• Journal of Navigation and Port Research
• /
• v.47 no.2
• /
• pp.85-92
• /
• 2023

The hardware of an LED lighting control system for coastal lighting at coastal pier entrance consists of a power supply unit, an AVR control unit, a CLCD output unit, an LED control unit, a scenario selection switch unit, and an operation speed display unit. It is made of an 8-channel. The CPU used ATmega128 and the FET was used to control the current signal. To operate the CPU, DC 12V was converted to DC 5V using a regulator 7805. A heat sink was used to remove heat generated in the FET. By connecting the load LED module to the manufactured 8-channel LED lighting control system, the operation was confirmed through various production scenarios. In addition, a control system was designed to show the most suitable color for the atmosphere of the coastal pier according to the input value of temperature and illumination using a fuzzy control system. Computer simulation was then conducted. Results confirmed that fuzzy control did not need to store many data inputs due to characteristics of artificial intelligence and that it could efficiently represent many output values with simple fuzzy rules.

• International journal of advanced smart convergence
• /
• v.12 no.3
• /
• pp.211-220
• /
• 2023

Surface electromyography (sEMG) is a noninvasive method used to capture electrically muscle activity, which can be easily measured even during exercise. The basic unit of muscle activity is the motor unit, and because an sEMG signal is a superposition of motor unit action potentials, analysis of muscle activity using sEMG should ideally be done from the perspective of motor unit activity. However, conventional techniques can only evaluate sEMG signals based on abstract signal features, such as root-mean-square (RMS) and mean-power-frequency (MPF), and cannot detect individual motor unit activities from an sEMG signal. On the other hand, needle EMG can only capture the activity of a few local motor units, making it extremely difficult to grasp the activity of the entire muscle. Therefore, in this study, a method to visualize the activities of motor units in a single-channel sEMG signal by relocating wavelet coefficients obtained by redundant discrete wavelet analysis is proposed. The information obtained through this method resides in between the information obtained through needle EMG and the information obtained through sEMG using conventional techniques.

• Korean Journal of Ecology and Environment
• /
• v.49 no.3
• /
• pp.187-196
• /
• 2016

This study was performed to improve the foaming generated in the effluent of wastewater treatment plant from March 2015 to July 2016. The main cause of foaming was air entrainment by an impinging jet and the internal accumulation by the diffusion barrier. Particularly, the foam growth was most active when there is low tide and larger discharge. To solve this problem, we experimented after installing fine mesh screen and the artificial channel device with underwater discharging outlet in the treated wastewater discharge channel and the outlet, respectively. As a result, the effects of foam reduction by devices ranged 85.0~92.0% and 70.7~85.6%, respectively. In addition, the foam and the noise were easily solved, first of all look to contribute to the prevention of complaints. Our device studies were applied to a single wastewater treatment plant. However, it is considered to be able to apply in other similar cases of domestic sewage treatment plants.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.7 no.2
• /
• pp.19-26
• /
• 2013

This paper propose a surface EMG signal based gait phase recognition method that selects features and channels adaptively. The proposed method can be used to control powered artificial prosthetic for lower limb amputees and can reduce overhead in real-time pattern recognition by selecting adaptive channels and features in an embedded device. The method can enhance the classification accuracy by adaptively selecting channels and features based on sensitivity and specificity of each subject because EMG signal patterns may vary according to subject's locomotion convention. In the experiments, we found that the muscles with highest recognition rate are different between human subjects. The results also show that the average accuracy of the proposed method is about 91% whereas those of existing methods using all channels and/or features is about 50%. Therefore we assure that sEMG signal based gait phase recognition using small number of adaptive muscles and corresponding features can be applied to control powered artificial prosthetic for lower limb amputees.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.14 no.1
• /
• pp.21-27
• /
• 2008

Triangular structure is used as basic shape of artificial structures for generating the upwelling current in order to make rich fishing ground at sea. Artificial upwelling current could bring the deep sea water containing a lot of nutrients from the bottom up to the surface. The purpose of this study is to examine the flow characteristics around underwater triangular structure with various stratification parameter. An experimental study was carried out for the triangular structure model in the circulating water channel to investigate flow characteristics by flow visualization method. A velocity fields around the underwater structure were measured by particle image velocimetry(PIV). The experimental results showed that the upwelling effect at the back and upper region of the structure could be best when the water depth was 2 times of the structure height and the stratification parameter was approximately 3.0. These quantitative data will be useful to determine the functional efficiency cf artificial upwelling structures.

• Advances in materials Research
• /
• v.6 no.2
• /
• pp.169-184
• /
• 2017

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism. This method works by forming of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena can arise and adversely affect the surface integrity of EDMed workpieces. These have to be taken into account and studied in order to optimize the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique that can provide reliable results and readily, be integrated into several technological areas. In this paper, we use an ANN, namely, the multi-layer perceptron and the back propagation network (BPNN) to predict the mean surface roughness of electro-discharge machined surfaces. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for getting a reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components.

