YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method.

Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

Owing to the integration of energy digitization and artificial intelligence technology,smart energy grids can realize the stable,efficient and clean operation of power systems.However,the emergence of cyber-physical attacks,such as dynamic load-altering attacks(DLAAs)has introduced great challenges to the security of smart energy grids.Thus,this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids.The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer.First,a data-driven method was proposed to predict the DLAA sequence in the cyber layer.By designing a double radial basis function network,the influence of disturbances on attack prediction can be eliminated.Based on the prediction results,an unknown input observer-based detection and localization method was further developed for the physical layer.In addition,an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs.Consequently,through the collaborative work of the cyber-physics layer,injected DLAAs were effectively detected and located.Compared with existing methodologies,the simulation results on IEEE 14-bus and 118-bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.

Visual SLAM in dynamic environments based on object detection

A great number of visual simultaneous localization and mapping(VSLAM)systems need to assume static features in the environment.However,moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption.To cope with this challenging topic,a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed.To reduce the influence of dynamic content,we incorporate the deep-learning-based object detection method in the visual odometry,then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system.Experiment with both on the TUM and KITTI benchmark dataset,as well as in a real-world environment,the results clarify that our method can significantly reduce the tracking error or drift,enhance the robustness,accuracy and stability of the VSLAM system in dynamic scenes.

Dynamic evolutionary community detection algorithms based on the modularity matrix

Motivated by the relationship of the dynamic behaviors and network structure, in this paper, we present two efficient dynamic community detection algorithms. The phases of the nodes in the network can evolve according to our proposed differential equations. In each iteration, the phases of the nodes are controlled by several parameters. It is found that the phases of the nodes are ultimately clustered into several communities after a short period of evolution. They can be adopted to detect the communities successfully. The second differential equation can dynamically adjust several parameters, so it can obtain satisfactory detection results. Simulations on some test networks have verified the efficiency of the presented algorithms.

Application of Bayesian Dynamic Forecast in Anomaly Detection

A macroscopical anomaly detection method based on intrusion statistic and Bayesian dynamic forecast is presented. A large number of alert data that cannot be dealt with in time are always aggregated in control centers of large-scale intrusion detection systems. In order to improve the efficiency and veracity of intrusion analysis, the intrusion intensity values are picked from alert data and Bayesian dynamic forecast method is used to detect anomaly. The experiments show that the new method is effective on detecting macroscopical anomaly in large-scale intrusion detection systems.

Overlapping Community Detection in Dynamic Networks

Due to the increasingly large size and changing nature of social networks, algorithms for dynamic networks have become an important part of modern day community detection. In this paper, we use a well-known static community detection algorithm and modify it to discover communities in dynamic networks. We have developed a dynamic community detection algorithm based on Speaker-Listener Label Propagation Algorithm (SLPA) called SLPA Dynamic (SLPAD). This algorithm, tested on two real dynamic networks, cuts down on the time that it would take SLPA to run, as well as produces similar, and in some cases better, communities. We compared SLPAD to SLPA, LabelRankT, and another algorithm we developed, Dynamic Structural Clustering Algorithm for Networks Overlapping (DSCAN-O), to further test its validity and ability to detect overlapping communities when compared to other community detection algorithms. SLPAD proves to be faster than all of these algorithms, as well as produces communities with just as high modularity for each network.

Dynamic obstacles detection of tram based on laser radar

The detection of obstacles in a dynamic environment is a hot and difficult problem.A method of autonomously detecting obstacles based on laser radar is proposed as a safety auxiliary structure of tram.The nearest neighbor method is used for spatial obstacles clustering from laser radar data.By analyzing the characteristics of obstacles,the types of obstacles are determined by time correlation.Experiments were carried out on the developed unmanned aerial vehicle(UAV),and the experimental results verify the effectiveness of the proposed method.

Wide dynamic detection range of methane gas based on enhanced cavity absorption spectroscopy

Integrated cavity output spectroscopy(ICOS) is an effective technique in trace gase detection.The strong absorption due to the long optical path of this method makes it challenging in the application scenes that have large gas concentration fluctuation,especially when the gas concentration is high.In this paper,we demonstrate an extension of the dynamic range of ICOS by using a detuned laser combined with an off-axis integrating cavity.With this,we improve the upper limit of the dynamic detection range from 0.1%(1000 ppm) to 20% of the gas concentration.This method provides a way of using ICOS in the applications with unpredictable gas concentrations such as gas leak detection,ocean acidification,carbon sequestration,etc.

