نشریه فرماندهی و کنترل
سال ششم شماره 2 (پیاپی 20، تابستان 1401)

Inconsistency as the prevailing conditions on the battlefield forces the military forces to adopt measures other than those corresponding to the symmetric or classic operations scene. The basis of adopting such measures is called the principles of war, which is the foundation of the science and art of war and guides the commander to achieve victory.The relevance of each of the principles of war depends on the type and environment of war. Like the doctrine, part of these principles can be used in general in all armed forces; But due to the existence of differences in assigned missions, the type of action of specialized forces, equipment models and the like, another part of these principles may be different in one force compared to other forces. Therefore, the researchers in the current study tried to identify the factors affecting air defense operations in an uneven war environment, to extract and present the "principles of air defense war in an uneven environment".The current type of research is applied-developmental and its approach is qualitative with descriptive-analytical method. The data has been collected by library and field methods. The obtained information has been analyzed using MaxQDA software. The validity of the interview questions was calculated through content validity and reliability with "Cohen's kappa formula". Finally, from the findings of the research on the principles of air defense warfare in an uneven environment, including the principle of information aristocracy, the principle of passive defense, the principle of deception, the principle of targeting, the principle of frequency management, the principle of offense in defense, the principle of all-round air defense, the principle of purpose, the principle decentralized support, the principle of initiative and creativity,The principle of surprise, the principle of all-round psychological operations, the principle of proportionality, the principle of agility, the principle of fire and movement, the principle of maintaining strength and defensive power, the principle of no need for repetition, the principle of maximum deterrence, the principle of safe and stable communications, the principle of continuity of defense operations. air, the principle of self-reliance and tactical independence, the principle of decentralized implementation, the principle of flexibility, the principle of non-interference in actions, the principle of speed of action, the principle of early engagement, the principle of defense in depth, the principle of attrition, the principle of centralized command, the principle of electronic warfare and the principle Cyber ​​defense is calculated and presented.

In this research we develop a deep reinforcement learning-based method for autonomous robot navigation. Our approach in this study is based on DDPG and one of its improved versions named SD3. We did some modifications on this algorithm to make it proper for autonomous navigation problems and optimize it for this problems. The modified algorithm can work with high dimensional state spaces because of using convolutional layers. Also we propose two reward terms include linear velocity reward and angular velocity penalty to encourage robot to move faster with smoother movements. For generalizing the algorithm we used an algorithm for randomly changing shape, layout and number of obstacles in the environment. And to speed up the learning process and improving the robot operation, we normalized all input data. Finally, the proposed algorithm is implemented with ROS and Gazebo and the results show improvement versus the main SD3 and DDPG algorithms.

In order to advance the goals of the military organization, one should have a special look at the role of information technology and develop the information technology strategy of the military organization. Remaining strategies at the general level or general directions are among the issues that prevent the implementation of strategies in organizations. In response to this problem, the concept of strategy map was proposed. The information technology strategy map of Netami organization tries to provide a general map of possible strategies for Netami organizations in order to improve the processes and strengthen the infrastructure and intangible assets of the organization. In this research based on analytical descriptive method, firstly by using documentary method and in the form of library and field studies, obtaining advice from experts, all the documents and documents available regarding the preparation of the strategy map, obtaining and according to the indices extracted from these studies and Based on the opinions of the experts, the final results have been presented in the form of a strategic map for the information technology of the military organization. The strategic map proposed in this research for military organizations has 3 dimensions (facets) of infrastructure, internal processes and effectiveness, and beneficiaries and has 47 strategic goals in the facet of the map, which are based on the goals model and have a hierarchical relationship.

Convolutional neural networks are considered as one of the most popular machine learning models in data classification. despite their significant success in data classification, they do not produce acceptable results when working with imbalanced data.  Imbalanced learning is one of the most challenging issues in machine learning, since in these problems, samples of one or more classes are usually much more than others, or misclassification costs are not equal for all classes, while CNN networks assume the distribution of classes and the cost of misclassification to be equal. The ensemble method is a popular method to deal with imbalanced data sets, which can achieve high accuracy by combining several basic estimators, and in comparison with using only one estimator, the reliability of the model could be improved. ensemble methods empower machine learning models to deal with imbalanced data. In this research, we have introduced a method based on ensemble learning for convolutional neural networks, which uses the cascade of CNN networks to work with imbalanced data. We use the focal loss function to train CNNs, the gamma parameter in the loss function determines the importance of hard and easy samples. CNNi+1 gives less importance to easy samples than hard ones in comparison with CNNi, this is done by increasing the gamma step by step (increasing γi+1 compared to γi). In our proposed FENIL ensemble network (Incremental Focal Ensemble method for multi-class Imbalalanced Learning), weights of the training data for CNNi+1 are determined by the classification result of the previous CNN i.e. CNNi. The combination of all CNNs is used to classify the new data. We applied our proposed FENIL ensemble network to several benchmark data sets. the results showed that the FENIL network not only has much higher accuracy and F1-score (18.63, 19.61 higher) In comparison with non-deep methods such as decision tree AdaBoost but also obtained better results In comparison with other common deep methods for imbalanced learning.

The use of scientific methods to meet the information needs of commanders and increase their knowledge on the battlefield is one of the necessities of command and control. The main goal of this research is to determine the effect of synoptic analysis of heavy rains in command and control. In this research, an environmental-to-circulation approach was used, and for this purpose, precipitation data in the cold season of the year and upper atmosphere data at levels 1000 and 500 were received and used from ECMWF. First, the rainfall above the 95th percentile of the data with high overlap in the stations of the region was identified and determined as a representative day. The results of the synoptic analysis of the research showed that there are three important patterns for heavy rainfall in the region and also the role of the Sudanese low pressure along with the deepening of the Mediterranean trough due to the blocking event of the low pressure cut in the eastern Mediterranean Sea plays a significant role in the heavy rainfall of the region. The effect of synoptic analysis in command and control was recognized in three dimensions, identification of atmospheric hazards, decision making and action.

In the age of communication and the digital world, the security of computer systems is considered one of the most controversial security issues. The upcoming research is trying to extract useful data from the Microsoft malware dataset named BIG 2015, a classifier that is very simple and has little computational complexity both in the field of feature extraction and in the field of the classifier mechanism. It provides security and malware detection. Of the 1804 extracted features, some of which have played a more important and colorful role in the classification, the section_name_headre feature has been calculated with a weight of 0.2160. The accuracy of the classifier is 99.81 and the predictor error is 0.00774. In this regard, in order to achieve better predictions and higher accuracy than the aggregate algorithm and methods of selecting suitable features from the data sets used, the techniques of Feature selection, Feature Importance Xgboost & Lgb, and Permutation Importance have been used. Therefore, by using the findings of this research in IDS and IPS systems, it is possible to increase the accuracy of malware detection and reduce the detection error rate.