• Journal of the Korea Society for Simulation
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• v.11 no.1
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• pp.71-82
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• 2002

One of the most important things in the research for MIN-based switch operation the management scheme of network cycle. In the traditional MIN, when the receving buffer module is empty, the sell has to move forward the front-most buffer position by the characteristic of the conventional FIFO queue. However, most of buffer modules are almost always full for practical amount of input loads. The long network cycle of the traditional scheme is thus a substantial waste of bandwidth. In this paper, we propose the modeling method for the input and multi-buffered MIN with unit step buffering scheme, In spite of simplicity, simulation results show that the proposed model is very accurate comparing to previous modeling approaches in terms of throughput and the trend of delay.

• Journal of the Semiconductor & Display Technology
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• v.9 no.2
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• pp.87-90
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• 2010

A new SRAM pixel circuit with RESET Transistor of a LCOS display module was designed for a pico-projector application. A dual-block PWM method was also proposed to realize the field sequential color system having only one LCOS panel. 0.29 inch LCOS panel in SVGA resolution was fabricated and the proposed dual-block PWM method was tested with it. Discontinuity of brightness curve was caused due to multi-pulses and it was improved by the adoption of proper mapping table. With the proposed SRAM with RESET pixel circuit and dual-block PWM method, the test images were successfully demonstrated.

• Annual Conference of KIPS
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• 2012.04a
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• pp.522-524
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• 2012

IT 사회의 변천에 따라 더욱더 중요시 되는 것이 컴퓨터 시스템 보안이다. 이 보안에 대한 위협 요소를 줄이기 위해서 TCG(Trusted Computing Group)은 TPM(Trusted Platform Module)이라는 반도체 칩을 기반으로 한 신뢰성 플랫폼을 제안하였다. 본 논문은 네트워크 서비스의 기술발전에 따른 모바일 환경에서의 TPM 칩을 기반으로 동작하는 컴퓨팅 환경의 신뢰 상태 및 시스템 자원에 대한 상태 정보를 실시간 모니터링한다. 또한 모니터링 중인 컴퓨터 시스템의 프로세스에 대해 BiT Profiling 기법을 통한 분기 명령 추적을 모니터링하며, 이를 통해 사용자가 능동적인 대처가 가능하도록 한다.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.179-186
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• 2021

For stereo matching based on deep learning, the design of network structure is crucial to the calculation of matching cost, and the time-consuming problem of convolutional neural network in image processing also needs to be solved urgently. In this paper, a method of stereo matching using sparse loss volume in parallax dimension is proposed. A sparse 3D loss volume is constructed by using a wide step length translation of the right view feature map, which reduces the video memory and computing resources required by the 3D convolution module by several times. In order to improve the accuracy of the algorithm, the nonlinear up-sampling of the matching loss in the parallax dimension is carried out by using the method of multi-category output, and the training model is combined with two kinds of loss functions. Compared with the benchmark algorithm, the proposed algorithm not only improves the accuracy but also shortens the running time by about 30%.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.695-700
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• 2021

The Faster RCNN-based shellfish recognition algorithm is introduced for shellfish recognition studies that currently do not have any deep learning-based algorithms in a practical setting. The original feature extraction module is replaced by DenseNet, which fuses multi-level feature data and optimises the NMS algorithm, network depth and merging method; overcoming the omission of shellfish overlap, multiple shellfish and insufficient light, effectively solving the problem of low shellfish classification accuracy. In the complexifier test environment, the test accuracy was improved by nearly 4%. Higher testing accuracy was achieved compared to the original testing algorithm. This provides favourable technical support for future applications of the improved Faster RCNN approach to seafood quality classification.

