Fast Detection Scheme Research Articles

Fast Two-Stage Max-Log MAP Detection Schemes for the Soft-Input Soft-Output Detection of Coded Generalized Spatial Modulation Signals

Fast Two-Stage Max-Log MAP Detection Schemes for the Soft-Input Soft-Output Detection of Coded Generalized Spatial Modulation Signals

An Optimized Fraud Identification Framework Using DCNN For Wireless Communication

Convolution Neural Network (Cnn) Is The Powerful Framework To Solve The Lot Of Issues Of Link Detection In Unlabeled Wireless Communication. Last Few Decades Lot Of Fraud Detection Strategies Has Been Projected Bit That All Are Inefficient For Detecting The Frauds. Therefore, The Great Need For Effective And Fast Fraud Detection Scheme With Higher Detection Accuracy. In This Research, Spider Based Convolution Neural Network (Sbcnn) Model To Detect The Frauds In The Wireless Communication. Initially, Create The Wireless Channel To Transmit The Messages From Source To Destination. Here, The Fraudulent Activities Are Detected Based On The Packet Delivery Time Of The Source To Destination Of The Wireless Medium. Moreover, The Proposed System Implementation Is Done In The Matlab Frame Work Additionally; The Obtained Results Are Validated With Prevailing Methods For Evaluating The Efficiency Of The Proposed Sbcnn Approach. Wireless communication fraud poses a significant threat as unauthorized use of services, compromising the security of cellular networks and infrastructure. With the surge in online services and users, the reliance on wireless communication for high-speed internet applications has grown. Despite the convenience brought by technologies like net banking, credit cards, and online services, financial frauds and unauthorized payments remain substantial risks. The intricate nature of wireless communication, illustrated in Fig. 1, where devices use signals between source and destination nodes, leads to challenges like network interference and loss rate, often exacerbated by fraudulent activities. Numerous techniques, including Artificial Intelligence (AI), hybrid ensemble models, oversampling, and machine learning, have been explored but haven't provided satisfactory solutions. In this study, an optimization-assisted intelligent framework is proposed to maximize communication performance, addressing the limitations of existing approaches. The subsequent sections analyse related research, identify issues with conventional methods, elaborate on the functioning of the proposed framework, discuss results, and conclude with research inferences

AF-FDS: An Accurate, Fast, and Fine-Grained Detection Scheme for DDoS Attacks in High-Speed Networks With Asymmetric Routing

Distributed Denial of Service (DDoS) attacks have posed severe threats to the Internet. Although researchers have proposed many DDoS detection schemes, there are still some challenging issues. Traditional per-flow-based DDoS methods are impractical for massive amounts of high-speed network traffic due to the huge resource consumption. In addition, existing methods are not designed to take into account the widespread asymmetric routing in high-speed networks, resulting in false positives when these methods are deployed on the Internet. Furthermore, existing methods can not achieve a good trade-off between detection accuracy and granularity when detecting hybrid DDoS attacks. This paper proposes an Accurate, Fast, and Fine-grained Detection Scheme (AF-FDS) for DDoS attacks in high-speed networks with asymmetric routing. We select features based on the characteristics of DDoS attacks and design a data structure Double Composite Structure Sketch (DCSS). DCSS can achieve fast recording and extraction of the selected features from the sampled traffic. Experimental results using real-world traces in a 10Gbps network with asymmetric routing show that AF-FDS can detect nine types of DDoS attacks at a fine-grained level within 15 seconds with over 98.0% precision and recall, even at a sampling rate of 1/1024. Furthermore, the comparison with several state-of-the-art methods illustrates that AF-FDS can detect DDoS attacks with a lower false positive rate (FPR) and shorter alarm time in asymmetric routing scenarios.

