Journal of the Korean Society for Geothermal and Hydrothermal Energy
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v.10
no.2
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pp.19-23
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2014
In the capital of Mongolia where the air quality is getting worse due to the coal consumption used for electricity generation and district heating, the application of geothermal systems in the housing sector is recently designed for high class resort. In this study, the case of applying a geothermal system in a house in Mongolia is examined. The effects of passive house design on the needed heat pump capacity, as well as the annual energy consumption are analyzed. Moreover, as the initial costs, except labor fee, are assumed similar to Korea, cost analysis for several cases is examined, too. From the results, if a house is designed according to passive house standard instead of ASHRAE standard, the heat pump capacity can be expected to be reduced from 16 to 5~6 RT. Furthermore, although the initial cost of architectural cost might increased by 29 M\, the total initial cost is reduced by 14 M\, while the annual energy consumption is reduced by 14%. This is mainly driven by the fact that the geothermal system which serves as the main system to cover the building needs, with a high initial cost for fulfilling the peak requirements.
The rapid change of the health and medical environment and the globalization of medicine has driven doctors to converge and analyse of new and up-to-date medical information and decide to what to make decision for diagnosis and treatments in clinical practice. Medical environment goes with the changes with social environment such as rapid increase of aging population, changes of disease pattern, formation of new area of experts except doctors, government intervention for the medical system, medical insurance of the charges of medical treatment, a increased desire for human rights. These trends should be adopted rapidly to the education system for the students of medical school. The learning objectives of the preventive medicine was developed in 1995 and underwent necessary revision of the contents to create the first revision in 2006. However, the required educational contents of health promotion and disease prevention have been changed by the new trends of medical education such as PBL and integrated curriculum and the 2006 revision does not satisfy these needs. We formed a task force which surveyed all the Western and Traditional Korean medical colleges to describe the state of preventive medicine education in Korea, analyzed the changing education demand according to the change of health environment and quantitatively measured the validity and usefulness of each learning objective in the previous curriculum. With these results, for the good education for preventive medicine, each Traditional Korean medicine schools need more preventive medicine faculties and teaching assistants and opening of some required subjects such as Yangsaeng and Qigong. And future studies of the learning process and ongoing development of teaching materials according to the new learning objectives should be undertaken with persistence in order to ensure the progress of preventive medicine education.
The rapid deployment of millions of mobile sensors and smart devices has resulted in high demand for opportunistic encounter-based networking. For the cooperative video surveillance of dashboard cameras in nearby vehicles, a fast and energy-efficient asynchronous neighbor discovery protocol is indispensable because a dashboard camera is an energy-hungry device after the vehicle's engine has turned off. In the existing asynchronous neighbor discovery protocols, all nodes always try to discover all neighbors. However, a dashboard camera needs to discover nearby dashboard cameras when an event is detected. In this paper, we propose a fast and energy-efficient asynchronous neighbor discovery protocol, which enables nodes : 1) to have different roles in neighbor discovery, 2) to discover neighbors within a search range, and 3) to report promptly the exact discovery result. The proposed protocol has two modes: periodic wake-up mode and active discovery mode. A node begins with the periodic wake-up mode to be discovered by other nodes, switches to the active discovery mode on receiving a neighbor discovery request, and returns to the periodic wake-up mode when the active discovery mode finishes. In the periodic wake-up mode, a node wakes up at multiples of number ${\alpha}$, where ${\alpha}$ is determined by the node's remaining battery power. In the active discovery mode, a node wakes up for consecutive ${\gamma}$ slots. Then, the node operating in the active discovery mode can discover all neighbors waking up at multiples of ${\beta}$ for ${\beta}{\leq}{\gamma}$ within ${\gamma}$ time slots. Since the proposed protocol assigns one half of the duty cycle to each mode, it consumes equal to or less energy than the existing protocols. A performance comparison shows that the proposed protocol outperforms the existing protocols in terms of discovery latency and energy consumption, where the frequency of neighbor discovery requests by car accidents is not constantly high.
