• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.33 no.4
• /
• pp.100-113
• /
• 2010

Manufacturing companies' cost competitiveness with respect to equipment management can be achieved by satisfying additional market demands by their own capacity without purchasing additional equipments. In essence, it can be accomplished by making continuous investigation into bottlenecks and improvement on them. Therefore, equipment performance measure systems should be designed so that they can support manufacturing companies' such endeavors. With the purpose of establishing an effective equipment performance measurement system for detecting and improving bottlenecks, this study (1) suggests some desirable features that such a system should have, (2) evaluates conventional equipment performance measurement systems in terms of their usefulness for the detection and improvement of bottlenecks, and (3) proposes an improved system. We also perform a simulation experiment to demonstrate the limitations of the conventional systems and show how the proposed system can resolve the problems.

• The KIPS Transactions:PartA
• /
• v.9A no.4
• /
• pp.525-534
• /
• 2002

Race conditions, races in short, need to be detected for debugging parallel programs, because the races result in unintended non-deterministic executions. To detect the races in an execution of program, previous techniques use a centralized data structure which may incur serious bottleneck in generating concurrency information, or show inefficient time complexity which depends on the degree of nested parallelism in comparing any two of them. We propose a new labeling scheme in this paper, which is scalable in generating the concurrency information without bottleneck by using private data structure, and improves time complexity into constant in checking concurrency. The scalability and time efficiency therfore makes on-the-fly race detection efficient not only for programs with either shared-memory or message-passing, but also for programs with mixed model of the two.

• Journal of radiological science and technology
• /
• v.46 no.4
• /
• pp.277-285
• /
• 2023

In this study, pneumonia identification networks with the small number of layers were constructed by using chest X-ray images. The networks had similar trainable-parameters, and the performance of the trained models was quantitatively evaluated with the modification of the network architectures. A total of 6 networks were constructed: convolutional neural network (CNN), VGGNet, GoogleNet, residual network with identity blocks, ResNet with bottleneck blocks and ResNet with identity and bottleneck blocks. Trainable parameters for the 6 networks were set in a range of 273,921-294,817 by adjusting the output channels of convolution layers. The network training was implemented with binary cross entropy (BCE) loss function, sigmoid activation function, adaptive moment estimation (Adam) optimizer and 100 epochs. The performance of the trained models was evaluated in terms of training time, accuracy, precision, recall, specificity and F1-score. The results showed that the trained models with the small number of layers precisely detect pneumonia from chest X-ray images. In particular, the overall quantitative performance of the trained models based on the ResNets was above 0.9, and the performance levels were similar or superior to those based on the CNN, VGGNet and GoogleNet. Also, the residual blocks affected the performance of the trained models based on the ResNets. Therefore, in this study, we demonstrated that the object detection networks with the small number of layers are suitable for detecting pneumonia using chest X-ray images. And, the trained models based on the ResNets can be optimized by applying appropriate residual-blocks.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.12 no.2
• /
• pp.150-161
• /
• 2012

Eye detection is widely used in applications, such as face recognition, driver behavior analysis, and human-computer interaction. However, it is difficult to achieve real-time performance with software-based eye detection in an embedded environment. In this paper, we propose a parallel hardware architecture for real-time eye detection. We use the AdaBoost algorithm with modified census transform(MCT) to detect eyes on a face image. We parallelize part of the algorithm to speed up processing. Several downscaled pyramid images of the eye candidate region are generated in parallel using the input face image. We can detect the left and the right eye simultaneously using these downscaled images. The sequential data processing bottleneck caused by repetitive operation is removed by employing a pipelined parallel architecture. The proposed architecture is designed using Verilog HDL and implemented on a Virtex-5 FPGA for prototyping and evaluation. The proposed system can detect eyes within 0.15 ms in a VGA image.

