Influence of roughness on the detection of mechanical characteristics of low-k film by the surface acoustic waves

The surface acoustic wave （SAW） technique is a precise and nondestructive method to detect the mechanical charac- teristics of the thin low dielectric constant （low-k） film by matching the theoretical dispersion curve with the experimental dispersion curve. In this paper, the influence of sample roughness on the precision of SAW mechanical detection is inves- tigated in detail. Random roughness values at the surface of low-k film and at the interface between this low-k film and the substrate are obtained by the Monte Carlo method. The dispersive characteristic of SAW on the layered structure with rough surface and rough interface is modeled by numerical simulation of finite element method. The Young＇s moduli of the Black DiamondTM samples with different roughness values are determined by SAWs in the experiment. The results show that the influence of sample roughness is very small when the root-mean-square （RMS） of roughness is smaller than 50 nm and correlation length is smaller than 20 μm. This study indicates that the SAW technique is reliable and precise in the nondestructive mechanical detection for low-k films.

An Efficient Stabbing Based Intrusion Detection Framework for Sensor Networks

Intelligent Intrusion Detection System(IIDS)for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a firewall.The efficiency of IIDS highly relies on the algorithm performance.The enhancements towards these methods are utilized to enhance the classification accuracy and diminish the testing and training time of these algorithms.Here,a novel and intelligent learning approach are known as the stabbing of intrusion with learning framework(SILF),is proposed to learn the attack features and reduce the dimensionality.It also reduces the testing and training time effectively and enhances Linear Support Vector Machine(l-SVM).It constructs an auto-encoder method,an efficient learning approach for feature construction unsupervised manner.Here,the inclusive certified signature(ICS)is added to the encoder and decoder to preserve the sensitive data without being harmed by the attackers.By training the samples in the preliminary stage,the selected features are provided into the classifier(lSVM)to enhance the prediction ability for intrusion and classification accuracy.Thus,the model efficiency is learned linearly.The multi-classification is examined and compared with various classifier approaches like conventional SVM,Random Forest(RF),Recurrent Neural Network(RNN),STL-IDS and game theory.The outcomes show that the proposed l-SVM has triggered the prediction rate by effectual testing and training and proves that the model is more efficient than the traditional approaches in terms of performance metrics like accuracy,precision,recall,F-measure,pvalue,MCC and so on.The proposed SILF enhances network intrusion detection and offers a novel research methodology for intrusion detection.Here,the simulation is done with a MATLAB environment where the proposed model shows a better trade-off compared to prevailing approaches.

Fusion network for small target detection based on YOLO and attention mechanism

Target detection is an important task in computer vision research, and such an anomaly detection and the topic of small target detection task is more concerned. However, there are still some problems in this kind of researches, such as small target detection in complex environments is susceptible to background interference and poor detection results. To solve these issues, this study proposes a method which introduces the attention mechanism into the you only look once(YOLO) network. In addition, the amateur-produced mask dataset was created and experiments were conducted. The results showed that the detection effect of the proposed mothed is much better.

Distributed Detection Mechanism and Resilient Consensus Strategy for Secure Voltage Control of AC Microgrids

Distributed secondary control,depending on the sparse communication topology,excels for its flexibility and expandability in microgrids.The communication network plays an important role in microgrid control,but it is vulnerable to cyber-attacks.In this paper,the mathematical model for false data injection(FDI)attacks in AC microgrids is established,and the corresponding detection mechanism based on the morphological gradient is designed for the location of cyber-attacks in communication topology.Then,we propose a median-based resilient consensus voltage control strategy to mitigate the negative effects caused by malicious cyber-attacks and ensure the safe operation of the microgrid.Combining the detection method and resilient consensus control,a novel eventdriven mitigation scheme is derived to improve the resilience of microgrids under cyber-attacks.Finally,a tested microgrid model composed of five different distributed generation(DG)units is simulated in the MATLAB/Simulink environment.The feasibility and effectiveness of the presented detection mechanism and resilient consensus strategy are verified by simulation results applying different scenarios.

Aeroengine Fault Diagnosis Method Based on Optimized Supervised Kohonen Network

To diagnose the aeroengine faults accurately,the supervised Kohonen(S-Kohonen)network is proposed for fault diagnosis.Via adding the output layer behind competitive layer,the network was modified from the unsupervised structure to the supervised structure.Meanwhile,the hybrid particle swarm optimization(H-PSO)was used to optimize the connection weights,after using adaptive inheritance mode(AIM)based on the elite strategy,and adaptive detecting response mechanism(ADRM),HPSO could guide the particles adaptively jumping out of the local solution space,and ensure obtaining the global optimal solution with higher probability.So the optimized S-Kohonen network could overcome the problems of non-identifiability for recognizing the unknown samples,and the non-uniqueness for classification results existing in traditional Kohonen(T-Kohonen)network.The comparison study on the GE90 engine borescope image texture feature recognition is carried out,the research results show that:the optimized S-Kohonen network has a strong ability of practical application in the classification fault diagnosis;the classification accuracy is higher than the common neural network model.

Comparison of Two Rhodamine-Polyamine Polystyrene Solid-phase Fluorescence Sensors for Hg(Ⅱ) Detection Based on Theoretical Calculation

Two novel rhodamine-based polystyrene solid-phase fluorescence sensors PS-PA-Ⅰ and PS・PA-Ⅱ with different lengths of polyamines were synthesized for Hg(Ⅱ)determination.Thedetection mechanism involving the Hg(Ⅱ)chelation-induced spirocycle open of rhodamine was proposed with the aid of theoretical calculation.The stronger N—Hg bond and the longer polyamine chain in PS-PA-Ⅱ led to a better selectivity,much higher and more quickly fluorescence response to Hg(Ⅱ).

Mechanics of molecular beacons for mRNA detecting

Decoding genetic information is crucial for gene therapy and cancer diagnosis,which has attracted growing interest in the field of clinical medicine and life science.In this study,we conducted a comprehensive exploration to obtain the detection mechanism of molecular beacons from a mechanics point of view.The potential energy function of molecular beacon/target system is established firstly,based on which the profile of molecular beacons is solved by genetic algorithm optimization.The length of stem and the total energy are further calculated when the target is hybridized with loop and stem.The results show that the hybridization between target and stem is energetically favorable compared with that between target and loop,indicating a new detection strategy.These analyses may cast light on understanding the mechanism of molecular beacons detection,and further help to design novel molecular beacons with high resolution and quantification.