• 한국전기전자재료학회논문지
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• 제20권9호
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• pp.821-827
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• 2007

The nitrogen oxides, NO and $NO_2$, abbreviated usually as NOx, emitted from combustion facilities such as power plants and automobiles are the typical air-pollutants causing acid rain and photochemical smog. In order to solve the NOx-related pollution problems effectively, we need efficient techniques to monitor NOx in the combustion exhausts and in environments. Development of solid-state electrochemical devices for detecting NOx is demonstrated based on various combination of solid electrolytes and auxiliary sensing materials. The object of this research is to develop various sensor performance for solid state amperometric sensor, and to test gas sensor performance manufactured. So we try to present a guidance for developing amperometric gas sensor. We concentrated on development of manufacturing process and performance test. Amperometric Nitrogen dioxide sensor was fabricated using NASICON and an $NaNO_2$ layer deposited on the counter electrode. The current response was almost linear with Nitrogen dioxide concentration in the range 1-350 ppb at $150^{\circ}C$.

• 센서학회지
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• 제21권4호
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• pp.283-286
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• 2012

This paper presents the optical hydrogen sensor based on transparent 3C-SiC membrane and photovoltaic effect. Gasochromic materials of Pd and Pd/$WO_3$ were deposited by sputter on 3C-SiC membrane for gas sensing area. Gasochromic materials change to transparency by exposure to hydrogen. The variations of light intensity by hydrogen generate the photovoltaic of P-N junction between N-type 3C-SiC and P-type Si. Single layer of Pd shows higher photovoltaic compared with Pd/$WO_3$. However, phase transition from ${\alpha}$ to ${\beta}$ is shown at 6 %. Pd/$WO_3$ structure show the more linear response to hydrogen range of 2 % ~10 %. Also, almost 2 times fast response and recovery characteristics are shown at Pd/$WO_3$. These fast performances are come from the fact that Pd promoted the chemical reaction between hydrogen and $WO_3$.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.304-306
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• 2003

Oceanic pycnocline depth is usually obtained from in situ measurements. As ocean internal waves occur on and propagate along oceanic pycnocline, it is possible to estimate the depth remotely. This paper presents a method for retrieving pycnocline depth from synthetic aperture radar (SAR) imagery where internal waves are visible. This model is constructed by combining a two-layer ocean model and a nonlinear internal wave model. It is also assumed that the observed groups of internal wave packets on SAR imagery are generated by local semidiurnal tides. Case study in East China Sea shows a good agreement with in situ CTD data.

• 한국수산과학회지
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• 제36권4호
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• pp.417-429
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• 2003

Abnormal oceanic conditions associated with the passage of typhoons are examined using hydrographic and satellite data 1990-2002. During the passage period of typhoons in the Korean waters, an abrupt decrease of sea surface temperature (SST) in range of 5 to $8^{\circ}C$ was observed. The areas of SST decrease were an order of 100-200 km, and the low SST lasted about 15-25 days after passage of typhoon. After passage of typhoon, the water temperatures in the surface mixed layer of 30 m show negative anomalies for quite a long period. In addition, stratification parameters were substantially decreased and chlorophyll a density was rapidly increased.

• 정보처리학회논문지D
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• 제9D권5호
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• pp.939-944
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• 2002

In this paper, we propose and apply new classification method to the remotely sensed image acquired from airborne multi-spectral scanner. This is a neuro-fuzzy image classifier derived from the generic model of a 3-layer fuzzy perceptron. We implement a classification software system with the proposed method for land cover image classification. Comparisons with the proposed and maximum-likelihood classifiers are also presented. The results show that the neuro-fuzzy classification method classifies more accurately than the maximum likelihood method. In comparing the maximum-likelihood classification map with the neuro-fuzzy classification map, it is apparent that there is more different as amount as 7.96% in the overall accuracy. Most of the differences are in the "Building" and "Pine tree", for which the neuro-fuzzy classifier was considerably more accurate. However, the "Bare soil" is classified more correctly with the maximum-likelihood classifier rather than the neuro-fuzzy classifier.

