrastructure-less. A node in a mobile ad hoc network communicates through wireless links without wired channels. When source nodes want to communicate with the destination outside its transmission range it uses multi-hop mechanisms. The intermediate node forwards the data packet to the next node until the data packet reaches its destination. Due wireless links and lack of centralized administration device, mobile ad hoc network is more vulnerable for security attacks. The rushing attack is one of the most dangerous attacks in the on-demand routing protocol of mobile ad hoc networks. Rushing attack highly transmits route request with higher transmission power than the genuine nodes and become participate between source and destination nodes, after that, it delays or drop actual data pass through it. In this study, the researcher incorporates rushing attack in one of the most commonly used mobile ad hoc network routing protocols namely Ad hoc on-demand multipath distance vector and provides a rushing attack prevention method based on the time threshold value and random route selection. Based on the time RREQ arrives a node takes a decision, if the RREQ packet arrives before threshold value, the RREQ packet consider as came from an attacker and discarded else RREQ packet received then randomly select RREQ to forward. In this study performance metrics like packet delivery ratio, end-to-end delay and throughput have been evaluated using Network simulation (NS-2.35). As a result of simulation shows newly proposed prevention mechanism improves network performance in all cases than the network under attacker. For example, the average packet delivery ratio enhanced from 54.37% to 97.69%, throughput increased from 20.84bps to 33.06bpsand the average delay decreased from 1147.22ms to 908.04ms. It is concluded that the new proposed techniques show improvement in all evaluated performance metrics.
A Grounded Capacitance Multiplier Circuit Employing VDTA

Capacitors occupy very large area on chips. To reduce the occupation area on chip for circuits employing large capacitances, capacitance multipliers are used. In this work, a new design of Voltage Differencing Transconductance Amplifier (VDTA) based capacitance multiplier and its CMOS implementation are proposed. The multiplication factor of the proposed circuit is 115. The design of the proposed grounded capacitor multiplier is verified with the simulations in Cadence environment with $0.18\mu\mathrm{m}$ TSMC technology.
Research on technological innovation opportunities based on patent analysis and technological evolution

Patent as a tool to measure innovation direction and innovation focus. Patent information can shorten the duration of technological innovation, improves the quality of technological innovation, and explores opportunities for technological innovation. Thus, this article takes patents as the starting point, and integrates methods and theories such as patent cluster analysis, technology function matrix, technology roadmap, and technology evolution theory, to build a model for identifying opportunities for technological innovation based on patent analysis and technology evolution to macro-level identify the current technology at hotspots, blank spots, and to micro-level identification of current technical routes and development. The validity of the model is verified by identifying the technical opportunities of gas turbines.
Mining Resident Activity Pattern Based on Spatio-temporal Trajectory Data

The research on residents' activity pattern based on spatio-temporal trajectory data is helpful to the optimization of urban operation. At present, one of the difficulties in the research is how to determine the regularity of residents' activities when the labeled samples are sparse. We propose an improved periodic decision algorithm of sliding window, combined with feedforward neural network to search outlier activity patterns. Experimental results show that our method can effectively classify the overall travel features and quantify individual activity abnormalities.
A Statistical Shape-Based Patient-Specific Anatomical Structure Model

A patient-specific anatomical structure model has been widely used in many medical applications. However, in practical applications, to effectively construct a patient-specific anatomical structure model is a challenge, the reasons are: (1) the manual marking process for landmark points is time-consuming and is prone to have false points; (2) the correspondence establishment is difficult; (3) the performance of the model is limited. Therefore, the purpose of this study is to automatically construct a patient-specific anatomical structure model to solve these difficulties. Firstly, the input data are preprocessed to enhance the region of interest in CT scan images. Then, the region of interest is regarded as a training specimen, and the triangle is used to mesh the training specimen. Meanwhile, vertices contraction strategy is introduced to iteratively contract triangle meshes, and the correspondences are established through improved B-spline free-form deformation. Finally, principal component analysis is used to generate the final patient-specific anatomical structure model. Experimental results on 30 pelvic CT scan images verify that the proposed method outperforms the compared methods.
MUTUAL ENTITY AUTHENTICATION PROTOCOL FOR MOBILE CLOUD COMPUTING