Journal of the Korean Society of Environmental Restoration Technology
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v.15
no.1
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pp.133-140
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2012
The study of Arundinaria munsuensis Y. Lee to this day was limited to the field of morphological species classification since its first discovery at Jiri mountain in the late 1990s. This native plants, one of Korean endemic species found in Jiri Mt. necessitates further studies about its use as revegetation plants or groundcover plants in urban areas. This study was launched from this need followed by summaries of results below related to its usage. This targeted species is a native Korean species which its natural distribution on the subalpine zone has merit for the wide range of climatic adaptation. Also, the coverage rate reached 78.3% in three years mostly by rhizome growth with an expectation of full coverage in four years providing less maintenance needs after planting. The net price for the construction of pot seedling methods of this plants is relatively economical than other normal revegetation construction methods. For these reasons, drilling for the plants on cut and fill soil slopes driven from pot seedling adapts Arundinaria munsuensis Y. Lee as efficient revegetation plant. Total construction cost for pot seedling work($30cm{\times}30cm$) of Arundinaria munsuensis was approximately 21,000won which is in reasonable price range when compared to other revegetation construction methods of disturbed slopes. As a native Korean plant, Arundinaria munsuensis has wide range of climatic adaptation with less maintenance requirement after planting. This study may suggest a strong possibility of Arundinaria munsuensis as a pot planting material in sloped area.
Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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v.29
no.10
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pp.73-82
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2015
This paper proposes an ripple reduction algorithm and analyzes the effects of offset and scale errors generated by voltage sensor while measuring grid voltage in grid-tied single-phase inverters. Generally, the grid-connected inverter needs to detect the phase angle information by measuring grid voltage for synchronization, so that the single-phase inverter can be accurately driven based on estimated phase angle information. However, offset and scale errors are inevitably generated owing to the non-linear characteristics of voltage sensor and these errors affect that the phase angle includes 1st harmonic component under using SRF-PLL(Synchronous Reference Frame - Phase Locked Loop) system for detecting grid phase angle. Also, the performance of the overall system is degraded from the distorted phase angle including the specific harmonic component. As a result, in this paper, offset and scale error due to the voltage sensor in single-phase grid connected inverter under SRF-PLL is analyzed in detail and proportional resonant controller is used to reduce the ripples caused by the offset error. Especially, the integrator output of PI(Proportional Integral) controller in SRF-PLL is selected as an input signal of the proportional resonant controller. Simulation and experiment are performed to verify the effectiveness of the proposed algorithm.
Unit testing is one of the test levels, which tests an individual unit or a group of related units. Recently, in Agile Development or Safety-critical System Development, the unit testing plays an important role for the qualities. According to the definition of unit testing, it is supposed to be done by the developers of units. That is because test models for the unit testing refers to the structure of units, and others but its original developers hardly can understand the structures. However, in practice, unit testing is often asked to be done without the original developers. For example, it is when faults are revealed in customer sites and the development team does not exit any more. In this case, instead of original developers, other developers or test engineers take a product and test it. The unit testing done by a non-developer, who is not the original developer, would cause some difficulties or cause more cost. In this paper, we tests an open source, JTopas, as a non-developer, with building test models, implementing test codes, and executing test cases. To fit this experiment to practical testing situations, we designed it based on the practices of unit testing, which were surveyed through SPIN(Software Process Improvement Network). This paper analyzes which part of unit testing done by non-developers needs more effort compared to the unit testing done by original developers. And it concludes that Agile Development contributes on reducing the extra effort caused by non-developers, since it implements test codes first before developing source code. That means all the units have already included their own tests code when they are released.
KIPS Transactions on Software and Data Engineering
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v.7
no.1
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pp.19-24
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2018
This paper presents an IoT platform of common interfaces that are flexible IoT applications and Connect the smart devices. The IoT platform includes automatic collaboration discovery the smart Device. It is different things case with connection each device through IoT Platforms are each device and application service. Due to these heterogeneities, it is quite advantages to develop applications working with derived IoT services. This circumstance needs the generic interface and results in a range of IoT architectures by not only the environments settings and discovery resource but also varied uniqe to implementation services working with IoT applications. Therefore, this suggestion of solve the problems and make it possible independent platforms always alive to connection with each devices based on the generic interface. The generic interface is heterogeneity-driven solve the problems and effectively contributions a platform that could be operated in diverse IoT Platforms.
