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Photovoltaic (PV) power can be a reasonable alternative as a carbon-free power source in a global warming environment. However, when many PV generators are interconnected in power systems, inaccurate forecasting of PV generation leads to unstable power system operation. In order to help system operators maintain a reliable power balance, even when renewable capacity increases excessively, an incentive program has been introduced in Korea. The program is expected to improve the self-forecasting accuracy of distributed generators and enhance the reliability of power system operation by using the predicted output for day-ahead power system planning. In order to maximize the economic benefit of the incentive program, the PV site should offer a strategic schedule. This paper proposes a PV generation scheduling method that considers incentives for accurate renewable energy forecasting. The proposed method adjusts the predicted PV generation to the optimal generation schedule by considering the characteristics of PV energy deviation, energy storage system (ESS) operation, and PV curtailment. It then maximizes incentives by mitigating energy deviations using ESS and PV curtailment in real-time conditions. The PV scheduling problem is formulated as a stochastic mixed-integer linear programming (MILP) problem, considering energy deviation and daily revenue under expected PV operation scenarios. The numerical simulation results are presented to demonstrate the economic impact of the proposed method. The proposed method contributes to mitigating daily energy deviations and enhancing daily revenue. [ABSTRACT FROM AUTHOR]

It is very difficult for us to accurately predict occurrence of a fire. But, this is very important to protect human life and property. So, we study fire hazard prediction and evaluation methods to cope with fire risks. In this paper, we propose three models based on statistical machine learning and optimized risk indexing for fire risk assessment. We build logistic regression, deep neural networks (DNN) and fire risk indexing models, and verify performances between proposed and traditional models using real investigated data related to fire occurrence in Korea. In general, fire prediction models currently in use do not provide satisfactory levels of accuracy. The reason for this result is that the factors affecting fire occurrence are very diverse and frequency of fire occurrence is very sparse. To improve accuracy of fire occurrence, we first build logistic regression and DNN models. In addition, we construct a fire risk indexing model for a more improved model of fire prediction. To illustrate comparison results between our research models and current fire prediction model, we use real fire data investigated in Korea between 2011 to 2017. From the experimental results of this paper, we can confirm that accuracy of prediction by the proposed method is superior to the existing fire occurrence prediction model. Therefore, we expect the proposed model to contribute to evaluating the possibility of fire risk in buildings and factories in the field of fire insurance and to calculate the fire insurance premium. [ABSTRACT FROM AUTHOR]

According to the climate change scenario, climate change in the Korean Peninsula is expected to worsen due to extreme temperatures, with effects such as rising average temperatures, heat waves, and droughts. In Republic of Korea, which relies on foreign countries for the supply of forage crops, a decrease in the productivity of forage crops is expected to cause increased damage to the domestic livestock industry. In this paper, to solve the issue of climate vulnerability for forage crops, we performed a study to predict the productivity of forage crops in relation to climate change. We surveyed and compiled not only forage crop production data from various regions, but also experimental cultivation production data over several years from reports of the Korea Institute of Animal Science and Technology. Then, we crawled related climate data from the Korea Meteorological Administration. Therefore, we were able to construct a basic database for forage crop production data and related climate data. Using the database, a production prediction model was implemented, applying a multivariate regression analysis and deep learning regression. The key factors were determined as a result of analyzing the changes in forage crop production due to climate change. Using the prediction model, it could be possible to forecast the shifting locations of suitable cultivation areas. As a result of our study, we were able to construct electromagnetic climate maps for forage crops in Republic of Korea. It can be used to present region-specific agricultural insights and guidelines for cultivation technology for forage crops against climate change. [ABSTRACT FROM AUTHOR]

Digital music is one of the most important commodities on the market due to music royalty distribution in Korea. As the music market has been transformed into a digital music market by means such as downloading and streaming, the distribution of music royalties via online service providers (OSPs) has become a highly important issue for music rights holders. Currently, one of the most important issues in music royalty distribution in Korea is the unfair distribution of royalties due to the indiscriminate repeat streaming of digital music. To prevent this, music consumption log data from several OSPs were collected via a day-based system; however, there was a limit on the identification of detailed information on the use of music in its current state. This paper analyzes the structural problems and limitations related to the settlement of music royalties and provides a structure in which there can be transparent settlement and distribution between users and rights holders as an institutional measure. We also propose various AI (artificial intelligence)-based applications using music consumption log data. The proposed system will hopefully be used for public purposes. [ABSTRACT FROM AUTHOR]

IoT technology is used in various industries, including the manufacturing, energy, finance, education, transportation, smart home, and medical fields. In the medical field, IoT applications can provide high-quality medical services through the efficient management of patients and mobile assets in hospitals. In this paper, we introduce an IoT system to the medical field using Sigfox, a low-power communication network for indoor location monitoring used as a hospital network. A proof-of-concept (PoC) was implemented to evaluate the effectiveness of medical device and patient safety management. Specific requirements should be considered when applying the IoMT system in a hospital environment. In this study, the location and temperature of various targets sending signals to the monitoring system using three different networks (Sigfox, Hospital and Non-Hospital) were collected and compared with true data, the average accuracy of which were 69.2%, 72.5%, and 83.3%, respectively. This paper shows the significance in the application of an IoMT using the Sigfox network in a hospital setting in Korea compared with existing hospital networks. [ABSTRACT FROM AUTHOR]

Despite their importance, relatively little attention has been paid to vehicle routing problems with asymmetric costs (ACVRPs), or their benchmark instances. Taking advantage of recent advances in map application programming interfaces (APIs) and shared spatial data, this paper proposes new realistic sets of ACVRP benchmark instances. The spatial data of urban distribution centers, postal hubs, large shopping malls, residential complexes, restaurant businesses and convenience stores are used. To create distance and time matrices, the T map API, one of the most frequently used real time path analysis and distance measurement tools in Korea, is used. This paper also analyzes some important issues prevailing in urban transportation environments. These include the challenges of accounting for the frequency and distance in which air travel differs from reality when measuring closeness, the differences in distance and time for outgoing and return trips, and the rough conversion ratios from air distance to road distance and to road time. This paper contributes to the research community by providing more realistic ACVRP benchmark instances that reflect urban transportation environments. In addition, the cost matrix analyses provide insights into the behaviors of urban road networks. [ABSTRACT FROM AUTHOR]