Dynamic unbalance detection of cardan shaft in high-speed train based on EMD-SVD-NHT

Contrary to the aliasing defect between the adjacent intrinsic model functions(IMFs) existing in empirical model decomposition(EMD), a new method of detecting dynamic unbalance with cardan shaft in high-speed train was proposed by applying the combination between EMD, Hankel matrix, singular value decomposition(SVD) and normalized Hilbert transform(NHT). The vibration signals of gimbal installed base were decomposed through EMD to get different IMFs. The Hankel matrix constructed through the single IMF was orthogonally executed through SVD. The critical singular values were selected to reconstruct vibration signs on the basis of the key stack of singular values. Instantaneous frequencys(IFs) of reconstructed vibration signs were applied to detect dynamic unbalance with shaft and eliminated clutter spectrum caused by the aliasing defect between the adjacent IMFs, which highlighted the failure characteristics. The method was verified by test data in the unbalance condition of dynamic cardan shaft. The results show that the method effectively detects the fault vibration characteristics caused by cardan shaft dynamic unbalance and extracts the nature vibration features. With comparison to the traditional EMD-NHT, clarity and failure characterization force are significantly improved.

Hierarchical adaptive stereo matching algorithm for obstacle detection with dynamic programming

An adaptive weighted stereo matching algorithm with multilevel and bidirectional dynamic programming based on ground control points （GCPs） is presented. To decrease time complexity without losing matching precision, using a multilevel search scheme, the coarse matching is processed in typical disparity space image, while the fine matching is processed in disparity-offset space image. In the upper level, GCPs are obtained by enhanced volumetric iterative algorithm enforcing the mutual constraint and the threshold constraint. Under the supervision of the highly reliable GCPs, bidirectional dynamic programming framework is employed to solve the inconsistency in the optimization path. In the lower level, to reduce running time, disparity-offset space is proposed to efficiently achieve the dense disparity image. In addition, an adaptive dual support-weight strategy is presented to aggregate matching cost, which considers photometric and geometric information. Further, post-processing algorithm can ameliorate disparity results in areas with depth discontinuities and related by occlusions using dual threshold algorithm, where missing stereo information is substituted from surrounding regions. To demonstrate the effectiveness of the algorithm, we present the two groups of experimental results for four widely used standard stereo data sets, including discussion on performance and comparison with other methods, which show that the algorithm has not only a fast speed, but also significantly improves the efficiency of holistic optimization.

A High-level Architecture for Intrusion Detection on Heterogeneous Wireless Sensor Networks: Hierarchical, Scalable and Dynamic Reconfigurable

Networks protection against different types of attacks is one of most important posed issue into the network and information security domains. This problem on Wireless Sensor Networks (WSNs), in attention to their special properties, has more importance. Now, there are some of proposed solutions to protect Wireless Sensor Networks (WSNs) against different types of intrusions;but no one of them has a comprehensive view to this problem and they are usually designed in single-purpose;but, the proposed design in this paper has been a comprehensive view to this issue by presenting a complete Intrusion Detection Architecture (IDA). The main contribution of this architecture is its hierarchical structure;i.e. it is designed and applicable, in one, two or three levels, consistent to the application domain and its required security level. Focus of this paper is on the clustering WSNs, designing and deploying Sensor-based Intrusion Detection System (SIDS) on sensor nodes, Cluster-based Intrusion Detection System (CIDS) on cluster-heads and Wireless Sensor Network wide level Intrusion Detection System (WSNIDS) on the central server. Suppositions of the WSN and Intrusion Detection Architecture (IDA) are: static and heterogeneous network, hierarchical, distributed and clustering structure along with clusters' overlapping. Finally, this paper has been designed a questionnaire to verify the proposed idea;then it analyzed and evaluated the acquired results from the questionnaires.

Three-dimensional coordinates test method with uncertain projectile proximity explosion position based on dynamic seven photoelectric detection screen

To objectively obtain the three-dimensional coordinates of the projectile fuze proximity explosion when projectile intersects the head of missile target, we propose a dynamic seven photoelectric detection screen test method, which is made up of six plane detection screens and a flash photoelectric dynamic detection screen. The three-dimensional coordinates calculation model of the projectile proximity explosion position based on seven plane detection screens with dynamic characteristics is established.According to the relation of the dynamic seven photoelectric detection screen planes and the time values,the analytical function of the projectile proximity explosion position parameters under non-linear motion is derived. The projectile signal filtering method based on discrete wavelet transform is explored in this work. Additionally, the projectile signal recognition algorithm using an improved particle swarm is proposed. Based on the characteristics of the time duration and the signal peak error for the projectile passing through the detection screen, the signals attribution of the same projectile passing through six detection screens are analyzed for obtaining precise time values of the same projectile passing through the detection screens. On the basis of the projectile fuze proximity explosion test, the linear motion model and the proposed non-linear motion model are used to calculate and compare the same group of projectiles proximity explosion position parameters. The comparison of test results verifies that the proposed test method and calculation model in this work accurately obtain the actual projectile proximity explosion position parameters.

