Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

Research Progress of Detection Methods for Microplastics

Microplastics are plastic particles or fibers with a diameter of less than 5 mm,and they widely exist in the environment and pose potential risks to the ecosystem and human health.Microplastics detection can provide basic data for formulating effective environmental protection strategies.In this paper,the physical,chemical and biological detection methods of microplastics are reviewed,and the advantages and disadvantages of different methods are analyzed.The problems and challenges encountered in microplastics detection are analyzed,and the future research is discussed.

Discussion on the application prospect of the transient electromagnetic method based on the dual launcher and mine advanced detection

The dual transmitter implements the equivalent anti-magnetic flux transient electromagnetic method, which can effectively reduce the scope of the transient electromagnetic detection blind area. However, this method is rarely reported in the detection of pipelines in urban geophysical exploration and the application of coal mines. Based on this, this paper realizes the equivalent anti-magnetic flux transient electromagnetic method based on the dual launcher. The suppression effect of this method on the blind area is analyzed by physical simulation. And the detection experiment of underground pipelines is carried out outdoors. The results show that the dual launcher can significantly reduce the turn-off time, thereby effectively reducing the impact of the blind area on the detection results, and the pipeline detection results verify the device’s effectiveness. Finally, based on the ground experimental results, the application prospect of mine advanced detection is discussed. Compared with other detection fields, the formation of blind areas is mainly caused by the equipment. If the dual launcher can be used to reduce the blind area, the accuracy of advanced detection can be improved more effectively. The above research results are of great significance for improving the detection accuracy of the underground transient electromagnetic method.

Effects of Standing Time during Pretreatment on the Nitrite Concentration Detected by Spectrophotometric Method

Food safety problems caused by excessive nitrite addition have been frequently reported and the detection of nitrite in food is particularly important. The standing time during the pretreatment of primary sample has a great influence on the concentration of nitrite tested by spectrophotometric method. In this context, three kinds of food samples are prepared, including canned mustard, canned fish and home-made pickled water. A series of standing times are placed during the sample pretreatments and the corresponding nitrite contents in these samples are detected by spectrophotometric method based on N-ethylenediamine dihydrochloride. This study aims to find out a reasonable standing time during the pretreatment of food sample, providing influence factor for precise detection of nitrite.

Efficient People Detection with Infrared Images

This work focuses on the problem of monitoring the coastline, which in Portugal’s case means monitoring 3007 kilometers, including 1793 maritime borders with the Atlantic Ocean to the south and west. The human burden on the coast becomes a problem, both because erosion makes the cliffs unstable and because pollution increases, making the fragile dune ecosystem difficult to preserve. It is becoming necessary to increase the control of access to beaches, even if it is not a popular measure for internal and external tourism. The methodology described can also be used to monitor maritime borders. The use of images acquired in the infrared range guarantees active surveillance both day and night, the main objective being to mimic the infrared cameras already installed in some critical areas along the coastline. Using a series of infrared photographs taken at low angles with a modified camera and appropriate filter, a recent deep learning algorithm with the right training can simultaneously detect and count whole people at close range and people almost completely submerged in the water, including partially visible targets, achieving a performance with F1 score of 0.945, with 97% of targets correctly identified. This implementation is possible with ordinary laptop computers and could contribute to more frequent and more extensive coverage in beach/border surveillance, using infrared cameras at regular intervals. It can be partially automated to send alerts to the authorities and/or the nearest lifeguards, thus increasing monitoring without relying on human resources.

Research and Application of Log Defect Detection and Visualization System Based on Dry Coupling Ultrasonic Method

In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood,this paper invented a set of log internal defect detection and visualization system by using the ultrasonic dry coupling agent method.The detection and visualization analysis of internal log defects were realized through log specimen test.The main conclusions show that the accuracy,reliability and practicability of the system for detecting the internal defects of log specimens have been effectively verified.The system can make the edge of the detected image smooth by interpolation algorithm,and the edge detection algorithm can be used to detect and reflect the location of internal defects of logs accurately.The content mentioned above has good application value for meeting the requirement of increasing demand for wood resources and improving the automation level of wood nondestructive testing instruments.