The ratio of students with a multicultural background relative to the total number of students in South Korea is consistently going up due to the increasing number of multicultural students and decreasing school age population. Yet, the low level of digitalization in multicultural households requires an effort to address digital divide. This paper, accordingly, held an SW (software) and AI (artificial intelligence) camp on four occasions to 314 multicultural students living in Jeju and observed how the perception of participating students changed on SW and AI. The education camp was organized after analyzing the limitations of existing multicultural education and computer education as well as their issues. To validate effects of the education, a paired sample t-test before and after education and an independent sample t-test were carried out to make an analysis by education period and analyze education effect by background variables. Furthermore, text network analysis on short answers was made for an in-depth analysis of research results. It shows the research participants' awareness of SW · AI changing for the positive post-camp in most sub-elements. However, self-efficacy of jobs related to SW · AI, which was one of the sub-elements, was lower post-camp than in pre-camp in a few cases. Since the average score of this particular element is noticeably lower than other average scores and research participants were not evenly distributed by grade, further improvement is warranted in follow-up research. [ABSTRACT FROM AUTHOR]

Semiconductor and display industries in the Republic of Korea make up the global electronics market with some of the greatest potential for growth due to accelerated digital transformation. Greenhouse gases (GHGs) present in the Earth's atmosphere could trap heat and contribute to the greenhouse effect, leading to global warming and climate change, and it is important to note that while GHGs are naturally present in the atmosphere and play a crucial role in regulating the Earth's temperature, human activities have significantly increased their concentration, leading to accelerated global warming and climate change. Volatile fluorinated compounds (FCs), including perfluorocompounds (PFCs), hydrofluorocompounds (HFCs), NF3, and SF6, are potent long-standing greenhouse gases that are used and emitted by electronics during the manufacturing and display stages of semiconductors. In accordance with global climate change, GHG reduction has developed as a demand of the times, and the electronics industry has also made efforts to reduce GHG emissions in response. Until now, process emissions from the use of fluorinated greenhouse gases (F-GHGs) in various industries have been calculated according to the '06 IPCC G/L, and emission factors of '06 IPCC G/L have also been applied. However, the reduction and emission factors proposed in the IPCC G/L are values that do not reflect the latest and advanced reduction technologies in South Korean electronics, and national GHG emissions are overestimated. In this paper, by preparing accurate measurement methods for destruction removal efficiency (DRE), the use rate of gas (Ui), and b-product emission factors (Bby-product, i), which are characteristic parameters for estimating GHG Tier 3a emissions, we aim to increase the accuracy of GHG emissions by advancing emission factors that are unique to the semiconductor and display industries within the Republic of Korea. [ABSTRACT FROM AUTHOR]

Despite various economic crisis situations around the world, the courier and delivery service market continues to be revitalized. The parcel shipping volume in Korea is currently 3.37 billion parcels, achieving a growth rate of about 140% compared to 2012, and 70% of parcels are from metropolitan areas. Given the above statistics, this paper focused on the development of an underground logistics system (ULS), in order to conduct a study to handle the freight volume in a more eco-friendly manner in the center of metropolitan areas. In this paper we first analyzed the points at which parcel boxes were damaged, based on a ULS. After collecting image data of the parcel boxes, the damaged parcel boxes were detected and classified using computerized methods, in particular, a convolutional neural network (CNN), MobileNet. For image classification, Google Colaboratory notebook was used and 4882 images were collected for the experiment. Based on the collected dataset, when conducting the experiment, the accuracy, recall, and specificity of classification for the testing set were 84.6%, 82% and 88.54%, respectively,. To validate the usefulness of the MobileNet algorithm, additional experiments were performed under the same conditions using other algorithms, VGG16 and ResNet50. The results show that MobileNet is superior to other image classification models when comparing test time. Thus, in the future, MobileNet has the potential to be used for identifying damaged boxes, and could be used to ensure the reliability and safety of parcel boxes based on a ULS. [ABSTRACT FROM AUTHOR]

Featured Application: The multilayer classification algorithm proposed in this paper can be applied to active sonar systems in order to detect long-range targets. The task of detecting and classifying highly maneuverable and unidentified underwater targets in complex environments is significant in active sonar systems. Previous studies have applied many detection schemes to this task using signals above a preset threshold to separate targets from clutter; this is because a high signal-to-noise ratio (SNR) target has sufficient feature vector components to be separated out. However, in real environments, the received target return's SNR is not always above the threshold. Therefore, a target detection algorithm is needed for varied target SNR conditions. When the clutter energy is too strong, false detection can occur, and the probability of detection is reduced due to the weak target signature. Furthermore, since a long pulse repetition interval is used for long-range detection and ambient noise tends to be high, classification processing for each ping is needed. This paper proposes a multilayer classification algorithm applicable to all signals in real underwater environments above the noise level without thresholding and verifies the algorithm's classification performance. We obtained a variety of experimental data by using a real underwater target and a hull-mounted active sonar system operated on Korean naval ships in the East Sea, Korea. The detection performance of the proposed algorithm was evaluated in terms of the classification rate and false alarm rate as a function of the SNR. Since experimental environment data, including the sea state, target maneuvering patterns, and sound speed, were available, we selected 1123 instances of ping data from the target over all experiments and randomly selected 1000 clutters based on the distribution of clutters for each ping. A support vector machine was employed as the classifier, and 80% of the data were selected for training, leaving the remaining data for testing. This process was carried out 1000 times. For the performance analysis and discussions, samples of scatter diagrams and feature characteristics are shown and classification tables and receiver operation characteristic (ROC) curves are presented. The results show that the proposed algorithm is effective under a variety of target strengths and ambient noise levels. [ABSTRACT FROM AUTHOR]

This study focused on the in-plane rocking behavior of unreinforced masonry (URM) walls. Three URM wall specimens were designed and fabricated based on a typical masonry house in Korea. The experimental parameters were the layout of openings (its presence or absence) and configuration of openings (window or door). Static cyclic loading tests were conducted to investigate nonlinear performance curves of masonry walls subjected to a rocking behavior in the in-plane direction. In this paper, the mortar-joint tensile crack strength and rocking strength of masonry walls (i.e., peak and residual strengths) were evaluated, and the effects of opening configurations on the masonry wall strength were examined, due to the proposed procedure. The deformation capacity of a rocking behavior was also identified by the procedure. As a result, specimens without initial cracks showed the rocking behavior after mortar-joint tensile crack failure, whereas a specimen with initial cracks exhibited only the rocking behavior. Since no remarkable strength deterioration was found until final loading in all specimens, an in-plane rocking URM wall may have very good deformation performance. The estimated mortar-joint tensile crack strength, rocking strength, stiffness, and ultimate deformation were in good agreement with the experimental results, regardless of the layout and configuration of openings. [ABSTRACT FROM AUTHOR]

