Experimental Study on Engineering Behavior of Solidified Soil for Scour Repair and Protection

A new scour countermeasure using solidified slurry for offshore foundation has been proposed recently.Fluidized solidified slurry is pumped to seabed area around foundation for scour protection or pumped into the developed scour holes for scour repair as the fluidized material solidifies gradually.In the pumping operation and solidification,the engineering behaviors of solidified slurry require to be considered synthetically for the reliable application in scour repair and protection of ocean engineering such as the pumpability related flow value,flow diffusion behavior related rheological property,anti-scour performance related retention rate in solidification and bearing capacity related strength property after solidification.In this study,a series of laboratory tests are conducted to investigate the effects of mix proportion(initial water content and binder content)on the flow value,rheological properties,density,retention rate of solidified slurry and unconfined compressive strength(UCS).The results reveal that the flow value increases with the water content and decreases with the binder amount.All the solidified slurry exhibits Bingham plastic behavior when the shear rate is larger than 5 s^(-1).The Bingham model has been employed to fit the rheology test results,and empirical formulas for obtaining the density,yield stress and viscosity are established,providing scientific support for the numerical assessment of flow and diffusion of solidified slurry.Retention rate of solidified slurry decreases with the water flow velocity and flow value,which means the pumpability of solidified slurry is contrary to anti-scour performance.The unconfined compressive strength after solidification reduces as the water content increases and binder content decreases.A design and application procedure of solidified soil for scour repair and protection is also proposed for engineering reference.

Design and prototyping of the readout electronics for the transition radiation detector in the high energy cosmic radiation detection facility

The high energy cosmic-radiation detection(HERD)facility is planned to launch in 2027 and scheduled to be installed on the China Space Station.It serves as a dark matter particle detector,a cosmic ray instrument,and an observatory for high-energy gamma rays.A transition radiation detector placed on one of its lateral sides serves dual purpose,(ⅰ)calibrating HERD's electromagnetic calorimeter in the TeV energy range,and(ⅱ)serving as an independent detector for high-energy gamma rays.In this paper,the prototype readout electronics design of the transition radiation detector is demonstrated,which aims to accurately measure the charge of the anodes using the SAMPA application specific integrated circuit chip.The electronic performance of the prototype system is evaluated in terms of noise,linearity,and resolution.Through the presented design,each electronic channel can achieve a dynamic range of 0–100 fC,the RMS noise level not exceeding 0.15 fC,and the integral nonlinearity was<0.2%.To further verify the readout electronic performance,a joint test with the detector was carried out,and the results show that the prototype system can satisfy the requirements of the detector's scientific goals.

Target acquisition performance in the presence of JPEG image compression

This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background,where the similarity is further used to determine the degree of clutter in the image.Four new clutter metrics based on image quality assessment are introduced,among which the Haar wavelet-based perceptual similarity index,known as HaarPSI,provides the best target acquisition prediction results.It is shown that the similarity between the target and the background at the boundary between visually lossless and visually lossy compression does not change significantly compared to the case when an uncompressed image is used.In future work,through subjective tests,it is necessary to check whether this presence of compression at the threshold of just noticeable differences will affect the human target acquisition performance.Similarity values are compared with the results of subjective tests of the well-known target Search_2 database,where the degree of agreement between objective and subjective scores,measured through linear correlation,reached a value of 90%.

An Intelligent Approach for Intrusion Detection in Industrial Control System

Supervisory control and data acquisition(SCADA)systems are computer systems that gather and analyze real-time data,distributed control systems are specially designed automated control system that consists of geographically distributed control elements,and other smaller control systems such as programmable logic controllers are industrial solid-state computers that monitor inputs and outputs and make logic-based decisions.In recent years,there has been a lot of focus on the security of industrial control systems.Due to the advancement in information technologies,the risk of cyberattacks on industrial control system has been drastically increased.Because they are so inextricably tied to human life,any damage to them might have devastating consequences.To provide an efficient solution to such problems,this paper proposes a new approach to intrusion detection.First,the important features in the dataset are determined by the difference between the distribution of unlabeled and positive data which is deployed for the learning process.Then,a prior estimation of the class is proposed based on a support vector machine.Simulation results show that the proposed approach has better anomaly detection performance than existing algorithms.

