• Proceedings of the IEEK Conference
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• 2000.06b
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• pp.250-253
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• 2000

In this paper, we Implement a new arithmetic computation for MPEG audio data to overcome the limitations of real number processing in the fixed-point arithmetics, such as: overheads in processing time and power consumption. We aims at efficiently dealing with real numbers by extending the fixed-point arithmetic manipulation for floating-point numbers in MPEG audio data, and implementing the DSP libraries to support the manipulation and computation of real numbers with the fixed-point resources.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2861-2873
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• 1997

An FPU(Floating Point unit) is the principle component in high performance computer and is placed on a chip together with main processing unit recently. As a Processing speed of the FPU is accelerated, the rounding stage, which occupies one of the floating point Processing steps for floating point operations, has a considerable effect on overall floating point operations. In this paper, by studying and analyzing the processing flows of the conventional floating point adder/subtractor, multipler and divider, which are main component of the FPU, efficient rounding mechanisms are presented. Proposed mechanisms do not require any additional execution time and any high speed adder for rounding operation. Thus, performance improvement and cost-effective design can be achieved by this approach.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.9
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• pp.33-42
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• 1997

Fixed-point optimization utility software that can aid scaling and wordlength determination of digital signal processign algorithms written in C or C$\^$++/ language is developed. This utility consists of two programs: the range estimator and the fixed-point simulator. The former estimates the ranges of floating-point variables for automatic scaling purpose, and the latter translates floating-point programs into fixed-point equivalents for evaluating te fixed-point performance by simulation. By exploiting the operator overloading characteristics of C$\^$++/ language, the range estimation and the fixed-point simulation can be conducted just by modifying the variable declaration of the original program. This utility is easily applicable to nearly all types of digital signal processing programs including non-linear, time-varying, multi-rate, and multi-dimensional signal processing algorithms. In addition, this software can be used for comparing the fixed-point characteristics of different implementation architectures.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.429-438
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• 2006

Neural Networks(NNs) are applied for solving a wide variety of nonlinear problems in several areas, such as image processing, pattern recognition etc. Although NN can be simulated by using software, many potential NN applications required real-time processing. Thus they need to be implemented as hardware. The hardware implementation of multi-layer perceptrons(MLPs) in several kind of NNs usually uses a fixed-point arithmetic due to a simple logic operation and a shorter processing time compared to the floating-point arithmetic. However, the fixed-point arithmetic-based MLP has a drawback which is not able to apply the MLP software that use floating-point arithmetic. We propose a design method for MLPs which has the floating-point arithmetic-based fully-pipelining architecture. It has a processing speed that is proportional to the number of the hidden nodes. The number of input and output nodes of MLPs are generally constrained by given problems, but the number of hidden nodes can be optimized by user experiences. Thus our design method is using optimized number of hidden nodes in order to improve the processing speed, especially in field of a repeated processing such as image processing, pattern recognition, etc.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.5
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• pp.330-337
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• 2022

Research on object detection algorithms using 2D data has already progressed to the level of commercialization and is being applied to various manufacturing industries. Object detection technology using 2D data has an effective advantage, there are technical limitations to accurate data generation and analysis. Since 2D data is two-axis data without a sense of depth, ambiguity arises when approached from a practical point of view. Advanced countries such as the United States are leading 3D data collection and research using 3D laser scanners. Existing processing and detection algorithms such as ICP and RANSAC show high accuracy, but are used as a processing speed problem in the processing of large-scale point cloud data. In this study, PointNet a representative technique for detecting objects using widely used 3D point cloud data is analyzed and described. And RandLA-Net, which overcomes the limitations of PointNet's performance and object prediction accuracy, is described a review of detection technology using point cloud data was conducted.