Increases in algal growth have been reported in rivers, reservoirs, and other water resources worldwide, including Korea. Algal overgrowth can result in algal bloom, which has several negative impacts, such as ecosystem degradation and economic losses. Mitigation measures employed in Korea include an algal warning system and survey-based water quality forecast systems. However, these methods are time-consuming and require sample collection from the site. On the other hand, remote sensing techniques that use chlorophyll a are unable to distinguish between different types of algal species. In this paper, we aimed to identify a classification technique based on remote sensing methods that can be used to distinguish between blue-green algae and green algae. We acquired and prepared an algal culture solution and used a hyperspectral sensor to obtain an algae spectrum. Thereafter, we measured the absorption and emission spectra of blue-green and green algae and distinguished them using the instantaneous slope change of the spectrum. The absorption spectra for green algae showed two peaks at 417–437 nm and 661–673 nm, whereas those of blue-green algae showed three peaks at 449–529 nm, 433–437 nm, and 669–677 nm. The results of this study could form a basis for developing mitigation measures for algal overgrowth. [ABSTRACT FROM AUTHOR]

An inrush current is generated when a transformer is energized. This current has a large magnitude and rich harmonics, thereby causing mal-operation of the protection relay. Therefore, the development of countermeasures against inrush current is necessary, and this study has been performed by computer simulations. However, it is difficult for a power system operator to perform a computer simulation as it is difficult to determine what data should be selected and entered. Therefore, this paper establishes the simulation process of transformer inrush current using the Electromagnetic Transients Program (EMTP). Two methods to simulate the transformer inrush current are described in detail. Based on the actual 154 kV transformer test report in Korea, the simulation results of the inrush current using the two methods are discussed. [ABSTRACT FROM AUTHOR]

The trash disposal system, using standard trash bags, has been adopted by the government of the Republic of Korea (ROK) for more than two decades. This has caused a sanitary problem, as well as some secondary pollution. It is possible to solve this problem by deploying more manpower, but considering the manpower and maintenance costs that impose a heavy burden on the local governments who are experiencing tight financial situations, it would not be feasible. Thus, an Internet of Things (IoT)-based Smart Trash Separation Bin model that can reduce the cost of trash separation work has been proposed in this paper. The three efficient designs that respectively use a sensor, image processing, or spectroscope technology are presented. These IoT-based designs can bring significant merit to reducing the manpower costs, as well as the administrative cost involved. [ABSTRACT FROM AUTHOR]

This study uses the API of Upbit, one of Korea's cryptocurrency exchanges, to predict continuous time series for a limited period and cryptocurrencies using LSTM, a machine learning technique. The trading (buying and selling) point algorithm presented in this study was used to conduct experimental research on efficient profit creation for cryptocurrency investment. Several related studies have shown the results of time series prediction for long-term forecasts, such as a week or several months. Still, they have not attempted to make an ultra-short-term prediction in units of one minute. This paper attempts such a 1 min prediction. This is an experiment to create efficient profits by setting efficient trading (buying and selling) points using machine learning techniques and repeating these operations by an algorithm. Applying it to cryptocurrency shows the possibility of time series prediction. [ABSTRACT FROM AUTHOR]

This paper presents a system, namely, the abnormal-weather monitoring and curation service (AWMC), which provides people with a better understanding of abnormal weather conditions. The service can analyze a set of multivariate weather datasets (i.e., 7 meteorological datasets from 18 cities in Korea) and show (i) which dates are mostly abnormal in a certain city, and (ii) which cities are mostly abnormal on a certain date. In particular, the dynamic graph-embedding-based anomaly detection method was employed to measure anomaly scores. We implemented the service and conducted evaluations. Regarding the results of monitoring abnormal weather, AWMC shows that the average precision was approximately 90.9%, recall was 93.2%, and F1 score was 92.1% for all the cities. [ABSTRACT FROM AUTHOR]

Since the mid-2000 s, longitudinal tinning has been applied to concrete pavements of expressways to improve the comfort and safety of road vehicle users. However, in certain longitudinal tinning sections, lateral vibrations occur during driving, which poses a safety hazard. This paper analyzes the cause behind this problem and proposes a longitudinal texturing specification that can minimize lateral vibration. To perform this analysis, the accelerations of driving vehicles and the degree of lateral vibration transmitted to panels in the vehicles were evaluated for each longitudinal texture applied in South Korea. Structural analysis was also conducted for the lateral force and moment according to the tire tread profile and longitudinal texturing specifications using the finite element method. In addition, field investigation, indoor drivability evaluation, and field application were performed to evaluate the optimal longitudinal texturing specification. The results indicate that the texture designated as 2 × 3 × 19 mm is the optimal longitudinal texture on hardened concrete pavement. However, because this specification is difficult to apply to fresh concrete, 3 × 3 × 16 mm is proposed as the optimal longitudinal texture owing to its excellent performance in the panel survey and in structural analysis. [ABSTRACT FROM AUTHOR]

A new line-of-sight (LOS) decision algorithm applicable to simulation of electronic warfare (EW) is developed. For accurate simulation, the digital terrain elevation data (DTED) of the region to be analyzed must be reflected in the simulation, and millions of datasets are necessary in the EW environment. In order to obtain real-time results in such an environment, a technology that determines line-of-sight (LOS) quickly and accurately is very important. In this paper, a novel algorithm is introduced for determining LOS that can be applied in an EW environment with three-dimensional (3D) DTED. The proposed method shows superior performance as compared with the simplest point-to-point distance calculation method and it is also 50% faster than the conventional interpolation method. The DTED used in this paper is the data applied as level 0 for the Republic of Korea, and the decision of the LOS at approximately 1.8 million locations viewed by a reconnaissance plane flying 10 km above the ground is determined within 0.026 s. [ABSTRACT FROM AUTHOR]