Fast and Distortion-Free Short-Circuit Detection and Protection Scheme With Fault Ride-Through Capability Applied to Four-Leg VSI

Fast and Distortion-Free Short-Circuit Detection and Protection Scheme With Fault Ride-Through Capability Applied to Four-Leg VSI

Fast and Stable Detection Scheme of Point of Common Coupling Voltage for Renewable Energy Systems Tied to Distortion Grids

Fast and Stable Detection Scheme of Point of Common Coupling Voltage for Renewable Energy Systems Tied to Distortion Grids

An Improved Setting-Free Scheme for Fast and Reliable Detection of Complete and Partial Loss-of-Excitation

In this article, an improved setting-free loss-of-excitation (LOE) protection is developed for fast and reliable classification of various events. In the proposed scheme, the internal voltage and reactive power of the synchronous generator (SG) have been exploited to fulfill the condition of not needing the threshold amount because they are directly dependent on the rotor field and continuously reduced during the LOE, leading to having a negative polarity of variation. However, due to slow dynamic variation during the LOE condition, they are multiplied to accelerate and highlight the reduction changes, giving rise to increasing the detection speed. Meanwhile, owing to the considerable difference between the value of internal voltage and reactive power and to highlight the effect of multiplication function, an adaptive gain on the basis of steady-state parameters of SG is suggested. Furthermore, given that the negative polarity of the multiplied parameters can be affected during the hybrid events, the limited integral function is implemented to eliminate the happened swings, resulting in keeping the negative polarity and thus clustering the hybrid events reliably. The performed simulations on the IEEE 9-bus and 39-bus test systems prove that the provided setting-free protection can detect all types of the events reliably and quickly than other schemes.

Information Leakage Tracking Algorithms in Online Social Networks

Aim: In order to explore the study on information leakage tracking algorithms in online social networks, solve the problem of information leakage in the current online social network. a deterministic leaker tracking algorithm based on digital fingerprints is proposed Background: : First, the basic working principle of the algorithm is that the platform uses plug-ins to embed a unique user-identifying information before users try to obtain digital media such as images and videos shared by others on the platform. Objective: Secondly, because the scale of users in social networks is extremely large and dynamic, while ensuring the uniqueness of digital fingerprints, it is also necessary to ensure the coding efficiency and scalability of digital fingerprint code words. Methods: Simulation experiments show that: 10 experiments are performed on 10,000 to 100,000 nodes, the Hamming distance threshold d is set to be 3, and the length of the hash code and the binary random sequence code are both 64 bits. Results: Compared with the traditional linear search, the proposed digital fingerprint fast detection scheme has better performance Conclusion: It is proved that an index table based on hash code and user ID is established and combines with community structure, to improve the detection efficiency of digital fingerprints

A Surveillance Video Real-Time Object Detection System Based on Edge-Cloud Cooperation in Airport Apron

Airport apron carries a lot of preparations for flight operation, and the advancement of its various tasks is of great significance to the flight operation. In order to build a more intelligent and easy-to-deploy airport apron operation analysis guarantee system, it is necessary to study a low-cost, fast, and real-time object detection scheme. In this article, a real-time object detection solution based on edge cloud system for airport apron operation surveillance video is proposed, which includes lightweight detection model Edge-YOLO, edge video detection acceleration strategy, and a cloud-based detection results verification mechanism. Edge-YOLO reduces the amounts of parameters and computational complexity by using model lightweight technology, which can achieve better detection speed performance on edge-end embedded devices with weak computing power, and adds an attention mechanism to compensate for accuracy loss. Edge video detection acceleration strategy achieves further detection acceleration for Edge-YOLO by utilizing the motion information of objects in the video to achieve real-time detection. Cloud-based detection results verification mechanism verifies and corrects the detection results generated by the edge through a multi-level intervention mechanism to improve the accuracy of the detection results. Through this solution, we can achieve reliable and real-time monitoring of airport apron video on edge devices with the support of a small amount of cloud computing power.

Fault-Tolerant Operation of Bidirectional ZSI-Fed Induction Motor Drive for Vehicular Applications

This paper presents an efficient and fast fault-tolerant control scheme for a bidirectional Z-source inverter (BiZSI)-fed induction-motor drive system for vehicular applications. The proposed strategy aims for the fault detection, localization and diagnosis of the proposed system during switch failures in the inverter module. Generally, power–semiconductor switch failures in inverter modules occur due to open- and short-circuit faults. An efficient modulation scheme is proposed and design specifications are thoroughly derived to obtain high voltage gains across the BiZSI network. A suitably fast detection and diagnosis scheme to isolate the faulty leg and resume the normal operation is discussed in this paper. The control scheme is provided such that the faulty leg is isolated and the motor phase is fed from a redundant leg to resume the operation. A feasible localization algorithm based on experimentally derived values and switching vectors is implemented. In addition, a fast fault diagnosis method based on current estimation and motor speed variation is designed and implemented. Moreover, the most important advantages of the proposed strategy include lower hardware requirements and less harmonic distortion in the output currents. Finally, the simulation and experimental results are presented to validate the feasibility of the theoretical analysis. An extensive performance evaluation of the proposed system with fault ride-through capabilities is performed to prove its suitability for vehicular applications. To validate its merits, the proposed strategy is compared with similar fault-tolerant schemes currently used in the industry.

