• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.785-788
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• 1993

This paper presents the prototype framework for automated integration of CMOS current-mode fuzzy logic circuits using an intelligent module approach. The library of modules representing the standard fuzzy logic operators was built. These modules were finally used to synthesized sophisticated fuzzy logic units. Fuzzy unit designs were made based upon the results of a newel methodology of the current mirror-based fuzzy logic function synthesis. This methodology is actually incorporated into the presented framework. As an example, the membership function unit was synthesized, simulated, and the final layout was generated using the presented framework. Finally, the fuzzy logic controller unit (FLC) was generated using the proposed framework. Simulation as well as measurement results show unquestionable advantages of the proposed fuzzy logic function integration system over the classical design methodology with respect to the area, relative error and performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1213-1237
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• 2019

Vehicular Ad-hoc Network (VANET) facilities envision future Intelligent Transporting Systems (ITSs) by providing inter-vehicle communication for metrics such as road surveillance, traffic information, and road condition. In recent years, vehicle manufacturers, researchers and academicians have devoted significant attention to vehicular communication technology because of its highly dynamic connectivity and self-organized, decentralized networking characteristics. However, due to VANET's high mobility, dynamic network topology and low communication coverage, dissemination of large data packets (e.g. multimedia content) is challenging. Clustering enhances network performance by maintaining communication link stability, sharing network resources and efficiently using bandwidth among nodes. This paper proposes a mobility-based, multi-hop clustering algorithm, (MBCA) for multimedia content broadcasting over an IEEE 802.11p-LTE-enabled hybrid VANET architecture. The OMNeT++ network simulator and a SUMO traffic generator are used to simulate a network scenario. The simulation results indicate that the proposed clustering algorithm over a hybrid VANET architecture improves the overall network stability and performance, resulting in an overall 20% increased cluster head duration, 20% increased cluster member duration, lower cluster overhead, 15% improved data packet delivery ratio and lower network delay from the referenced schemes [46], [47] and [50] during multimedia content dissemination over VANET.

• Smart Media Journal
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• v.10 no.2
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• pp.92-99
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• 2021

Smart space is a technology that supports humans by interacting with the surrounding environment. Smart space has a built-in dynamic intelligent environment. This paper proposes a framework that provides user-customized dynamic intelligent environments in smart spaces. In the existing research that provides user-customized intelligent services, users' interests are only explicitly analyzed, and smart spaces are not considered. Implicit interest analysis can suggest a service that may be of interest to users rather than explicit interest analysis, but it requires higher performance than explicit interest analysis. Smart spaces can obtain useful information by interacting with information in the space. The framework proposed in the study uses a proximity-based social network of things to fit into a smart space. In addition, the implicit interest analysis provides intelligent information for smart spaces using the social media information and spatial information objects. In addition, we propose a method to prevent performance degradation while maintaining accuracy in consideration of the characteristics of the smart space.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.5
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• pp.325-333
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• 2021

A 3 kW grid-tied PV inverter with Gallium nitride high-electron mobility transistor (GaN HEMT) for domestic commercialization was developed using boost converter and full-bridge inverter with LCL filter topology. Recently, many GaN HEMTs are manufactured as surface mount packages because of their lower parasitic inductance characteristic than standard TO (transistor outline) packages. A surface mount packaged GaN HEMT releases heat through either top or bottom cooling method. IGOT60R070D1 is selected as a key power semiconductor because it has a top cooling method and fairly low thermal resistances from junction to ambient. Its characteristics allow the design of a 3 kW inverter without forced convection, thereby providing great advantages in terms of easy maintenance and high reliability. 1EDF5673K is selected as a gate driver because its driving current and negative voltage output characteristics are highly optimized for IGOT60R070D1. An LCL filter with passive damping resistor is applied to attenuate the switching frequency harmonics to the grid-tied operation. The designed LCL filter parameters are validated with PSIM simulation. A prototype of 3 kW PV inverter with GaN HEMT is constructed to verify the performance of the power conversion system. It achieved high power density of 614 W/L and peak power efficiency of 99% for the boost converter and inverter.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.166-173
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• 2022

Recently, studies have been conducted on intelligent transportation systems (ITS) that provide safety and convenience to humans. Systems that compose the ITS adopt architectures that applied the cloud computing which consists of a high-performance general-purpose processor or graphics processing unit. However, an architecture that only used the cloud computing requires a high network bandwidth and consumes much power. Therefore, applying edge computing to ITS is essential for solving these problems. In this paper, we propose an edge artificial intelligence (AI) device based ITS. Edge AI which is applicable to various systems in ITS has been applied to license plate recognition. We implemented edge AI on a field-programmable gate array (FPGA). The accuracy of the edge AI for license plate recognition was 0.94. Finally, we synthesized the edge AI logic with Magnachip/Hynix 180nm CMOS technology and the power consumption measured using the Synopsys's design compiler tool was 482.583mW.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.2027-2046
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• 2024

