جستجوی مقالات مرتبط با کلیدواژه « Data Mining » در نشریات گروه « مکانیک »

Artificial intelligence refers to algorithms that can have correct and logical reactions as well as behaviors similar to intelligent human behaviors; including understanding complex situations, simulating human thought processes and reasoning methods, successfully responding to them, learning and the ability to acquire knowledge and reason to solve problems. Artificial intelligence is a kind of knowledge of knowing and designing intelligent agents and its scope of application is global. Artificial intelligence is a vast field of intersection of many knowledges as well as a connecting link of old and new sciences and techniques. Artificial intelligence is undoubtedly a new phenomenon in the field of science and technology, which has had a great impact on the field of naval military equipment. Artificial intelligence, like any other technology, has advantages and disadvantages, and as a result, many opportunities and threats. In this article, after stating the introduction and presenting the problem plan and its description, the opportunities of using artificial intelligence and data mining to improve the performance of electronic warfare systems in the maritime domain are discussed. Then, the advantage of using data mining with artificial intelligence compared to other methods to identify cyber-electronic threats in the maritime domain is discussed. Finally, the identification phases of cyber-electronic threats in the maritime domain are introduced by combining artificial intelligence and data mining. In the end, after the solutions and proposals are proposed, discussions, summaries and conclusions are presented.

n GEO communicational satellites, thrusts are being used for many missions such as station keeping, longitude change maneuver and actuators desaturation. These types of actuators need fuel and its estimation requires many complicated calculations. In this paper, a novel method based on availed data for previous satellites is proposed for estimation of satellite fuel mass and it does not need mathematical modeling of satellite dynamics. Two methods, least square method and artificial intelligence, are used to this aim and two mathematical model are proposed for satellite fuel mass estimation. By applying the models to several previous satellites, it is shown that the models have lower than 5% average error. The proposed method in this paper is quick and accurate and can be utilized for GEO satellites feasibility study and conceptual design

Inability to timely detection of pre-engineered attacks are the most obvious weaknesses in cyber defense. The possession and efficient indigenous methods of intrusion detection in computer networks, defense against cyber attacks will increase. In this study, by using intrusion detection system based on the combination of data mining and clustering methods and artificial neural network is presented. Learning and assessment method using the training data set of Cambridge and by using software and Matlab been Weka. The results show that the three criteria of article Precision, Recall, F- value works better than other methods.

In this research، an intelligent and automatic procedure is introduced for diagnosis of some common faults of timing belt bases on its vibration signals. For this goal، vibration signals were gained in different faulty conditions of timing belt. In data mining step، six features namely، standard deviation، kurtosis، skewness، impulse factor، shape factor and crest factor were extracted from vibration signals aim to better monitoring of signals behavior. After extracting the fault characteristics، Artificial Neural Network (ANN) classifier was used for intelligent detection of faulty belts. The ANN was trained with 60 percent of signals and was tested with other signals. The results show the faulty belts have a turbulence vibration and impulsive behavior. Also، ANN classifier detected and classified the timing belts faults with 90 percent accuracy. The results show which the use of vibration signals for small faults detection of timing belt was effective aim to prevent its complete rupture. The results demonstrate the combination of data mining and artificial intelligence techniques is a powerful and successful procedure for precision timing belt troubleshooting.

The Timing belt is one of important parts of modern cars. "Heat extreme" and "overloading" are two common faults of these belts, which cause fatigue, erosion, sudden failures, and consequently damages to the engine. The present article introduces an intelligent method to diagnose these two major faults of belts, using vibration signals. The method presented in this research was designed based on a combination of experimental analyses, data mining of time domain signals and the artificial neural network (ANN) classifier. To do so, firstly, vibration signals of the belt were measured by a laser sensor in three conditions: (1) normal condition, (2) belt overloading and (3) heat extreme. In the data mining stage, five statistical features including mean, standard deviation, root mean square (RMS), kurtosis and impulse factors were extracted from vibration signals. Extracted features were used as ANN inputs to the fault diagnosis and the classification of different defects of the belt. Finally, the ANN, with the average accuracy of 76%, could diagnose and classify relevant faults in the timing belt of an engine. Results showed that intelligent methods could be used to diagnose faults of belts and therefore, to protect engines from serious damages, caused by their failures.