• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.26 no.12C
• /
• pp.261-267
• /
• 2001

This paper presents the architecture of multiple-queue manager for input-queued switch, which has arbitration latency, and the design of the chip. The proposed architecture of multiple-queue manager provides wire-speed routing with a pipelined buffer management, and the tolerance of requests and grants data transmission latency between the input queue manager and central arbiter using a new request control method, which is based on a high-speed shifter. The multiple-input-queue manager has been implemented in a field programmable gate array chip, which provides OC-48c port speed. It enhances the maximum throughput of the input queuing switch up to 98.6% with 128-cell shared input buffer in 16$\times$16 switch size.

• Journal of the Korean Society for Precision Engineering
• /
• v.26 no.10
• /
• pp.32-39
• /
• 2009

Most mobile devices provide limited input interfaces in order to maximize the mobility and the portability. In this paper, the author proposes a small cubic-shaped tangible input interface which tracks the location, the direction, and the velocity using MEMS sensor technology to overcome the physical limitations of the poor input devices in mobile computing environments. As the preliminary phase for implementing the proposed tangible input interface, the prototype design and implementation methods are described in this paper. Various experiments such as menu manipulation, 3-dimensional contents control, and sensor data visualization have been performed in order to verify the validity of the proposed interface. The proposed tangible device enables direct and intuitive manipulation. It is obvious that the mobile computing will be more widespread and various kinds of new contents will emerge in near future. The proposed interface can be successfully employed for the new contents services that cannot be easily implemented because of the limitation of current input devices. It is also obvious that this kind of interface will be a critical component for future mobile communication environments. The proposed tangible interface will be further improved to be applied to various contents manipulation including 2D/3D games.

• Journal of Positioning, Navigation, and Timing
• /
• v.7 no.4
• /
• pp.189-203
• /
• 2018

In this paper, a Software Defined Radio (SDR) architecture was designed and implemented for rapid prototyping of GNSS receiver. The proposed SDR can receive various GNSS and direct sequence spread spectrum (DSSS) signals without software modification by expanded input parameters containing information of the desired signal. Input parameters include code information, center frequency, message format, etc. To receive various signal by parameter controlling, a correlator, a data bit extractor and a receiver channel were designed considering the expanded input parameters. In navigation signal processing, pseudorange was measured based on Coordinated Universal Time (UTC) and appropriate navigation message decoder was selected by message format of input parameter so that receiver position can be calculated even if SDR is set up various GNSS combination. To validate the proposed SDR, the software was implemented using C++, CUDA C based on GPU and USRP. Experimentation has confirmed that changing the input parameters allows GPS, GLONASS, and BDS satellite signals to be received. The precision of the position from implemented SDR were measured below 5 m (Circular Error Probability; CEP) for all scenarios. This means that the implemented SDR operated normally. The implemented SDR will be used in a variety of fields by allowing prototyping of various GNSS signal only by changing input parameters.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.11
• /
• pp.521-526
• /
• 2016

As on-line informal text data have massive in its volume and have unstructured characteristics in nature, there are limitations in applying traditional relational data model technologies for data storage and data analysis jobs. Moreover, using dynamically generating massive social data, social user's real-time reaction analysis tasks is hard to accomplish. In the paper, to capture easily the semantics of massive and informal on-line documents with unsupervised learning mechanism, we design and implement automatic topic extraction systems according to the mass of the words that consists a document. The input data set to the proposed system are generated first, using N-gram algorithm to build multiple words to capture the meaning of the sentences precisely, and Hadoop and Spark (In-memory distributed computing framework) are adopted to run topic model. In the experiment phases, TB level input data are processed for data preprocessing and proposed topic extraction steps are applied. We conclude that the proposed system shows good performance in extracting meaningful topics in time as the intermediate results come from main memories directly instead of an HDD reading.

• Proceedings of the ESK Conference
• /
• 1997.04a
• /
• pp.196-199
• /
• 1997

This paper describes the implementation process of Virtual Modeling system for a customer-oriented product. The human sense is measured and analyzed by physical design factors and can be applied also for the product design. The first step implementing virtual modeling is to make a human sensibility("Kansei") database. Human sensibility database is constructed with the relational data of Kansei words and design factors. The next step is extraction the design information from the human sensibility database by fuzzy inference algorithm. This design information is used for the input data for the graphic database. Virtual implementation software compounds 3D shape of product. The final product can be modified according to the customer's requirement.quirement.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.11
• /
• pp.473-478
• /
• 2022

Evaluation learning based on code testing is becoming a popular solution in programming education via Online judge(OJ). In the recent past, many papers have been published on how to detect plagiarism through source code similarity analysis to support OJ. However, deep learning-based research to support automated tutoring is insufficient. In this paper, we propose Input & Output side FiLM models to predict whether the input code will pass or fail. By applying Feature-wise Linear Modulation(FiLM) technique to GRU, our model can learn combined information of Java byte codes and problem information that it tries to solve. On experimental design, a balanced sampling technique was applied to evenly distribute the data due to the occurrence of asymmetry in data collected by OJ. Among the proposed models, the Input Side FiLM model showed the highest performance of 73.63%. Based on result, it has been shown that students can check whether their codes will pass or fail before receiving the OJ evaluation which could provide basic feedback for improvements.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.3
• /
• pp.639-647
• /
• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of IKEEE
• /
• v.25 no.2
• /
• pp.261-266
• /
• 2021

This paper suggests design methodology for thermal infrared data correction algorithms considering environmental temperature variations. First, a thermal infrared measurement model is suggested by a parameter-dependent first-order input-output equation using the relationship between infrared measurement data and model environmental parameters. In order to compensate the influence of environmental temperatures on infrared data, a compensation function is identified. Through experiments, the proposed algorithm is shown to reduce the influence of environmental temperatures on the infrared data effectively.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.41 no.6
• /
• pp.415-421
• /
• 2017

As stricter environmental regulation have led to an increase in the water treatment cost, it is necessary to quantitatively study the input power of the aeration process to improve the energy efficiency of the water treatment processes. The objective of this study is to propose the empirical correlations for the mass transfer coefficient with the gas hold-up and input power in order to investigate the mass transfer characteristics of the aeration process. It was found that as the input power increases, the mass transfer coefficient increases because of the decrease of gas hold-up and increase of Reynolds number, the penetration length, and dispersion of mixed flow. The correlations for the volumetric mass transfer coefficients with gas hold-up and input power were consistent with the experimental data, with the maximum deviation less than approximately ${\pm}10.0%$.

• Design & Manufacturing
• /
• v.15 no.4
• /
• pp.24-31
• /
• 2021

In this study, an artificial neural network model was constructed to convert CAE analysis data into similar experimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through the design of experiment and random selection method. The injection molding conditions and the weight, height, and diameter of the product derived from CAE results were used as the input parameters for learning of the convert model. Also the product qualities of experimental results were used as the output parameters for learning of the convert model. The accuracy of the convert model showed RMSE values of 0.06g, 0.03mm, and 0.03mm in weight, height, and diameter, respectively. As the next step, additional randomly selected conditions were created and CAE analysis was performed. Then, the additional CAE analysis data were converted to similar experimental data through the conversion model. An artificial neural network model was constructed to predict the quality of injection molded product by using converted similar experimental data and injection molding experiment data. The injection molding conditions were used as input parameters for learning of the predicted model and weight, height, and diameter of the product were used as output parameters for learning. As a result of evaluating the performance of the prediction model, the predicted weight, height, and diameter showed RMSE values of 0.11g, 0.03mm, and 0.05mm and in terms of quality criteria of the target product, all of them showed accurate results satisfying the criteria range.