• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.1-8
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• 2019

Developing reliable soil moisture prediction techniques at agricultural regions is a pivotal issue for sustaining stable crop productions. In this study, a physically-based SWAP(Soil-Water-Atmosphere-Plant) model was suggested to estimate soil moisture dynamics at the study sites. ROSETTA was also integrated to derive the soil hydraulic properties(${\alpha}$, n, ${\Theta}_r$, ${\Theta}_s$, $K_s$) as the input variables to SWAP based on the soil information(Sand, Silt and Clay-SSC, %). In order to predict the soil moisture dynamics in future, the mid-term TIGGIE(THORPEX Interactive Grand Global Ensemble) and long-term S2S(Subseasonal to Seasonal) weather forecasts were used, respectively. Our proposed approach was tested at the six study sites of RDA(Rural Development Administration). The estimated soil moisture values based on the SWAP model matched the measured data with the statistics of Root Mean Square Error(RMSE: 0.034~0.069) and Temporal Correlation Coefficient(TCC: 0.735~0.869) for validation. When we predicted the mid-/long-term soil moisture values using the TIGGE(0~15 days)/S2S(16~46 days) weather forecasts, the soil moisture estimates showed less variations during the TIGGE period while uncertainties were increased for the S2S period. Although uncertainties were relatively increased based on the increased leading time of S2S compared to those of TIGGE, these results supported the potential use of TIGGE/S2S forecasts in evaluating agricultural drought. Our proposed approach can be useful for efficient water resources management plans in hydrology, agriculture, etc.

• Journal of Information Technology Applications and Management
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• v.26 no.2
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• pp.41-60
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• 2019

O2O(Online-to-Offline) commerce, a kind of multi-channel, is used in various industries such as accommodation, food and beverage, transportation, and real estate. The O2O commerce, a channel of integrating online and offline, overcomes the limitations of traditional online commerce, where a customer made a purchase decision without direct experience. Despite this advantage of O2O, if the online-offline channel is not effectively linked, customer's complaints would occur due to service failure. This study, with regard to O2O service failure circumstances, intends to investigate the effect of customer's perceived justice on their emotional response, recovery satisfaction, followed by intention to repurchase. Perceived justice in this study is composed of distributive justice, interactional justice, and procedural justice. Furthermore, this study explores the moderation effect of O2O industry types in the relationship between perceived justice and emotion. An O2O industry type is classified into accommodation, food and beverage, and transportation. A hypothesized research model was empirically tested using a structural equation model. The current study collected 433 questionnaires and the target respondents are customers who have experienced service failure in O2O commerce. The empirical results showed that O2O commerce more effectively conducts service recovery strategy and causes positive customer response by integrating online and offline channel. One of the ultimate purposes of O2O service providers is to reduce the likelihood of service failures and to recover more quickly and efficiently by linking two channels rather than using a single channel. This study suggested that the O2O channel is effective in influencing customer satisfaction and loyalty by inducing customer's positive emotions in recovering service failure.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.2
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• pp.55-67
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• 2022

Land use is a critical factor that affects the hydrological characteristics of watersheds, thereby determining the biological condition of streams. This study analyzes the effects of land uses in the watersheds on biological indicators of streams across the Han River basin using a linear model (LM) and generalized additive model (GAM). LULC and biological monitoring data of streams were obtained from the Korean Ministry of Environment. The proportions of urban, agricultural, and forest areas in the watersheds were regressed to the three biological indicators, including diatom, benthic macroinvertebrate, and fish of streams. The estimated LM and GAM models for the biological indicators were then compared, using regression determination R² and AIC values. The results revealed that GAM models performed better than the LM models in explaining the variances of biological indicators of streams, indicating the non-linear relationships between biological indicators and land uses in watersheds. Also, the results suggested that the indicator of macroinvertebrates was the most sensitive indicator to land uses in watersheds. Although non-linear relationships between watershed land uses and biological indicators of streams could vary among biological indicators, it was consistent that streams' biological integrity significantly deteriorated by a relatively low percentage of urban areas. Meanwhile, biological indicators of streams were negatively affected by the relatively high percentage of agricultural areas. The results of this study can be integrated into effective quantitative criteria for the watershed management and land use plans to enhance the biological integrity of streams. In specific, land uses management plans in watersheds may need more close attention to urban land use changes than agricultural land uses to sustain the biological integrity of streams.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.473-484
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• 2022

This study aimed to verify the reciprocal casual relationship between self-rated health status and depression in the elderly through longitudinal analysis. An autoregressive cross-lagged model was identified. This study analyzed 3,363 elderly people aged 65 or older using the 5th, 6th, and 7th wave data from Korean Longitudinal Study of Ageing(KLoSA). The results are as follows. First, self-rated health status had a positive(+) autoregressive effect. Second, depression had a positive(+) autoregressive effect. Third, self-rated health status had a negative(-) cross-lagged effect on depression, but depression was not a casual predictor of self-rated health status. Based on these findings, it was suggested that there is a need for systematic policies to improve basic fitness in the early stages of old age, as well as the need to expand health promotion programs and implement integrated depression management programs.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.173-182
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• 2023

