• Journal of the Korean Professional Engineers Association
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• v.43 no.5
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• pp.21-24
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• 2010

The development of the mountain areas is the economic solution to the forest-mountain region that is becoming more hollw by the day. It can also be said as providing a rest area for those that live in the cities who long for their child-hood memories. This development will bring the vitalization of the regional economy and the balanced development of the national land planning. In order to bring the continuous development of the mountain areas, educating a dedicated professional forest-mountain expert is a must.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.1
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• pp.1-2
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• 2023

2-Bromopropane, a causative chemical that caused the outbreak of reproductive toxicity 28 years ago, was classified as Group 2A in the recently held IARC monograph 133 meeting. Korean research data were used as supporting data in the carcinogenicity evaluation of 2-bromopropane and other carcinogens. I would like to share my memories with the researchers at the Occupational Safety and Health Research Institute who worked hard to identify the cause.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.3
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• pp.136-142
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• 2006

Existing buffer management schemes for shared-memory output queueing switches can be classified into two types: In the first type, some constant amount of memory space is guaranteed to each virtual queue using static queue thresholds. The static threshold method (ST) belongs to this type. On the other hand, the second type of approach tries to maximize the buffer utilization in 머 locating buffer memories. The complete sharing (CS) method is classified into this type. In the case of CS, it is very hard to protect regular traffic from mis-behaving traffic flows while in the case of ST the thresholds can not be adjusted according to varying traffic conditions. In this paper, we propose a new buffer management method called weighted dynamic thresholds (WDT) which can process packet flows based on loss priorities for quality-of-service (QoS) functionalities with fairly high memory utilization factors. We verified the performance of the proposed scheme through computer simulations.

• Journal of KIISE:Software and Applications
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• v.27 no.5
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• pp.463-472
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• 2000

In this paper, we consider the problem of realizing associative memories via cellular neural network(CNN). After introducing qualitative properties of the CNN model, we formulate the synthesis of CNN that can store given binary vectors with optimal performance as a constrained optimization problem. Next, we observe that this problem's constraints can be transformed into simple inequalities involving linear matrix inequalities(LMIs). Finally, we reformulate the synthesis problem as a generalized eigenvalue problem(GEVP), which can be efficiently solved by recently developed interior point methods. Proposed method can be applied to both space varying template CNNs and space-invariant template CNNs. The validity of the proposed approach is illustrated by design examples.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.08a
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• pp.108-109
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• 2012

This talk will begin with the demonstration of facile synthesis of silicon nanostructures using the magnesiothermic reduction on silica nanostructures prepared via self-assembly, which will be followed by the characterization results of their performance for energy storage. This talk will also report the fabrication and characterization of highly porous, stretchable, and conductive polymer nanocomposites embedded with carbon nanotubes (CNTs) for application in flexible lithium-ion batteries. It will be presented that the porous CNT-embedded PDMS nanocomposites are capable of good electrochemical performance with mechanical flexibility, suggesting these nanocomposites could be outstanding anode candidates for use in flexible lithium-ion batteries. Directed self-assembly (DSA) of block copolymers (BCPs) can generate uniform and periodic patterns within guiding templates, and has been one of the promising nanofabrication methodologies for resolving the resolution limit of optical lithography. BCP self-assembly processing is scalable and of low cost, and is well-suited for integration with existing semiconductor manufacturing techniques. This talk will introduce recent research results (of my research group) on the self-assembly of Si-containing block copolymers for the achievement of sub-10 nm resolution, fast pattern generation, transfer-printing capability onto nonplanar substrates, and device applications for nonvolatile memories. An extraordinarily facile nanofabrication approach that enables sub-10 nm resolutions through the synergic combination of nanotransfer printing (nTP) and DSA of block copolymers is also introduced. This simple printing method can be applied on oxides, metals, polymers, and non-planar substrates without pretreatments. This talk will also report the direct formation of ordered memristor nanostructures on metal and graphene electrodes by the self-assembly of Si-containing BCPs. This approach offers a practical pathway to fabricate high-density resistive memory devices without using high-cost lithography and pattern-transfer processes. Finally, this talk will present a novel approach that can relieve the power consumption issue of phase-change memories by incorporating a thin $SiO_x$ layer formed by BCP self-assembly, which locally blocks the contact between a heater electrode and a phase-change material and reduces the phase-change volume. The writing current decreases by 5 times (corresponding to a power reduction of 1/20) as the occupying area fraction of $SiO_x$ nanostructures varies.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.12
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• pp.61-70
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• 2007