The fishing vessel buyback program, that is, the project of fishing vessel reduction is a complex challenge that needs to take into consideration for both fishing resource protection and industrial restructuring. By the way, the fishing vessel buyback program in Korea is still poorly fruitful and there are few applications for the project in the country. For example, the buyback program of this nation has some problems like uncertainly set goals, conflicts among targets, no flexibility in the program implementation, low participation in the project and lack in follow-up actions. To solve these problems, this study offers the following alternatives. First, it is setting up detailed targets for fishing vessel buyback. Those targets should be what can be actually driven under policy support, such as reduction in illegal fishing, decrease in an exact number of fishing vessels and decline in fishing capacity rather than what seem to be comprehensive such as protection of coastal or offshore fishery and industrial restructuring. Second, it is taking measures for the livelihood of those who would face disadvantages due to fishing vessel reduction. Those measures providing or supporting the re-education and re-employment of fishery workers and building up systems that help the workers transfer fishing to another job. Third, it is adopting the governance system that overcomes lacks in communications between the fishing vessel buyback program and improves the performance effectiveness of that program. Here, governance means that businesses and authorities concerned in the program interact and cooperate with one another in making and implementing policies related to the project of fishing vessel reduction and assessing the results of that project. Fourth, it is taking actions for fishery reorganization after the fishing vessel buyback program. Factors that can be considered as those actions include supporting fishery workers' cost of fishing vessel reduction, decreasing and modernizing fishing fleets and improving the quality of fishing equipment. In conclusion, the fishing vessel buyback program should be prepared with comprehensive actions or measures that can cope well with a variety of problems that may be arisen from it while keeping itself on the track of its original goals.
Water quality models are scientific tools that simulate and interpret the relationship between physical, chemical and biological reactions to external pollutant loads in water systems. They are actively used as a key technology in environmental water management. With recent advances in computational power, water quality modeling technology has evolved into a coupled three-dimensional modeling of hydrodynamics, water quality, and ecological inputs. However, there is uncertainty in the simulated results due to the increasing model complexity, knowledge gaps in simulating complex aquatic ecosystem, and the distrust of stakeholders due to nontransparent modeling processes. These issues have become difficult obstacles for the practical use of water quality models in the water management decision process. The objectives of this paper were to review the theoretical background, needs, and development status of water quality modeling technology. Additionally, we present the potential future directions of water quality modeling technology as a scientific tool for national environmental water management. The main development directions can be summarized as follows: quantification of parameter sensitivities and model uncertainty, acquisition and use of high frequency and high resolution data based on IoT sensor technology, conjunctive use of mechanistic models and data-driven models, and securing transparency in the water quality modeling process. These advances in the field of water quality modeling warrant joint research with modeling experts, statisticians, and ecologists, combined with active communication between policy makers and stakeholders.
Forward osmosis (FO) process is a chemical potential driven process, where highly concentrated draw solution (DS) is used to take water through semi-permeable membrane from feed solution (FS) with lower concentration. Recently, commercial FO membrane modules have been developed so that full-scale FO process can be applied to seawater desalination or water reuse. In order to design a real-scale FO plant, the performance prediction of FO membrane modules installed in the plant is essential. Especially, the flux prediction is the most important task because the amount of diluted draw solution and concentrate solution flowing out of FO modules can be expected from the flux. Through a previous study, a theoretical based FO module model to predict flux was developed. However it needs an intensive numerical calculation work and a fitting process to reflect a complex module geometry. The idea of this work is to introduce deep learning to predict flux of FO membrane modules using 116 experimental data set, which include six input variables (flow rate, pressure, and ion concentration of DS and FS) and one output variable (flux). The procedure of optimizing a deep learning model to minimize prediction error and overfitting problem was developed and tested. The optimized deep learning model (error of 3.87%) was found to predict flux better than the theoretical based FO module model (error of 10.13%) in the data set which were not used in machine learning.
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