• 센서학회지
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• 제10권3호
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• pp.180-186
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• 2001

최근 전류 법적인 Clark형 센서의 단점을 극복하기 위해 FET형 용존 산소 센서가 제안되었다. 그러나 제안된 센서의 출력은 작업전극에서 발생하는 pH의 변화를 감지하기 때문에 유속에 대한 영향을 받는다. 본 논문에서는 FIA(flow injection analysis)를 이용하여 유속의 영향을 최소화 할 수 있는 방향을 결정하였다. 그리고 완충막을 작업전극과 감지막 위에 형성시켜 작업전극과 감지부가 측정용액에 직접 노출되어 용액의 유동에 의해 센서의 출력이 불안정한 문제를 개선하였다. TEOS(tetraethylorthosilicate)와 DEDMS(diethoxydimethylsilane)를 혼합하여 졸-겔법으로 완충막을 형성하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.989-992
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• 2006

The new approach to the problem of oil spill detection consisting in combined use of all available quasiconcurrent satellite information (AVHRR NOAA, TOPEX/Poseidon, Jason-1, MODIS Terra/Aqua, QuikSCAT) is suggested. We present the results of the application of the proposed approach to the operational monitoring of seawater condition and pollution in the coastal zone of northeastern Black Sea conducted in 2006. This monitoring is based on daily receiving, processing and analysis of data different in nature (microwave radar images, optical and infrared data), resolution and surface coverage. These data allow us to retrieve information on seawater pollution, sea surface and air-sea boundary layer conditions, seawater temperature and suspended matter distributions, chlorophyll a concentration, mesoscale water dynamics, near-surface wind and surface wave fields. The focus is on coastal seawater circulation mechanisms and their impact on the evolution of pollutants.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.922-925
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• 2006

The objective of this study is first to resolve the spatial and seasonal variability of BL in the bay using 'the most comprehensive' data set available for the bay and then to understand the formation mechanisms and variability in the light of the known dynamical and thermodynamical processes. The most recent study [Masson et al., 2002] on the BL variability in the bay was based on the World Ocean Atlas (WOA98) of Levitus [1998]. The temperature and salinity profiles in the bay have increased considerably after the release of WOA98. The WOA98, itself has been updated to WOA01 in 2001. Further, the deployment of ARGO profiling floats in the bay since 2002 has generated many additional profiles. In addition to the ARGO data and the updated WOA01, the hydrographic data collected from the bay under several Indian national programs and archived in the Indian Oceanographic Data Centre (IODC) was also considered in the present study. The WOA98 and WOA01 consist of only limited data from the IODC archive, especially from the Exclusive Economic Zone of India. Therefore, the combination of these data from the three different sources (WOA01, ARGO and IODC) provides ‘the most comprehensive data set’ for the bay to resolve the BLT structure and its variability in a much better scale than in the past.

• 한국지능시스템학회논문지
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• 제11권7호
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• pp.675-679
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• 2001

In this paper, we present a new competitive learning algorithm called Dynamic Competitive Learning (DCL). DCL is a supervised learning method that dynamically generates output neurons and initializes automatically the weight vectors from training patterns. It introduces a new parameter called LOG (Limit of Grade) to decide whether an output neuron is created or not. If the class of at least one among the LOG number of nearest output neurons is the same as the class of the present training pattern, then DCL adjusts the weight vector associated with the output neuron to learn the pattern. If the classes of all the nearest output neurons are different from the class of the training pattern, a new output neuron is created and the given training pattern is used to initialize the weight vector of the created neuron. The proposed method is significantly different from the previous competitive learning algorithms in the point that the selected neuron for learning is not limited only to the winner and the output neurons are dynamically generated during the learning process. In addition, the proposed algorithm has a small number of parameters, which are easy to be determined and applied to real-world problems. Experimental results for pattern recognition of remote sensing data and handwritten numeral data indicate the superiority of DCL in comparison to the conventional competitive learning methods.

• 한국군사과학기술학회지
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• 제15권1호
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• pp.16-27
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• 2012

In multi-interface multi-channel(MIMC) based tactical ad hoc networks, QoS support for required operational capacity is one of the main challenging issues for multi-hop transmissions. To support QoS in such a harsh environment, we propose a novel MAC scheme to minimize multi-hop as well as per-hop delay. The current IEEE 802.11 MAC protocols should contend to reserve the channel resource at every hop by each sender. The every-hop channel contention results in a degradation of end-to-end delay for multi-hop transmissions. The basic idea of our scheme is to make a "multi-hop reservation" at the MAC layer by using the modified RTS frame. It contains additional information such as destination information, packet priority, and hop count, etc. In addition, we differentiate the contention window area according to the packet priority and the number of hops to deliver packets in the predefined allowed latency. Our scheme can minimize the multi-hop delay and support the QoS of the critical data in real time(i.e., VoIP, sensing video data, Video conference between commanders). Our simulation study and numerical analysis show that the proposed scheme outperforms the IEEE 802.11 MAC.