• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.4
• /
• pp.744-751
• /
• 2008

Ubiquitous computing is a new paradigm of telecommunication technology and is embedded with advanced computing technology to process a large amount of data in a normal environment. Generally, ail equipment is embedded with sensors and operating devices to interaction with communication functions. That is why ubiquitous computing must be able to access any devices anywhere at anytime in order to perform appropriate functions. Unfortunately, however, it is difficult to make an optimized design for applications which can effectively interaction with various functions in distributed environment like ubiquitous computing. Therefore, this paper is aimed at deploying interface with server nodules and virtual prototyping by utilizing LabVIEW and embedded application software with additional network function. In addition, given information about sensors collected from context-awareness and location-awareness, it will suggest the ideal ubiquitous application based on context-awareness and apply the advanced application to device control and monitoring through context awareness of lab.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.4
• /
• pp.791-799
• /
• 2008

An image recognition for surrounding conditions is very important in image transmission. In recently rears, as the information infrastructure is more general, the user-centered demands in which they want to identify by object's states image using wire or wireless environment have increased. However, existing mobile solution could be hard to expect high quality mage, because limitation of software processing according as network based on mobile terminal which has low band width supports software codec. To solve this weak point, this paper describes on hardware codec design based on MPEG-4 which is international video compression standard. Implemented system contains the embedded CPU for optimized design and it works high quality service as transmission speed and resolution in mobile circumstance.

• KEPCO Journal on Electric Power and Energy
• /
• v.5 no.3
• /
• pp.157-163
• /
• 2019

K-BEMS System was introduced to reduce peak load and to save total energy of the 120 buildings that KEPCO headquarter and branch offices use. K-BEMS system is composed of PV, battery, and hybrid PCS. In this system, ESS, PV, lighting is used to save building energy based on demand prediction. Currently, neural network technique for short past data is applied to demand prediction, and fixed scheduling method by operator for ESS charging/discharging is used. To enhance this system, KEPCO research institute has carried out this K-BEMS research project for 3 years since January 2016. As the result of this project, we developed new real-time highly reliable building demand prediction technique with error free and optimized automatic ESS charging/discharging technique. Through several field test, we can certify the developed algorithm performance successfully. So we will describe the details in this paper.

• Journal of the Korea Society for Simulation
• /
• v.24 no.3
• /
• pp.61-68
• /
• 2015

Mobile ad hoc networks (MANETs) are self-organized dynamic networks populated by mobile nodes. This paper presents the improved cross-layer approach to complement the recent works for video transmission services on MANET. We use a statistical design of experiment and analysis in order to investigate interactions between major factors of each layer effectively with minimizing ns-3 simulation run time. The proposed cross-layer approach considers MANET protocol layers (i.e., physical, network and transmission layers) and an application layer (i.e., a video encoder) as factors simultaneously. In addition, the approach defines an objective video quality metric as a response variable. The result of this paper can be applicable as a preliminary research to design an optimized video transmission application which has ability to adjust controllable factors to dynamic uncontrollable factors.

• Korean Journal of Veterinary Research
• /
• v.58 no.4
• /
• pp.211-217
• /
• 2018

Mesenchymal stem cells (MSCs) are useful candidates for tissue engineering and cell therapy. Physiological cell environment not only connects cells to each other, but also connects cells to the extracellular matrix that provide mechanical support, thus exposing the entire cell surface and activating signaling pathways. Hydrogel is a polymeric material that swells in water and maintains a distinct 3-dimensional (3D) network structure by cross linking. In this study, we investigated the optimized cellular function for canine adipose tissue-derived MSCs (cAD-MSCs) using hydrogel. We observed that the expression levels of Ki67 and proliferating cell nuclear antigen, which are involved in cell proliferation and stemness, were increased in transwell-hydrogel (3D-TN) compared to the transwell-normal (TN). Also, transforming growth factor-${\beta}1$ and SOX9, which are typical bone morphogenesis-inducing factors, were increased in 3D-TN compared to the TN. Collagen type II alpha 1, which is a chondrocyte-specific marker, was increased in 3D-TN compared to the TN. Osteocalcin, which is a osteocyte-specific marker, was increased in 3D-TN compared to the TN. Collectively, preconditioning cAD-MSCs via 3D culture systems can enhance inherent secretory properties that may improve the potency and efficacy of MSCs-based therapies for bone regeneration process.

• Journal of the Korean Geotechnical Society
• /
• v.35 no.9
• /
• pp.37-45
• /
• 2019

Quick identification of the condition of tunnel face and optimized determination of support patterns during tunnel excavation in underground construction projects help engineers prevent tunnel collapse and safely excavate tunnels. This study investigates a CNN technique for quick determination of rock quality classification depending on the condition of tunnel face, and presents the procedure for rock quality classification using a deep learning technique and the improved method for accurate prediction. The VGG16 model developed by tens of thousands prestudied images was used for deep learning, and 1,469 tunnel face images were used to classify the five types of rock quality condition. In this study, the prediction accuracy using this technique was up to 83.9%. It is expected that this technique can be used for an error-minimizing rock quality classification system not depending on experienced professionals in rock quality rating.

