مجله علوم و فناوری های پدافند نوین
سال سیزدهم شماره 3 (پیاپی 49، پاییز 1401)

The feeding of off-grid AC loads, like military loads or defense systems is of high importance. This paper proposes a novel structure for Switched-Capacitor based Multi-Level Inverter (SCMLI) that produces a high quality (low THD) 7-level output-voltage waveform for supplying off-grid AC loads. The proposed inverter requires only single DC-source and can boost the input voltage up to three times, at the output port. The boosting capability is very crucial at PV applications. The reduced device count, high quality (low THD) output voltage, and natural voltage balancing of capacitors are other main merits of suggested inverter. The proposed inverter can effectively supply any load type, including pure resistive, resistive-inductive and pure inductive loads. The short discharging interval of capacitors leads to reduced capacitors' voltage-ripple, high voltage quality, low voltage-ripple losses and better inverter efficiency. Comparison results approve the superiority of proposed converter over existed counterparts. Also, the correct operation of proposed inverter during different operational conditions, has been validated by simulation (performed in MATLAB-Simulink software) and experimental analysis.
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Unmanned aerial vehicles (UAVs) search dangerous or difficult places for humans and collect various environmental data. Human face recognition in drones is essential for various applications, such as surveillance, search, and security. Previous methods for face recognition are highly sensitive to limitations such as height, angle and distance from the face. Therefore, locating and identifying faces at high altitudes and long distances reduces the accuracy and efficiency of previous methods. In this paper, a new deep leanring-based face detection and recognition is presented. The proposed method is performed in three steps. In the first step, input images are segmented with the selective search algorithm. In the second step, a deep network is proposed for filtering bounding boxs to identify the target boxes with high accuracy and speed. In fact, a two-class classification problem is performed by deep learning forfacelocalization. In the third step, the localized faces are usedto perform face recognition using the proposed deep network. In the proposed architecture, the properties of widely used deep networks are used, and a quantitative comparison of the proposed method with new methods in terms of computational complexity shows that training the proposed model requires less runing time than other methods. In addition, the evaluation of the proposed method on the DroneFace dataset for different distance and height from the target shows thatthe proposed method has an average face detection rate of 75.9 and an average face recognition rate of 84.6. Therefore, the proposed method has higher accuracy and efficiency than state-of-the-art methods in this field and can be used for surveillance and security applications.

The most important limitations for wireless underwater telecommunication systems are multipath, fading, attenuation, and extensive interference in underwater channels. In these channels, due to wide changes in the signal-to-noise ratio, transmitting and receiving in fixed modulation mode does not have the desired performance. For this reason, in this paper, the use of turbo coding to encode an adaptive modulation system based on orthogonal frequency division multiplexing (OFDM) is introduced for the first time, which reduces the transmission error and also achieves high efficiency. In the proposed system model, the optimal system was achieved by selecting appropriate parameters for communication in the underwater acoustic environment and also choosing proper modulation modes, which decreases the bit error rate by 16% compared to other systems introduced so far for underwater acoustic communication

The science of robotics has been widely applied to develop industries and specifically to reduce human injuries. By adjusting the controller of these robots to achieve proper speed and accuracy, their performance can be improved which it can lead to reduction in the number of injuries. Until now, robotic arm controllers have often been modeled using equations of direct and inverse kinematics, aiming to control the position of the final actuator of the arm. Difficulty in solving these equations, errors in solving them, lack of user-friendly environment, inflexibility in decision-making and the volume of calculations are among the problems of existing robotic control systems. In this paper, the robotic arm with four degrees of freedom is controlled by two classic and Fuzzy control methods and three PI, PD and PID controllers. Matlab-Simulink is used as a tool to test the movement characteristics of the robot. It was observed that the PD controller, despite not having overshoot, leads to a steady state error in most of the cases. Also, the PI controller provides a proper settling time, while, it has a higher overshoot than the PID controller. Finally, the results showed that the Fuzzy PID controller provides better responses than the classical PID controller and the classical and Fuzzy PI and PD controllers.

Progressive failure in the structure due to the occurrence of accidental actions and the chain effect of the failure, leads to the failure of a wide range of the structure or even the total collapse of the structure. Power transmission lines are one of the integral components in the power system. Failure of power transmission line structures is one of the main problems of power companies in different regions of the world. In this research, the progressive failure behavior of a case study of 230 kV power transmission line in Fars province has been investigated. In the present study, two methods of linear dynamic analysis and nonlinear dynamic analysis have been used to identify damage with the alternative load path method. The finite element model of the passing tower (DC0), the end tower (DC90) and the conductors of the 230 kV power transmission line have been modeled in SAP2000 software. In this research, the behavior of progressive failure due to the removal of the base of power transmission towers has been investigated and evaluated. The results showed that the passing and end masts are stable under gravity load due to the failure of one leg. Also, the results showed that due to the removal of one pillar in the progressive failure analysis, the end masts have higher resistance than the passing masts.
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In this paper, the performance of the multi-arched doors against explosion loads has been investigated using numerical modeling in LS-DYNA finite element software. Using the multi-arched structures reduces the used material weight and as a result, decreases the cost of building the door. Furthermore, this structure causes a more uniform stress distribution and reduces the stress concentration compared to conventional doors with a smooth surface. The parameters were studied in this research were the number of arches, the height of the arch, the weight of the explosive material, the dimensions of the door, the materials used in the construction of the door and the type of the hardened. The numerical modeling results show that by increasing the number of arch from one to five, the maximum displacement created in the center of the sample decreases by about 86%. By increasing the height of the arch from 5 to 10 and 20 cm, based on the number of different arches, the maximum displacement of the center of the door decreases between 5 and 32%. Among the different methods of strengthening the door, the multi-arch method increases the resistance. This method creates this resistance by consuming less steel and is more economical.