The density of Memory has been increased by great challenge for memory technology. Therefore, elements of memory become more smaller than before and the sensitivity to faults increases. As a result of these changes, memory testing becomes more complex. In addition, as the number of storage elements per chip increases, the test cost becomes more remarkable as the cost per transistor drops. Recent development in system-on-chip (SOC) technology makes it possible to incorporate large embedded memories into a chip. However, it also complicates the test process, since usually the embedded memories cannot be controlled from the external environment. Proposed design doesn't need controls from outside environment, because it integrates into memory. In general, there are a variety of memory modules in SOC, and it is not possible to test all of them with a single algorithm. Thus, the proposed scheme supports the various memory testing process. Moreover, it is able to At-Speed test in a memory module. consequently, the proposed is more efficient in terms of test cost and test data to be applied.

• JSTS:Journal of Semiconductor Technology and Science
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• v.12 no.4
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• pp.449-457
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• 2012

The program/erase (P/E) cyclic endurances including bias temperature instability (BTI) behaviors of Metal-$Al_2O_3$-Nitride-Oxide-Semiconductor (MANOS) memories are investigated in comparison with those of Semiconductor-Oxide-Nitride-Oxide-Semiconductor (SONOS) memories. In terms of BTI behaviors, the SONOS power-law exponent n is ~0.3 independent of the P/E cycle and the temperature in the case of programmed cell, and 0.36~0.66 sensitive to the temperature in case of erased cell. Physical mechanisms are observed with thermally activated $h^*$ diffusion-induced Si/$SiO_2$ interface trap ($N_{IT}$) curing and Poole-Frenkel emission of holes trapped in border trap in the bottom oxide ($N_{OT}$). In terms of the BTI behavior in MANOS memory cells, the power-law exponent is n=0.4~0.9 in the programmed cell and n=0.65~1.2 in the erased cell, which means that the power law is strong function of the number of P/E cycles, not of the temperature. Related mechanism is can be explained by the competition between the cycle-induced degradation of P/E efficiency and the temperature-controlled $h^*$ diffusion followed by $N_{IT}$ passivation.

• Journal of KIISE
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• v.43 no.5
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• pp.524-530
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• 2016

The desire to access data faster and the growth of next-generation memories such as non-volatile memories, contribute to the development of research on memory file systems. It is recommended that memory mapped file I/O, which has less overhead than read-write I/O, is utilized in a high-performance memory file system. Memory mapped file I/O, however, brings a page table overhead, which becomes one of the big overheads that needs to be resolved in the entire file I/O performance. We find that same overheads occur unnecessarily, because a page table of a file is removed whenever a file is opened after being closed. To remove the duplicated overhead, we propose the mapping cache, a technique that does not delete a page table of a file but saves the page table to be reused when the mapping of the file is released. We demonstrate that mapping cache improves the performance of traditional file I/O by 2.8x and web server performance by 12%.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.57-64
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• 2006

Tn this paper, We propose a user friendly object-based multimedia retrieval system using the HCNN(HippoCampus Neural Network. Most existing approaches to content-based retrieval rely on query by example or user based low-level features such as color, shape, texture. In this paper we perform a scene change detection and key frame extraction for the compressed video stream that is video compression standard such as MPEG. We propose a method for automatic color object extraction and ACE(Adaptive Circular filter and Edge) of content-based multimedia retrieval system. And we compose multimedia retrieval system after learned by the HCNN such extracted features. Proposed HCNN makes an adaptive real-time content-based multimedia retrieval system using excitatory teaming method that forwards important features to long-term memories and inhibitory learning method that forwards unimportant features to short-term memories controlled by impression.

• Journal of Sensor Science and Technology
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• v.5 no.2
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• pp.37-46
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• 1996

This paper describes a system that can be used to recognize unknown materials regardless of the change in ambient temperature by using temperature response curve fitting and fuzzy neural network(FNN). There are problems with a recognition system which utilize temperature responses. It requires too many memories to store the vast temperature response data and it has to be filtered to remove the noise which occurs in experiments. Thus, this paper proposes a practical method using curve fitting to remove the above problems of memories and noise. Also, the FNN is proposed to overcome the problem caused by the change of ambient temperature. Using the FNN which is learned by temperature responses on fixed ambient temperatures and known thermal conductivity, the thermal conductivity of the material can be inferred on various ambient temperatures. So the material can be recognized via its thermal conductivity.