DRaNN_PSO: A deep random neural network with particle swarm optimization for intrusion detection in the industrial internet of things

The Industrial Internet of Things (IIoT) is a rapidly emerging technology that increases the efficiency and productivity of industrial environments by integrating smart sensors and devices with the internet. The advancements in communication technologies have introduced stable connectivity and a higher data transfer rate in the IIoT. The IIoT devices generate a massive amount of information that requires intelligent data processing techniques for the development of cybersecurity mechanisms. In this regard, deep learning (DL) can be an appropriate choice. This paper proposes a Deep Random Neural Network (DRaNN) based fast and reliable attack detection scheme for IIoT environments. The RaNN is an advanced variant of the traditional Artificial Neural Network (ANN) with a highly distributed nature and better generalization capabilities. To attain a higher attack detection accuracy, the proposed RaNN is optimally trained by incorporating hybrid particle swarm optimization (PSO) with sequential quadratic programming (SQP). The SQP-enabled PSO facilitates the neural network to select optimal hyperparameters. The efficacy of the suggested scheme is analyzed in both binary and multiclass configurations by conducting extensive experiments on three new IIoT datasets. The experimental outcomes demonstrates the promising performance of the proposed design for all datasets.

A Robust Half-Cycle Single-Phase Prefiltered Open-Loop Frequency Estimator for Fast Tracking of Amplitude and Phase

This article presents a fast phase-locking and amplitude detection scheme for single-phase applications. The proposed scheme employs a pseudo-rectification process in order to generate even harmonics from the grid signal. The dc-offset component present in the grid signal increases the sensitivity of the pseudo-rectification process, and the design of the prefiltering stage must, therefore, be completely focused on the rejection of dc-offset and the even-harmonics. The application of a half-cycle (HC) prefilter would consequently appear to be a highly attractive option as regards improving the dynamic response time and even harmonic rejection abilities. However, the elimination of the dc-offset component and the estimation of phase and amplitude information is still a very challenging task. The aforementioned issues are addressed by employing an HC comb filter and an HC nonadaptive Lyapunov orthogonal signal generator. The estimation of single-phase grid parameters is thereby made simpler by employing an improved open-loop frequency estimation, which is capable of providing accurate frequency deviation information with good precision. The research shown herein also reveals that the estimation of the amplitude and phase information differs slightly from the usual mathematical notations owing to the pseudo-rectification method adopted in the proposed work. Note that the prefiltering stage is nonadaptive in nature, which may lead to steady-state errors in the amplitude and phase information under off-nominal frequency conditions. An online mathematical correction approach dependent on frequency deviation is, therefore, employed in order to obtain the error-free amplitude and phase information. Finally, the robustness of the current proposal is validated by experimentally comparing several known grid parameter estimation schemes using a real-time controller.

Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China

Although radar observations capture storm structures with high spatiotemporal resolutions, they are limited within the storm region after the precipitation formed. Geostationary satellites data cover the gaps in the radar network prior to the formation of the precipitation for the storms and their environment. The study explores the effects of assimilating the water vapor channel radiances from Himawari-8 data with Weather Research and Forecasting model data assimilation system (WRFDA) for a severe storm case over north China. A fast cloud detection scheme for Advanced Himawari imager (AHI) radiance is enhanced in the framework of the WRFDA system initially in this study. The bias corrections, the cloud detection for the clear-sky AHI radiance, and the observation error modeling for cloudy radiance are conducted before the data assimilation. All AHI radiance observations are fully applied without any quality control for all-sky AHI radiance data assimilation. Results show that the simulated all-sky AHI radiance fits the observations better by using the cloud dependent observation error model, further improving the cloud heights. The all-sky AHI radiance assimilation adjusts all types of hydrometeor variables, especially cloud water and precipitation snow. It is proven that assimilating all-sky AHI data improves hydrometeor specifications when verified against the radar reflectivity. Consequently, the assimilation of AHI observations under the all-sky condition has an overall improved impact on both the precipitation locations and intensity compared to the experiment with only conventional and AHI clear-sky radiance data.

