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  1. Book ; Online: Seawater Reverse Osmosis (SWRO) Desalination

    Hong, Seungkwan / Park, Kiho / Kim, Jungbin / Alayande, Abayomi Babatunde / Kim, Youngjin

    Energy consumption in plants, advanced low-energy technologies, and future developments for improving energy efficiency

    2023  

    Keywords Environmental science, engineering & technology ; Industrial applications of scientific research & technological innovation ; Mining technology & engineering ; Science ; Environmental Science ; Applied Sciences ; Technology & Engineering ; Mining
    Language English
    Size 1 Online-Ressource
    Publisher IWA Publishing
    Document type Book ; Online
    Note English
    HBZ-ID HT030374517
    ISBN 9781789061222 ; 1789061229
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Book ; Online: Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies

    Kim, Young-Jin

    2022  

    Abstract: The attainment of carbon neutrality requires a research agenda that addresses the technical and economic challenges that will be encountered as we progress toward 100% renewable electricity generation. Increasing proportions of variable renewable energy ( ...

    Abstract The attainment of carbon neutrality requires a research agenda that addresses the technical and economic challenges that will be encountered as we progress toward 100% renewable electricity generation. Increasing proportions of variable renewable energy (VRE) sources (such as wind turbines and photovoltaic systems) render the supply-and-demand balance of VRE-dominated power grids difficult. The operational characteristics and effects of VRE inverters also require attention. Here, we examine the implications of the paradigm shift to carbon neutrality and summarize the associated research challenges in terms of system planning, operation, and sta-bility, and the need for energy storage integration, demand-side participation, distributed con-trol and estimation, and energy sector coupling. We also highlight the existing literature gaps, and our recent studies that can fill in the gaps, thereby facilitating the improvement of grid op-eration and estimation. The numerical results of comparative case studies are also provided on the operational stability and economics of power grids with a high level of VRE sources, assist-ing stakeholders in establishing specific roadmaps and making relevant decisions.
    Keywords Electrical Engineering and Systems Science - Systems and Control
    Subject code 690
    Publishing date 2022-02-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  3. Book ; Online: Online Learning of Interconnected Neural Networks for Optimal Control of an HVAC System

    Kim, Youngjin

    2021  

    Abstract: Optimizing the operation of heating, ventilation, and air-conditioning (HVAC) systems is a challenging task, requiring the modeling of complex nonlinear relationships among HVAC load, indoor temperatures, and outdoor environments. This paper proposes a ... ...

    Abstract Optimizing the operation of heating, ventilation, and air-conditioning (HVAC) systems is a challenging task, requiring the modeling of complex nonlinear relationships among HVAC load, indoor temperatures, and outdoor environments. This paper proposes a new strategy for optimal operation of an HVAC system in a commercial building. The system for indoor temperature control is divided into three sub-systems, each of which is modeled using an artificial neural network (ANN). The ANNs are then interconnected and integrated into an optimization problem for temperature set-point scheduling. The problem is reformulated to determine the optimal set-points using a deterministic search algorithm. After the optimal scheduling is initiated, the ANNs undergo online learning repeatedly, mitigating the overfitting. Case studies are performed to analyze the performance of the proposed strategy, compared to the strategies with a pre-determined temperature set-point, an ideal physics-based building model, and conventional ANN-based building models. The case study results confirm that the proposed strategy is effective in terms of the HVAC energy cost, practical applicability, and training data requirement.
    Keywords Electrical Engineering and Systems Science - Systems and Control
    Subject code 690
    Publishing date 2021-01-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Relieving the Need for Bi-Level Decision-Making for Optimal Retail Pricing via Online Meta-Prediction of Data-Driven Demand Response of HVAC Systems

    Kim, Youngjin

    2020  

    Abstract: Price-based demand response (DR) of heating, ventilating, and air-conditioning (HVAC) systems is a challenging task, requiring comprehensive models to represent the building thermal dynamics and game theoretic interactions among participants. This paper ... ...

    Abstract Price-based demand response (DR) of heating, ventilating, and air-conditioning (HVAC) systems is a challenging task, requiring comprehensive models to represent the building thermal dynamics and game theoretic interactions among participants. This paper proposes an online learning-based strategy for a distribution system operator (DSO) to determine optimal electricity prices, considering the optimal DR of HVAC systems in commercial buildings. An artificial neural network (ANN) is trained with building energy data and represented using an explicit set of linear and nonlinear equations, without physics-based model parameters. An optimization problem for price-based DR is then formulated using this equation set and repeatedly solved offline, producing data on optimal DR schedules for various conditions of electricity prices and building thermal environments. Another ANN is then trained online to directly predict DR schedules for day-ahead electricity prices, which is referred to as meta-prediction (MP). By replacing the DR optimization problem with the MP-enabled ANN, an optimal electricity pricing strategy can be implemented using a single-level decision-making structure, which is simpler and more practical than a bi-level one. In simulation case studies, the proposed single-level strategy is verified to successfully reflect the game theoretic relations between the DSO and commercial building operators, so that they effectively exploit the operational flexibility of the HVAC systems to make the DR application profitable, while ensuring the grid voltage stability and occupants thermal comfort.
    Keywords Electrical Engineering and Systems Science - Systems and Control
    Subject code 690
    Publishing date 2020-12-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Feedforward Control of DGs for a Self-healing Microgrid

    Kim, Young-Jin

    2021  

    Abstract: Network reconfiguration (NR) has recently received significant attention due to its potential to improve grid resilience by realizing self-healing microgrids (MGs). This paper proposes a new strategy for the real-time frequency regulation of a ... ...