Recent Advances in the Rapid Detection and Performance Evaluation Methods of Detergent Additives for Gasoline

Although detergent additives for gasoline have been widely commercialized,their formulas are often kept confidential and there is still no standardized method for quickly detecting the main active ingredients and evaluating their effectiveness,which makes their regulation difficult.An overview of the current state of the development and application of detergent additives for gasoline in China and other regions,as well as a review of the rapid detection and performance evaluation methods available for analyzing detergent additives are given herein.The review focuses on the convenience,cost,efficiency,and feasibility of on-site detection and the evaluation of various methods,and also looks into future research directions,such as detecting and evaluating detergent additives in ethanol gasoline and with advanced engine technologies.

A procedural study of building up a performance evaluation indicator system of intellectual property protection

The intellectual property protection, whether judicial or administrative, is evaluated through a performance evaluation indicator system. To building up such a system, we must follow certain working procedures which usually consist of four steps： to determine the performance objectives, to design the structure of indicator system, to specify the indicators and to set up the weight of indicators. Each step plays a different role in performance evaluation indicator system and has its own impact on the realization of performance evaluation objectives respectively. So the scientifically building up a performance evaluation indicator system is the key to determine whether the intellectual property is protected well or not.

Design and implementation of self-protection agent for network-based intrusion detection system

Static secure techniques, such as firewall, hierarchy filtering, distributed disposing,layer management, autonomy agent, secure communication, were introduced in distributed intrusion detection. The self-protection agents were designed, which have the distributed architecture,cooperate with the agents in intrusion detection in a loose-coupled manner, protect the security of intrusion detection system, and respond to the intrusion actively. A prototype self-protection agent was implemented by using the packet filter in operation system kernel. The results show that all the hosts with the part of network-based intrusion detection system and the whole intrusion detection system are invisible from the outside and network scanning, and cannot apperceive the existence of network-based intrusion detection system. The communication between every part is secure. In the low layer, the packet streams are controlled to avoid the buffer leaks exist ing in some system service process and back-door programs, so as to prevent users from misusing and vicious attack like Trojan Horse effectively.

MCMOD:The Multi-Category Large-Scale Dataset for Maritime Object Detection

The marine environment is becoming increasingly complex due tothe various marine vehicles,and the diversity of maritime objects poses a challengeto marine environmental governance.Maritime object detection technologyplays an important role in this segment.In the field of computer vision,there is no sufficiently comprehensive public dataset for maritime objects inthe contrast to the automotive application domain.The existing maritimedatasets either have no bounding boxes(which are made for object classification)or cover limited varieties of maritime objects.To fulfil the vacancy,this paper proposed the Multi-Category Large-Scale Dataset for MaritimeObject Detection(MCMOD)which is collected by 3 onshore video camerasthat capture data under various environmental conditions such as fog,rain,evening,etc.The whole dataset consists of 16,166 labelled images alongwith 98,590 maritime objects which are classified into 10 classes.Comparedwith the existing maritime datasets,MCMOD contains a relatively balancedquantity of objects of different sizes(in the view).To evaluate MCMOD,this paper applied several state-of-the-art object detection approaches fromcomputer vision research on it and compared their performances.Moreover,a comparison between MCMOD and an existing maritime dataset was conducted.Experimental results indicate that the proposed dataset classifies moretypes of maritime objects and covers more small-scale objects,which canfacilitate the trained detectors to recognize more types of maritime objects anddetect maritime objects over a relatively long distance.The obtained resultsalso showthat the adopted approaches need to be further improved to enhancetheir capabilities in the maritime domain.

