• Journal of Aerospace System Engineering
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• v.16 no.2
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• pp.63-72
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• 2022

As interest in microsatellites increases, research has been actively conducted recently on the performance and use, as well as the orbital design and deployment techniques, for the microsatellite constellations. The purpose of this study was to investigate orbital deployment techniques using thrust and differential atmospheric drag control (DADC) for the Walker-delta constellation. When using thrust, the time and thrust required for orbital deployment vary, depending on the separation speed and direction of the satellite with respect to the launch vehicle. A control strategy to complete the orbital deployment with limited performance of the propulsion system is suggested and it was analyzed. As a result, the relationship between the deployment period and the total thrust consumption was derived. It takes a relatively longer deployment time using differential air drag rather than consuming thrusts. It was verified that the satellites can be deployed only with differential air drag at a general orbit of a microsatellite constellation. The conclusion of this study suggests that the deployment strategy in this paper can be used for the microsatellite constellation.

• Korean Journal of Construction Engineering and Management
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• v.23 no.5
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• pp.87-97
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• 2022

Although the use of BIM in architectural projects has increased, repetitive modeling tasks and frequent design errors remain as obstacles to the practical application of BIM. In particular, interior finishing elements include the most varied and detailed requirements, and thus requires improving its modelling efficiency and resolving potential design errors. Recently, visual programming-based modules has gained traction as a way to automate a series of repetitive modeling tasks. However, existing approaches do not adequately reflect the practical modeling needs and focus only on replacing siimple, repetitive tasks. This study developed and evaluated the performance of three modules for automatic detailing of walls, floors and ceilings. The three elements were selected by analyzing the man-hours and the number of errors that typically occur when detailing BIM models. The modules were then applied to automatically detail a sample commercial facility BIM model. Results showed that the implementations met the practical modeling requirements identified by actual modelers of an construction management firm.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.449-466
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• 2022

The establishment of a complete geological model that includes information about all the various components at a site (such as underground structures and the compositions of rock and soil underground space) is difficult, and geological modeling is a developing field. This study uses commercial software for the relatively easy composition of geological models. Our digital modeling process integrates a model of Jeju Island's 3D geological information, models of cave shapes, and information on the state of a road at the site's upper surface. Among the numerous natural caves that exist in Jeju Island, we studied the Jaeamcheon lava tube near Hallim town, and the selected site lies below a road. We developed a digital model by applying the principles of building information modeling (BIM) to the cave (CaveBIM). The digital model was compiled through gathering and integrating specific data: relevant processes include modeling the cave's shape using a laser scanner, 3D geological modeling using geological information and geophysical exploration data, and modeling the surrounding area using drones. This study developed a global-scale model of the Hallim region and a local-scale model of the Jaeamcheon cave. Cross-validation was performed when constructing the LSM, and the results were compared and analyzed.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.541-546
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• 2021

Recently, there is a trend of developing various identification and prediction models for hypertension using clinical information based on artificial intelligence and machine learning around the world. However, most previous studies on identification or prediction models of hypertension lack the consideration of the ideas of non-invasive and cost-effective variables, race, region, and countries. Therefore, the objective of this study is to present hypertension prediction model that is easily understood using only general and simple sociodemographic variables. Data used in this study was based on the Korea National Health and Nutrition Examination Survey (2018). In men, the model using the naive Bayes with the wrapper-based feature subset selection method showed the highest predictive performance (ROC = 0.790, kappa = 0.396). In women, the model using the naive Bayes with correlation-based feature subset selection method showed the strongest predictive performance (ROC = 0.850, kappa = 0.495). We found that the predictive performance of hypertension based on only sociodemographic variables was higher in women than in men. We think that our models based on machine leaning may be readily used in the field of public health and epidemiology in the future because of the use of simple sociodemographic characteristics.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.449-456
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• 2021

An intrusion detection system is a technology that detects abnormal behaviors that violate security, and detects abnormal operations and prevents system attacks. Existing intrusion detection systems have been designed using statistical analysis or anomaly detection techniques for traffic patterns, but modern systems generate a variety of traffic different from existing systems due to rapidly growing technologies, so the existing methods have limitations. In order to overcome this limitation, study on intrusion detection methods applying various machine learning techniques is being actively conducted. In this study, a comparative study was conducted on data preprocessing techniques that can improve the accuracy of anomaly detection using NGIDS-DS (Next Generation IDS Database) generated by simulation equipment for traffic in various network environments. Padding and sliding window were used as data preprocessing, and an oversampling technique with Adversarial Auto-Encoder (AAE) was applied to solve the problem of imbalance between the normal data rate and the abnormal data rate. In addition, the performance improvement of detection accuracy was confirmed by using Skip-gram among the Word2Vec techniques that can extract feature vectors of preprocessed sequence data. PCA-SVM and GRU were used as models for comparative experiments, and the experimental results showed better performance when sliding window, skip-gram, AAE, and GRU were applied.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.19-27
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• 2022

In general, the performance of ML(Machine Learning) application is determined by various factors such as the type of ML model, the size of model (number of parameters), hyperparameters setting during the training, and training data. In particular, the recognition accuracy of ML may be deteriorated or experienced overfitting problem if the amount of dada used for training is insufficient. Existing studies focusing on image recognition have widely used open datasets for training and evaluating the proposed ML models. However, for specific applications where the sensor used, the target of recognition, and the recognition situation are different, it is necessary to build the dataset manually. In this case, the performance of ML largely depends on the quantity and quality of the data. In this paper, training data used for motion recognition application is augmented using the kernel density estimation algorithm which is a type of non-parametric estimation method. We then compare and analyze the recognition accuracy of a ML application by varying the number of original data, kernel types and augmentation rate used for data augmentation. Finally experimental results show that the recognition accuracy is improved by up to 14.31% when using the narrow bandwidth Tophat kernel.