• Journal of Internet Computing and Services
• /
• v.10 no.2
• /
• pp.15-28
• /
• 2009

This paper proposes HWbF(Hit and WLC based Firewall) design which consists of an PFS(Packet Filter Station) and APS(Application Proxy Station). PFS is designed to reduce bottleneck and to prevent the transmission delay of them by distributing packets with PLB(Packet Load Balancing) module, and APS is designed to manage a proxy cash server by using PCSLB(Proxy Cash Server Load Balancing) module and to detect a DoS attack with packet traffic quantity. Therefore, the proposed HWbF in this paper prevents packet transmission delay that was a drawback in an existing Firewall, diminishes bottleneck, and then increases the processing speed of the packet. Also, as HWbF reduce the 50% and 25% of the respective DoS attack error detection rate(TCP) about average value and the fixed critical value to 38% and 17%. with the proposed expression by manipulating the critical value according to the packet traffic quantity, it not only improve the detection of DoS attack traffic but also diminishes the overload of a proxy cash server.

• 한국HCI학회:학술대회논문집
• /
• 2008.02a
• /
• pp.451-455
• /
• 2008

In this paper, we present how we implement the low-level, triangle intersection routine in a massively parallel fashion using n VIDIA's new GPGPU language, CUDA. Triangle intersection often becomes a computational bottleneck in the collision detection problem. Due to the relatively low bandwidth between CPU and GPU, it has been challenging to implement efficient, object-space collision detection between triangle sets. However, thanks to the improved data transmission rates in CUDA architecture, in this paper, we improved the performance of triangle intersection substantially better than the optimized CPU counterpart.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.05a
• /
• pp.9-11
• /
• 2022

Community detection is a hot topic in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper focuses on detecting absolute fair maximal balanced cliques in signed attributed social networks, which can satisfy ensuring the fairness of complex networks and break the bottleneck of the "information cocoon".

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.10
• /
• pp.345-352
• /
• 2022

Recent LiDAR(Light Detection And Ranging) sensor is used for scanning object around in real-time. This sensor can detect movement of the object and how it has changed. As the production cost of the sensors has been decreased, LiDAR begins to be used for various industries such as facility guard, smart city and self-driving car. However, LiDAR has a large input data size due to its real-time scanning process. So another way for processing a large amount of data are needed in LiDAR system because it can cause a bottleneck. This paper proposes edge computing to compress massive point cloud for processing quickly. Since laser's reflection range of LiDAR sensor is limited, multiple LiDAR should be used to scan a large area. In this reason multiple LiDAR sensor's data should be processed at once to detect or recognize object in real-time. Edge computer compress point cloud efficiently to accelerate data processing and decompress every data in the main cloud in real-time. In this way user can control LiDAR sensor in the main system without any bottleneck. The system we suggest solves the bottleneck which was problem on the cloud based method by applying edge computing service.

• Journal of IKEEE
• /
• v.25 no.3
• /
• pp.493-500
• /
• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Journal of Communications and Networks
• /
• v.8 no.1
• /
• pp.93-105
• /
• 2006

The domination of the Internet by TCP-based services has spawned many efforts to provide high network utilization with low loss and delay in a simple and scalable manner. Active queue management (AQM) algorithms attempt to achieve these goals by regulating queues at bottleneck links to provide useful feedback to TCP sources. While many AQM algorithms have been proposed, most suffer from instability, require careful configuration of nonintuitive control parameters, or are not practical because of slow response to dynamic traffic changes. In this paper, we propose a new AQM algorithm, hybrid random early detection (HRED), that combines the more effective elements of recent algorithms with a random early detection (RED) core. HRED maps instantaneous queue length to a drop probability, automatically adjusting the slope and intercept of the mapping function to account for changes in traffic load and to keep queue length within the desired operating range. We demonstrate that straightforward selection of HRED parameters results in stable operation under steady load and rapid adaptation to changes in load. Simulation and implementation tests confirm this stability, and indicate that overall performances of HRED are substantially better than those of earlier AQM algorithms. Finally, HRED control parameters provide several intuitive approaches to trading between required memory, queue stability, and response time.