Featured Application: The underwater target classification algorithm proposed in this paper can be applied to an active sonar system to detect long-range targets. Detection and classification of unidentified underwater targets maneuvering in complex underwater environments are critical for active sonar systems. In previous studies, many detection methods were applied to separate targets from the clutter using signals that exceed a preset threshold determined by the sonar console operator. This is because the high signal-to-noise ratio target has enough feature vector components to separate. However, in a real environment, the signal-to-noise ratio of the received target does not always exceed the threshold. Therefore, a target detection algorithm for various target signal-to-noise ratio environments is required; strong clutter energy can lead to false detection, while weak target signals reduce the probability of detection. It also uses long pulse repetition intervals for long-range detection and high ambient noise, requiring classification processing for each ping without accumulating pings. In this study, a target classification algorithm is proposed that can be applied to signals in real underwater environments above the noise level without a threshold set by the sonar console operator, and the classification performance of the algorithm is verified. The active sonar for long-range target detection has low-resolution data; thus, feature vector extraction algorithms are required. Feature vectors are extracted from the experimental data using Power-Normalized Cepstral Coefficients for target classification. Feature vectors are also extracted with Mel-Frequency Cepstral Coefficients and compared with the proposed algorithm. A convolutional neural network was employed as the classifier. In addition, the proposed algorithm is to be compared with the result of target classification using a spectrogram and convolutional neural network. Experimental data were obtained using a hull-mounted active sonar system operating on a Korean naval ship in the East Sea of South Korea and a real maneuvering underwater target. From the experimental data with 29 pings, we extracted 361 target and 3351 clutter data. It is difficult to collect real underwater target data from the real sea environment. Therefore, the number of target data was increased using the data augmentation technique. Eighty percent of the data was used for training and the rest was used for testing. Accuracy value curves and classification rate tables are presented for performance analysis and discussion. Results showed that the proposed algorithm has a higher classification rate than Mel-Frequency Cepstral Coefficients without affecting the target classification by the signal level. Additionally, the obtained results showed that target classification is possible within one ping data without any ping accumulation. [ABSTRACT FROM AUTHOR]

This study is to obtain the equivalent (effective) mechanical properties of corrugated paperboard specifically that is widely applied in Korea for packaging of agricultural products. To analyze the equivalent properties of corrugated paperboard, finite element modeling, measurement of the reaction force, and superposition theory for the unit cell were applied. The stress-strain behavior obtained by applying the calculated equivalent mechanical properties to the simplified model of the corrugated paperboard is compared with experimental results. Nine equivalent mechanical properties governing the orthotropic behavior of corrugated paperboard were analyzed through finite element analysis (FEA). The stress-strain behavior of the corrugated paperboard experimentally showed elastic-plastic behavior, although the equivalent mechanical properties applied to the simplified model were elastic properties based on the theoretical approach, so that the finite element analysis results showed linearity. Therefore, when applying the calculated equivalent mechanical properties through FEA, the characteristics of the section where the strain only increases without the increase in the load due to the flute should be taken into consideration. [ABSTRACT FROM AUTHOR]

This paper introduces a large-scale spontaneous speech corpus of Korean, named KsponSpeech. This corpus contains 969 h of general open-domain dialog utterances, spoken by about 2000 native Korean speakers in a clean environment. All data were constructed by recording the dialogue of two people freely conversing on a variety of topics and manually transcribing the utterances. The transcription provides a dual transcription consisting of orthography and pronunciation, and disfluency tags for spontaneity of speech, such as filler words, repeated words, and word fragments. This paper also presents the baseline performance of an end-to-end speech recognition model trained with KsponSpeech. In addition, we investigated the performance of standard end-to-end architectures and the number of sub-word units suitable for Korean. We investigated issues that should be considered in spontaneous speech recognition in Korean. KsponSpeech is publicly available on an open data hub site of the Korea government. [ABSTRACT FROM AUTHOR]

Metaverse is a compound word of "Meta" and "Universe", meaning a world that transcends reality, a new virtual world. Due to the COVID-19 pandemic, non-face-to-face further accelerated the activation of the Metaverse. The Metaverse has the attractiveness of arousing user interest, high service scalability, and the potential to combine multiple revenue models. Therefore, many industries are adopting the Metaverse. The Republic of Korea has been on a ceasefire, since 1953, after the Korean War. After the war, the DMZ was decided in an area of 2 km from north to south (total of 4 km), centered on the Military Demarcation Line (MDL). The DMZ area is the largest nature conservation area in Asia and a military area where public access is strictly restricted. The Ministry of Unification is trying to reduce tensions and establish peace in the DMZ area by raising the interest of Korean people, as well as people around the world, through the DMZ Metaverse application. The Korean government has its own e-Government framework and cloud operating environments. In order to adopt the Metaverse to Korean government agencies and operate them sustainably, it is essential to support the e-Government framework and the cloud environments. In this paper, the design features, architecture, models, and functions of OnTwins, a Metaverse platform serviceable in the Korean government IT operating environment, are elaborated in detail. The DMZ Metaverse public service built on the platform will also be introduced. Finally, after comparing it with other Metaverse platforms, future research directions are discussed. [ABSTRACT FROM AUTHOR]

Featured Application: Our research not only provides a method for estimating evapotranspiration, but also provides the possibility for additional remote sensing models to appear on a "minute" or even "second" time scale. Using remote sensing to estimate evapotranspiration minute frequency is the basis for accurately calculating hourly and daily evapotranspiration from the regional scale. However, from the existing research, it is difficult to use remote sensing data to estimate evapotranspiration minute frequency. This paper uses GF-4 and moderate-resolution imaging spectroradiometer (MODIS) data in conjunction with the Surface Energy Balance Algorithm for Land (SEBAL) model to estimate ET at a 3-min time interval in part of China and South Korea, and compares those simulation results with that from field measured data. According to the spatial distribution of ET derived from GF-4 and MODIS, the texture of ET derived from GF-4 is more obvious than that of MODIS, and GF-4 is able to express the variability of the spatial distribution of ET. Meanwhile, according to the value of ET derived from both GF-4 and MODIS, results from these two satellites have significant linear correlation, and ET derived from GF-4 is higher than that from MODIS. Since the temporal resolution of GF-4 is 3 min, the land surface ET at a 3-min time interval could be obtained by utilizing all available meteorological and remote sensing data, which avoids error associated with extrapolating instantaneously from a single image. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) has emerged as one of the key trends in the architecture, engineering, and construction (AEC) industry. As interest in BIM rapidly increased, the quantity of related literature also increased, thus, it has become important to analyze and identify the key topics and trends in BIM research over this period. Therefore, in order to analyze the research and technology trends related to BIM in Korea, we used the text-mining technique. In addition, by analyzing BIM-related papers using text-mining technology, we can analyze the patterns, main research trends, and trends in specific fields through data preprocessing, which formalizes the unstructured data of sentences, thus presenting us with a specific strategic plan for the future direction of research and technology related to BIM in Korea. In order to propose a strategic direction for future BIM-related research and technology in Korea, in this study, many researches related to BIM in Korea were collected and changes in the patterns and research trends of the BIM research periods were analyzed by dividing them it into the periods of introduction, development, and adaptation using text-mining methods and techniques, frequency analysis, and topic modeling methods. Therefore, in the future, BIM will be developed not only in the field of architecture but in all fields related to the construction industries, such as civil engineering, aviation, and shipping, and more active research will be conducted in various fields. [ABSTRACT FROM AUTHOR]

