• Wind and Structures
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• v.39 no.4
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• pp.305-311
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• 2024

This paper reviews measurements of wind profiles in the atmospheric boundary layer in strong wind (thermally neutral) conditions over open water and the ocean, and the variation of the roughness parameters with mean wind speed. Based on the wind data recorded on the coast of the island of Frøya (Norway) in the 1980s, and dropwindsonde profiles in hurricanes, the paper shows that 'capping', or saturation, of the surface drag coefficient becomes apparent at a mean wind speed at 10m height of about 25 m/s. Wind speed models used in the offshore industries were investigated, (the ISO model, the API 'tropical cyclone' model and the IEC model). The ISO model, although based on good quality data from Frøya, does not allow for the saturation of the roughness above about 25-30 m/s, even though that was apparent in the Frøya data. 'Capping' of the aerodynamic roughness length for wind speeds greater than 28 m/s is represented appropriately in the API 'tropical cyclone'model, and hence the model represents the mean wind properties reasonably well in severe tropical cyclone conditions. However, the turbulence intensities in the API 'tropical cyclone' model, based on over-land measurements (ESDU), are overpredicted for winds over the ocean, at heights above 20m. The IEC models are entirely based on over-land measurements, and hence are not representative of over-water conditions such as those required for offshore wind farms. New model profiles for over-ocean strong winds are proposed for wind speeds up to hurricane strength, based on the ISO profiles, but with capping of the surface drag coefficient at a value of 0.0025, at a mean wind speed at 10m height of 25 m/s. The proposed turbulence intensity model is also a revision of the ISO profile, also with capping above 25 m/s. The proposed model profiles are in better general agreement with recorded data in strong winds than those currently specified in international standards, and are applicable to all wind speeds in synoptic-scale events, including those in tropical cyclones, typhoons and hurricanes. As well as the Frøya data, the revised strong-wind models are supported by measurements from Atlantic hurricanes, gales in the North Sea, landfalling typhoons in Japan and Cyclone 'Yasi' in Queensland, Australia.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.71-78
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• 2009

A demand on the spatial data management has been rapidly increased with the introduction and diffusion process of ITS, Telematics, and Wireless Sensor Network. And many different users use the digital map that offers various thematic spatial data. Spatial data for digital map can be managed by tile-based and feature-based data. The existing tile-based digital map management systems have difficult problems such as data construction, history management, and update data based on a spatial object. In order to solve these problems, we proposed the data model for feature-based digital map management system for representation of feature-based seamless map, history management, real-time update of spatial data, and analyzed the validity and utility of the proposed model.

• Journal of Information Technology Services
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• v.12 no.3
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• pp.311-321
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• 2013

In the community of software engineering, diverse modeling languages are used for representing all relevant information in the form of models. Also many different models such as business model, business process model, product models, interface models etc. are generated through software life cycles. In this situation, models need to be integrated for enterprise integration and enhancement of software productivity. Researchers propose rebuilding models by a specific modeling language, using a intemediate modeling language and using common reference for model integration. However, in the current approach it requires a lot of cost and time to integrate models. Also it is difficult to identify common objects from several models and to update objects in the repository of common model objects. This paper proposes software model integration using metadata model based on Linked data. We verify the effectiveness of the proposed approach through a case study.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.805-814
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• 2001

The problem of 'outliers', observations which look suspicious in some way, has long been one of the most concern in the statistical structure to experimenters and data analysts. We propose a model for outliers problem and also analyze it in linear regression model using a Bayesian approach with the variance-inflation model. We will use Geweke's(1996) ideas which is based on the data augmentation method for detecting outliers in linear regression model. The advantage of the proposed method is to find a subset of data which is most suspicious in the given model by the posterior probability The sampling based approach can be used to allow the complicated Bayesian computation. Finally, our proposed methodology is applied to a simulated and a real data.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2016-2021
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• 2003

This paper deals with a control technique of eliminating the transient vibration with respect to a waist axis of an articulated robot. This control technique is based on a model-based control in order to establish the damping effect on the driven mechanical part. The control model is composed of reduced-order electrical and mechanical parts related to the velocity control loop. The parameters of the control model can be obtained from design data or experimental data. This model estimates a load speed converted to the motor shaft. The difference between the estimated load speed and the motor speed is calculated dynamically, and it is added to the velocity command to suppress the transient vibration. This control method is applied to an articulated robot regarded as a time-invariant system. The effectiveness of the model-based control integrated into the position control loop is verified by simulations. Simulations show satisfactory control results to reduce the transient vibration at the end-effector.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.860-879
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• 2002

Traditionally, Geographical Information Systems can only process spatial data in a procedure-oriented way, and the data can't be treated integrally. This method limits the development of spatial data applications. A new and promising method to solve this problem is the spatial structural query language, which extends SQL and provides integrated accessing to spatial data. In this paper, the theory of spatial structural query language is discussed, and a new geographical data model based on the concepts and data model in OGIS is introduced. According to this model, we implemented a spatial structural query language G/SQL. Through the studies of the 9-Intersection Model, G/SQL provides a set of topological relational predicates and spatial functions for GIS application development. We have successfully developed a Web-based GIS system-WebGIS-using G/SQL. Experiences show that the spatial operators G/SQL offered are complete and easy-to-use. The BNF representation of G/SQL syntax is included in this paper.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.295-302
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• 2022

This paper contains the development of a smart power device designed to collect load power data from industrial manufacturing machines, predict future variations in load power data, and detect abnormal data in advance by applying a machine learning-based prediction algorithm. The proposed load power data prediction model is implemented using a Long Short-Term Memory (LSTM) algorithm with high accuracy and relatively low complexity. The Flask and REST API are used to provide prediction results to users in a graphical interface. In addition, we present the results of experiments conducted to evaluate the performance of the proposed approach, which show that our model exhibited the highest accuracy compared with Multilayer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM) models. Moreover, we expect our method's accuracy could be improved by further optimizing the hyperparameter values and training the model for a longer period of time using a larger amount of data.

• Journal of KIBIM
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• v.5 no.1
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• pp.25-34
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• 2015

In the initial step of BIM based architectural design process, workloads are increased and the decision making process becomes more complex than those of the conventional design process. Technologies regarding distribution, exchange, classification, verification of BIM data are fundamental elements of construct environment for information sharing based on BIM. Interoperability of BIM model data is another issue to integrate BIM model. To improve interoperability in BIM-based collaboration, a model for utilizing formal&unformal design informations is suggested. Futhermore, Prototyping the model and practical test is conducted for advancement of data exchange making design data richen.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.81-86
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• 2018

Software defect severity is very important in projects with limited historical data or new projects. But general software defect prediction is very difficult to collect the label information of the training set and cross-project defect prediction must have a lot of data. In this paper, an unclassified data set with defect severity is clustered according to the distribution ratio. And defect severity-based prediction model is proposed by way of labeling. Proposed model is applied CLAMI in JM1, PC4 with the least ambiguity of defect severity-based NASA dataset. And it is evaluated the value of ACC compared to original data. In this study experiment result, proposed model is improved JM1 0.15 (15%), PC4 0.12(12%) than existing defect severity-based prediction models.

• ETRI Journal
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• v.46 no.3
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• pp.513-525
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• 2024

Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction-based self-supervised anomaly detection model to overcome these challenges. It has a dual-subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy-based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.