Dynamic categorization of 3D objects for mobile service robots

Purpose This paper aims to present an object detection methodology to categorize 3D object models in an efficient manner. The authors propose a dynamically generated hierarchical architecture to compute very fast objects’ 3D pose for mobile service robots to grasp them. Design/methodology/approach The methodology used in this study is based on a dynamic pyramid search and fast template representation, metadata and context-free grammars. In the experiments, the authors use an omnidirectional KUKA mobile manipulator equipped with an RGBD camera, to localize objects requested by humans. The proposed architecture is based on efficient object detection and visual servoing. In the experiments, the robot successfully finds 3D poses. The present proposal is not restricted to specific robots or objects and can grow as much as needed. Findings The authors present the dynamic categorization using context-free grammars and 3D object detection, and through several experiments, the authors perform a proof of concept. The authors obtained promising results, showing that their methods can scale to more complex scenes and they can be used in future applications in real-world scenarios where mobile robot are needed in areas such as service robots or industry in general. Research limitations/implications The experiments were carried out using a mobile KUKA youBot. Scalability and more robust algorithms will improve the present proposal. In the first stage, the authors carried out an experimental validation. Practical implications The current proposal describes a scalable architecture, where more agents can be added or reprogrammed to handle more complicated tasks. Originality/value The main contribution of this study resides in the dynamic categorization scheme for fast detection of 3D objects, and the issues and experiments carried out to test the viability of the methods. Usually, state-of-the-art treats categories as rigid and make static queries to datasets. In the present approach, there are no fixed categories and they are created and combined on the fly to speed up detection.

Formulation of the Non-Parametric Value at Risk Portfolio Selection Problem Considering Symmetry

In this research, we study the non-parametric portfolio selection problem with Value at Risk (VaR) minimization and establish a new enhanced Mixed Integer Linear Programming (MILP) formulation to obtain the optimal solutions considering the symmetric property of VaR. We identify that the new MILP formulation can significantly reduce the computation burden of the MILP solver CPLEX. To solve larger-scale practical portfolio selection problems in reasonable computation time, we also develop the Particle Swarm Optimization (PSO) algorithm integrating an efficient Fast Feasible Solution Detection (FFSD) scheme to obtain the near-optimal solutions. Using the simulated datasets with different distribution parameters and skewness and kurtosis patterns, some preliminary numerical results are provided to show the efficiency of the new formulation and FFSD scheme.

Protection Scheme for Fast Detection and Interruption of High-Impedance Faults on Rate-Limited DC Distribution Networks

This article presents a protection scheme for dc power distribution systems based on a unique rate-limited operating mode. The concept of this protection scheme is that extremely slow ramp-rate limits can be imposed on the dc network voltage and all currents drawn from the network through control of the power electronic interfaces. Meanwhile, all fast transients produced by loads and DGs can be absorbed by local energy storage-typically a battery-behind the interface converter. The removal of all transients from the distribution network enables a very effective method to differentiate the normal operation from fault conditions, including high-impedance faults, such as vegetation faults and human-body faults. In the proposed scheme, the ramp rates of the current and voltage on the network are sensed by each interfaced converter to check for compliance with the defined rate limits. It is unlikely that a given fault on the network will comply with the stringently slow rate limits, so this scheme allows the system to quickly detect the fault and deploy suitable protections. Experimental results show that the presented protection scheme is capable of detecting and interrupting a human-body-impedance fault quickly enough to prevent electrocution on a dc distribution cable operating at voltages of 1000 V and above, leading to unprecedented safety on a power distribution network of this voltage level.

Improving the reliability of Byzantine fault‐tolerant distributed software‐defined networks

SummaryThis paper presents a method to improve the reliability and fault tolerance of distributed software‐defined networks. This method is called “BIRDSDN (Byzantine‐Resilient Improved Reliable Distributed Software‐Defined Networks).” In BIRDSDN, a group communication is implemented among the controllers of the whole clusters. This method can detect the crash failure and Byzantine failure of any controller and undertakes a fast detection and recovery scheme to select the controllers to take over the orphan switches. BIRDSDN takes into account the reliability of the nodes considering the failure probability of intracluster and intercluster links, topology, load, and latency. The numerical results show that this approach performs better than the other approaches regarding failure detection, recovery, latency, throughput, reliability, and packet loss.

