• Clean Technology
• /
• v.28 no.4
• /
• pp.278-284
• /
• 2022

This study presents an excellent planar heater based on low-dimensional composites. By optimizing the ratio of 1D carbon nanotubes (CNT) and 2D MXene (Ti₃C₂T_X), it is possible to create a planar heater that has superior electrical conductivity and high heat generation characteristics. Low-dimensional composites were prepared by mixing CNT paste and MXene solution with eco-friendly waterborne polyurethane (WPU). In order to find the optimal mixing ratio for the MXene-CNT-WPU composites, samples with MXene to CNT weight ratios of 3:1, 1:1, 1:3, 1:7, and 1:14 were investigated. In addition to these different weight ratios, 5 wt% WPU was equally applied to each sample. It was confirmed that the higher the weight ratio of CNT, the lower the sheet resistance and the higher the heating temperature. In particular, when the MXene-CNT-WPU planar heater was fabricated by mixing MXene and CNT at a weight ratio of 1:7 and 1:14, the heating temperature was higher than the heating temperature of a CNT-WPU planar heater. These characteristics are due to the optimized mixture of the 1D materials (CNT) and the 2D materials (MXene) causing the formation of a flat surface and a dense network structure. The low-dimensional composites manufactured with the optimized mixing ratios found in this study are expected to be applied in flexible electronic devices.

• Clean Technology
• /
• v.28 no.3
• /
• pp.232-237
• /
• 2022

Biorefineries, in which renewable resources are utilized, are an eco-friendly alternative based on biomass feedstocks. Alginic acid, a major component of brown algae, which is a type of marine biomass, is widely used in various industries and can be converted into value-added chemicals such as sugars, sugar alcohols, furans, and organic acids via catalytic hydrothermal decomposition under certain conditions. In this study, ruthenium-supported activated carbon and magnesium oxide were mixed and applied to the depolymerization of alginic acid in a batch reactor. The addition of magnesium oxide as a basic promoter had a strong influence on product distribution. In this heterogeneous catalytic system, the separation and purification processes are also simplified. After the reaction, low molecular weight alcohols and organic acids with 5 or fewer carbons were produced. Specifically, under the optimal reaction conditions of 30 mL of 1 wt% alginic acid aqueous solution, 100 mg of ruthenium-supported activated carbon, 100 mg of magnesium oxide, 210 ℃ of reaction temperature, and 1 h of reaction time, total carbon yields of 29.8% for alcohols and 43.8% for a liquid product were obtained. Hence, it is suggested that this catalytic system results in the enhanced hydrogenolysis of alginic acid to value-added chemicals.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.5
• /
• pp.497-502
• /
• 2022

Information delivery among young generation has a distinct tendency to prefer visual to text as means of information distribution and sharing recently, and it is natural to distribute information through Youtube or one-man broadcasting on Internet. That is, young generation usually get their information through this kind of distribution procedure. Many young generation are also drastic and more aggressive for decorating themselves very uniquely. It tends to create personal characteristics freely through drastic expression and attempt of face makeup, hair styling and fashion coordination without distinction of sex. Especially, face makeup becomes an object of major concern among males nowadays, and female of course, then it is the major means to express their personality. In this study, to meet the demands of the times, we design and implement the intellectual smart mirror application that efficiently retrieves and recommends the related videos among Youtube or one-man broadcastings produced by famous professional makeup artists to implement the face makeup congruous with our face shape, hair color & style, skin tone, fashion color & style in order to create the face makeup that represent our characteristics. We also introduce the AI technique to provide optimal solution based on the learning of user's search patterns and facial features, and finally provide the detailed makeup face images to give the chance to get the makeup skill stage by stage.