Community Detection in Dynamic Social Networks

There are many community detection algorithms for discovering communities in networks, but very few deal with networks that change structure. The SCAN (Structural Clustering Algorithm for Networks) algorithm is one of these algorithms that detect communities in static networks. To make SCAN more effective for the dynamic social networks that are continually changing their structure, we propose the algorithm DSCAN (Dynamic SCAN) which improves SCAN to allow it to update a local structure in less time than it would to run SCAN on the entire network. We also improve SCAN by removing the need for parameter tuning. DSCAN, tested on real world dynamic networks, performs faster and comparably to SCAN from one timestamp to another, relative to the size of the change. We also devised an approach to genetic algorithms for detecting communities in dynamic social networks, which performs well in speed and modularity.

Extraordinary tunable dynamic range of electrochemical aptasensor for accurate detection of ochratoxin A in food samples

We report the design of a sensitive,electrochemical aptasensor for detection of ochratoxin A(OTA)with an extraordinary tunable dynamic sensing range.This electrochemical aptasensor is constructed based on the target induced aptamer-folding detection mechanism and the recognition between OTA and its aptamers results in the conformational change of the aptamer probe and thus signal changes for measurement.The dynamic sensing range of the electrochemical aptasensor is successfully tuned by introduction of free assistant aptamer probes in the sensing system.Our electrochemical aptasensor shows an extraordinary dynamic sensing range of 11-order magnitude of OTA concentration from 10^−8 to 10^2 ng/g.Of great significance,the signal response in all OTA concentration ranges is at the same current scale,demonstrating that our sensing protocol in this research could be applied for accurate detections of OTA in a broad range without using any complicated treatment of signal amplification.Finally,OTA spiked red wine and maize samples in different dynamic sensing ranges are determined with the electrochemical aptasensor under optimized sensing conditions.This tuning strategy of dynamic sensing range may offer a promising platform for electrochemical aptasensor optimizations in practical applications.

A Nonparametric Approach to Foreground Detection in Dynamic Backgrounds

Foreground detection is a fundamental step in visual surveillance.However,accurate foreground detection is still a challenging task especially in dynamic backgrounds.In this paper,we present a nonparametric approach to foreground detection in dynamic backgrounds.It uses a history of recently pixel values to estimate background model.Besides,the adaptive threshold and spatial coherence are introduced to enhance robustness against false detections.Experimental results indicate that our approach achieves better performance in dynamic backgrounds compared with several approaches.

Dynamic load-altering attack detection based on adaptive fading Kalman filter in power systems

This paper presents an effective and feasible method for detecting dynamic load-altering attacks(D-LAAs)in a smart grid.First,a smart grid discrete system model is established in view of D-LAAs.Second,an adaptive fading Kalman filter(AFKF)is designed for estimating the state of the smart grid.The AFKF can completely filter out the Gaussian noise of the power system,and obtain a more accurate state change curve(including consideration of the attack).A Euclidean distance ratio detection algorithm based on the AFKF is proposed for detecting D-LAAs.Amplifying imperceptible D-LAAs through the new Euclidean distance ratio improves the D-LAA detection sensitivity,especially for very weak D-LAA attacks.Finally,the feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified based on simulations.

Dynamic Simulation of Cracked Buildings for Damage Detection

A dynamic simulation method for cracked structures is implemented to determine their dynamic response with the purpose of evaluating their structural behavior.The procedure makes possible the si mulation of three-dimensional cracked structures.The excitation force is randomly generated to simulate wind gusts.It is assumed the structure remains in the elastic range,which allows for each mode that contributes to its dynamic response to be decoupled.The results indicate that the presence of damage causes changes in the modals parameters of the structure as accurate as other similar methods proposed for simpler structures.Therefore,it is concluded that the proposed method is a reliable way to evaluate the dynamic behavior of three-dimensi onal cracked building structures.

Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis

In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events;however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.

PHOTO-ELECTRIO MEASURING SYSTEM FOR TOOL EDGE DETECTION BY THE TEOHNOLOGY OF COMPUTER DYNAMIO DISPLAY MOVING IMAGE

This paper describes a technology of dynamic display moving image by computer monitor,which is initially used in the design of tool detection system. The paper presents the hardware and software principie and edge detection process. The way of marking edge point coordinates and stability of moving image also is analyzed. The method reforms the conventional design of the 2-D vision detection system. Moreover,it facilitates the design of the systematic mechanical construction,is convenient to compile instrument systemsoftware,and realizes to detect and track display image simultaneously. By the work,the tool detection system is improved to practical application.