    Abstract Network reconfiguration (NR) has recently received significant attention due to its potential to improve grid resilience by realizing self-healing microgrids (MGs). This paper proposes a new strategy for the real-time frequency regulation of a reconfigurable MG, wherein the feedforward control of synchronous and inverter-interfaced distributed generators (DGs) is achieved in coordination with the operations of sectionalizing and tie switches (SWs). This enables DGs to compensate more quickly, and preemptively, for a forthcoming variation in load demand due to NR-aided restoration. An analytical dynamic model of a reconfigurable MG is developed to analyze the MG frequency response to NR and hence determine the desired dynamics of the feedforward controllers, with the integration of feedback loops for inertial response emulation and primary and secondary frequency control. A small-signal analysis is conducted to analyze the contribution of the supplementary feedforward control to the MG frequency regulation. Simulation case studies of NR-aided load restoration are also performed. The results of the small-signal analysis and case studies confirm that the proposed strategy is effective for improving the MG frequency regulation under various conditions of load demand, model parameter errors, and communication time delays.
    Keywords Electrical Engineering and Systems Science - Systems and Control
    Subject code 600
    Publishing date 2021-10-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: Effect of silicon or cerium doping on the anti-inflammatory activity of biphasic calcium phosphate scaffolds for bone regeneration.

    Kim, Hyun-Woo / Kim, Young-Jin

    Progress in biomaterials

    2022  Volume 11, Issue 4, Page(s) 421–430

    Abstract: Biphasic calcium phosphate (BCP) bioceramics composed of hydroxyapatite and β-tricalcium phosphate have attracted considerable attention as ideal bone substitutes for reconstructive surgery, orthopedics, and dentistry, owing to their similar chemical ... ...

    Abstract Biphasic calcium phosphate (BCP) bioceramics composed of hydroxyapatite and β-tricalcium phosphate have attracted considerable attention as ideal bone substitutes for reconstructive surgery, orthopedics, and dentistry, owing to their similar chemical composition to bone mineral and biocompatibility. The addition of trace elements to BCP bioceramics, such as magnesium (Mg), cerium (Ce), and silicon (Si), can alter the physicochemical and biological properties of the resulting materials. To improve the anti-inflammatory activity of a pure BCP scaffold, this study developed a simple wet chemical precipitation and gel-casting method to fabricate microporous BCP scaffolds containing Si or Ce. The BCP scaffolds exhibited interconnected microporous structures with uniform micropores and unequiaxed grains. No changes in the phase composition and microstructure of the scaffolds with the Si or Ce doping were observed. Conversely, Si or Ce doping into the BCP crystal lattice influenced the in vitro biological activity of the scaffolds and the bone-forming ability of the cells cultured on the BCP scaffolds. The results of biological activity assays demonstrated that Ce-BCP promoted cell proliferation and osteogenic differentiation more effectively than the other scaffolds. In particular, Ce-BCP significantly suppressed the expression of bone-active cytokines via the anti-inflammatory and anti-oxidative effects. Therefore, Si- or Ce-doped BCP scaffolds can contribute to providing a new generation of bone graft substitutes.
    Language English
    Publishing date 2022-10-12
    Publishing country Germany
    Document type Journal Article
    ISSN 2194-0509
    ISSN 2194-0509
    DOI 10.1007/s40204-022-00206-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: An integrated agent-based simulation modeling framework for sustainable production of an Agrophotovoltaic system

    Kim, Youngjin / Kim, Sumin / Kim, Sojung

    Journal of Cleaner Production. 2023 Sept., v. 420 p.138307-

    2023  

    Abstract: The Agrophtovoltaic (APV) system is an alternative for sustainable crop production, where solar power is generated via Photovoltaic (PV) modules. Since both crop production and solar power generation activities are heavily dependent on dynamic ... ...