• Tunnel and Underground Space
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• v.32 no.5
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• pp.298-311
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• 2022

Some problems, such as aging workers, a decreased population due to a low birth rate, and shortage of skilled workers, are rising in construction sites. Therefore research for smart construction technology that can be improved for productivity, safety, and quality has been recently developed with government support by replacing traditional construction technology with advanced digital technology. In particular, the motor grader that mainly performs road surface flattening is a construction machine that requires the application of automation technology for repetitive construction. It is predicted that the construction period will be shortened if the construction automation technology such as trajectory tracking, automation work, and remote control technology is applied. In this study, we introduce the hardware and software architecture of the smart motor grader to apply unmanned and automation technology and then analyze the traditional earthwork method of the motor grader. We suggested the application plans for the path pattern and blade control method of the smart motor grader based on this. In addition, we verified the performance of waypoint-based path-following depending on scenarios and the blade control's performance through tests.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.331-338
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• 2022

Successor representation (SR) is a model of human reinforcement learning (RL) mimicking the underlying mechanism of hippocampal cells constructing cognitive maps. SR utilizes these learned features to adaptively respond to the frequent reward changes. In this paper, we evaluated the performance of SR under the context where changes in latent variables of environments trigger the reward structure changes. For a benchmark test, we adopted SR-Dyna, an integration of SR into goal-driven Dyna RL algorithm in the 2-stage Markov Decision Task (MDT) in which we can intentionally manipulate the latent variables - state transition uncertainty and goal-condition. To precisely investigate the characteristics of SR, we conducted the experiments while controlling each latent variable that affects the changes in reward structure. Evaluation results showed that SR-Dyna could learn to respond to the reward changes in relation to the changes in latent variables, but could not learn rapidly in that situation. This brings about the necessity to build more robust RL models that can rapidly learn to respond to the frequent changes in the environment in which latent variables and reward structure change at the same time.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.315-324
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• 2022

In this paper, we propose KoEPT, a Transformer-based generative model for automatic math word problems solving. A math word problem written in human language which describes everyday situations in a mathematical form. Math word problem solving requires an artificial intelligence model to understand the implied logic within the problem. Therefore, it is being studied variously across the world to improve the language understanding ability of artificial intelligence. In the case of the Korean language, studies so far have mainly attempted to solve problems by classifying them into templates, but there is a limitation in that these techniques are difficult to apply to datasets with high classification difficulty. To solve this problem, this paper used the KoEPT model which uses 'expression' tokens and pointer networks. To measure the performance of this model, the classification difficulty scores of IL, CC, and ALG514, which are existing Korean mathematical sentence problem datasets, were measured, and then the performance of KoEPT was evaluated using 5-fold cross-validation. For the Korean datasets used for evaluation, KoEPT obtained the state-of-the-art(SOTA) performance with 99.1% in CC, which is comparable to the existing SOTA performance, and 89.3% and 80.5% in IL and ALG514, respectively. In addition, as a result of evaluation, KoEPT showed a relatively improved performance for datasets with high classification difficulty. Through an ablation study, we uncovered that the use of the 'expression' tokens and pointer networks contributed to KoEPT's state of being less affected by classification difficulty while obtaining good performance.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.255-265
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• 2022

We compared and analyzed the factors affecting the lecture evaluation of IT subjects, which are mandatory for all students of H University. The purpose of this study is to determine whether lecture satisfaction has a significant correlation with academic achievement, attendance rate, and categories of courses. In this study, we check whether the lecture satisfaction of IT liberal arts subjects that require a lot of computer-based practice differs from that of other liberal arts subjects. We used the 2,149 evaluation data of 12 lectures submitted by 2,322 students in the first and second semesters of year 2019 at University H. As for the lecture evaluation results, in addition to the evaluation scores of the multiple choice questions, the subjective questions were also quantified by classifying the statements submitted by the students into positive and negative types to make the results of the lecture evaluation objective. Our research results show that student group who have the higher attendance rates and academic achievements have higher level of lecture satisfaction and they also use more positive words than negative words in subjective evaluation questions. Students with the lower score use the more negative words, but the ratio between positive and negative words does not differ between groups. Higher attendance rates groups in the basic programming courses and software applications courses have higher lecture satisfaction ratio. But in the intermediate programming courses, the higher attendances rate and the lecture satisfaction do not have any significant relationship. Also students in the intermediate programming courses use more negative words than those in the basic programming courses.