• Journal of Practical Agriculture & Fisheries Research
• /
• v.17 no.1
• /
• pp.183-204
• /
• 2015

Papua New Guinea, birthplace of the South Pacific, is a natural nation which have potential of increasing crops output because it has optimum condition for crop growth as tropical rain forest climate under hot and humid climate. Farming village of Papua New Guinea want to produce crops for create income beyond the self-sufficiency. It needs the technological transfer such as irrigation facilities and understanding of agricultural weather condition for good crops production. In particular, it needs a improvement through pH, EC, ORP for make optimum soil condition and it need the standardization production and farm products what the consumer wants. Internationally technical cooperation is needed for agricultural development of Papua New Guinea and maintenance of international cooperation will help for economic development between the two countries. In particular, basic environment research for agricultural development of Papua New Guinea is expected to play a larger role of technical cooperation of agriculture.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.206-208
• /
• 2022

As the big data industry has recently developed significantly, interest in privacy violations caused by personal information leakage has increased. There have been attempts to automate this through named entity recognition in natural language processing. In this paper, named entity recognition data is constructed semi-automatically by identifying sentences with de-identification information from de-identification information in Korean Wikipedia. This can reduce the cost of learning about information that is not subject to de-identification compared to using general named entity recognition data. In addition, it has the advantage of minimizing additional systems based on rules and statistics to classify de-identification information in the output. The named entity recognition data proposed in this paper is classified into twelve categories. There are included de-identification information, such as medical records and family relationships. In the experiment using the generated dataset, KoELECTRA showed performance of 0.87796 and RoBERTa of 0.88.

• Journal of Creative Information Culture
• /
• v.5 no.2
• /
• pp.183-191
• /
• 2019

The purpose of this paper is to develop an unplugged education program that develops the 4C (Creativity, Critical thinking, Communication ability, Collaboration) and CT (Computational Thinking) competencies required in modern society. This study discovered "Fine Dust Robot" as a theme suitable for the unplugged education program, and designed the Unplugged 4-hour education program which can develop 4C and CT competencies. The first stage motivates learning, and the second and third stages develop unplugged activity to develop CT. In the fourth stage, the algorithms created through unplugged activities were programmed through the natural language instruction card and produced the output. We developed educational materials that can be utilized in the unplugged education program. Finally, education programs were conducted for elementary school students, and pre- and post-tests of computational thinking were conducted for general students and gifted students. Educational effective was found in both groups.

• Journal of Radiation Protection and Research
• /
• v.48 no.1
• /
• pp.9-14
• /
• 2023

Background: The detectors of both computed radiography (CR) and direct digital radiography (DR) have a wide dynamic range that could tolerate high values of exposure factors without an adverse effect on image quality. Therefore, this study aims to assess patient radiation dose and proposes institutional diagnostic reference levels (DRLs) for two teaching hospitals in Ghana. Materials and Methods: CR and DR systems were utilized in this study from two teaching hospitals. The CR system was manufactured by Philips Medical Systems DMC GmbH, while the DR system was manufactured by General Electric. The entrance skin doses (ESDs) were calculated using the standard equation and the tube output measurements. Free-in-air kerma (µGy) was measured using a calibrated radiation dosimeter. The proposed institutional DRLs were estimated using 75^th percentiles values of the estimated ESDs for nine radiographic projections. Results and Discussion: The calculated DRLs were 0.4, 1.6, 3.4, 0.5, 0.4, 1.1, 1.0, 1.2, and 1.7 mGy for chest posteroanterior (PA), lumbar spine anteroposterior (AP), lumbar spine lateral (LAT), cervical spine AP, cervical spine LAT, skull PA, pelvis AP, and abdomen AP, respectively in CR system. In the DR system, the values were 0.3, 1.6, 3.1, 0.4, 0.3, 0.7, 0.6, 0.9, and 1.3 for chest PA, lumbar spine AP, lumbar spine LAT, cervical spine AP, cervical spine LAT, skull PA, pelvis AP, and abdomen AP, respectively. Conclusion: Institutional DRLs in nine radiographic projections have been proposed for two teaching hospitals in Ghana for the first time. The proposed DRLs will serve as baseline data for establishing local DRLs in the hospitals and will be a valuable tool in optimizing patient doses.

• Computers and Concrete
• /
• v.32 no.2
• /
• pp.217-232
• /
• 2023

Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m²), pile length (m), internal friction angle along the pile body and pile tip (Ø°), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trial-and-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles.

• Steel and Composite Structures
• /
• v.46 no.3
• /
• pp.319-334
• /
• 2023

Localizing damages is an essential task to monitor the health of the structures since they may not be able to operate anymore. Among the damage detection techniques, non-destructive methods are considerably more preferred than destructive methods since damage can be located without affecting the structural integrity. However, these methods have several drawbacks in terms of detecting abilities, time consumption, cost, and hardware or software requirements. Employing artificial intelligence techniques could overcome such issues and could provide a powerful damage detection model if the technique is utilized correctly. In this study, the crack localization in flat and folded plate structures has been conducted by employing a Backpropagated Artificial Neural Network (BPANN). For this purpose, cracks with 18 different dimensions in thin, flat, and folded structures having 15⁰, 30⁰, 45⁰, and 60⁰ folding angle have been modeled and subjected to free vibration analysis by employing the Classical Plate Theory with Finite Element Method. A Four-nodded quadrilateral element having six degrees of freedom has been considered to represent those structures mathematically. The first ten natural frequencies have been obtained regarding healthy and cracked structures. To localize the crack, the ratios of the frequencies of the cracked flat and folded structures to those of healthy ones have been taken into account. Those ratios have been given to BPANN as the input variables, while the crack locations have been considered as the output variables. A total of 500 crack locations have been regarded within the dataset obtained from the results of the free vibration analysis. To build the best intelligent model, a feature search has been conducted for BAPNN regarding activation function, the number of hidden layers, and the number of hidden neurons. Regarding the analysis results, it is concluded that the BPANN is able to localize the cracks with an average accuracy of 95.12%.