Reconfigurable Intelligent Surface (RIS) is an innovative technique to precisely control the phase of incident signals with the help of low-cost passive reflective elements. It shows excellent potential in the sixth generation of mobile communication systems, which not only extends wireless coverage but also boosts channel capacity. Considering that multipath propagation and a high number of antennas are involved in RIS in assisted mega multiple-input multiple-output (MIMO) systems, it suffers from severe channel fading and multipath effects, which in turn lead to signal instability and degradation of transmission performance. To overcome this obstacle, this essay suggests an improved gradient optimization algorithm to dynamically and optimally adjust the phase of the reflective elements to counteract channel fading and multipath effects as a strategy. In order to overcome the optimization problem of falling into local minima, this paper proposes an adaptive learning rate algorithm based on Adagrad improvement, which searches for the global optimal solution more efficiently and improves the robustness of the optimization algorithm. The suggested technique helps to enhance the estimate of channel efficiency of RIS-assisted large MIMO systems, according to simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1759-1772
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• 2023

Chinese named entity recognition (NER) is a challenging work that seeks to find, recognize and classify various types of information elements in unstructured text. Due to the Chinese text has no natural boundary like the spaces in the English text, Chinese named entity identification is much more difficult. At present, most deep learning based NER models are developed using a bidirectional long short-term memory network (BiLSTM), yet the performance still has some space to improve. To further improve their performance in Chinese NER tasks, we propose a new NER model, IDCNN-BiLSTM-Highway, which is a combination of the BiLSTM, the iterated dilated convolutional neural network (IDCNN) and the highway network. In our model, IDCNN is used to achieve multiscale context aggregation from a long sequence of words. Highway network is used to effectively connect different layers of networks, allowing information to pass through network layers smoothly without attenuation. Finally, the global optimum tag result is obtained by introducing conditional random field (CRF). The experimental results show that compared with other popular deep learning-based NER models, our model shows superior performance on two Chinese NER data sets: Resume and Yidu-S4k, The F1-scores are 94.98 and 77.59, respectively.

• Journal of Communications and Networks
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• v.15 no.2
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• pp.142-152
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• 2013

The network mobility basic support (NEMO BS) protocol has been investigated to provide Internet connectivity for a group of nodes, which is suitable for intelligent transportation systems (ITS) applications. NEMO BS often increases the traffic load and handover latency because it is designed on the basis of mobile Internet protocol version 6 (MIPv6). Therefore, schemes combining proxy MIPv6 with NEMO (P-NEMO) have emerged to solve these problems. However, these schemes still suffer from packet loss and long handover latency during handover. Fast P-NEMO (FP-NEMO) has emerged to prevent these problems. Although the FP-NEMO accelerates handover, it can cause a serious tunneling burden between the mobile access gateways (MAGs) during handover. This problem becomes more critical as the traffic between the MAGs increases. Therefore, we propose a scheme for designing an improved FP-NEMO (IFP-NEMO) to eliminate the tunneling burden by registering a new address in advance. When the registration is completed before the layer 2 handover, the packets are forwarded to the new MAG directly and thereby the IFP-NEMO avoids the use of the tunnel between the MAGs during handover. For the evaluation of the performance of the IFP-NEMO compared with the FP-NEMO, we develop an analytical framework for fast handovers on the basis of P-NEMO. Finally, we demonstrate that the IFP-NEMO outperforms the FP-NEMO through numerical results.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.3
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• pp.126-132
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• 2004

This paper proposed trajectory tracking control based on genetic algorithm. Trajectory tracking control scheme are real coding genetic algorithm(RCGA) and back-propagation algorithm(BPA). Control scheme ability experience proposed simulation. Stable tracking control problem of mobile robots have been studied in recent years. These studies have guaranteed stability of controller, but the performance of transient state has not been guaranteed. In some situations, constant gain controller shows overshoots and oscillations. So we introduce better control scheme using real coding genetic algorithm and neural network. Using RCGA, we can find proper gains in several situations and these gains are generalized by neural network. The generalization power of neural network will give proper gain in untrained situation. Performance of proposed controller will verity numerical simulations and the results show better performance than constant gain controller.

• Journal of Mechanical Science and Technology
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• v.18 no.4
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• pp.535-549
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• 2004

This paper proposes a sliding mode controller with fuzzy adaptive perturbation compensator(FAPC) to get a good control performance and reduce the chatter, The proposed algorithm can reduce the chattering because the proposed fuzzy adaptive perturbation compensator compensates the perturbation terms. The compensator computes the control input for compensating unmodeled dynamic terms and disturbance by using the observer-based fuzzy adaptive network(FAN) The weighting parameters of the compensate. are updated by on-line adaptive scheme in order to minimize the estimation error and the estimation velocity error of each actuator. Therefore, the combination of sliding mode control and fuzzy adaptive network gives the robust and intelligent routine to get a good control performance. To evaluate the control performance of the proposed approach, tracking control is experimentally carried out for the hydraulic motion platform which consists of a 6-DOF parallel manipulator.