• Journal of Intelligence and Information Systems
• /
• v.29 no.1
• /
• pp.107-119
• /
• 2023

The technology that recognizes a soldier's motion and movement status has recently attracted large attention as a combination of wearable technology and artificial intelligence, which is expected to upend the paradigm of troop management. The accuracy of state determination should be maintained at a high-end level to make sure of the expected vital functions both in a training situation; an evaluation and solution provision for each individual's motion, and in a combat situation; overall enhancement in managing troops. However, when input data is given as a timer series or sequence, existing feedforward networks would show overt limitations in maximizing classification performance. Since human behavior data (3-axis accelerations and 3-axis angular velocities) handled for military motion recognition requires the process of analyzing its time-dependent characteristics, this study proposes a high-performance data-driven classifier which utilizes the long-short term memory to identify the order dependence of acquired data, learning to classify eight representative military operations (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). Since the accuracy is highly dependent on a network's learning conditions and variables, manual adjustment may neither be cost-effective nor guarantee optimal results during learning. Therefore, in this study, we optimized hyperparameters using Bayesian optimization for maximized generalization performance. As a result, the final architecture could reduce the error rate by 62.56% compared to the existing network with a similar number of learnable parameters, with the final accuracy of 98.39% for various military operations.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.4
• /
• pp.157-164
• /
• 2023

This paper suggests O(n²) polynomial time heuristic algorithm for corporate tax structure optimization problem that has been classified as NP-complete problem. The proposed algorithm constructs tax tree levels that the target holding company is located at root node of Level 1, and the tax code categories(T_e) 1,4,3,2 are located in each level 2,3,4,5 sequentially. To find the maximum tax-relief path from source(S) to target(T), firstly we connect the minimum witholding tax rate _minr_w(u, v) arc of node u point of view for transfer the profit from u to v node. As a result we construct the spanning tree from all of the source nodes to a target node, and find the initial feasible solution. Nextly, we find the alternate path with minimum foreign tax rate _minr_fi(u, v) of v point of view. Finally we choose the minimum tax-relief path from of this two paths. The proposed heuristic algorithm performs better optimal results than linear programming and Tabu search method that is a kind of metaheuristic method.

• Clean Technology
• /
• v.16 no.2
• /
• pp.117-123
• /
• 2010

In this study, we propose two methods able to recover different type of gold from gold-cyanide solutions: biosorption and desorption process for mono-valent gold recovery and biosorption and incineration process for zero-valent gold recovery. The waste bacterial biomass of Corynebacterium glutamicum generated from amino acid fermentation industry was used as a biosorbent. The pH edge experiments indicated that the optimal pH range was pH 2 - 3. From isothermal experiment and its fitting with Langmuir equation, the maximum uptake capacity of Au(I) at pH 2.5 were determined to be 35.15 mg/g. Kinetic tests evidenced that the process is very fast so that biosorption equilibrium was completed within the 60 min. To recover Au(I), the gold ions were able to be successfully eluted from the Au-loaded biosorbent by changing the pH to pH 7 and the desorption efficiency was 91%. This indicates that the combined process of biosorption and desorption would be effective for the recovery of Au(I). In order to recover zero-valent gold, the Au-loaded biosorbents were incinerated. The content of zero-valent gold in the incineration ash was as high as 85%. Therefore, we claim on the basis of the results that two suggested combined processes could be useful to recover gold from cyanide solutions and chosen according to the type of gold to be recovered.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
• /
• v.29 no.1
• /
• pp.1-7
• /
• 2023

Chlorine dioxide (ClO₂) is widely used for post-harvest sterilization treatment. However, there are limitations in the retail application of ClO₂ due to difficulties in handling, expensive facilities, and safety concerns. Therefore, it is necessary to develop a ClO₂ technology that can be easily applied and continuously released for a long period. In this study, a series of ClO₂ self-releasing sachets were developed. First, poly(ether-block-amide) (PEBAX) and polyethylene-glycol (PEG) composite films containing different ratios of citric acid (CA) were prepared using the solution casting method. The as-prepared PEBAX/PEG-CA composite films were evaluated using FT-IR, DSC, and TGA to confirm chemical structure and thermal properties. Subsequently, PEBAX/PEG-CA composite films were designed in the form of a sachet and NaClO₂ powder was transferred into the sachet to achieve a ClO₂ self-releasing system. The ClO₂-releasing behavior of the sachet was investigated by measuring the release amount of the gas using UV-vis. The release amount of ClO₂ increased with increasing CA contents owing to the existence of higher protons (trigger) in the polymer matrix. Further, ClO₂ gas was released for a longer time. Therefore, the as-prepared smart sachet can be tuned according to applications and packaging sizes to serve an optimal sterilization effect.