Detection performance and inversion processing of logging-while-drilling extra-deep azimuthal resistivity measurements

We present systematic investigations on the physics,detection performance and inversion of logging-while-drilling extradeep azimuthal resistivity measurements(EDARM).First,the definitions of EDRAM measurements are discussed,followed by the derivation of the attenuation and phase-shift geometrical factors to illustrate the relative contributions of formation units to the observed signals.Then,a new definition of detection depth,which considers the uncertainty of inversion results caused by the data noise,is proposed to quantify the detection capability of ED ARM.Finally,the B ayesian theory associated with Markov chain Monte Carlo sampling is introduced for fast processing of EDARM data.Numerical results show that ED ARM is capable of detecting the azimuth and distance of remote bed boundaries,and the detection capability increases with increasing spacing and resistivity contrast.The EDARM tool can accommodate a large range of formation resistivity and is able to provide the resistivity anisotropy at arbitrary relative dipping angles.In addition,multiple bed boundaries and reservoir images near the borehole are readily obtained by using the Bayesian inversion.

Cued search algorithm with uncertain detection performance for phased array radars

A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, and the statistical characteristic of the detection probability is calculated by using the fluctuant model of the target radar cross section （RCS）. Secondly, when the detection probability is completely unknown, its probability density function is modeled with a beta distribution, and its posterior probability distribution with the radar observation is derived based on the Bayesian theory. Finally simulation results show that the cued search algorithm with a known RCS fluctuant model can achieve the best performance, and the algorithm with the detection probability modeled as a beta distribution is better than that with a random selected detection probability because the model parameters can be updated by the radar observation to approach to the real value of the detection probability.

Research on Evaluating Index of Lightning Protection Safety Performance of High Voltage Overhead Transmission Line

In order to improve the lightning protection performance of transmission lines, lightning protection management has been divided into every tower that lightning protection performance has been evaluated respectively. According to factors such as landform, span, tower type, grounding resistance, isolator type, and so on, relative ratio of tripping operation of every tower in the line has been calculated to evaluate its lightning protection safety performance, it is beneficial to operation maintenance and lightning reconstruction of transmission lines.

Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)systems.In light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated environment.To achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the system.The localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the GCPV.To develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation signals.Further,the developed algorithm is combined with the PMCU implemented with the experimental setup of GCPV.The results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

Fault Detection Based on Hierarchical Cluster Analysis in Wide Area Backup Protection System

In wide area backup protection of electric power systems, the prerequisite of protection device's accurate, fast and reliable performance is its corresponding fault type and fault location can be discriminated quickly and defined exactly. In our study, global information will be introduced into the backup protection system. By analyzing and computing real-time PMU measurements, basing on cluster analysis theory, we are using mainly hierarchical cluster analysis to search after the statistical laws of electrical quantities' marked changes. Then we carry out fast and exact detection of fault components and fault sections, and finally accomplish fault isolation. The facts show that the fault detection of fault component (fault section) can be performed successfully by hierarchical cluster analysis and calculation. The results of hierarchical cluster analysis are accurate and reliable, and the dendrograms of hierarchical cluster analysis are in intuition.

Effect of Phase Change Materials on the Thermal Protective Performance of the Multi-layered Fabrics Examined by TPP Tester under Flash Fire

Cotton fabrics treated with phase change materials( PCMs)were used in multi-layered fabrics of the fire fighter protective clothing to study its effect on thermal protection. The thermal protective performance( TPP) of the multi-layered fabrics was measured by a TPP tester under flash fire. Results showed that the utilization of the PCM fabrics improved the thermal protective performance of the multi-layered fabrics. The fabric with a PCM add on of 41. 9% increased the thermal protection by 50. 6% and reduced the time to reach a second degree burn by 8. 4 s compared with the reference fabrics( without PCMs). The employment of the PCM fabrics also reduced the blackened areas on the inner layers. The PCM fabrics with higher PCM melting temperature could bring higher thermal protective performance.