A data platform collecting the whole metadata held by government agencies and a knowledge graph showing the relationship between the collected open-government data are proposed in this paper. By practically applying the data platform and the knowledge graph to the public sector in Korea, three improvements were expected: (1) enhancing user accessibility across open-government data; (2) allowing users to acquire relevant data as well as desired data with a single query; and (3) enabling data-driven decision-making. In particular, the barriers for citizens to acquire the necessary data have been greatly reduced by using the proposed knowledge graph, which is considered to be important for data-driven decision-making. The reliability and feasibility of constructing a metadata-based open-data platform and a knowledge graph are estimated to be considerably high as the proposed approach is applied to a real service of the public sector in Korea. [ABSTRACT FROM AUTHOR]

In South Korea, Honam High-Speed Railway has a relatively large residual settlement issue and high fines content has been pointed out as one of the causes. Design guidelines regulate not to use soils containing fines content higher than 25%. However, there is no background information on the effect of fines content on settlement. Therefore, this paper aims to investigate compressional behavior according to fines content using sand and kaolinite. Oedometer test results showed that the compression index is lowest with fines content of 15% to 20% at which the mixture produced maximum density. The optimum fines content for inducing low settlement would be 15% to 20% for the sand–kaolinite mixture. Transition fines content (TFC), which shows sand-like to claylike behavior, was observed to have between 21% and 26% of fines content. Critical fines content (fcrit) where a minimum void ratio occurs was estimated as 21.67%. These behavioral changes appear when fines content is greater than the optimum fines content. SEM also shows that the kaolinite particles were overlapped, creating flat surfaces with a fines content higher than 30%, and showing clay-like behavior. Based on the analysis results, engineers can simply identify the behavior of embankment materials to ensure optimum fines content and consequently minimize long-term settlement potential. [ABSTRACT FROM AUTHOR]

India has a longstanding reputation in the film industry, whereas South Korean films have only recently achieved notable success globally. Despite their significant positions in the global film market, there are very few studies that compare and analyze the competitive advantage of the two countries in the film industry. This paper adopts the ABCD model as a complementary framework to the two mainstream theories of strategic management (i.e., industry-based view and resource-based view) to analyze and compare the competitiveness of the industrial success of emerging countries. For the empirical test, this paper uses natural language processing methods to operationalize the theoretical model. After collecting text data from news articles in English related to the Korean and Indian film industries, this study analyzes how many keywords with regards to the 8 sub-factors of the ABCD model are mentioned in the articles using the document similarity measurement. The results reveal the different but complementary areas of strengths. India has higher competitiveness in the factor of Agility while Korea has higher competitiveness in Convergence. This study also highlights the areas for further development and potential partnership between the two countries by leveraging each other's strengths. [ABSTRACT FROM AUTHOR]

Airport development and operation has a significant impact on local economy. However, the impact is assessed differently from different perspectives. In this paper, the authors investigated how the public think on the benefit of airport development and operation from a regional perspective. More specifically, the paper focuses on what the main factors are in public perceptions on the regional benefit of airport development and operation. The authors analyzed a survey collected in South Korea using structural equation models. The authors assigned five categories of perception factors and examined these factor relationships regarding the ripple effects caused by airport development and operation on each region. The five factors were "increased use in air transportation", "increased exchange with other regions", "regional economic benefit", "regional development", and "dis-benefit to the region". The model was verified with structural equations and path analysis results for each factor. It was revealed that people perceive the increased use in air transportation and the increased exchange with other regions as bringing about the most significant ripple effects, followed by regional development and regional economic benefit in this order, according to the order of path groups. The results of this paper are expected to provide stakeholders' perspectives and insights when planning airport development and operation. [ABSTRACT FROM AUTHOR]

Seaweeds or marine macroalgae are known for producing potentially bioactive substances that exhibit a wide range of nutritional, therapeutic, and nutraceutical properties. These compounds can be applied to treat chronic diseases, such as cancer, cardiovascular disease, osteoporosis, neurodegenerative diseases, and diabetes mellitus. Several studies have shown that consumption of seaweeds in Asian countries, such as Japan and Korea, has been correlated with a lower incidence of chronic diseases. In this study, we conducted a review of published papers on seaweed consumption and chronic diseases. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method for this study. We identified and screened research articles published between 2000 and 2021. We used PubMed and ScienceDirect databases and identified 107 articles. This systematic review discusses the potential use of bioactive compounds of seaweed to treat chronic diseases and identifies gaps where further research in this field is needed. In this review, the therapeutic and nutraceutical properties of seaweed for the treatment of chronic diseases such as neurodegenerative diseases, obesity, diabetes, cancer, liver disease, cardiovascular disease, osteoporosis, and arthritis were discussed. We concluded that further study on the identification of bioactive compounds of seaweed, and further study at a clinical level, are needed. [ABSTRACT FROM AUTHOR]

Ginseng is an important medicinal plant in Korea. The roots of the ginseng plant have medicinal properties; thus, it is very important to maintain the quality of ginseng roots. Root rot disease is a major disease that affects the quality of ginseng roots. It is important to predict this disease before it causes severe damage to the plants. Hence, there is a need for a non-destructive method to identify root rot disease in ginseng plants. In this paper, a method to identify the root rot disease by analyzing the RGB plant images using image processing and deep learning is proposed. Initially, plant segmentation is performed, and then the noise regions are removed in the plant images. These images are given as input to the proposed linear deep learning model to identify root rot disease in ginseng plants. Transfer learning models are also applied to these images. The performance of the proposed method is promising in identifying root rot disease. [ABSTRACT FROM AUTHOR]

The 3D point cloud reconstruction from photos taken by an unmanned aerial vehicle (UAV) is a promising tool for monitoring and managing risks of cut-slopes. However, surface changes on cut-slopes are likely to be hidden by seasonal vegetation variations on the cut-slopes. This paper proposes a vegetation removal method for 3D reconstructed point clouds using (1) a 2D image segmentation deep learning model and (2) projection matrices available from photogrammetry. For a given point cloud, each 3D point of it is reprojected into the image coordinates by the projection matrices to determine if it belongs to vegetation or not using the 2D image segmentation model. The 3D points belonging to vegetation in the 2D images are deleted from the point cloud. The effort to build a 2D image segmentation model was significantly reduced by using U-Net with the dataset prepared by the colour index method complemented by manual trimming. The proposed method was applied to a cut-slope in Doam Dam in South Korea, and showed that vegetation from the two point clouds of the cut-slope at winter and summer was removed successfully. The M3C2 distance between the two vegetation-removed point clouds showed a feasibility of the proposed method as a tool to reveal actual change of cut-slopes without the effect of vegetation. [ABSTRACT FROM AUTHOR]