    Abstract The Agrophtovoltaic (APV) system is an alternative for sustainable crop production, where solar power is generated via Photovoltaic (PV) modules. Since both crop production and solar power generation activities are heavily dependent on dynamic environmental conditions, it is challenging to design an APV system based on accurate estimation of its performance. To this end, this study aims to introduce an agent-based simulation (ABS) framework integrated with polynomial regression and ridge regression. In particular, two agent types are devised, as follow: (1) The photovoltaic agent calculates electricity produced via PV modules, and estimates its profits; and (2) the crop production agent calculates crop harvests underneath the PV modules, and estimates their profits. To validate the proposed framework, field experiment data with five types of crops (i.e., corn, sesame, soybean, mung bean, and red bean) at the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea with three different shading ratios of 21.3%, 25.6%, and 32.0% have been used. In addition, for the sustainable operation of an APV system in terms of electricity generation as well as crop production, three climate change scenarios based on the shared socioeconomic pathway (SSP) are considered. The proposed framework identifies that the agrophotovoltaic system with 32% sharing ratio increases up to 20% of the total profit of normal farmland. As a result, the proposed framework enables the performance of an APV system under dynamic climate conditions to be accurately estimated, so that APV system designers can utilize it to identify a profitable long-term APV system.
    Keywords agricultural land ; agricultural research ; climate ; climate change ; corn ; crop production ; electricity ; electricity generation ; field experimentation ; mung beans ; power generation ; red beans ; regression analysis ; solar energy ; soybeans ; sustainable agriculture ; South Korea ; Agent-based modeling ; Agrophotovoltaic ; ALMANAC ; Renewable energy ; Simulation
    Language English
    Dates of publication 2023-09
    Publishing place Elsevier Ltd
    Document type Article ; Online
    ISSN 0959-6526
    DOI 10.1016/j.jclepro.2023.138307
    Database NAL-Catalogue (AGRICOLA)

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  8. Article ; Online: Multitask Learning-Based Deep Signal Identification for Advanced Spectrum Sensing.

    Kim, Hanjin / Kim, Young-Jin / Kim, Won-Tae

    Sensors (Basel, Switzerland)

    2023  Volume 23, Issue 24

    Abstract: The explosive demand for wireless communications has intensified the complexity of spectrum dynamics, particularly within unlicensed bands. To promote efficient spectrum utilization and minimize interference during communication, spectrum sensing needs ... ...

    Abstract The explosive demand for wireless communications has intensified the complexity of spectrum dynamics, particularly within unlicensed bands. To promote efficient spectrum utilization and minimize interference during communication, spectrum sensing needs to evolve to a stage capable of detecting multidimensional spectrum states. Signal identification, which identifies each device's signal source, is a potent method for deriving the spectrum usage characteristics of wireless devices. However, most existing signal identification methods mainly focus on signal classification or modulation classification, thus offering limited spectrum information. In this paper, we propose DSINet, a multitask learning-based deep signal identification network for advanced spectrum sensing systems. DSINet addresses the deep signal identification problem, which involves not only classifying signals but also deriving the spectrum usage characteristics of signals across various spectrum dimensions, including time, frequency, power, and code. Comparative analyses reveal that DSINet outperforms existing shallow signal identification models, with performance improvements of 3.3% for signal classification, 3.3% for hall detection, and 5.7% for modulation classification. In addition, DSINet solves four different tasks with a 65.5% smaller model size and 230% improved computational performance compared to single-task learning model sets, providing meaningful results in terms of practical use.
    Language English
    Publishing date 2023-12-13
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s23249806
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: Experimental Study on Effects of CO

    Kim, Young-Jin / Sim, Sang-Rak / Ryu, Dong-Woo

    Materials (Basel, Switzerland)

    2023  Volume 16, Issue 22

    Abstract: Human survival is threatened by the rapid climate change due to global warming caused by the increase in ... ...

    Abstract Human survival is threatened by the rapid climate change due to global warming caused by the increase in CO
    Language English
    Publishing date 2023-11-09
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2487261-1
    ISSN 1996-1944
    ISSN 1996-1944
    DOI 10.3390/ma16227107
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: The Study of an Adaptive Bread Maker Using Machine Learning.

    Lee, Jooho / Kim, Youngjin / Kim, Sangoh

    Foods (Basel, Switzerland)

    2023  Volume 12, Issue 22

    Abstract: Bread is one of the most consumed foods in the world, and modern food processing technologies using artificial intelligence are crucial in providing quality control and optimization of food products. An integrated solution of sensor data and machine ... ...

    Abstract Bread is one of the most consumed foods in the world, and modern food processing technologies using artificial intelligence are crucial in providing quality control and optimization of food products. An integrated solution of sensor data and machine learning technology was determined to be appropriate for identifying real-time changing environmental variables and various influences in the baking process. In this study, the Baking Process Prediction Model (BPPM) created by data-based machine learning showed excellent performance in monitoring and analyzing real-time sensor and vision data in the baking process to predict the baking stages by itself. It also has the advantage of improving the quality of bread. The volumes of bread made using BPPM were 127.54 ± 2.54, 413.49 ± 2.59, 679.96 ± 1.90, 875.79 ± 2.46, and 1260.70 ± 3.13, respectively, which were relatively larger than those made with fixed baking time (
    Language English
    Publishing date 2023-11-17
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2704223-6
    ISSN 2304-8158
    ISSN 2304-8158
    DOI 10.3390/foods12224160
    Database MEDical Literature Analysis and Retrieval System OnLINE

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