In order to Resolve LongAnalyze and Design and Provide an Easy and accessible Method for Calculating the Response of Concrete Beams to Explosion Load, the Dynamic Analysis Method for One Degree of Freedom System has been used to Analyze this Structure and its Deflection. To Obtain the Answers, the Explosive Load is used exponentially and the Behavior of the Concrete Structure is Considered Elasto-Plastic Equivalent. Then, using two Parameters of Load Duration and Time to reach the End of the Elastic Region to Determine the Range of Responses, Different Modes have been Considered to Calculate the Response Equation and Determine the Maximum Deflection of the Concrete Beam by Theoretical Analysis of SDOF Equations of Motion for the Specified Ranges, the Parametric Responses of Concrete Beams to the Explosion Load were Obtained. Finally, by Comparing the output of these Relationships with the Results of Laboratory Tests, the Finite Element Model and the Relationships of the UFC 3-340-02, the Accuracy of the Proposed Method is Ensured. By Performing different Analyzes in Different Loading Condition and Different Structural Properties, It Was Founf that the Accuracy of the Propoesed Method Is above 90%.

In recent years, the very rapid development of technology in the field of UAVs (Unmanned Aerial Vehicles), along with its advantages, has brought serious threats at various social and security levels. Among these problems, we can mention the issue of unauthorized flights in protected and security areas. Therefore, real time detection of these devices is necessary in order to quickly carry out relevant measures. In this regard, in this research, using the YOLOv5l algorithm, which is part of the latest version of one-stage computer vision algorithms, two models with SGD and Adam optimizers have been developed for the real time detection of UAVs. To develop the models in this research, a dataset containing 10046 images of different types and states of UAVs has been used. The processing of the models has been done with the Google Colab platform, which provides a free powerful processing system for developers. The models have been evaluated on four test sets of 1000 images including normal, low volume, night mode, gray scale and also a test set including 100 images from several UAVs. according to the results presented in this research, the developed model with The Adam optimizer performed better than the model developed with the SGD optimizer

Identifying and classifying marine vehicles is of great significance in military applications. Traditionally, this task was performed by sonar officers based on acoustic signals received by sonar hydrophones. Nowadays, developing systems that perform this task automatically and with minimal human intervention is inevitable. In this study, using different augmentation methods, the size of this dataset is expanded under different scenarios. With the help of signal processing and machine learning knowledge, different features are extracted and next these features are classified using different classification algorithms. Finally, the performance of different scenarios is evaluated by calculating different measures. Results of this study show the success of the Mel-Frequency Cepsteral Coeffiecent (MFCC) feature extraction method when using various classification algorithms, including Artificial Neural Network (ANN) and also the failure of Convolutional Neural Network (CNN) when training with small datasets. The overall comparison reveals that due to the small number of samples in the dataset ANN performs the best.

The Motor Imagery (MI)-based Brain-Computer Interface (BCI) has been proposed as an effective method for direct communication between the brain and external electronic devices. In BCI systems, the main challenge is converting brain signals into reliable commands to control electronic devices. Electroencephalogram (EEG) is the most widely used signal in BCI-related research. Recently, it has been considered with some other biological signals, such as Near-Infrared Spectroscopy (NIRS), to increase the efficiency of BCI systems. The most important challenge in multi-modal BCI systems is combining the extracted features from different signals. For this purpose, in this paper, EEG and NIRS components, including HbO and HbR, were first decomposed into different frequency bands. Next, deep features are extracted from each band using a One-Dimensional (1D) Convolutional Neural Network (CNN). Since the final feature vector has a high dimension, Kernel Principal Component Analysis (KPCA) is employed to remove the irrelevant features, and the remaining ones are classified using the Support Vector Machine (SVM). The results show that the proposed method has high accuracy and improves the recently presented methods.

In recent years, cyberspace has been filled with cyber attacks such as denial of service (DoS) attacks, information phishing, financial fraud, spam and so on. One of the most common cyber attacks that have caused significant economic damage to the financial infrastructure of different countries is denial of service attacks. As a preventive measure, intrusion detection systems equipped with machine learning classification algorithms have been developed to detect anomalies in network traffic. These classification algorithms, depending on the type of DoS attack, have varying degree of success in detecting these attacks and allow users to effectively identify between normal traffic and malicious DoS traffic. In the proposed approach, three steps are used to identify and classify the most common denial of service attacks. The first step is to pre-process the actual SNMP-MIB dataset to scale the data and delete the defective data. In the second stage, by reducing the number of data set features, only the features of the interface group are used, which leads to a reduction in attack detection time. The results show that using the proposed approach, normal traffic and five DoS attacks can be detected from the MIB-SNMP dataset with 100% accuracy rate. Only the detection accuracy of two attacks, UDP Flood and Slowloris, with 99.87 and 99.94% respectively, had a very small error of detection rate.

Railgun launchers are emerging weapons that can be used as offensive or defensive weapons. Since the recorded speeds of firing shots of railgun launcher have reached several thousand meters per second, this launcher is a very good option to be implemented as a defensive artillery gun against enemy’s missiles. In this paper, first, the dynamic behavior of a railgun launcher is modeled by a system of nonlinear differential equations. Then, by numerical solving of this system of equations, the physical variables of the launcher in terms of time including location and speed of movement of armature (bullet), current injected in the rails and voltage of capacitor bank are calculated. Finally, the optimal parameters of railgun launcher in order to achieve the highest firing speed are determined by means of differential evolution optimization method. The results have shown that an optimally designed 300 MJ energy-stored railgun could achieve firing shot speeds of more than 5000 m/s which are suitable for defensive applications..