X-ray: Discovering DRAM Internal Structure and Error Characteristics by Issuing Memory Commands

The demand for accurate information about the internal structure and characteristics of dynamic random-access memory (DRAM) has been on the rise. Recent studies have explored the structure and characteristics of DRAM to improve processing in memory, enhance reliability, and mitigate a vulnerability known as rowhammer. However, DRAM manufacturers only disclose limited information through official documents, making it difficult to find specific information about actual DRAM devices. This paper presents reliable findings on the internal structure and characteristics of DRAM using activate-induced bitflips (AIBs), retention time test, and row-copy operation. While previous studies have attempted to understand the internal behaviors of DRAM devices, they have only shown results without identifying the causes or have analyzed DRAM modules rather than individual chips. We first uncover the size, structure, and operation of DRAM subarrays and verify our findings on the characteristics of DRAM. Then, we correct misunderstood information related to AIBs and demonstrate experimental results supporting the cause of rowhammer. We expect that the information we uncover about the structure, behavior, and characteristics of DRAM will help future DRAM research.Comment: 4 pages, 7 figure

Fabrication and Fundamental Properties of Two-Dimensional Magnetophotonic Crystals for Magneto-Optic Micro-Devices

豊橋技術科学大

Efficient Multitask Reinforcement Learning Without Performance Loss

11Nsciescopu

A New Adaptive Sliding-Mode Control and Its Application to Robot Manipulators

This paper proposes a new adaptive law based on sliding-mode control(SMC) control. The proposed adaptive law adjusts the switching gain near the sliding manifold to have the appropriate attractivity property. This appropriate attractivity property prevents unexpected errors caused by the excessive or insufficient adaptation of switching gain. Furthermore, this adaptive law ensures the fast adaptation speed of switching gain, and this yields the reduction of chattering. For the more practical implementation based on a model-free controller, the proposed adaptive SMC (ASMC) works together with a time-delay controller(TDC). To validate the proposed adaptive law, the results of simulations are carried out and comparisons are made with existing ASMC control schemes. © 2018 IEEE.1

An Adaptive Model Uncertainty Estimator Using Delayed State Based Model-free Control and Its Application to Robot Manipulator

In this article, we propose an innovative model-free control (MFC) algorithm using an adaptive model uncertainty estimator (AMUE) that provides stable torque input while allowing more precise control, even in the presence of instantaneous disturbances, such as friction, payload, or trajectory changes. Unlike traditional time-delay estimation (TDE)-based controllers that directly use one-sample delayed signals to estimate unmodeled dynamics and uncertainties, the proposed algorithm achieves better tracking performance by considering not only the one-sample delayed signal but also its gradient with an adaptive gain. Furthermore, the proposed adaptive estimator works well independently of conventional TDE-based controllers, providing a wide range of control gains. This implies that the proposed approach provides the opportunity to strategically improve TDE-based controllers, which have performance limitations caused by conventional TDE technique errors. The proposed algorithm can be easily extended to different TDE-based controllers. Finally, the stability of the AMUE-MFC is guaranteed through the Lyapunov stability theory, and its performance is demonstrated via simulations and experiments with robotic manipulators. © 1996-2012 IEEE.11Nsciescopu

A Reinforcement Learning-based Adaptive Time-Delay Control and Its Application to Robot Manipulators

This study proposes an innovative reinforcement learning-based time-delay control (RL-TDC) scheme to provide more intelligent, timely, and aggressive control efforts than the existing simple-structured adaptive time-delay controls (ATDCs) that are well-known for achieving good tracking performances in practical applications. The proposed control scheme adopts a state-of-the-art RL algorithm called soft actor critic (SAC) with which the inertia gain matrix of the timedelay control is adjusted toward maximizing the expected return obtained from tracking errors over all the future time periods. By learning the dynamics of the robot manipulator with a data-driven approach, and capturing its intractable and complicated phenomena, the proposed RL-TDC is trained to effectively suppress the inherent time delay estimation (TDE) errors arising from time delay control, thereby ensuring the best tracking performance within the given control capacity limits. As expected, it is demonstrated through simulation with a robot manipulator that the proposed RL-TDC avoids conservative small control actions when large ones are required, for maximizing the tracking performance. It is observed that the stability condition is fully exploited to provide more effective control actions.1

Position Control of Brushless DC Motor using Adaptive Sliding mode Control with Time-delay Control

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Communication-Efficient Event-Triggered Time-Delay Control and Its Application to Robot Manipulators

This paper proposes a communicationefficient event-triggered time-delay control (ET-TDC) scheme, which is more intelligent, consumes less network resources, and is more suitable for practical applications, such as network control systems, over bandwidth-limited communication channels. The proposed controller ensures a fast convergence rate and high tracking performance because it is based on TDCs that are well-known for achieving high tracking performance. To maintain such properties, the proposed control scheme adopts a performance-preserving ET strategy, whereby the communication efforts between the controllers and actuators can be significantly reduced. The ET condition of the proposed controller is devised to ensure the system stability by using a Lyapunov function. Finally, the performance of the proposed ET-TDC is demonstrated through a numerical simulation and an experiment using a two-link robot manipulator.11Nsciescopu

How to easily make a policy network of reinforcement learning robust without physical modeling

In this paper, we propose ensemble inverse model network based disturbance observer (EIMN-DOB) to improve the robustness of the policy network (PN) which is a training result of policy based reinforcement learning (RL), without physical modeling. EIMN-DOB uses the ensemble model of the inverse model network (IMN), which acts as a nominal inverse model, and can estimate and cancel model uncertainty and disturbance like a typical disturbance observer (DOB) without a physical modeling. Because EIMN is trained from the data used in training RL, the additional training data for expressing the inverse model are not required. The experiments in this paper appeared that the PN of soft actor critic(SAC) combined with EIMN-DOB maintains control performance even in the presence of disturbance in continuous control benchmark tasks based on Mujoco physics engine. When the trained PN is used with EIMN-DOB in the real environment, the control performance in simulator can be preserved in the real environment, and it is expected to be utilized to minimize the sim-to-real gap of RL.1

An Uncertainty and Disturbance Estimator based on Model-free Time-delay Control to Robot manipulators

An Uncertainty and Disturbance Estimator based on Model-free Time-delay Control to Robot manipulators1