Rapid Determination of Dopamine and Its Metabolites During in vivo Cerebral Microdialysis by Routine High Performance Liquid Chromatography With Electrochemical Detection

To determine dopamine and its metabolites during in vivo cerebral microdialysis by routine high performance liquid chromatography with electrochemical detection. Methods Microdialysis probes were placed into the right striatum of Wistar rat brains and perfused with Ringer＇s solution at a rate of 1.5 pL/min. A reverse phase HPLC with electrochemistry was used to assay DA, DOPAC, and HVA after cerebral microdialysates were collected every 20 minutes from awake and freely moving rats. In order to identify the reliability of this method, its selectivity, linear range, precision and accuracy were tested and the contents of DA, DOPAC, and HVA in rat microdialysates were determined. Results The standard curve was in good linear at the concentration ranging from 74 nmol/L to 1.5 pmol/L for DOPAC （r^2= 0.9996）, from 66 nmol/L to 1.3 gmol/L for DA （r^2=l.0000） and from 69 nmol/L to 1.4 pmol/L for HVA （r^2=0.9992）. The recovery of DOPAC （0.30, 0.77, 1.49 gmol/L）, DA （0,26, 0.69, 1.32 gmol/L）, and HVA （0.27, 0.71, 1.37 gmol/L） was 82.00±1.70%, 104.00±4.00%, 98.70±3.10%; 92.30± 1.50%, 105.30±2.30%, 108.00±2.00%; 80.00±7.80%, 107.69±8.00%, and 108.66±3.10%, respectively at each concentration. Their intra-day RSD was 3.3%, 3.4%, and 2.5%, and inter-day RSD was 4.2%, 2.3%, and 5.6%, respectively. The mean extracellular concentrations of DOPAC, DA, and HVA in rat brain microdialysates were 10.7, 2.4, and 9.2 gmol/L （n=6）, respectively. Conclusion The findings of our study suggested that the simple, accurate and stable method can be applied to basic researches of diseases related to monoamines neurotransmitters by cerebral microdialysis in rats.

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Educational Data Mining(EDM)is an emergent discipline that concen-trates on the design of self-learning and adaptive approaches.Higher education institutions have started to utilize analytical tools to improve students’grades and retention.Prediction of students’performance is a difficult process owing to the massive quantity of educational data.Therefore,Artificial Intelligence(AI)techniques can be used for educational data mining in a big data environ-ment.At the same time,in EDM,the feature selection process becomes necessary in creation of feature subsets.Since the feature selection performance affects the predictive performance of any model,it is important to elaborately investigate the outcome of students’performance model related to the feature selection techni-ques.With this motivation,this paper presents a new Metaheuristic Optimiza-tion-based Feature Subset Selection with an Optimal Deep Learning model(MOFSS-ODL)for predicting students’performance.In addition,the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of outliers.Besides,the Chaotic Monarch Butterfly Optimization Algo-rithm(CBOA)is used for the selection of highly related features with low com-plexity and high performance.Then,a sailfish optimizer with stacked sparse autoencoder(SFO-SSAE)approach is utilized for the classification of educational data.The MOFSS-ODL model is tested against a benchmark student’s perfor-mance data set from the UCI repository.A wide-ranging simulation analysis por-trayed the improved predictive performance of the MOFSS-ODL technique over recent approaches in terms of different measures.Compared to other methods,experimental results prove that the proposed(MOFSS-ODL)classification model does a great job of predicting students’academic progress,with an accuracy of 96.49%.