• Journal of the Korean Recycled Construction Resources Institute
• /
• v.11 no.3
• /
• pp.234-243
• /
• 2023

The electromagnetic pulse(EMP) is a general term for high-output electromagnetic waves, and is classified into EMP generated from nuclear weapons, non-nuclear EMP, and EMP generated by natural phenomena. Electromagnetic pulses are means that can cause fatal damage to all electronic devices with electromagnetic elements, such as communication devices, mobile phones, computers, TVs, and means of transportation. In this study, the electromagnetic pulse(EMP) shielding effectiveness evaluation of paints according to the type and amount of carbon material was conducted to develop EMP shielding inorganic paint using carbon materials. In order to analyze the improvement of compatibility and dispersibility between materials, experiments were conducted two times with about 27 types of mixture proportions, and the electromagnetic pulse shielding effectiveness was evaluated by the electrical resistance measurement method. As a result of applying the EMP shielding paint developed through this study to shielding concrete, it was confirmed that the shielding performance was improved from about 25 dB to a maximum of 40 dB.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.37 no.1
• /
• pp.106-111
• /
• 2024

A triboelectric nanogenerator is a promising energy harvester operated by the combined mechanism of electrostatic induction and contact electrification. It has attracting attention as eco-friendly and sustainable energy generators by harvesting wasting mechanical energies. However, the power generated in the natural environment is accompanied by low frequencies, so that the output power under such input conditions is normally insufficient amount for a variety of industrial applications. In this study, we introduce a non-contact rotational triboelectric nanogenerator using pedaling and gear systems (called by P-TENG), which has a mechanism that produces high power by using rack gear and pinion gear when a large force by a pedal is given. We design the system can rotate the shaft to which the rotor is connected through the conversion of vertical motion to rotational motion between the rack gear and the pinion gear. Furthermore, the system controls the one directional rotation due to the engagement rotation of the two pinion gears and the one-way needle roller bearing. The TENG with a 2 mm gap between the rotor and the stator produces about the power of 200 ㎼ and turns on 82 LEDs under the condition of 800 rpm. We expect that P-TENG can be used in a variety of applications such as operating portable electronics or sterilizing contaminated water.

• Membrane and Water Treatment
• /
• v.14 no.4
• /
• pp.155-162
• /
• 2023

Global climate change and urbanization have various demerits, such as water pollution, flood damage, and deterioration of water circulation. Thus, attention is drawn to Nature-based Solution (NbS) that solve environmental problems in ways that imitate nature. Among the NbS, urban wetlands are facilities that perform functions, such as removing pollutants from a city, improving water circulation, and providing ecological habitats, by strengthening original natural wetland pillars. Frequent monitoring and maintenance are essential for urban wetlands to maintain their performance; therefore, there is a need to apply the Internet of Things (IoT) technology to wetland monitoring. Therefore, in this study, we attempted to develop a real-time wetland monitoring device and interface. Temperature, water temperature, humidity, soil humidity, PM1, PM2.5, and PM10 were measured, and the measurements were taken at 10-minute intervals for three days in both indoor and wetland. Sensors suitable for conditions that needed to be measured and an Arduino MEGA 2560 were connected to enable sensing, and communication modules were connected to transmit data to real-time databases. The transmitted data were displayed on a developed web page. The data measured to verify the monitoring device were compared with data from the Korea meteorological administration and the Korea environment corporation, and the output and upward or downward trend were similar. Moreover, findings from a related patent search indicated that there are a minimal number of instances where information and communication technology (ICT) has been applied in wetland contexts. Hence, it is essential to consider further research, development, and implementation of ICT to address this gap. The results of this study could be the basis for time-series data analysis research using automation, machine learning, or deep learning in urban wetland maintenance.

• Journal of Animal Science and Technology
• /
• v.66 no.4
• /
• pp.645-662
• /
• 2024

Climate change, driven by the natural process of global warming, is a worldwide issue of significant concern because of its adverse effects on livestock output. The increasing trend of environmental temperature surging has drastically affected meat production and meat product quality, hence result in economic losses for the worldwide livestock business. Due to the increasing greenhouse gas emissions, the situation would get prolonged, and heat exposure-related stress is expected to worsen. Heat exposure causes metabolic and physiological disruptions in livestock. Ruminants and monogastric animals are very sensitive to heat stress due to their rate of metabolism, development, and higher production levels. Before slaughter, intense hot weather triggers muscle glycogen breakdown, producing pale, mushy, and exudative meat with less water-holding capacity. Animals exposed to prolonged high temperatures experience a decrease in their muscle glycogen reserves, producing dry, dark, and complex meat with elevated final pH and increased water-holding capacity. Furthermore, heat stress also causes oxidative stresses, especially secondary metabolites from lipid oxidation, severely affects the functionality of proteins, oxidation of proteins, decreasing shelf life, and food safety by promoting exfoliation and bacterial growth. Addressing the heat-related issues to retain the sustainability of the meat sector is an essential task that deserves an inclusive and comprehensive approach. Considering the intensity of the heat stress effects, this review has been designed primarily to examine the consequences of hot environment temperatures and related stresses on the quality and safety of meat and secondarily focus on cutting edge technology to reduce or alleviate the situational impact.