Design and Implementation of a High-Speed Real-Time Position Detection System in Electromagnetic Coil Launcher

Cun, Guan Xiao; Wang, Shuai; Guo, Daoxing; Guan, Shaohua; Liu, Baolong; Wu, Biao

doi:10.1109/tps.2020.3019045

Cited by 4 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Throughout literature, there are more researches have been done in this area. In addition to ransomware [3], Trojans [4], Keyloggers [5], Backdoors [6], Launchers [7], there are other categories of malware. Both signature-based and behavior-based techniques are used for malware detection.…”

Section: A Malware Analysismentioning

confidence: 99%

Deep Image: A precious image based deep learning method for online malware detection in IoT Environment

Ghahramani¹,

Taheri²,

Shojafar³

et al. 2022

Preprint

View full text Add to dashboard Cite

The volume of malware and the number of attacks in IoT devices are rising everyday, which encourages security professionals to continually enhance their malware analysis tools. Researchers in the field of cyber security have extensively explored the usage of sophisticated analytics and the efficiency of malware detection. With the introduction of new malware kinds and attack routes, security experts confront considerable challenges in developing efficient malware detection and analysis solutions. In this paper, a different view of malware analysis is considered and the risk level of each sample feature is computed, and based on that the risk level of that sample is calculated. In this way, a criterion is introduced that is used together with accuracy and FPR criteria for malware analysis in IoT environment. In this paper, three malware detection methods based on visualization techniques called the clustering approach, the probabilistic approach, and the deep learning approach are proposed. Then, in addition to the usual machine learning criteria namely accuracy and FPR, a proposed criterion based on the risk of samples has also been used for comparison, with the results showing that the deep learning approach performed better in detecting malware.

show abstract

Section: A Malware Analysismentioning

confidence: 99%

Deep Image: A precious image based deep learning method for online malware detection in IoT Environment

Ghahramani¹,

Taheri²,

Shojafar³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The velocity of the electromagnetic launchers is higher than the conventional launchers [1]. Therefore, number of the studies on electromagnetic launchers are increasing.…”

Section: Introductionmentioning

confidence: 99%

Effect of Change of Reluctance Launcher Parameters on Projectile Velocity

Sari

2023

IEEE Access

View full text Add to dashboard Cite

In this study, it is aimed to increase the projectile velocity by changing some parameters of the reluctance launcher. Studied parameters are the initial position of the projectile, the coating of the coil with ferrite, the coil length and the barrel outer diameter. A 3D Maxwell model of the reluctance launcher was generated to examine the effect of the change of these parameters on the projectile velocity. After the 3D model of the launcher was generated, analysis was done for each parameter. During the analysis for a parameter, the other parameters were kept constant. As a result of the analysis of the projectile position, it was determined that the projectile velocity is the highest when the projectile position is -2. It has been determined that the velocity of the projectile increases if the coil is coated with ferrite. It has been determined that the projectile velocity is the highest when the coil length is 7 cm. It has been determined that the highest projectile velocity is obtained when the barrel outer diameter is 18 mm. Using these results, the Maxwell model of the improved launcher was generated. The projectile velocity in the Maxwell model of the initial launcher is 19.24 m/sec. The projectile velocity of the improved launcher in the Maxwell model is 25.8 m/sec. By improving the launcher, a velocity increase of 34.09% was achieved. Later, this launcher was built and the parameters were investigated experimentally. In the experimental work, the projectile velocity of the initial launcher was measured as 19.11 m/sec, and the projectile velocity of the improved launcher was measured as 24.9 m/sec. As a result of the experimental work, a velocity increase of 30.29% was obtained by improving the launcher.

show abstract

Target Electromagnetic Detection Method in Underground Environment: A Review

Wang

Zhang

et al. 2022

IEEE Sensors J.

View full text Add to dashboard Cite

Design and Implementation of a High-Speed Real-Time Position Detection System in Electromagnetic Coil Launcher

Cited by 4 publications

References 16 publications

Deep Image: A precious image based deep learning method for online malware detection in IoT Environment

Deep Image: A precious image based deep learning method for online malware detection in IoT Environment

Effect of Change of Reluctance Launcher Parameters on Projectile Velocity

Target Electromagnetic Detection Method in Underground Environment: A Review

Contact Info

Product

Resources

About