Virtual Difference Voltage Scheme for Fault Detection in VSC-HVDC Transmission Systems

In this paper, a fast fault detection scheme for voltage source converter based high-voltage direct current (VSC-HVDC) transmission systems is proposed. Based on Bergeron model equations, the remote terminal voltage of an adopted transmission system is calculated in terms of the local measured current and voltage signals. Subsequently, the computed voltage of the remote terminal is compared with the corresponding actual measured-communicated value. Provided that the considered transmission system is functioning well, the difference between the computed and measured voltages is almost zero. However, a considerable virtual voltage arises for fault conditions. When the voltage difference exceeds a predetermined threshold, a fault condition can be detected. Although a reliable communication link is required, the delay for detecting the fault is not caused by the communication time. For evaluation purpose, a detailed simulation is developed using PSCAD/EMTDC with various fault locations, including the cases near the inside or outside of the protected transmission system. The results corroborate a fast detection scheme depending on a moderate sampling/processing frequency level. A high security level is verified even with the worst external faults, or with the misaligned measured samples at the terminals. This corroborates the suitability of the proposed scheme for protecting multi-terminal HVDC systems.

Inertial Sensor-Based Slip Detection in Human Walking

Slip is one of the leading causes of fall-related injuries among occupational and elderly population. Existing research supports proactive slip and fall prevention approaches, while active strategies remain underdeveloped. Development of active slip-induced fall prevention systems requires fast, effective slip detection. This paper aims to develop a novel, real-time slip detection and estimation algorithm during human walking. The slip estimation is built on a slip dynamic model for biped walkers with the integration of the human locomotion constraints. The slip detection uses a set of wearable inertial sensors attached on the lower limbs. A slip indicator is introduced to detect the slip shortly after the heel-strike event. We present an extended Kalman filter-based slip estimation to characterize the slipping distance. One attractive property of the algorithm is its fast, accurate slip onset detection and slipping distance estimation with low-cost, nonintrusive sensing features. Experiments are conducted to validate and demonstrate the performance of the proposed slip detection and estimation scheme. Note to Practitioners —Slip-induced falls often lead to injuries among elderly and professional workers. Active slip and fall prevention could potentially mitigate the negative societal and economic impacts due to slip-induced injuries. However, no assistive device is yet commercially available or reported for slip and fall prevention. This paper presents a first step toward this goal by development and evaluation of a fast slip detection scheme. The wearable slip detection system is small size and lightweight, consisting of five small inertial measurements units placed on the leg. The system can detect the initiation of foot slip several times faster compared to the human sensorimotor mechanisms. The simplicity of the system design, easy implementation, and the fast detection results demonstrate superior reliability and robustness compared to the other slip detection approaches. The proposed slip detection algorithm can find potential applications, such as humanoid robots and control, and healthcare, or home automation.

Rapid Separation of Enantiomeric Impurities in Chiral Molecules by a Self-Referential Weak Measurement System.

We propose a self-referential fast detection scheme for a frequency domain weak measurement system for the detection of enantiomeric impurities in chiral molecules. In a transmissive weak measurement system, the optical rotation (OR) is used to modify the pre-selected polarization state and the post-selection polarization state. We obtained the sum and difference of the optical rotations produced by the sample and the standard by rotating the quarter wave plate in the system. Then, we estimate the ratio of chiral molecules to enantiomeric impurities using the ratio of the central wavelength shifts caused by the addition and subtraction states described above. In this paper, our system has an optical resolution of 1.88 × 10−5°. At the same time, we completed the detection of the ratio of the two substances in the mixture of L-proline and D-proline in different proportions, which proved that our system can quickly detect the content of enantiomeric impurities in chiral molecules.

New scheme for fast copy-move detection

Image forgery detection is a challenging and important problem for many years. One of the most frequently used types of forgery (copy-move) is copying and pasting image fragment within the same image. There are exist two main types of copy-move forgeries: on the one hand, they are created without applying transformations (plain copy-move) and on the other – different digital transforms can be used to hide the traces of copy-move forgery (transformed copy-move). To solve the task of copy-move forgery detection in a complex way we propose several novel techniques, described further. The carried out research showed high quality of copy-move detection with intensity shift, contrast enhancement and additive noise distortions. In conclusion part we provide limitations of the scheme and the ranges of distortion parameters that can be detected.