Application of SPAC method and electromagnetic wave CT in karst detection of Wuhan Metro Line 8

In recent years,karst construction projects in the built-up area of Wuhan(capital of Hubei Province,China)are increasing,and the karst geological disasters have aroused social concerns.The actual engineering projects usually use shallow geophysical exploration methods to explore karst.This paper uses Spatial Auto-Correlation Method(SPAC)and electromagnetic Computerized Tomography(CT)to detect karst in urban built-up areas.Depending on the different physical properties of rock and soil,the SPAC method can better reveal the interface between soil and rock strata and the interface between soil layers.The electromagnetic CT method can identify strata according to the apparent absorption coefficient,which can better reveal the interface between soil and rock,the interface between the more intact and weathered rock.The SPAC method is mainly qualitative to measure the low-speed area,namely,the wrong geological body i.e.,karst cave,but also can detect the fracture zone or filling mode of karst cave,and at the same time,cannot use exploration holes or logging observation.The electromagnetic CT method can accurately detect the location and scale of the karst caves and has a higher accuracy detecting karst bands.In addition,exploration holes or well logging observations are also expected to be conducted,and their detection effect is greatly affected by lithology.

Iterative Dichotomiser Posteriori Method Based Service Attack Detection in Cloud Computing

Cloud computing(CC)is an advanced technology that provides access to predictive resources and data sharing.The cloud environment represents the right type regarding cloud usage model ownership,size,and rights to access.It introduces the scope and nature of cloud computing.In recent times,all processes are fed into the system for which consumer data and cache size are required.One of the most security issues in the cloud environment is Distributed Denial of Ser-vice(DDoS)attacks,responsible for cloud server overloading.This proposed sys-tem ID3(Iterative Dichotomiser 3)Maximum Multifactor Dimensionality Posteriori Method(ID3-MMDP)is used to overcome the drawback and a rela-tively simple way to execute and for the detection of(DDoS)attack.First,the pro-posed ID3-MMDP method calls for the resources of the cloud platform and then implements the attack detection technology based on information entropy to detect DDoS attacks.Since because the entropy value can show the discrete or aggregated characteristics of the current data set,it can be used for the detection of abnormal dataflow,User-uploaded data,ID3-MMDP system checks and read risk measurement and processing,bug ratingfile size changes,orfile name changes and changes in the format design of the data size entropy value.Unique properties can be used whenever the program approaches any data error to detect abnormal data services.Finally,the experiment also verifies the DDoS attack detection capability algorithm.

Application Research of Sonar Detection Method in Melting Exploration at the Bottom of Piles

Karst landforms are widely distributed in China,and are most common in Yunnan,Guizhou and Guangxi.If the development of karst caves at the bottom of the piles cannot be accurately ascertained before the construction of bridge pile foundations,accidents such as hole collapse,slurry leakage,and drill sticking will easily occur.In this paper,the principle and method of sonar detection for detecting karst caves at the bottom of bridge piles was introduced,and the sonar detection data and the cave situation at the bottom of the pile during the construction process in combination with the case of Yunnan Zhenguo Highway Project was analyzed,which verifies the practicability and reliability of sonar detection method reliability.

The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

The pupil recognition method is helpful in many real-time systems,including ophthalmology testing devices,wheelchair assistance,and so on.The pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size,occlusion of eyelids,and eyelashes.Deep Convolutional Neural Networks(DCNN)are being used in pupil recognition systems and have shown promising results in terms of accuracy.To improve accuracy and cope with larger datasets,this research work proposes BOC(BAT Optimized CNN)-IrisNet,which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil region.The proposed method is based on very deep architecture and many tricks from recently developed popular CNNs.Experiment results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight,easy to implement,and of cutting-edge accuracy.Finally,the region-based black box method for determining pupil center coordinates was introduced.The proposed architecture was tested using various IRIS databases,including the CASIA(Chinese academy of the scientific research institute of automation)Iris V4 dataset,which has 99.5%sensitivity and 99.75%accuracy,and the IIT(Indian Institute of Technology)Delhi dataset,which has 99.35%specificity and MMU(Multimedia University)99.45%accuracy,which is higher than the existing architectures.

Application of clan member signal method in structural damage detection

It is well known that in most cases, a reference is necessary for structural health diagnosis, and it is very difficult to obtain such a reference for a given structure. In this paper, a clan member signal method （CMSM） is proposed for use in structures consisting of groups （or clans） that have the same geometry, i.e., the same cross section and length, and identical boundary conditions. It is expected that signals measured on any undamaged member in a clan after an event could be used as a reference for any other members in the clan. To verify the applicability of the proposed method, a steel truss model is tested and the results show that the CMSM is very effective in detecting local damage in structures composed of identical slender members.