• The Transactions of the Korea Information Processing Society
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• v.7 no.1
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• pp.252-265
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• 2000

In this thesis, the analysis of data processing method and the amount of computation in the whole geometry processing is conducted step by step. Floating-point ALU design is based on the characteristics of geometry processing operation. The performance of the devised ALU fitting with the geometry processing operation is analyzed by simulation after the description of the proposed ALU and geometry processor. The ALU designed in the paper can perform three types of floating-point operation simultaneously-addition/subtraction, multiplication, division. As a result, the 23.5% of improvement is achieved by that floating-point ALU for the whole geometry processing and in the floating-point division and square root operation, there is another 23% of performance gain with adding area-performance efficient SRT divisor.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.787-800
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• 2021

Three-dimensional (3D) object classification tasks using point clouds are widely used in 3D modeling, face recognition, and robotic missions. However, processing raw point clouds directly is problematic for a traditional convolutional network due to the irregular data format of point clouds. This paper proposes a pointwise convolution neural network (CNN) structure that can process point cloud data directly without preprocessing. First, a 2D convolutional layer is introduced to percept coordinate information of each point. Then, multiple 2D convolutional layers and a global max pooling layer are applied to extract global features. Finally, based on the extracted features, fully connected layers predict the class labels of objects. We evaluated the proposed pointwise CNN structure on the ModelNet10 dataset. The proposed structure obtained higher accuracy compared to the existing methods. Experiments using the ModelNet10 dataset also prove that the difference in the point number of point clouds does not significantly influence on the proposed pointwise CNN structure.

• KIEAE Journal
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• v.14 no.3
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• pp.55-63
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• 2014

This study selected and analyzed filter media that can be applied in non-point pollution reduction devices aimed at processing the source of pollution on site for road runoff that increases rapidly in rainfall-runoff in order to improve the water quality of urban areas. First, the factors that affect the quality of runoff caused by sources of non-point pollution include physical and social factors such as the usage of land around the area of water collection, type of pavement and movement of cars and people, as well as rainfall characteristics such as frequency, intensity, amount and duration of rainfall. Second, the purification tests of the filter media were processed for pH, BOD, COD and T-P, and the filter media showed to have initial purification effect at that items. However, the filter media showed to be very effective for the processing of SS, T-N, Zn and Cd from the beginning to the end. Third, for filter media, zeolite and vermiculite showed to be effective for processing SS, T-N, Zn and CD constantly, and composite filter media including zeolite showed to have strong processing effects. The authors conclude that this study can be applied to technical areas and policies aimed at reducing non-point pollution in urban areas and can also contribute to allowing eco-friendly management of rainfall as well as improvement of water quality.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.491-499
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• 2017

In this study, we proposed a new approach to segment ground and nonground points gained from a 3D laser range sensor. The primary aim of this research was to provide a fast and effective method for ground segmentation. In each frame, we divide the point cloud into small groups. All threshold points and start-ground points in each group are then analyzed. To determine threshold points we depend on three features: gradient, lost threshold points, and abnormalities in the distance between the sensor and a particular threshold point. After a threshold point is determined, a start-ground point is then identified by considering the height difference between two consecutive points. All points from a start-ground point to the next threshold point are ground points. Other points are nonground. This process is then repeated until all points are labelled.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.49-52
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• 2010

디지털 신호처리 알고리즘들은 실제 시스템에 적용할 때 임베디드 시스템 등 하드웨어의 성능과 소비전력 및 비용에 제약이 있을 경우 연산 정밀도가 높은 floating-point 연산 대신 제한된 정밀도와 적은 연산 비용을 요구하는 fixed-point 연산을 사용하여 구현한다. 시스템의 개발단계에서는 적용할 알고리즘을 floating-point 연산을 이용한 코드를 먼저 작성한 후 이를 fixed-point 연산으로 대체하는 과정을 거치게 되는데, 이는 숙련된 개발자와 상당한 양의 개발기간을 요하는 까다로운 작업이다. 이에 본 연구에는 코드작성 편의를 높이고 개발기간을 단축하기 위해 C++ template 기반의 fixed-point 연산 라이브러리를 개발하였다. 이는 floating-point 연산 코드와 fixed-point 연산 코드를 별도로 개발할 필요 없이 하나의 코드를 이용하여 자유로이 연산 정밀도를 지정할 수 있으며 개발자는 기존의 floating-point 연산을 이용하는 코드를 작성하는 것처럼 쉽게 코드를 작성할 수 있도록 한다. 또한, template 기반으로 작성되어 기존의 연구들과 달리 추가적인 작업도구 없이도 범용 C++ 컴파일러가 최적화된 코드를 생성할 수 있도록 되어있는 것이 특징이다.