• Journal of the Korean Society of Industry Convergence
• /
• v.22 no.1
• /
• pp.1-9
• /
• 2019

In electric agricultural machine the gearbox is used to increase torque and lower the output speed of the motor shaft. The gearbox consists of several shafts, helical gears and spur gears works in series. Optimization plays an important role in gear design as reducing the weight or volume of a gear set will increase its service life and improve the bearing capacity. In this paper the basic design parameters for gear like shaft diameter and face width are considered as the input variables. The bending stress and material volume is considered as the objective function. ANSYS was used to investigate the bending stress when the variable was changed. Artificial Neural Network (ANN) was used to obtain the mathematical model of the system based on the bending stress behaviour. The ANN was used since the output system is nonlinear. The Genetic Algorithm (GA)　technique of optimization is used to obtain the optimized values of shaft diameter and face width on the pinion based on the ANN mathematical model and the results are compared as that obtained using the traditional method. The ANN and GA were performed using MATLAB. The simulation results were shown that the proposed algorithm was successfully calculated the value of shaft diameter and face width to obtain the minimal bending stress and material volume of the gearbox.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.2
• /
• pp.711-732
• /
• 2019

Many unmanned aerial vehicle (UAV) applications have been employed for performing data collection in facilitating tasks such as surveillance and monitoring objectives in remote and dangerous environments. In light of the fact that most of the existing UAV relaying applications operate in conventional half-duplex (HD) mode, a full-duplex (FD) based UAV relay aided wireless network is investigated, in which the UAV relay helps forwarding information from the source (S) node to the destination (D). Since the activated UAV relays are always floating and flying in the air, its channel state information (CSI) as well as channel capacity is a time-variant parameter. Considering decode-and-forward (DF) relaying protocol in UAV relays, the cooperative relaying channel capacity is constrained by the relatively weaker one (i.e. in terms of signal-to-noise ratio (SNR) or signal-to-interference-plus-noise ratio (SINR)) between S-to-relay and relay-to-D links. The channel capacity can be optimized by adaptively optimizing the transmit power of S and/or UAV relay. Furthermore, a hybrid HD/FD mode is enabled in the proposed UAV relays for adaptively optimizing the channel utilization subject to the instantaneous CSI and/or remaining self-interference (SI) levels. Numerical results show that the channel capacity of the proposed UAV relay aided wireless networks can be maximized by adaptively responding to the influence of various real-time factors.

• Smart Structures and Systems
• /
• v.23 no.1
• /
• pp.1-14
• /
• 2019

Control algorithms are the most important aspects in successful control of structures against earthquakes. In recent years, intelligent control methods rather than classical control methods have been more considered by researchers, due to some specific capabilities such as handling nonlinear and complex systems, adaptability, and robustness to errors and uncertainties. However, due to lack of learning ability of fuzzy controller, it is used in combination with a genetic algorithm, which in turn suffers from some problems like premature convergence around an incorrect target. Therefore in this research, the introduction and design of the Fuzzy Cooperative Coevolution (Fuzzy CoCo) controller and Adaptive Neural-Fuzzy Inference System (ANFIS) have been innovatively presented for semi-active seismic control. In this research, in order to improve the seismic behavior of structures, a semi-active control of building using Magneto Rheological (MR) damper is proposed to determine input voltage of Magneto Rheological (MR) dampers using ANFIS and Fuzzy CoCo. Genetic Algorithm (GA) is used to optimize the performance of controllers. In this paper, the design of controllers is based on the reduction of the Park-Ang damage index. In order to assess the effectiveness of the designed control system, its function is numerically studied on a 9-story benchmark building, and is compared to those of a Wavelet Neural Network (WNN), fuzzy logic controller optimized by genetic algorithm (GAFLC), Linear Quadratic Gaussian (LQG) and Clipped Optimal Control (COC) systems in terms of seismic performance. The results showed desirable performance of the ANFIS and Fuzzy CoCo controllers in considerably reducing the structure responses under different earthquakes; for instance ANFIS and Fuzzy CoCo controllers showed respectively 38 and 46% reductions in peak inter-story drift ($J_1$) compared to the LQG controller; 30 and 39% reductions in $J_1$ compared to the COC controller and 3 and 16% reductions in $J_1$ compared to the GAFLC controller. When compared to other controllers, one can conclude that Fuzzy CoCo controller performs better.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.1
• /
• pp.93-98
• /
• 2021

Due to the development of deep learning and AI, the scale of the model has grown, and it has been integrated into other fields to blend into our lives. However, in environments with limited resources such as embedded devices, it is exist difficult to apply the model and problems such as power shortages. To solve this, lightweight methods such as clouding or offloading technologies, reducing the number of parameters in the model, or optimising calculations are proposed. In this paper, quantization of learned models is applied to ONNX models used in various framework interchange formats, neural network structure and inference performance are compared with existing models, and various module methods for quantization are analyzed. Experiments show that the size of weight parameter is compressed and the inference time is more optimized than before compared to the original model.