• Annual Conference of KIPS
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• 2013.11a
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• pp.95-97
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• 2013

최근 지능형 자동차에 대한 연구가 활발하게 진행 되면서, 차량에서 발생하는 데이터를 처리하기 위한 다양한 기술들이 연구되고 있다. 더불어 운전자의 상태 인식을 위한 다양한 기술들이 되면서 지능형 자동차에 대한 구색을 갖추기 위한 연구도 활발히 이루어지고 있다. 하나의 MCU에서 차량상태표현을 위한 데이터 및 운전자 상태 정보를 표현하는 데이터를 동시에 처리하려면 병목현상이 발생되기 때문에 정상적이 데이터 처리가 어려울 것이다. 본 연구에서는 실시간 데이터 처리가 가능한 RT-eCos 기반의 태스크 처리에 우선순위를 두어 차량상태정보와 운전자 상태 정보의 원활한 데이터 처리를 위한 데이터 처리 모듈을 설계하기 위한 내용을 기술한다.

• Progress in Superconductivity
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• v.3 no.1
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• pp.17-22
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• 2001

Practical implementation of the SFQ technology in most application requires more than single-chip-level circuit complexity. Multiple chips have to be integrated with a technology that is reliable at cryogenic temperatures and supports an inter-chip data transmission speed of tens of GHz. In this work, we have studied two basic issues in building large RSFQ circuits. The first is the reliable inter-chip SFQ pulse transfer technique using Multi-Chip-Module (MCM) technology. By noting that the energy contained in an SFQ pulse is less than an attojoule, it is not very surprising that the direct transmission of a single SFQ pulse through MCM solder bump connectors can be difficult and an innovative technique is needed. The second is the recycling of the bias currents. Since RSFQ circuits are dc current biased the large RSFQ circuits need serial biasing to reduce the total amount of current input to the circuit.

• Advances in nano research
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• v.15 no.6
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• pp.513-520
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• 2023

The exponential proliferation of cutting-edge computing technologies has spurred organizations to outsource their data and computational needs. In the realm of cloud-based computing environments, ensuring robust security, encompassing principles such as confidentiality, availability, and integrity, stands as an overarching imperative. Elevating security measures beyond conventional strategies hinges on a profound comprehension of malware's multifaceted behavioral landscape. This paper presents an innovative paradigm aimed at empowering cloud service providers to adeptly model user behaviors. Our approach harnesses the power of a Particle Swarm Optimization-based Probabilistic Neural Network (PSO-PNN) for detection and recognition processes. Within the initial recognition module, user behaviors are translated into a comprehensible format, and the identification of malicious nano-structures behaviors is orchestrated through a multi-layer neural network. Leveraging the UNSW-NB15 dataset, we meticulously validate our approach, effectively characterizing diverse manifestations of malicious nano-structures behaviors exhibited by users. The experimental results unequivocally underscore the promise of our method in fortifying security monitoring and the discernment of malicious nano-structures behaviors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1208-1222
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• 2024

Music resources are characterized by quantization, diversification and complication. With the rapid increase of the demand for music resources, the storage of music resources is very large. In order to improve the retrieval effect of music resources, a massive music resources retrieval method based on ant colony algorithm is proposed to effectively use music resources. This paper constructs autocorrelation function to extract pitch feature of music resource, classifies the music resource information by calculating feature similarity. Using ant colony algorithm to correlate the feature of music resource, gain the result of correlative, locate the result of detection and get the result of multi-module. Simulation results show that the proposed method has high precision and recall, short retrieval time and can effectively retrieve massive music resources.

• Smart Media Journal
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• v.13 no.4
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• pp.9-15
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• 2024

Tree-based algorithms have been the dominant methods used build a prediction model for tabular data. This also includes personal credit data. However, they are limited to compatibility with categorical and numerical data only, and also do not capture information of the relationship between other features. In this work, we proposed an ensemble model using the Transformer architecture that includes text features and harness the self-attention mechanism to tackle the feature relationships limitation. We describe a text formatter module, that converts the original tabular data into sentence data that is fed into FinBERT along with other text features. Furthermore, we employed FT-Transformer that train with the original tabular data. We evaluate this multi-modal approach with two popular tree-based algorithms known as, Random Forest and Extreme Gradient Boosting, XGBoost and TabTransformer. Our proposed method shows superior Default Recall, F1 score and AUC results across two public data sets. Our results are significant for financial institutions to reduce the risk of financial loss regarding defaulters.