The UAE government has set its sights on creating a smart, electronic-based government system that utilizes AI. The country's collaboration with India aims to bring substantial returns through AI innovation, with a target of over $20 billion in the coming years. To achieve this goal, the UAE launched its AI strategy in 2017, focused on improving performance in key sectors and becoming a leader in AI investment. To ensure public safety as the role of AI in government grows, the country is working on developing integrated cyber security solutions for SCADA systems. A questionnaire-based study was conducted, using the AI IQ Threat Scale to measure the variables in the research model. The sample consisted of 200 individuals from the UAE government, private sector, and academia, and data was collected through online surveys and analyzed using descriptive statistics and structural equation modeling. The results indicate that the AI IQ Threat Scale was effective in measuring the four main attacks and defense applications of AI. Additionally, the study reveals that AI governance and cyber defense have a positive impact on the resilience of AI systems. This study makes a valuable contribution to the UAE government's efforts to remain at the forefront of AI and technology exploitation. The results emphasize the need for appropriate evaluation models to ensure a resilient economy and improved public safety in the face of automation. The findings can inform future AI governance and cyber defense strategies for the UAE and other countries.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.270-272
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• 2022

Safety is one of the factors that prevent clinical drugs from being distributed on the market. In the case of neurotoxicity, which is the main cause of safety problems caused by drug side effects, risk assessment of drugs and compounds is required in advance. Currently, experiments for testing drug safety are based on animal experimetns, which have the disadvantage of being time-consuming and expensive. Therefore in order to solve the above problem, a neurotoxic prediction model through an in silico experiment was suggested. In this study, the category of neurotoxicity was expanded using a unified medical language system and various related compound data were obtained based on an integrated database. The SMILES (Simplified Molecular Input Line Entry System) of the obtained compounds were converted into fingerprints and it is used as input of machine learning. The model finally predicts the presence or absence of neurotoxicity. The experiment proposed in this study can reduce the time and cost required for the in vivo experiment. Furthermore, it is expected to shorten the research period for new drug development and reduce the burden of suspension of development.

• Earthquakes and Structures
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• v.22 no.5
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• pp.503-515
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• 2022

This paper presents the development of seismic fragility curves for a precast reinforced concrete bridge instrumented with a structural health monitoring (SHM) system. The bridge is located near an active seismic fault in the Dominican Republic (DR) and provides the only access to several local communities in the aftermath of a potential damaging earthquake; moreover, the sample bridge was designed with outdated building codes and uses structural detailing not adequate for structures in seismic regions. The bridge was instrumented with an SHM system to extract information about its state of structural integrity and estimate its seismic performance. The data obtained from the SHM system is integrated with structural models to develop a set of fragility curves to be used as a quantitative measure of the expected damage; the fragility curves provide an estimate of the probability that the structure will exceed different damage limit states as a function of an earthquake intensity measure. To obtain the fragility curves a digital twin of the bridge is developed combining a computational finite element model and the information extracted from the SHM system. The digital twin is used as a response prediction tool that minimizes modeling uncertainty, significantly improving the predicting capability of the model and the accuracy of the fragility curves. The digital twin was used to perform a nonlinear incremental dynamic analysis (IDA) with selected ground motions that are consistent with the seismic fault and site characteristics. The fragility curves show that for the maximum expected acceleration (with a 2% probability of exceedance in 50 years) the structure has a 62% probability of undergoing extensive damage. This is the first study presenting fragility curves for civil infrastructure in the DR and the proposed methodology can be extended to other structures to support disaster mitigation and post-disaster decision-making strategies.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.95-102
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• 2023

The emergence of Internet of Things (IoT) into our daily lives has grown rapidly. It's been integrated to our homes, cars, and cities, increasing the intelligence of devices involved in communications. Enormous amount of data is exchanged over smart devices through the internet, which raises security concerns in regards of privacy evasion. This paper is focused on the forensics and intrusion detection on one of the most common protocols in IoT environments, especially smart home environments, which is the Message Queuing Telemetry Transport (MQTT) protocol. The paper covers general IoT infrastructure, MQTT protocol and attacks conducted on it, and multiple network forensics frameworks in smart homes. Furthermore, a machine learning model is developed and tested to detect several types of attacks in an IoT network. A forensics tool (MQTTracker) is proposed to contribute to the investigation of MQTT protocol in order to provide a safer technological future in the warmth of people's homes. The MQTT-IOT-IDS2020 dataset is used to train the machine learning model. In addition, different attack detection algorithms are compared to ensure the suitable algorithm is chosen to perform accurate classification of attacks within MQTT traffic.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2215-2221
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• 2023

In order to further meet the requirements of weight, volume, and dose minimization for new nuclear energy devices, the bare-bones multi-objective particle swarm optimization algorithm is used to automatically and iteratively optimize the design parameters of radiation shielding system material, thickness, and structure. The radiation shielding optimization program based on the bare-bones particle swarm optimization algorithm is developed and coupled into the reactor radiation shielding multi-objective intelligent optimization platform, and the code is verified by using the Savannah benchmark model. The material type and thickness of Savannah model were optimized by using the BBMOPSO algorithm to call the dose calculation code, the integrated optimized data showed that the weight decreased by 78.77%, the volume decreased by 23.10% and the dose rate decreased by 72.41% compared with the initial solution. The results show that the method can get the best radiation shielding solution that meets a lot of different goals. This shows that the method is both effective and feasible, and it makes up for the lack of manual optimization.