İlgi alanları

• MakaleYalnız künye2027/r/gumushane/13789
  Analytical UWB waveform design based on linear combination of Gaussian-modulated sinusoids

  Oğuz, Yasin; Kaya, Haydar

  This study proposes an analytical ultra wide-band (UWB) waveform design based on linear combination of Gaussian-modulated sine waves to prevent interference with existing narrowband systems, in accordance with the Federal Communications Commission (FCC) UWB spectral mask. The proposed model determines the common pulse duration and the center frequencies of the components in closed-form using the relationships among the number of components, the overlap of components and the design band frequency limits. As a result, UWB waveforms for different design scenarios can be generated without computationally intensive algorithms. The method was evaluated in terms of spectrum utilization efficiency (SUE) within the 3.1-10.6 GHz indoor FCC UWB band. Results show that same SUE can be achieved with fewer components by appropriate overlap level. Comparisons with existing studies demonstrate that the proposed approach provides high performance for similar component numbers while avoiding complex optimization-based models with high computational cost. Moreover, the normalized spectral form and SUE are preserved for different sub-band widths when the analytical parameters are updated according to the new frequency limits. The results demonstrate that the proposed method is an effective waveform design approach due to its low computational complexity, high SUE, and scalability to multi-subband full-band UWB designs.

  İncele
• MakaleAçık Erişim2026/r/gumushane/19459
  Enhanced control of a two-wheeled gyroscopic combat robot with variable CMG disk speeds using LQR-Based neural network

  Oğuz, Yasin; Ünker, Faruk; Cetin, Gokhan

  This study proposes a hybrid control approach to improve the stability and adaptability of a two-wheeled gyroscopic combat robot by integrating deep learning with Linear Quadratic Regulator (LQR) control. Due to their inherently unstable nature, two-wheeled robots face challenges maintaining balance, especially when Control Moment Gyroscope (CMG) disk speeds vary, which significantly affect system dynamics. While LQR provides effective stabilization by minimizing a cost function, its performance degrades when disk speeds change. To overcome this limitation, a Neural Network (NN) was trained on 42001 precomputed LQR gain sets optimized for CMG disk speeds ranging from 800 to 5000 rad/s. The trained NN predicts appropriate coefficients in real time, enabling adaptive control under dynamic operating conditions. Simulation results demonstrate that the hybrid NN-LQR controller maintains stability under recoil forces up to 3000 N, where the conventional LQR loses balance about 4–6 seconds. Furthermore, the proposed controller reduces energy consumption by 21.6% during steady-state operation within the first 5 seconds and 10.6% under recoil conditions within the first 3 seconds compared to classical LQR. Although developed for a combat robot, the proposed framework offers a scalable solution for broader robotic applications such as search and rescue, surveillance, and autonomous navigation in complex environments, contributing to the advancement of intelligent control in robotics.

  İncele
• MakaleAçık Erişim2026/r/gumushane/13855
  Regression-Based Controller Design Using LQR Data for a Two-Wheeled Gyroscopic Combat Robot

  Oğuz, Yasin; Çetin, Gökhan; Ünker, Faruk

  Two-wheeled gyroscopic robots are challenging to balance due to their intrinsic instability and sensitivity to external disturbances. This study proposes a hybrid control approach that combines a linear quadratic regulator (LQR) and a regression-based machine learning model to increase stability and adaptability. The regression model is trained with data collected from an LQR-controlled system to predict the optimal control responses under different conditions. The proposed approach utilizes state-space representation and the Jacobian linearization technique to design and simulate the robot’s performance under both controlled and random disturbance profiles. Simulation results indicate that the proposed method ensures steady motion even in dynamic, uncertain environments and increases resistance to external disturbances. Compared to the conventional LQR control method, the regression-enhanced model dynamically adjusts the control parameters, effectively reducing oscillations throughout the robot’s motion and achieving an average energy saving of approximately 80 %. These results demonstrate that robust, adaptive control in autonomous two-wheeled robotic systems can be achieved by fusing data-driven models with classical control techniques.

  İncele
• MakaleYalnız künye2021/r/gumushane/17486
  Energy Efficient Driving Optimization of Electrical Vehicles Considering the Road Characteristics

  Eroğlu, Hasan; Oğuz, Yasin

  Electric vehicles, which are an important part of sustainable energy technologies, occupy an important place in our daily life. More efficient use of electric vehicles will ensure more efficient use of sustainable energy sources. It is not possible for the human brain to determine the most efficient driving characteristics. In this study, energy efficient driving optimization of electric vehicles was realized. Along the route, optimum speeds were determined in terms of energy, by using the road and engine characteristics. Geographical information systems and genetic algorithm have been used effectively in the solution of the problem. The effectiveness of the proposed algorithm was revealed with many test studies. With this study, an algorithm that provides an energy-efficient driving for electrical vehicles was developed. The results will contribute to the development of electric vehicle technologies.

  İncele
• BildiriYalnız künye2014/r/gumushane/16805
  A Linear Signal Combination Model for UWB Applications

  Oguz, Yasin; Kaya, Haydar

  Systems that use UWB technology based on communication with baseband signals of very short duration recently attract attention in communication and radar application, and thereof have been improving rapidly. Because UWB signals occupy a spectrum that is spread about very wide frequency band, one of the principal issues that need to be considered in UWB applications is the frequency allocation. In order to minimize or prevent possible distorting interferences with existing communication systems, spectral distribution of mandatory EIRP limit values has been determined by FCC in UWB applications. In this study, a linear signal combination model has been established and on mathematical expressions have been provided for production of wave forms are suitable UWB emission mask specified by FCC.

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