• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.691-708
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• 2010

An important issue of supporting multi-users with diverse quality-of-service (QoS) requirements over wireless networks is how to optimize the systematic scheduling by intelligently utilizing the available network resource while, at the same time, to meet each communication service QoS requirement. In this work, we study the problem of a variety of communication services over multi-media heterogeneous cognitive OFDM system. We first divide the communication services into two parts. Multimedia applications such as broadband voice transmission and real-time video streaming are very delay-sensitive (DS) and need guaranteed throughput. On the other side, services like file transmission and email service are relatively delay tolerant (DT) so varying-rate transmission is acceptable. Then, we formulate the scheduling as a convex optimization problem, and propose low complexity distributed solutions by jointly considering channel assignment, bit allocation, and power allocation. Unlike prior works that do not care computational complexity. Furthermore, we propose the FAASA (Fairness Assured Adaptive Sub-carrier Allocation) algorithm for both DS and DT users, which is a dynamic sub-carrier allocation algorithm in order to maximize throughput while taking into account fairness. We provide extensive simulation results which demonstrate the effectiveness of our proposed schemes.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.2
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• pp.83-91
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• 2018

With the development of IT (Information Technology) technology, embedded system and network technology are combined and used in various environments such as military environment as well as everyday life. In this paper, we propose a new airdropped distributed mobility model (ADMM) modeling the dispersion falling of the direct shot of a cluster bomb, and we compare and analyze some representative MANET routing protocols in ADMM in ns-3 simulator. As a result of the analysis, we show OLSR routing protocol is promising in ADMM environment in the view points of packet delivery ratio (PDR), end to end delay, and jitter. In addition, we propose a new adaptation scheme for OLSR, AND-OLSR (Average Node Distance based adaptive-OLSR) to improve the original OLSR in ADMM environment. The new protocol calculates the average node distance, adapts the period of the control message based on the average node distance increasing rate. Through the simulation study, we show that the proposed AND-OLSR outperforms the original OLSR in PDR and control message overhead.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.14-19
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• 1998

In this paper, a target approachable force-guided control with adaptive accommodation for the complex assembly is presented. The complex assembly (CA) is defined as a task which deals with complex shaped parts including concavity or whose environment is so complex that unexpected contacts occur frequently during insertion. CA tasks are encountered frequently in the field of the manufacturing automation and various robot applications. To make CA successful, both the bounded wrench condition and the target approachability condition should be satisfied simultaneously during insertion. By applying the convex optimization technique, an optimum target approaching twist can be determined at each instantaneous contact state as a global minimum solution. Incorporated with an admissible perturbation method, a new CA algorithm using only the sensed resultant wrench and the target twist is developed without motion planning nor contact analysis which requires the geometry of the part and the environment. Finally, a VME-bus based real-time control system is built to experiment various CA task. T-insertion task as a planar CA and double-peg assembly task as a spacial assembly were successfully executed by implementing the new force-guided control with adaptive accommodation.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.555-566
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• 2012

The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batch-processing scheme in a local computing farm and data storage. In the case of a large-scale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resource-provisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.

• Journal of Power Electronics
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• v.17 no.6
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• pp.1469-1479
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• 2017

Primary-side regulation (PSR) scheme is widely applied in low power applications, such as cell phone chargers, network adapters, and LED drivers. However, the efficiency and standby power requirements have been improved to a high standard due to the new trends of DOE (Department Of Energy) Level VI and COC (Code Of Conduct specifications) V5. The major drawbacks of PSR include poor regulation due to inaccurate feedback and difficulty in acquiring acceptable regulation. A novel adaptive blanking strategy for constant current and constant voltage regulation is proposed in this paper. An accurate model for the sample blanking time related to transformer leakage inductance and the metal-oxide-semiconductor field-effect transistor (MOSFET) parasitic capacitance is established. The proposed strategy can achieve accurate detection for ultra-low standby power. In addition, numerous control factors are analyzed in detail to eliminate the influence of leakage inductance on the loop stability. A dedicated controller integrated circuit (IC) with a power MOSFET is fabricated to verify the effectiveness of the proposed control strategy. Experimental results demonstrated that the prototype based on the proposed IC has excellent performance.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.4
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• pp.137-143
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• 2021

We propose an adaptive non-singular terminal sliding-mode control for the fast finite-time convergence (FANTSMC) in robot manipulator. The proposed FANTSMC approach is developed to be applied without singularity in robot manipulator, which has a new pole-placement control with the non-singular terminal sliding variable while generating the desirable control torque. Moreover, the switching gain is designed to suppress the time-delayed estimation error appropriately, which aims at providing the high robust tracking performance. Also, the proposed one employs one-sample delayed information to cancel out the system uncertainties and disturbances. For these reasons, it offers strong attraction within the finite time. It is shown that the tracking performance of the proposed FANTSMC approach is guaranteed to be uniformly ultimately bounded through the Lyapunov stability. The effectiveness of the proposed FANTSMC approach is illustrated in simulations, which is compared with that of the up-to-date control approach.

