• Journal of KIISE:Databases
• /
• v.33 no.2
• /
• pp.188-200
• /
• 2006

Once a continuous query, which is commonly used in sensor networks, is issued, the query is executed many times with a certain interval and the results of those query executions are collected to the base station. Since this comes many communication messages continuously, it is important to reduce communication cost for collecting data to the base station. In sensor networks, in-network processing reduces the number of message transmissions by partially aggregating results of an aggregate query in intermediate nodes, or merging the results in one message, resulting in reduction of communication cost. In this paper, we propose a routing tree for sensor nodes that qualify the given query predicate, called the query specific routing tree(QSRT). The idea of the QSRT is to maximize in-network processing opportunity. A QSRT is created seperately for each query during dissemination of the query. It is constructed in such a way that during the collection of query results partial aggregation and packet merging of intermediate results can be fully utilized. Our experimental results show that our proposed method can reduce message transmissions more than 18% compared to the existing one.

• Korean Journal of Community Nutrition
• /
• v.4 no.1
• /
• pp.46-56
• /
• 1999

A deep understanding of the dietary patterns and nutrient intake is important for assessment of possilbe nutritional risk and for establishing nutrition improvement strategies. This study was conducted toexamine the dietary characteristics of a nutritionally poor elderly group compared to the middle-and highly-nourished group. Elderly participant was recruited from local elderly centers in Suwon city in 1998. Trained dietitians interviewed 119 elderly(35 males, 84 females) aged 60 years and over for collecting dietary data(24-hour recall) and related variables. Male and female subjects were grouped into high, middle, and low according to the mean nutrient adequancy ratio(MAR) tertiles. An analysisof the percentage of RDA(Recommended Daily Allowances of Korea) for each of the 10 nutrients showed that the male low-MAR group consumed below the RDA in all kinds of nutrients, and the female low-MAR group consumed nutrients below the RDA except vitamin C. An evaluation of nutrient density by Index of Nutritonal Quality(INQ) also showed a similar tendency. Thus, the INQ level of the male low-MAR group was significantly lower than the middle-or high-MAR group, especially in protein, vitamin A, thiamin, riboflavin, and phosphorus(p<0.05). Moreover, INQ level of female low-MAR group was significantly lower than that of the high group(p<0.05) in all nutrients. The female low-MAR group's daily food intake were also lower than those of the high-MARgroup in gains, fish, fruits, oil and beverages. The energy distribution from carbohydrates, fats and proteins showed that the male low-MAR group had significantly higher carbohydrate and lower fat proportions compared to each gender high-MAR group, respectively. The male and female low-MAR group had low scores about eating all side dishes. These findings indicate that a moderate increase of the meat/egg/fishes intake was needed by the male low-MAR group for improving nutrition adequacy, and an overall increase of the food quantity and quality was desired for the female low-MAR group. These data could be used for planning a community elderly nutrition program and establishing strategies for tailored guidelines for the individuals.

• Journal of Power System Engineering
• /
• v.22 no.6
• /
• pp.51-57
• /
• 2018

In Korea, the Air Quality Conservation Act and the Petroleum and Petroleum Substitute Fuel Business Act stipulate certain quality standards for fuels distributed in Korea, thereby striving to reduce vehicle performance and emissions. Domestic petroleum products import and produce all the crude oil from each oil refiner so that the quality of the petroleum product is different according to the characteristics of the crude oil. As a result, vehicles have been improved by using the physical properties calculated through the physical property measurement that has tried to improve the accuracy of the measurement of the energy consumption efficiency of the automobile by using standard fuel from abroad. In this study, the same test procedure and method as the test method of domestic gasoline vehicle emission are applied using four samples of gasoline and the latest gasoline vehicle which are actually distributed, and the performance evaluation is performed. The purpose of this study is to contribute to improvement of vehicle technology and fuel quality by collecting necessary basic data and obtaining data on the effect of differences in gasoline property on vehicle emissions. The results of the test showed that the emission of gases (NMOG, CO) from gasoline vehicles was the most influenced by the sulfur content, unlike the previous studies that the vehicles emission had the greatest influence on the distillation characteristics and the specific gravity of aromatic compounds. The catalytic reaction such as the poisoning action of the three-way catalyst which is the abatement device was interfered and the emission was increased. The distillation characteristics and specific gravity of aromatic compounds were found to affect the emission of vehicles. According to the physical properties of the fuel, the emission difference was 28.0% in the urban mode and 17.6 % in the highway mode.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.63 no.3
• /
• pp.97-105
• /
• 2021

Most of the rural development projects for the welfare of residents are mainly new construction and remodeling projects for community buildings such as village halls and senior citizens. However, in the case of the construction industry, it has been studied that 23% of the total carbon dioxide emissions generated in Korea are generated in the building-related sector. (GGIC, 2015) In order to reduce the emission of environmental pollutants resulting from construction of rural community buildings, there is a need to establish a system for rural buildings by predicting the environmental impact. As a result of this study, the emissions of air pollutants from buildings in rural communities were analyzed by dividing into seven stages: material production, construction, operation, maintenance, demolition, recycling, and transportation activities related to disposal. As a result, 12 kg of carbon dioxide (CO), 0.06 kg of carbon monoxide (CO), 0.02 kg of methane (CH), 0.04 kg of nitrogen oxides (NO), 0.02 kg of sulfurous acid gas (SO), and non-methane volatile organics per 1m of buildings in rural communities It was analyzed that 0.02 kg of compound (NMVOC) and 0.00011 kg of nitrous oxide (NO) were released. This study proved that environmentally friendly design is possible with a quantitative methodology for the comparison of operating energy and air pollutant emissions through the design specification change based on the statement of the rural community building. It is considered that it can function as basic data for further research by collecting major structural changes and materials of rural community buildings.