Mobile cloud computing offers a combination of both mobile computing and cloud computing services. Mobile cloud computing has many advantages like high mobility, high availability, high accessibility, adaptability to different types of devices of various configurations and screen sizes. Despite these advantages, MCC has its disadvantages. The disadvantages include connectivity issues, performance issues, and security threats to the data stored in the cloud. [5] Mobile cloud computing provides data transmission through an insecure channel with the help of the internet. So there is a possible risk of security threats or attacks like man-in-the-middle attack, impersonation, masquerading, etc. For providing a solution to these problems, there is a need for an authentication protocol that ensures secure data transmission. A protocol named Mutual Entity Authentication Protocol is defined, which will help us overcome the limitations possessed by Mobile Cloud Computing. This protocol uses an OTP to authenticate the user. [4] The OTP is formed by concatenating the username, password, and IMEI number of our mobile phone. [6] The OTP generated is encrypted by Triple Data Encryption Standard Algorithm in which a randomly generated key given to the algorithm is helpful for encryption. This protocol also uses Data Encryption Standard Algorithm to encrypt the files uploaded in the cloud. Not only for the 3DES algorithm, the key generated for the DES algorithm is also generated randomly. The protocol proposed is implemented and tested using Java EE to show its efficiency in authenticating the user and providing security during the data transmission. Compared with the existing protocols, the proposed protocol results demonstrates that it delivers better security for user's authentication and data transmission.
Characterization of the Ethanol-water Blend by Acoustic Signature Analysis in Ultrasonic Signals

The use of ethanol as fuel in Brazil stimulated the competition between distribution companies and resellers, which aggravated the practice of adulteration of fuels, aiming for illicit gains and tax evasion. The most common practice of adulteration in fuel alcohol is the addition of water. The classic techniques for measuring the water content in ethanol offer good precision and good detailing as to the presence of water. However, they present disadvantages such as the need for sample collection, long analysis time, in addition to the need for specialized laboratory and labor. This work aims to propose digital signal processing techniques to analyze and quantify the presence of water in ethanol fuel using a combination of Principal Component Analysis (PCA), and Singular Spectrum Analysis (SSA) applied on ultrasonic signals. This method resulted in the proposal of a new score, which relates to the ethanol/water ratio information present in the mixture. The results were promising when relating the proposed score to the presence of moisture in ethanol to a greater or lesser degree. Experiments performed prove the technique’s feasibility and pave the way for a new method for real-time monitoring.
Research on Performance Test Method of Integrated Electro-Hydraulic Braking System

Integrated Electro-Hydraulic Braking System (I-EHB) is the latest development achievement of automobile braking industry. Based on the lack of domestic industry standards for test bench performance test, a set of I-EHB performance test method is provided. Aiming at the three working states of I-EHB and referring to the bench test method of vacuum booster, the basic braking performance test items and performance indexes under different working states of I-EHB are designed. After studying the control strategy of I-EHB's intelligent driving assistance function, including ABS (Anti-Lock Braking System) function and AEB (Autonomous Emergency Braking) function, and designs ABS and AEB performance test items. With the existing detection system which can simulate the real vehicle working conditions, the products of an automobile company are tested, and the test results are complete and comprehensive, which is of great significance to the I-EHB bench performance test method.
Survey on Bayesian Network Development and Application

Bayesian Network(BN)is a graphical network model for the description of the variables between causal uncertainties.It is built for uncertainty modeling and reasoning,processing related to forecasting,intelligent reasoning,diagnosis,decision making,and risk/reliability analysis.Firstly,a brief presentation on BN was given.Then the development and function expansion of BN within the recent 30years was outlined,and BN application in the field of engineering technology including fault diagnosis,reliability analysis etc.was reviewed.Finally BN existing deficiencies and future research trends was summarized and discussed.
Modified DFIG Convertor Control Strategy for High Voltage Ride Through

