• Proceedings of the IEEK Conference
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• 2004.06b
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• pp.399-402
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• 2004

This paper presents a system-on-chip prototype implementing a full integration for a portable digital audio system. The chip is composed of a audio processor block to implements audio decoding and voice compression or decompression software, a system control block including 8-bit MCU core and Memory Management Unit (MMU) a low power 16-bit ${\Sigma}{\Delta}$ CODEC, two DC-to-BC converter, and a flash memory controller. In order to support other audio algorithms except Mask ROM type's fixed codes, a novel 16-bit fixed-point DSP core with the program-download architecture is proposed. Funker, an efficient power management technique such as task-based clock management is implemented to reduce power consumption for portable application. The proposed chip has been fabricated with a 4 metal 0.25um CMOS technology and the chip area is about 7.1 mm ${\times}$ 7.1mm with 100mW power dissipation at 2.5V power supply.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.3
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• pp.333-340
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• 2017

In this paper, we describe HW/SW co-optimizations for reconfigurable application specific instruction-set processors (ASIPs). Based on our previous very long instruction word (VLIW) ASIP, the proposed framework realizes various forward error-correction (FEC) algorithms for wireless communication systems. In order to enhance the energy efficiency, we newly introduce several design methodologies including high-radix algorithms, task-level out-of-order executions, and intensive resource allocations with loop-level rescheduling. The case study on the radix-4 turbo decoding shows that the proposed techniques improve the energy efficiency by 3.7 times compared to the previous architecture.

• Smart Structures and Systems
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• v.23 no.6
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• pp.553-564
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• 2019

Modeling of magnetorheological (MR) dampers for cable vibration control to facilitate the design of even more effective and economical systems is still a challenging task. In this study, a parameter-adaptive three-element model is first established for a full-scale MR damper based on laboratory tests. The parameters of the model are represented by a set of empirical formulae in terms of displacement amplitude, voltage input, and excitation frequency. The model is then incorporated into the governing equation of cable-damper system for investigation of open-loop vibration control of stay cables in a cable-stayed bridge. The concept of optimal voltage/current input achieving the maximum damping for the system is put forward and verified. Multi-mode suboptimal and Single-mode optimal open-loop control method is then developed. Important conclusions are drawn on application issues and unique characteristics of open-loop cable vibration control using MR dampers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.861-880
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• 2023

Semantic segmentation of road scene is the key technology of autonomous driving, and the improvement of convolutional neural network architecture promotes the improvement of model segmentation performance. The existing convolutional neural network has the simplification of learning knowledge and the complexity of the model. To address this issue, we proposed a road scene semantic segmentation algorithm based on multi-task collaborative learning. Firstly, a depthwise separable convolution atrous spatial pyramid pooling is proposed to reduce model complexity. Secondly, a collaborative learning framework is proposed involved with saliency detection, and the joint loss function is defined using homoscedastic uncertainty to meet the new learning model. Experiments are conducted on the road and nature scenes datasets. The proposed method achieves 70.94% and 64.90% mIoU on Cityscapes and PASCAL VOC 2012 datasets, respectively. Qualitatively, Compared to methods with excellent performance, the method proposed in this paper has significant advantages in the segmentation of fine targets and boundaries.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.21-22
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• 2021

Early academic warning is considered as an inherent problem in education data mining. Early and timely concern and guidance can save a student's university career. It is widely assumed as a multi-class classification system in view of machine learning. Therefore, An accurate and precise methodical solution is a complicated task to accomplish. For this issue, we present a hybrid model employing rough set theory with a back-propagation neural network to ameliorate the predictive capability of the system with an illustrative example. The experimental results show that it is an effective early academic warning model with an escalating improvement in predictive accuracy.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.124-128
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• 2024

Cloud computing is a technology for delivering information in which resources are retrieved from the internet through a web-based tools and applications, rather than a direct connection with the server. It is a new emerging computing based technology in which any individual or organization can remotely store or access the information. The structure of cloud computing allows to store and access various information as long as an electronic device has access to the web. Even though various merits are provided by the cloud from the cloud provides to cloud users, it suffers from various flaws in security. Due to these flaws, data integrity and confidentiality has become a challenging task for both the storage and retrieval process. This paper proposes a novel approach for data protection by an improved auditing based methodology in cloud computing especially in the process of cloud storage. The proposed methodology is proved to be more efficient in auditing the threats while storing data in the cloud computing architecture.

• Journal of architectural history
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• v.26 no.1
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• pp.7-16
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• 2017

One of the essential characteristics of Lewis Mumford's architectural criticism is the coherent emphasis on symbolism. Such emphasis stems from his understanding of city and humanity in the context of civilization: first, that the architecture symbolizes institutions of urban civilization; second, that the technical aspect of human nature should be balanced with its artistic aspects. Mumford believed that each architectural type requires an appropriate symbolic expression corresponding to its purpose and that a new symbolic expression, in a new cultural context, should replace the conventional expression. He took symbolism for an intuitional expression, and read multi-layered meanings of architecture: 'practical function' by way of rational reason and 'symbolic function' by way of intuition. He pursued a balance between practicality and beauty to rectify the situation of modern civilization, in which symbolism, the expression of its intuitional aspect, is in crisis. Ultimately, for Mumford, the essential task of architectural critic is of the interpretation of symbolism, aiming at the correspondence and communication between the architect(artist)'s intuition and critic(interpreter)'s via the media of symbol. The critic can play some privileged role of interpreting even symbols unintended by the architect. The ideal architectural critic, after all, would be the one who is able to understand the city, technology and human beings in the perspective of civilization and to interpret the architect's artistic expression in its highest form through intuition. Mumford established himself as such a critic and evaluated the status of aesthetic accomplishment of his contemporary architecture and technological civilization, giving emphasis on the artistic practice in architecture as a solution.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1786-1795
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• 2011

The trajectory control for omni-directional mobile robot is not easy. Especially, the tracking control which system uncertainty problem is included is much more difficult. This paper develops trajectory controller of 3-wheels omni-directional mobile robot using fuzzy multi-layered algorithm. The fuzzy control method is able to solve the problems of classical adaptive controller and conventional fuzzy adaptive controllers. It explains the architecture of a fuzzy adaptive controller using the robust property of a fuzzy controller. The basic idea of new adaptive control scheme is that an adaptive controller can be constructed with parallel combination of robust controllers. This new adaptive controller uses a fuzzy multi-layered architecture which has several independent fuzzy controllers in parallel, each with different robust stability area. Out of several independent fuzzy controllers, the most suited one is selected by a system identifier which observes variations in the controlled system parameter. This paper proposes a design procedure which can be carried out mathematically and systematically from the model of a controlled system; related mathematical theorems and their proofs are also given. Finally, the good performance of the developed mobile robot is confirmed through live tests of path control task.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.227-233
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• 2019

Recently, along with the recent development of deep learning technique, neural networks are achieving success in computer vision filed. Convolutional neural network have shown outstanding performance in not only for a simple image classification task, but also for tasks with high difficulty such as object segmentation and detection. However many such deep learning models are based on supervised-learning, which requires more annotation labels than image-level label. Especially image semantic segmentation model requires pixel-level annotations for training, which is very. To solve these problems, this paper proposes a weakly-supervised semantic segmentation method which requires only image level label to train network. Existing weakly-supervised learning methods have limitations in detecting only specific area of object. In this paper, on the other hand, we use multi-classifier deep learning architecture so that our model recognizes more different parts of objects. The proposed method is evaluated using VOC 2012 validation dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.311-326
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• 2024

The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.