Intrusion Detection Using Federated Learning for Computing

The integration of clusters,grids,clouds,edges and other computing platforms result in contemporary technology of jungle computing.This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time.Federated learning is a collaborative machine learning approach without centralized training data.The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior,potentially caused by malicious adversaries and it can emerge with new and unknown attacks.The main objective is to learn overall behavior of an intruder while performing attacks to the assumed target service.Moreover,the updated system model is send to the centralized server in jungle computing,to detect their pattern.Federated learning greatly helps the machine to study the type of attack from each device and this technique paves a way to complete dominion over all malicious behaviors.In our proposed work,we have implemented an intrusion detection system that has high accuracy,low False Positive Rate(FPR)scalable,and versatile for the jungle computing environment.The execution time taken to complete a round is less than two seconds,with an accuracy rate of 96%.

Ballistic design and testing of a composite armour reinforced by CNTs suitable for armoured vehicles

This paper is investigating the use of composite armour reinforced by nanomaterials, for the protection of light armoured(LAV) and medium armoured military vehicles(MAV), and the interaction between the composite materials and high-performance ballistic projectiles. Four armour materials, consisted of front hybrid fibre reinforced polymer cover layer, ceramic strike-face, fibre reinforced polymer intermediate layer and the metal matrix composite reinforced backplate, were manufactured and assembled by adhesive technology. The proposed laminated protection system is suitable for armoured ground vehicles;however, it could be used as armour on ground, air and naval platforms. The design of the protection system, including material selection and thickness, was elaborated depending on the performance requirements of Level 4 + STANAG 4569 military standard(projectile 14.5 mm × 114 mm API B32) and especially on a design philosophy which is analysed with the specifications. The backplate of this new composite is a hybrid material of Metal Matrix Composite(MMC) reinforced with carbon nanotubes(CNTs), manufactured with the use of powder metallurgy technique. The composite backplate material was morphologically, mechanically and chemically analysed. Results show that all plates are presenting high mechanical properties and ballistic characteristics, compared to commonly used armour plates. Real military ballistic tests according to AEP-STANAG 4569 were carried out for the total composite armour systems. After the ballistic tests, AA2024-CNT3 showed the best protection results, compared with the other plates(AA2024-CNT1 and AA2024-CNT2), with the projectile being unable to fully penetrate the composite plate.

Determination of neomycin in water samples by high performance anion chromatography with pulsed amperometric detection

A simple, fast and reliable method, using high performance anion chromatography with pulsed amperometric detection, had been developed for the analysis of neomycin in water samples. The elution and separation were carried out with an isocratic mobile phase, containing 10 mmol/L NaOH. The influence of the concentration and pH of the mobile phase on the separation and detection was investigated. A quadruple-potential waveform used for the detection was optimized. The detection limit of neomycin was down to 0.027 μg/mL. The linearity of neomycin calibration curve ranged from 0.050 to 0.505 μg/mL with correlation coefficient of 0.9997. R.S.D. (n = 11) was 4.0%.

Enhanced Intrusion Detection System for an EH IoT Architecture Using a Cooperative UAV Relay and Friendly UAV Jammer

In this paper,the detection capabilities and system performance of an energy harvesting(EH)Internet of Things(Io T)architecture in the presence of an unmanned aerial vehicle(UAV)eavesdropper(UE)are investigated.The communication protocol is divided into two phases.In the first phase,a UAV relay(UR)cooperates with a friendly UAV jammer(UJ)to detect the UE,and the UR and UJ harvest energy from a power beacon(PB).In the second phase,a ground base station(GBS)sends a confidential signal to the UR using non-orthogonal multiple access(NOMA);the UR then uses its harvested energy to forward this confidential signal to IoT destinations(IDs)using the decode-and-forward(DF)technique.Simultaneously,the UJ uses its harvested energy to emit an artificial signal to combat the detected UE.A closed-form expression for the probability of detecting the UE(the detection probability,DP)is derived to analyze the detection performance.Furthermore,the intercept probability(IP)and throughput of the considered IoT architecture are determined.Accordingly,we identify the optimal altitudes for the UR and UJ to enhance the system and secrecy performance.Monte Carlo simulations are employed to verify our approach.