With the rapid expansion of renewable energy and digital devices, there is a need for direct current (DC) distribution technology that can increase energy efficiency. As a result, DC distribution research is actively underway to cope with the sudden digitization and decentralization of load environment and power supply. To verify the possibility of DC distribution, Korea Electric Power Corporation (KEPCO) Research Institute made a DC distribution system connected with a real power system in Gwangju. The construction of the demonstration area mainly includes design of protection and grounding systems, operating procedures of insulation monitoring device (IMD), and construction of power converters. Furthermore, this paper goes beyond the simulation and the lab testing to apply DC distribution to a real system operation in advance. It is designed as a long-distance low-loaded customer for rural areas and operated by the DC distribution. In addition, safety and reliability are confirmed through field tests of DC distribution elements such as power conversion devices, protection and grounding systems. In particular, to improve the reliability of non-grounding system, the insulation monitoring device was installed and the algorithms of its operational procedures are proposed. Finally, this paper analyzes the problems caused by operating the actual DC distribution and suggests solutions accordingly. [ABSTRACT FROM AUTHOR]

In Korea, there is a rule for Renewable Energy Certification with weighting 5.0, to expand grid linkage capacity and to improve the stability of the grid to accommodate photovoltaic (PV) systems in a distributed power system. Due to this rule, many power companies and operators are trying to install electrical energy storage systems that are able to operate in conjunction with PV system power. These systems operate in parallel at the same grid connection point. This paper presents the results of case studies on the failure to detect islanding operation. Test evaluation devices that could be bi-directionally charged and discharged were implemented for an islanding detection test. Testing was conducted under a variety of operating conditions. When a single inverter was operated under the islanding condition, it was stably stopped within 0.5 s using the Korean grid-code standard. However, when two inverters were operated at the same time under the islanding condition, islanding detection failed and the two inverters continued to feed the connected RLC (resistor, inductor, capacitor) loads in the isolated section known as an island. Different algorithms used by PCS (power conversion system) manufacturers to detect islanding might cause this phenomenon. Therefore, it is necessary for a new PCS test standard to detect islanding. [ABSTRACT FROM AUTHOR]

Integrated circuit (IC) chip cards are commonly used in payment system applications since they can provide security and convenience simultaneously. More precisely, Europay, MasterCard, and VISA (EMV) are widely known to be well equipped with security frameworks that can defend against malicious attacks. On the other hand, there are other payment system applications at the national level. In the case of the Republic of Korea, standards for financial IC card specifications are established by the Korea Financial Telecommunications and Clearings Institute. Furthermore, security features defending against timing analysis, power analysis, electromagnetic analysis, and TEMPEST are required. This paper identifies side channel leakages in the financial IC cards of the Republic of Korea, although there may be side channel countermeasures. Side channel leakages in the financial IC cards of the Republic of Korea are identified for the first time since the side channel countermeasures were included in the standards. The countermeasure that is applied to the IC card from a black box perspective is estimated to measure security features against power analysis. Then, in order to investigate whether an underlying countermeasure is applied, first-order and second-order power analyses are performed on the main target, e.g., a S-box of the block cipher SEED that is employed in the financial system. Furthermore, the latest proposal in ICISC 2017 is examined to apply block cipher SEED to the financial IC card protocol. As a result, it is possible to identify some side channel leakages while expanding the lemma of the paper accepted in ICISC 2017. Algebraic logic is also constructed to recover the master key from some round keys. Finally, it is found that only 20,000 traces are required to find the master key. [ABSTRACT FROM AUTHOR]

In recent years, the governments in many countries have recognized the importance of data in boosting their economies. As a result, they are implementing the philosophy of open government data (OGD) to make public data easily and freely available to everyone in standardized formats. Because good quality OGD can boost a country's economy, whereas poor quality can jeopardize its efficient use and reuse, it is very important to maintain the quality of data stored in open government data portals (OGDP). However, most OGDPs do not have a feature that indicates the quality of the data stored there, and even if they do, they do not provide real-time service. Moreover, most recent studies focused on developing approaches to quantify the quality of OGD, either qualitatively or quantitatively, but did not offer an approach to automatically calculate and visualize it in real-time. To address this problem to some extent, this paper proposes a framework that can automatically assess the quality of data in the form of a data completeness ratio (DCR) and visualize it in real-time. The framework is validated using the OGD of South Korea, whose DCR is displayed in real-time using the Django-based dashboard. [ABSTRACT FROM AUTHOR]

Depression disorder is a disease that causes a deterioration of daily function and can induce thoughts of suicide. The Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5), which is the official reference of the American Psychiatry Association and is also used in Korea to identify depressive disorders, sets nine criteria for diagnosing depressive disorders. The lack of counseling personnel, including psychiatrists, and negative social perceptions of depressive disorders prevent counselors from being treated for depressive disorders. Natural language processing-based artificial intelligence (AI) services such as chatbots can help fill this need, but labeled datasets are needed to train AI services. In this study we collected data from AI Hub wellness consultations and crawls of the Reddit website to augment and build word dictionaries and analyze morphemes using the Kind Korean Morpheme Analyzer and Word2Vec. The collected datasets were labeled based on word dictionaries built according to nine DSM-5 depressive disorder diagnostic criteria. [ABSTRACT FROM AUTHOR]

The Republic of Korea is one of the largest consumers and a leading exporter of electronics, medical appliances, and heavy and light vehicles. Rare-earth (RE)-based magnets are indispensable for these technologies, and Korea is totally dependent on imports of compounds or composites of REEs, as the country lacks natural resources. Effect on rare earth supply chain significantly affects Korea's transition towards a green economy. This study investigates the Republic of Korea's approach to developing a secure rare earth supply chain for REE magnets via a recycling and materialization process known as ReMaT. It investigates the progress Korea has made so far regarding ReMaT from both technical and non-technical perspectives. Rare earth elements are successfully recycled as part of this process while experiments at the industrial scale is carried out. In this paper, the research results in terms of the extraction efficiency of rare earth elements are discussed and a comparison with previous relevant studies is provided. This study also highlights the opportunities and challenges regarding the implementation of the ReMaT process in order to create a downstream rare earth value chain based on circular economy principles. [ABSTRACT FROM AUTHOR]