• Journal of the Korean GEO-environmental Society
• /
• v.24 no.8
• /
• pp.5-11
• /
• 2023

Due to frequent occurrences of concentrated heavy rainfall caused by abnormal climate conditions in recent years, collapses of steep slopes have been occurring frequently due to surface erosion and increased pore water pressure. Various methods are being applied to prevent slope collapses, such as increasing the resistance to movement and reducing pore water pressure. Research on these methods has been consistently conducted as they provide an efficient response to slope collapses by satisfying both the conditions of resistance to movement and pore water pressure simultaneously. Therefore, in this study, we propose an upward slope reinforcement method by burying drainage materials with an upward slope inclination, instead of the conventional horizontal application. This approach aims to satisfy both slope reinforcement and drainage functions effectively, offering a comprehensive solution for slope stabilization. Furthermore, to determine the optimal burial angle that exhibits the most effective reinforcement and drainage effects of the proposed method, we investigated the reinforcement and drainage effects under conditions where the horizontal drainage materials were set at angles ranging from 0° to 60° in increments of 10° on a representative cross-section. Additionally, indoor model experiments were conducted under the conditions of 40°, which showed the most outstanding drainage effect, and 20°, which exhibited the highest safety factor, to validate the numerical analysis results. The results showed that the burial angle of 40° exhibits a relatively higher drainage effect as with the numerical analysis results, while the angle of 20° results in inadequate drainage and observed slope collapse.

• Journal of the Korean Geosynthetics Society
• /
• v.22 no.4
• /
• pp.17-26
• /
• 2023

To consider the risk of damage and fracture of P.C strands, the existing post-maintenance system alone has the limitations, hence it is necessary to quantitatively evaluate and predict the deterioration, durability and safety of facilities and establish a reasonable maintenance system considering the asset value of facilities. Therefore, it is worth considering a preventive maintenance plan that allows proactive measures to be taken before a major defect occurs in the temporary anchor. This study devised a preventive over tension method, reviewed its effectiveness through design and field tests, by calculating the resistance factors by performing a reliability-based optimization design. At this time, the over tension anchor method was evaluated using the ratio of the residual tension force after the fracture of P.C strands to the effective tension force before the fracture of P.C strand, followed by the resistance factor calculated by the optimal solution for each random variables using Excel solver and applying it to the limit state equations. As a result of the study, if the over tension ratio is 125% to 130%, the remaining strands showed a high resistance effect even after the fracture of P.C strand. As a result of the optimization design, it was found that it is appropriate to apply the load factor (γ) of 1.25, and the resistance factors of Φ₁, Φ₂, Φ₃ as 0.7, 0.5, 0.6.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.10
• /
• pp.27-35
• /
• 2023

In this paper, we propose a process of increasing productivity by applying a deep learning-based defect detection and classification system to the prepreg fiber manufacturing process, which is in high demand in the field of producing composite materials. In order to apply it to toe prepreg manufacturing equipment that requires a solution due to the occurrence of a large amount of defects in various conditions, the optimal environment was first established by selecting cameras and lights necessary for defect detection and classification model production. In addition, data necessary for the production of multiple classification models were collected and labeled according to normal and defective conditions. The multi-classification model is made based on CNN and applies pre-learning models such as VGGNet, MobileNet, ResNet, etc. to compare performance and identify improvement directions with accuracy and loss graphs. Data augmentation and dropout techniques were applied to identify and improve overfitting problems as major problems. In order to evaluate the performance of the model, a performance evaluation was conducted using the confusion matrix as a performance indicator, and the performance of more than 99% was confirmed. In addition, it checks the classification results for images acquired in real time by applying them to the actual process to check whether the discrimination values are accurately derived.