• Journal of Korean Society for Quality Management
• /
• v.49 no.4
• /
• pp.551-567
• /
• 2021

Purpose: The purpose of this paper is to present implications by analyzing research trends on smart factories by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on smart factories. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "SMART FACTORY" and "Smart Factory" as search terms, and the titles and Korean abstracts were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, 739 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; Smart factory research slowed down from 2005 to 2014, but until 2019, research increased rapidly. According to the analysis by fields, smart factories were studied in the order of engineering, social science, and complex science. There were many 'engineering' fields in the early stages of smart factories, and research was expanded to 'social science'. In particular, since 2015, it has been studied in various disciplines such as 'complex studies'. Overall, in keyword analysis, the keywords such as 'technology', 'data', and 'analysis' are most likely to appear, and it was analyzed that there were some differences by fields and years. Conclusion: Government support and expert support for smart factories should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to smart factories. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.

• International Journal of Computer Science & Network Security
• /
• v.21 no.1
• /
• pp.137-142
• /
• 2021

The Internet of Things (IoT) paradigm is at the forefront of present and future research activities. The huge amount of sensing data from IoT devices needing to be processed is increasing dramatically in volume, variety, and velocity. In response, cloud computing was involved in handling the challenges of collecting, storing, and processing jobs. The fog computing technology is a model that is used to support cloud computing by implementing pre-processing jobs close to the end-user for realizing low latency, less power consumption in the cloud side, and high scalability. However, it may be that some resources in fog computing networks are not suitable for some kind of jobs, or the number of requests increases outside capacity. So, it is more efficient to decrease sending jobs to the cloud. Hence some other fog resources are idle, and it is better to be federated rather than forwarding them to the cloud server. Obviously, this issue affects the performance of the fog environment when dealing with big data applications or applications that are sensitive to time processing. This research aims to build a fog topology job scheduling (FTJS) to schedule the incoming jobs which are generated from the IoT devices and discover all available fog nodes with their capabilities. Also, the fog topology job placement algorithm is introduced to deploy jobs into appropriate resources in the network effectively. Finally, by comparing our result with the state-of-art first come first serve (FCFS) scheduling technique, the overall execution time is reduced significantly by approximately 20%, the energy consumption in the cloud side is reduced by 18%.

• International Journal of Advanced Culture Technology
• /
• v.9 no.1
• /
• pp.177-182
• /
• 2021

The fourth industrial revolution based on information and communication technology (ICT) becomes the center of society, and the overall industrial structure is also changing significantly. ICT refers to the hardware of information devices and the software technologies required for the operation and information management of these devices, and any means of collecting, producing, processing, preserving, communicating and utilizing them. ICT is integrated into industries and services or combined with new technologies in various fields such as robotics and nanotechnology to connect all products and services to the network. The development of ICT, which continuously creates new products and services, has spread to all sectors of the industry, affecting not only daily life but also the livestock sector recently. In agriculture, ICT technology can reduce production costs by efficiently managing labor and energy because it can improve quality and yield based on data on environmental and growth information such as temperature, humidity, light and soil. In particular, smart livestock is considered suitable for achieving livestock management goals because it can reduce labor force and improve productivity by remotely and automatically managing accurate information necessary for raising and breeding livestock with ICT devices. The purpose of this study is to propose the need for ICT technology by comparing farm productivity before and after ICT is introduced. The method of the study is to compare the productivity before and after the introduction of ICT in Korean beef farms, pig farms, and poultry farms. The effectiveness of the study proved the excellence of ICT technology through the production results before ICT introduction and the productivity improvement case of livestock farms that efficiently operated manpower management and reduced labor force after ICT introduction. The conclusion of this paper is to present the need for smart livestock through ICT adoption through case study results.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.48 no.12
• /
• pp.1013-1020
• /
• 2020

Spark-induced breakdown spectroscopy (SIBS) utilizes an electric spark to induce a strong plasma for collecting atomic emissions. This study analyses the potential for usinga compact SIBS instead of conventional laser-induced breakdown spectroscopy (LIBS) in discriminating rocks and soils for planetary missions. Targeting bulky solids using SIBS has not been successful in the past, and therefore a series of optimizations of electrode positioning and electrode materials were performed in this work. The limit of detection (LOD) was enhanced up to four times compared to when LIBS was used, showing a change from 78 to 20 ppm from LIBS to SIBS. Because of the higher energy of plasma generated, the signal intensity by SIBS was higher than LIBS in three orders of magnitude with the same spectrometer setup. Changing the electrode material and locating the optimum position of the electrodes were considered for optimizing the current SIBS setup being tested for samples of planetary origin.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.44 no.4
• /
• pp.227-233
• /
• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.3
• /
• pp.469-478
• /
• 2022

This paper contains the results of developing smart devices and applications to monitor the load power of the industrial manufacturing machine and evaluate its performance. The smart devices in this paper are divided into two functionalities, which are collecting load power along with operating environment data of industrial manufacturing machines and transmitting the data to servers. Load power data collected from the smart devices are uploaded to MariaDB inside the Amazon Web Service (AWS) server. Using the RESTFul API, the uploaded power data can be retrieved and shown on the web and mobile application in the form of a graph to provide monitoring capability. To evaluate the performance of the developed system, the response time from MariaDB to web and mobile applications was measured. The results is ranging from 0.0256 to 0.0545 seconds in a 4G (LTE) network environment and from 0.6126 to 1.2978 seconds in a 3G network environment, which is considered a satisfactory result.