• Coupled systems mechanics
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• v.11 no.4
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• pp.285-295
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• 2022

Laminated composite plates are utilized extensively in different fields of construction and industry thanks to their advantages such as high stiffness-to-weight ratio. Additionally, they are characterized by their directional properties that permit the designer to optimize their stiffness for specific applications. This paper presents a numerical analysis and optimization study of plates made of composite subjected to low velocity impact. The main aim is to identify the optimum fiber orientations of the composite plates that resist low velocity impact load. First, a three-dimensional finite element model is built using LS DYNA computer software package to perform the impact analyses. The composite plate has been modeled using solid elements. The failure criteria of Tsai-Wu's criterion have been used to control the strength of the composite material. A good agreement has been found between the predicted numerical results and experimental results in the literature which validate the finite element model. Then, an Adaptive Simulated Annealing (ASA) has been used to optimize the response of impacted composite laminate where its objective is to maximize the safety factor by varying the ply angles. The results show that the ASA is robust in the sense that it is capable of predicting the best optimal designs.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.297-307
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• 2022

The simple procedural segment selection algorithm commonly used in Dynamic Adaptive Streaming over HTTP (DASH) reveals severe weakness to provide high-quality streaming services in the integrated mobile networks of various wired and wireless links. A major issue could be how to properly cope with dynamically changing underlying network conditions. The key to meet it should be to make the segment selection algorithm much more adaptive to fluctuation of network traffics. This paper presents a system architecture that replaces the existing procedural segment selection algorithm with a deep reinforcement learning algorithm based on the Asynchronous Advantage Actor-Critic (A3C). The distributed A3C-based deep learning server is designed and implemented to allow multiple clients in different network conditions to stream videos simultaneously, collect learning data quickly, and learn asynchronously, resulting in greatly improved learning speed as the number of video clients increases. The performance analysis shows that the proposed algorithm outperforms both the conventional DASH algorithm and the Deep Q-Network algorithm in terms of the user's quality of experience and the speed of deep learning.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.25-25
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• 2023

The ensemble optimal interpolation (EnOI) scheme is a sub-optimal alternative to the ensemble Kalman filter (EnKF) with a reduced computational demand making it potentially more suitable for operational applications. Since only one model is integrated forward instead of an ensemble of model realizations, online estimation of the background error covariance matrix is not possible in the EnOI scheme. In this study, we investigate two Gaussian noise based ensemble generation strategies to produce dynamic covariance matrices for assimilation of water level observations into a distributed hydrological model. In the first approach, spatially correlated noise, sampled from a normal distribution with a fixed fractional error parameter (which controls its standard deviation), is added to the model forecast state vector to prepare the ensembles. In the second method, we use an adaptive error estimation technique based on the innovation diagnostics to estimate this error parameter within the assimilation framework. The results from a real and a set of synthetic experiments indicate that the EnOI scheme can provide better results when an optimal EnKF is not identified, but performs worse than the ensemble filter when the true error characteristics are known. Furthermore, while the adaptive approach is able to reduce the sensitivity to the fractional error parameter affecting the first (non-adaptive) approach, results are usually worse at ungauged locations with the former.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.107-114
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• 2024

Mobile ad hoc networks represent self-configuring networks of mobile devices that communicate without relying on a fixed infrastructure. However, traditional routing protocols in such networks encounter challenges in selecting efficient and reliable routes due to dynamic nature of these networks caused by unpredictable mobility of nodes. This often results in a failure to meet the low-delay and low-energy consumption requirements crucial for such networks. In order to overcome such challenges, our paper introduces a novel multi-objective and adaptive routing scheme based on the Q-learning reinforcement learning algorithm. The proposed routing scheme dynamically adjusts itself based on measured network states, such as traffic congestion and mobility. The proposed approach utilizes Q-learning to select routes in a decentralized manner, considering factors like energy consumption, load balancing, and the selection of stable links. We present a formulation of the multi-objective optimization problem and discuss adaptive adjustments of the Q-learning parameters to handle the dynamic nature of the network. To speed up the learning process, our scheme incorporates informative shaped rewards, providing additional guidance to the learning agents for better solutions. Implemented on the widely-used AODV routing protocol, our proposed approaches demonstrate better performance in terms of energy efficiency and improved message delivery delay, even in highly dynamic network environments, when compared to the traditional AODV. These findings show the potential of leveraging reinforcement learning for efficient routing in ad hoc networks, making the way for future advancements in the field of mobile ad hoc networking.