Online Detection of Broken Rotor Bar Fault in Induction Motors by Combining Estimation of Signal Parameters via Min-norm Algorithm and Least Square Method

Current research in broken rotor bar （BRB） fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estima- tion cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the fre- quencies of the fundamental and fault characteristic com- ponents with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.

Toxicology,biosynthesis,bio-control of aflatoxin and new methods of detection

Mycotoxins and their derivatives since their discoveries and until the present time are behind unspecified economic and medical damages.Aflatoxins are classified according to their physical–chemical and toxicological characters in the most dangerous row of the mycotoxins.These aflatoxins are in part responsible,of irreversible medical disasters that are not easily manageable such as cancer of the liver and kidneys,and in the other part,of losses in the stored cereal products.Based on these crucial findings,monitoring of this toxin became imperative in post-harvest food products,during storage,during transformation chain and even during the long phases of conservation.Vigilance of this toxin is delivered by detection methods using very advanced technologies to respond in the shortest possible times.In addition,the knowledge of factors supporting the biosynthesis of aflatoxins such as the temperature,moisture content,concentration of nitrogen and carbon,and the molecules responsible for the genetic control of the synthesis will be reflected later in the choice of bio-control techniques.This control is currently based on new strategies using the bioactives substances of the plants,the lactic bacteria and some strains of actinomycetes that have good inhibiting activity against aflatoxins with fewer side effects on Man.On the other hand,this brief review summarizes the results of new studies demonstrating the toxicity of the toxin,new detection methods and bio-control.

Enzyme Inhibition Rate Method for Rapid Detection of Organophosphorus and Carbamate Pesticides in Cowpea

[Objectives ] The paper was to explore enzyme inhibition rate method for rapid detection of organophosphorus and carbamate pesticides in cowpea. [ Methods ] Acetylcholinesterase （ACHE） was added to cowpea extract, to determine the inhibition rate of extract against enzyme. The influences of different sampiing methods and sampling parts on detection results were compared. [ Results] The positive rate of standard sampling was 18.18% higher than that of non-stand- ard sampling, and the positive rate of samples collected from cowpea tail was 16.67% higher than that collected from other parts. [ Condmions] Enzyme inhibi- tion rate method is suitable for rapid detection of organophosphorus and carbamate pesticides in cowpea.

Statistical damage detection method for frame structures using a confidence interval

A novel damage detection method is applied to a 3-story frame structure, to obtain statistical quantification control criterion of the existence, location and identification of damage. The mean, standard deviation, and exponentially weighted moving average （EWMA） are applied to detect damage information according to statistical process control （SPC） theory. It is concluded that the detection is insignificant with the mean and EWMA because the structural response is not independent and is not a normal distribution. On the other hand, the damage information is detected well with the standard deviation because the influence of the data distribution is not pronounced with this parameter. A suitable moderate confidence level is explored for more significant damage location and quantification detection, and the impact of noise is investigated to illustrate the robustness of the method.

Blind detection of liquefaction by existing methods for New Zealand M_L6.3 earthquake on Feb. 22,2011

Real-time liquefaction monitoring and warning techniques are new ways to mitigate liquefaction hazard. A key point is to establish a reverse liquefaction detection method based on seismic records. However, the existing methods are quite limited and the reliability requires verification. On Feb. 22, 2011 an earthquake of magnitude 6.3 struck at New Zealand＇s South Island. Remarkable liquefaction phenomena were reported, which provide an opportunity to verify the existing liquefaction detection methods. 27 acceleration records within 50 km to the epicenter were selected to perform a blind detection by using the existing methods, including Miyajima method, Suzuki method, Kostadinov-Yamazaki method and Yuan-Sun method. The blind detection results indicate that Yuan-Sun method gives correct results for seven confirmed sites, and Suzuki method and Yuan-Sun method yield correct detection for a reported non-liquefied site. Four methods including the Yuan-Sun method give identical detection for four sites and three methods also including the Yuan-Sun method give identical detection for ten sites. Besides, there are five sites, for which the four methods give opposite detection.