Currently, wind power penetration is increased, and chain failures caused by DC blocking expose wind turbines to greater risk of high voltage ride-through (HVRT). In this paper, a modified HVRT strategy for rotor-side and grid-side converter of doubly-fed induction generators (DFIGs) is proposed. A reactive power control strategy based on voltage bias is proposed for the grid-side converter (GSC) to maintain the DC-link voltage. To support the recovery of grid voltage after voltage dip, a modified control strategy for DFIG’s rotor side converter is proposed. A model of the grid-connected wind farm is built in MATLAB/Simulink to test the feasibility of the proposed method. Without adding additional hardware, the proposed strategy can ensure that the DC-link voltage does not exceed the limit during HVRT process of the wind turbine, and DFIG can absorb a certain amount of inductive reactive power to support the grid voltage recovery. Simulation results show that the proposed method improves the high voltage ride- through capability of DFIG under various operating conditions.
Low-Power High-Gain Op-Amp With Cascoded Current Mirror Loads

This study describes a simple method for creating extremely power efficient class AB-AB Miller op-amps. The op-amp is designed with cascoded current mirror loads and it provides high open loop gain, band width and high phase margin which improves the stability of op-amp with only a modest increase in power dissipation. These improved parameters are achieved by using a LCMFB (local common mode feedback resistor) in the input stage, which also provides class AB operation. The suggested technique is validated in LTspice with all ac, dc and transient analysis and this stufy have applications in wireless sensors and bio-medical equipment with experimental findings of 109-dB open loop gain, 62-degree phase margin, 44-KHz bandwidth with 50- μ w power consumptions.
A Customer Preference-centered Method of Exhibition Design Trend Forecasting

In exhibition design, there is usually a difference between the design intention and the customer's actual experience. Therefore, it is necessary to incorporate customer preferences into the design strategy generation system. This article proposes a customer preference-centered trend forecasting method for exhibition design, and verifies its feasibility through a case study. This method is based on a set of standardized procedures, including fitting the relationship between customer preferences and design features, and trend prediction based on historical data. The article aims to support the proposal of user preference-centered design strategies, eliminate the perception difference between designers and customers, and provide guidance for practical applications.
Research on QMEMS Gyro Drift Processing Method based on Adaptive Kalman Filter

In view of the problems of qmems gyroscope vulnerable to environmental impact, poor stability and statistical characteristics change of random noise, the methods of random signal processing and time series analysis are designed. The combination of arithmetic average method and median value method is used to process the abnormal value of the series, the reverse order test method is used to complete the stability test, and the difference method is used to extract the trend term of drift data After testing the normality and periodicity of the preprocessed sequence, the ARIMA model of non-stationary random drift of gyroscope is established, and the applicability and accuracy of the model are tested according to the white noise test criterion. Based on the classical Kalman filtering theory and the transformation between ARIMA model and time series state space model, a segmented noise threshold Kalman real-time filtering method is proposed, and the effectiveness of this method is verified by experiments. The results show that the variance of ARIMA model after segmented noise threshold Kalman filter is reduced from 8.40148e-006 to 5.60701e-006, and the noise is reduced by 30% on the basis of classical Kalman filter. The experimental results show that this method can more effectively compensate the random drift error of qmems gyroscope in real time, and has good adaptability and stability.
Investigation of Deep Learning Model Based Intrusion Detection in Traditional and Ad Hoc Networks

Evolution and proliferation of the state-of-the-art technologies enable a human life marked by convenience and ease of access. Due to this technological advancement, difficulties are also created which are observed across various segments. Security issues in the field of networking are no exceptions to this. Networking also experiences lots of troubles produced by diffident types of complex intrusions. As a countermeasure to these troubles, a sophisticated and well-organized system, defined as network intrusion detection system (NIDS), has been introduced. It is aimed not only at the improvement of detecting accuracy but also at the correct identification of unspecified attacks. Over the last few decades, several technologies are introduced for developing NIDS to ensure a high degree of privacy and reliability. With the progression of modern deep learning technologies, NIDS performs wonders in the field of information security. In this paper, a number of deep learning based NIDSs are reviewed in a wide range of network oriented infrastructures, viz., traditional and ad hoc networks. This paper mainly discusses about the applications, limitations and methodologies of various NIDSs to provide a complete and transparent overview of network security to the readers.