Featured Application: Energy saving hull-form design for a fishing vessel. In response to the increasing number of free trade agreements (FTA) and the Trans-Pacific Partnership (TTP), it is necessary to develop next-generation fishing vessels for the littoral sea of the Republic of Korea. The main objectives of such fishing vessels are to enhance the comfort of the crew and to improve fuel efficiency under the newly established principal dimension. This paper employs the hull form variation method to obtain a series of forebody hull forms of the 19 gross tonnage class of a coastal composite fishing vessel. Chine breadth modification has been used for hull form variations. The objective function is the minimum value of wavemaking resistance. A parametric study was conducted to observe the effects of design parameters on the objective functions, and hull-form optimization was performed. In addition to the hull-form optimization, the effect of the stern flap was investigated in terms of both resistance and operational performances. [ABSTRACT FROM AUTHOR]

In this paper we demonstrate the threshold effects of infectious diseases on livestock prices. Daily retail prices of pork and chicken were used as structured data; news and SNS mentions of African Swine Fever (ASF) and Avian Influenza (AI) were used as unstructured data. Models were tested for the threshold effects of disease-related news and SNS frequencies, specifically those related to ASF and AI, on the retail prices of pork and chicken, respectively. The effects were found to exist, and the values of ASF-related news on pork prices were estimated to be −9 and 8, indicating that the threshold autoregressive (TAR) model can be divided into three regimes. The coefficients of the ASF-related SNS frequencies on pork prices were 1.1666, 0.2663 and −0.1035 for regimes 1, 2 and 3, respectively, suggesting that pork prices increased by 1.1666 Korean won in regime 1 when ASF-related SNS frequencies increased. To promote pork consumption by SNS posts, the required SNS frequencies were estimated to have impacts as great as one standard deviation in the pork price. These values were 247.057, 1309.158 and 2817.266 for regimes 1, 2 and 3, respectively. The impact response periods for pork prices were estimated to last 48, 6, and 8 days for regimes 1, 2 and 3, respectively. When the prediction accuracies of the TAR and autoregressive (AR) models with regard to pork prices were compared for the root mean square error, the prediction accuracy of the TAR model was found to be slightly better than that of the AR. When the threshold effect of AI-related news on chicken prices was tested, a linear relationship appeared without a threshold effect. These findings suggest that when infectious diseases such as ASF occur for the first time, the impact on livestock prices is significant, as indicated by the threshold effect and the long impact response period. Our findings also suggest that the impact on livestock prices is not remarkable when infectious diseases occur multiple times, as in the case of AI. To date, this study is the first to suggest the use of SNS to promote meat consumption. [ABSTRACT FROM AUTHOR]

A pressuremeter test (PMT) is a representative and highly reliable in situ test for assessing the stress–strain behavior of weathered granite. Its application, however, is restricted by its cost and time requirements. Many researchers have also investigated the correlations between the SPT-N value and the pressuremeter modulus (Em) and limit pressure (PL) of soils, but they have mostly focused on sand, silt, and clay and have employed simple regression analysis. In this study, equations for Em and PL were derived for weathered granite through multiple nonlinear regression analyses using a chemical weathering index that quantitatively represents the degree of weathering. Nonlinear multiple regression analyses were conducted by combining the allometric models that produced the optimal correlations between Em, PL, energy corrected SPT-N (SPT-N60), and normalized VR (Vogt' ratio) with vertical effective stress. The obtained equations for Em and PL had higher R2 values (0.76 and 0.46, respectively) compared with the simple regression equations reported in previous studies. Because local characteristics are important determinants of the engineering properties of geo-materials, the Em and PL equations proposed in this paper are intended for use in geotechnical surveys of weathered granite in South Korea. [ABSTRACT FROM AUTHOR]

Page 15, the sentence in the original paper is I "...The rMBE of the hybrid model is fairly constant regardless of i HT ht I under the maximum value of 3.7%. i ". Therefore, the authors would like to correct the statement to be I "...The rMBE of the hybrid model is fairly constant regardless of i HT ht I under the maximum absolute value of 3.35%... i ". We considered different Linke turbidity models ( HT ht ), and mistakes that may have possibly occurred when we accidentally mixed up the turbidity input for global horizontal irradiance (GHI) models. [Extracted from the article]

Countries such as Korea adopt design codes, evaluation criteria and specifications from standards originating abroad; this leads to a lack of distinction of the separate applications of dynamic stability evaluation parameters between various track structures of different track moduli. This paper discusses the applicability of the dynamic stability evaluation method of railway track structures by assessing 10 different types of railway track sections of a newly constructed railway operation line (5 ballasted and 5 concrete type track structures) by field instrumentation testing. Parameters of track support stiffness (TSS), wheel load fluctuation, derailment coefficient, and rail displacement are measured. The respective results are first compared to the standard criteria (design specification) and comparisons between the different track types are presented as ratios. Findings show that while all of the tracks satisfy the design specification requirements, each track type measurement result varies by a noticeable degree, particularly when comparing between concrete and ballast type track structures. Results of the study demonstrate that using the same dynamic stability evaluation criteria can lead to an incorrect assessment of the track performance evaluation of track structure, and a separate evaluation parameter for ballasted and concrete track structures is required. [ABSTRACT FROM AUTHOR]

Building Information Modeling (BIM) can act as a solution to improve various difficulties faced in the construction industry. BIM is expanding into infrastructure facilities for integrated information management, productivity improvement, and risk management. There are many cases where BIM is applied to buildings with horizontal and linear characteristics, but the infrastructure is different from the method of applying BIM to buildings with vertical characteristics. Using premanufactured libraries in BIM-based design can shorten the design time and simplify the design method, thus increasing the 3D design productivity. This paper develops BIM prototype libraries based on the standards from the Ministry of Land, Infrastructure, and Transport (MOLIT) of South Korea for railway infrastructure. BIM tools such as Civil 3D, Revit, and AECOsim were used by reflecting the characteristics of each facility, and the libraries, with a total of 762 types in 363 files, were constructed. The specifications were created by linking the common and facility-specific attribute items to the libraries. The result of the test modeling using the developed library showed significant productivity improvement, such as an average difference of 38.2% was observed for roadbed modeling and a mean difference of 50.2% for bridge modeling. [ABSTRACT FROM AUTHOR]