Sources, distribution, behavior, and detection techniques of microplastics in soil: A review

In recent years, the problem of environmental pollution caused by microplastics has attracted widespread attention. This paper reviews the latest research progress in terms of the source, content and distribution characteristics, harm, and detection technology of soil microplastics by referring to the relevant literature on soil microplastics worldwide. It concludes that:(1) Existing studies worldwide have detected the presence of microplastics in soil, water, and atmosphere, and the use of agricultural films, sewage sludge,and other man-made activities are the main sources of microplastics in soil;(2) microplastics can adsorb heavy metals, persistent organic pollutants and antibiotics in soil, change the physical and chemical properties of soil. This will result in composite pollution and harm to the ecosystem;(3) microplastics in soil not only can destroy the activity of key soil microorganisms, but also enter the body of crops and soil animals, affecting normal growth of crops and soil animals, and further threaten human health;(4) at present, there is no unified operating standard for the sampling, processing, and detection process of microplastics. Analysis methods such as visual inspection, spectroscopy, and thermal analysis have both advantages and disadvantages, and emerging detection technologies require urgent development.Microplastics have become a new pollutant in soil and their distribution characteristics are closely related to human activities. They pollute the environment and threaten human health through the food chain.Although related research on soil microplastics has just begun, it will become the focus of research in the future.

Robust fault detection for delta operator switched fuzzy systems with bilateral packet losses

Considering packet losses, time-varying delay, and parameter uncertainty in the switched fuzzy system, this paper designs a robust fault detection filter at any switching rate and analyzes the H∞ performance of the system. Firstly, the Takagi-Sugeno(T-S) fuzzy model is used to establish a global fuzzy model for the uncertain nonlinear time-delay switched system,and the packet loss process is modeled as a mathematical model satisfying Bernoulli distribution. Secondly, through the average dwell time method and multiple Lyapunov functions, the exponentially stable condition of the nonlinear network switched system is given. Finally, specific parameters of the robust fault detection filter can be obtained by solving linear matrix inequalities(LMIs). The effectiveness of the method is verified by simulation results.

Detection and quantification of Pb and Cr in oysters using laser-induced breakdown spectroscopy

The quantitative determination of heavy metals in aquatic products is of great importance for food security issues.Laser-induced breakdown spectroscopy(LIBS)has been used in a variety of foodstuff analysis,but is still limited by its low sensitivity when targeting trace heavy metals.In this work,we compare three sample enrichment methods,namely drying,carbonization,and ashing,for increasing detection sensitivity by LIBS analysis for Pb and Cr in oyster samples.The results demonstrate that carbonization can remove a significant amount of the contributions of organic elements C,H,N and O;meanwhile,the signals of the metallic elements such as Cu,Pb,Sr,Ca,Cr and Mg are enhanced by3–6 times after carbonization,and further enhanced by 5–9 times after ashing.Such enhancement is not only due to the more concentrated metallic elements in the sample compared to the dried ones,but also the unifying of the matter in carbonized and ashed samples from which higher plasma temperature and electron density are observed.This condition favors the detection of trace elements.According to the calibration curves with univariate and multivariate analysis,the ashing method is considered to be the best choice.The limits of detection of the ashing method are 0.52 mg kg-1 for Pb and0.08 mg kg-1 for Cr,which can detect the presence of heavy metals in the oysters exceeding the maximum limits of Pb and Cr required by the Chinese national standard.This method provides a promising application for the heavy metal contamination monitoring in the aquatic product industry.

Method for electromagnetic detection satellites scheduling based on genetic algorithm with alterable penalty coefficient

The electromagnetic detection satellite （EDS） is a type of earth observation satellites （EOSs）. The Information collected by EDSs plays an important role in some fields, such as industry, science and military. The scheduling of EDSs is a complex combinatorial optimization problem. Current research mainly focuses on the scheduling of imaging satellites and SAR satellites, but little work has been done on the scheduling of EDSs for its specific characteristics. A multi-satellite scheduling model is established, in which the specific constrains of EDSs are considered, then a scheduling algorithm based on the genetic algorithm （GA） is proposed. To deal with the specific constrains of EDSs, a penalty function method is introduced. However, it is hard to determine the appropriate penalty coefficient in the penalty function. Therefore, an adaptive adjustment mechanism of the penalty coefficient is designed to solve the problem, as well as improve the scheduling results. Experimental results are used to demonstrate the correctness and practicability of the proposed scheduling algorithm.