• Journal of Advanced Navigation Technology
• /
• v.16 no.5
• /
• pp.801-809
• /
• 2012

Dispersion management (DM) is the typical technique compensating for the distorted signals due to interaction of group velocity dispersion (GVD) and optical nonlinear effects for transmitting wavelength division multiplexed (WDM) channel with the excellent performance. Optimal net residual dispersion (NRD) and effective launching power range of optical transmission links with random distribution and artificial distribution of single mode fiber (SMF) length and residual dispersion per span (RDPS) required to flexibly design of optical links in DM. It is confirmed that optimal net residual dispersion (NRD) are +10 ps/nm and -10 ps/nm controlled by precompensation and postcompensation, respectively, in both of the considered distribution patterns of SMF length and RDPS. And, in optimal NRD, system performance in optical links with the descending distribution of SMF length and the ascending distribution of RDPS among the artificial distribution patterns are more improved, consequently, effective launching power range is expanded by almost 2 dB than those in optical links with the uniform distribution.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.29 no.11 s.242
• /
• pp.1229-1238
• /
• 2005

The objective of the present study was to investigate the efficacy of physical water treatment (PWT) technologies using different catalytic materials and an electronic anti-fouling device in the mitigation of mineral fouling in a once-through flow system with mini-channel heat exchanger. Effects of flow velocity and water hardness on the effectiveness of PWT technologies were experimentally studied. The artificial water hardness varied from 5.0 to 10 mo1/m$^{3}$ as CaCO$_{3}$. For 10 mo1/m$^{3}$ solution, fouling resistance reduced by 13-40$\%$ depending on flow velocity and types of PWT devices. On the other hand, fouling resistance reduced by 21-29$\%$ depending on the PWT devices for 5 mo11m3 solutions. The PWT device using alloy of Cu and Zn as catalyst (CM2) was slightly more effective than the others. SEM photographs of scale produced from the 10 mol/m$^{3}$ solution at 1.0 m/s indicated that calcium carbonate scales without PWT devices were needle-shaped aragonite, which is sticky, dense and difficult to remove. Scales with the PWT devices showed a cluster of spherical or elliptic shape crystals. Both the heat transfer test results and SEM photographs strongly support the efficacy of PWT technologies using catalytic materials and an electronic anti-fouling device in the mitigation of mineral fouling.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2008.03a
• /
• pp.130-135
• /
• 2008

An aerodynamic optimization design process of multistage axial turbine is presented in this article: first, applying quasi-three dimensional(Q3D) design methods to conduct preliminary design and then adopting modern optimization design methods to implement multistage local optimization. Quasi-three dimensional(Q3D) design methods, which mainly refer to S2 flow surface direct problem calculation, adopt the S2 flow surface direct problem calculation program of Harbin Institute of Technology. Multistage local optimization adopts the software of Numeca/Design3D, which jointly adopts genetic algorithm and artificial neural network. The major principle of the methodology is that the successive design evaluation is performed by using an artificial neural network instead of a flow solver and the genetic algorithms may be used in an efficient way. Flow computation applies three-dimensional viscosity Navier Stokes(N-S) equation solver. Such optimization process has three features: (i) local optimization based on aerodynamic performance of every cascade; (ii) several times of optimizations being performed to every cascade; and (iii) alternate use of coarse grid and fine grid. Such process was applied to optimize a three-stage axial turbine. During the optimization, blade shape and meridional channel were respectively optimized. Through optimization, the total efficiency increased 1.3% and total power increased 2.4% while total flow rate only slightly changed. Therefore, the total performance was improved and the design objective was achieved. The preliminary design makes use of quasi-three dimensional(Q3D) design methods to achieve most reasonable parameter distribution so as to preliminarily enhance total performance. Then total performance will be further improved by adopting multistage local optimization design. Thus the design objective will be successfully achieved without huge expenditure of manpower and calculation time. Therefore, such optimization design process may be efficiently applied to the aerodynamic design optimization of multistage axial turbine.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2022.06a
• /
• pp.205-208
• /
• 2022

Multi-exposure high dynamic range (HDR) image reconstruction, the task of reconstructing an HDR image from multiple low dynamic range (LDR) images in a dynamic scene, often produces ghosting artifacts caused by camera motion and moving objects and also cannot deal with washed-out regions due to over or under-exposures. While there has been many deep-learning-based methods with motion estimation to alleviate these problems, they still have limitations for severely moving scenes. They also require large parameter counts, especially in the case of state-of-the-art methods that employ attention modules. To address these issues, we propose a frequency domain approach based on the idea that the transform domain coefficients inherently involve the global information from whole image pixels to cope with large motions. Specifically we adopt Residual Fast Fourier Transform (RFFT) blocks, which allows for global interactions of pixels. Moreover, we also employ Depthwise Overparametrized convolution (DO-conv) blocks, a convolution in which each input channel is convolved with its own 2D kernel, for faster convergence and performance gains. We call this LFFNet (Lightweight Frequency Fusion Network), and experiments on the benchmarks show reduced ghosting artifacts and improved performance up to 0.6dB tonemapped PSNR compared to recent state-of-the-art methods. Our architecture also requires fewer parameters and converges faster in training.