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A Novel Capacitorless 1T DRAM Cell for Data Retention Time Improvement
Woojun Lee,Woo Young Choi IEEE 2011 IEEE TRANSACTIONS ON NANOTECHNOLOGY Vol.10 No.3
<P>This paper proposes a silicon-with-partially-insulating-layer-on-silicon-on-insulator (SISOI) one-transistor dynamic random access memory (1T DRAM) cell to increase data retention time. A conventional 1T DRAM cell has a data retention problem because it stores holes in an SOI layer, which is not separated from the source/drain region. However, the proposed SISOI 1T DRAM cell can keep holes electrically separated from the source/drain region, which leads to the increase of data retention time.</P>
Recognition of Human Motion with Deep Reinforcement Learning
Woojun Seok,Cheolsoo Park 대한전자공학회 2018 IEIE Transactions on Smart Processing & Computing Vol.7 No.3
Human.computer interaction (HCI) has become an important research area for improving the user experience on Internet of Things (IoT) devices. In particular, gesture recognition and dailyactivity recognition have attracted the interest of numerous researchers. Human motions have been predicted by analyzing accelerometer data from which features were extracted to be classified into a specific activity. However, due to the memory limitations of IoT devices, it is hard to utilize all the raw data from an accelerometer sensor. This paper proposes a deep reinforcement learning algorithm to recognize human arm movements using a commercial wearable device, the Myo armband. Agents learn the patterns that are the acceleration data of human motion. In addition, using raw accelerometer sensor data without feature extraction could make an end-to-end structure. In order to demonstrate the performance of the proposed method, a deep neural network (DNN) and a deep reinforcement learning algorithm are compared. As a result, a deep reinforcement learning agent yielded accuracy similar to a DNN using less data, and the agent could learn time-series human motion acceleration data.
Influence of Inversion Layer on Tunneling Field-Effect Transistors
Woojun Lee,WooYoung Choi IEEE 2011 IEEE electron device letters Vol.32 No.9
<P>The influence of inversion layer on tunneling field-effect transistors (TFETs) has been investigated. Simulation results show that drain current (<I>I</I><SUB>D</SUB>) saturation is related to inversion layer formation. Surface channel potential (Ψ<SUB>)</SUB> pinning due to the inversion layer formation makes <I>I</I><SUB>D</SUB> less sensitive to the gate voltage. Also, it has been shown that most of inversion carriers of TFETs are thermally injected from the drain. Inversion carriers supplied from the source by band-to-band tunneling are negligible.</P>
An extended Access Control with Uncertain Context
Woojun Kang 한국인터넷방송통신학회 2018 Journal of Advanced Smart Convergence Vol.7 No.4
While new information technology advances have made information access and acquisition methods much more diverse and easier, there are side effects that allow illegal access using diverse and high-performance tools. In order to cope with such threats, there are access control methods in database technology, and various studies are being conducted to extend traditional access control to cope with new computing environments. In this paper, we propose an extended access control with uncertain context-awareness. It enables appropriate security policy enforcement even if the contextual constraints specified by the security policy does not match those accompanied by access request query. We extract semantic implications from context tree, and define the argument that can quantitatively measure the semantic difference between two nodes in the context tree. It is used to semantically enforce the security policy, and to prevent the excessive authorization caused by the implication.