After the Industry 4.0 discussion in Germany in 2011, much attention has been paid to smart factory in Korea. Since 2014, smart factories have been established and expanded in Korea. However, about 80% of them were established at a low level. In this paper, we analyze smart factory statuses in detail through an empirical research on 113 manufacturing companies that have established smart factories in Korea. We build a framework based on the resource-based view (RBV) and IT value creation process and analyze the results of five constructs—manufacturing strategy, organization, system, process, and performance—using basic statistical methodologies to derive the current statuses of manufacturing companies that have established smart factories. Our results show that implementing advanced technologies such as AI technology that can implement semi-finished and finished product quality inspection, manufacturing process optimization and product demand forecast is a challenge, particularly for SMEs. We also find that securing and managing facility data is a difficult problem. In addition, while output and material management ranked high, the utilization of integration systems, which is important when building a smart factory, was found to be extremely low. Lastly, the performance indicator results showed that yield management and defect rate were most important, while job creation through the introduction of smart factories was low. Based on the results of this study, the government may be able to determine effective smart factory policies and provide manufacturing companies with a guide on establishing a smart factory. [ABSTRACT FROM AUTHOR]

The opening of the high-speed rail (HSR) resulted in significant changes in the transportation network of Korea. The new HSR construction was expected to become a new engine of local economic growth. However, there was a controversy regarding whether the connection between regions intensifies the concentration of socio-economic activities in the metropolis (straw effect) or contributes to the balance of regional development (sprawl effect). More increasing attention had been devoted to studying the "straw effects" caused by the newly built HSR networks on interregional social-economic activities. Despite considerable research on the benefit achieved from HSR construction, little has focused on the negative externalities resulting from it. This paper examined the potential "straw effects" of two new HSR lines through constructing the indicator of interregional dependency that measured one city's level of dependency on another one. In order to exclude the interference of lurking variables, five metropolitan cities were selected as case studies. The empirical results, based on a panel data model, revealed that the larger the economic scale of the target city, the lower the level of dependency on other cities, and there existed a "straw effect" on HSR development in terms of Seoul. [ABSTRACT FROM AUTHOR]

Based on a SEIRD model (Susceptible, Exposed, Infective, Recovered and Deceased) for COVID-19 infection with a new parametrization using a high infection rate, and a low fatality, we define the model in System Dynamics, Python, and Specification and Description Language (SDL). The different implementations obtained can be improved depending on the capabilities of the approach and, more interestingly, can be used to improve the Validation and Verification processes. In this paper, we are focused on describing how this comparison with other models' validation processes allows us to find the parameters of the system dynamics model, hence the parameters of the pandemic. This is a crucial element, specifically in this case, because the data are not complete or validated for different reasons. We use using existing data from Korea and Spain and showing that the proposed method and the obtained parametrization for the model fit with the empirical evidence. We discuss some implications of the validation process and the model parametrization. We use this approach to implement a Decision support system that shows the current pandemic situation in Catalonia. [ABSTRACT FROM AUTHOR]

The main contents of this paper are to verify the environmental factors affecting the power generation of floating photovoltaic systems and to present the power generation prediction model considering environmental factors by using regression analysis and neural networks studied during the last decade. This study focused on a comparative analysis of which model is best suited for the power generation prediction of the floating photovoltaic (PV) system. To compare the power generation characteristics of a floating and a land-based PV system, two identical 2.5 kW PV systems were installed—one on the water surface in the Boryeong Dam, Korea, and the other nearby on dry land—and their performances were compared. The solar irradiance of the floating PV system was 1.1% lower than that of the land-based PV. Nevertheless, the floating PV module temperature was 4.9% lower than that of the land-based PV, generating approximately 3% more power. Using the correlation analysis of data mining techniques, environmental factors affecting the efficiency of the floating PV system were investigated. The correlation coefficient between the module temperature and water temperature was r = 0.6317 which proves that the high efficiency and low module temperature characteristics of the floating PV system, when compared with that of the land-based PV, are due to the water evaporation effect. Considering environmental factors, power-generation prediction models based on regression analysis and neural networks are presented, and their accuracies are compared. This comparison confirms that the accuracy of the power generation prediction model using neural networks was approximately 2.59% higher than that of the regression analysis method. As a result of adjusting the hidden nodes in the neural network algorithm, it was confirmed that a neural network algorithm with ten hidden nodes was most suitable for calculating the amount of power generation. [ABSTRACT FROM AUTHOR]

Broadband power line communication (PLC) is used as a communication technique for advanced metering infrastructure (AMI) in Korea. High-speed (HS) PLC specified in ISO/IEC12139-1 and HomePlug Green PHY (HPGP) are deployed for remote metering. Recently, internet of things (IoT) PLC has been proposed for reliable communications on harsh power line channels. In this paper, the physical layer performance of IoT PLC, HPGP, and HS PLC is evaluated and compared. Three aspects of the performance are evaluated: the bit rate, power spectrum, and bit error rate (BER). An expression for the bit rate for IoT PLC and HPGP is derived while taking the padding bits and number of tones in use into consideration. The power spectrum is obtained through computer simulations. For the BER performance comparisons, the upper bound of the BER for each PLC standard is evaluated through computer simulations. [ABSTRACT FROM AUTHOR]

Featured Application: Sonar system. In this paper, a depth-bistatic bottom reverberation model that employs the ray theory is presented. The model can be applied to an active towed array in the ocean. The reverberation time series are modeled under the depth-bistatic assumption and their Doppler shift is calculated based on the actual source–receiver geometry. This model can handle N × 2D range-dependent bathymetry, the geometry of a triplet array, and the Doppler motion of the source, targets, and receiver. The model predictions are compared with the mid-frequency reverberation data measured by an active triplet towed array during August 2015 in the East Sea, Korea. These data are collected with a variable depth source at mid-frequency and the triplet line array in a deep-water environment. Model predictions of the beam time series and its spectrogram are in good agreement with the measurement. In particular, we discuss the effects of the source and receiver depths on the reverberation in deep water observed in both the measured and modeled results. [ABSTRACT FROM AUTHOR]

In this paper, we show the development of a demand-side management solution (DSMS) for demand response (DR) aggregator and actual demand response operation cases in South Korea. To show an experience, Korea's demand response market outline, functions of DSMS, real contracted capacity, and payment between consumer and load aggregator and DR operation cases are revealed. The DSMS computes the customer baseline load (CBL), relative root mean squared error (RRMSE), and payments of the customers in real time. The case of 10 MW contracted customers shows 108.03% delivery rate and a benefit of 854,900,394 KRW for two years. The results illustrate that an integrated demand-side management solution contributes by participating in a DR market and gives a benefit and satisfaction to the consumer. [ABSTRACT FROM AUTHOR]