강민구 교수

Semiconductor metal oxide (SMO) gas sensors are attracting great attention as next-generation environmental monitoring sensors. However, there are limitations to the actual application of SMO gas sensors due to their low selectivity. Although the electronic nose (E-nose) systems based on a sensor array are regarded as a solution for the selectivity issue, poor accuracy caused by the nonuniformity of the fabricated gas sensors and difficulty of real-time gas detection have yet to be resolved. In

This study considers a scheduling algorithm and mathematical models for a real polyvinyl chloride (PVC) plant. According to general rules of the PVC plant, the basic PVC scheduling model is formulated as mixed integer linear programming (MILP). This model includes production, inventory management, packing, and shipment processes. The basic PVC scheduling model is customized for two PVC production processes with different characteristics, resulting in two detailed PVC scheduling models. An optimi

In order to carry out river basin management, it is necessary to evaluate the state of the river basin and make site-specific measures on the basis of management goals and objectives. A river basin is divided into several watersheds, which are composed of several components: water resources, social and economic systems, law and institution, user, land, ecosystems, etc. They are connected among them and form network holistically. In this study, a methodology for evaluating watershed management wa

The scaling down of metal oxide semiconductor field-effect transistors (MOSFETs) for the last several years has contributed to the reduction of the scaling variables and device parameters as well as the operating voltage of the MOSFET. At the same time, the variation in the electrical characteristics of MOSFETs is one of the major issues that need to be solved. Especially because the issue with variation is magnified as the drive voltage is decreased. Therefore, this paper will focus on the vari

Recently, malware classification based on Deep Neural Networks (DNN) has gained significant attention due to the rise in popularity of artificial intelligence (AI). DNN-based malware classifiers are a novel solution to combat never-before-seen malware families because this approach is able to classify malwares based on structural characteristics rather than requiring particular signatures like traditional malware classifiers. However, these DNNbased classifiers have been found to lack robustness

본 연구에서는 전역탐색기법 중 Simplex법의 원리를 이용한 SCE-UA법과 A-S법을 탱크모형의 매개변수 보정에 적용하여 지역탐색기법인 Downhill Simplex법의 적용결과와 비교하여 탐색능력을 평가하였다. 목적함수에 대한 매개변수의 반응특성을 분석하기 위해서 모형의 매개변수 중, a11, a12, h12의 값을 참값 부근에서 변화를 주어 반응표면을 분석한 결과 DRMS와 ABSERR이 매개변수의 변화에 민감한 결과를 나타냈다. 최적화 기법별 탐색능력을 비교하기 위해 오차가 없는 합성자료와 실측자료를 사용하였다. 합성자료를 사용한 보정에서는 A-S법이 목적함수에 관계없이 최적해를 탐색한 결과를 나타냈으며, SCE-UA법은 ABSERR를 사용할 경우에 최적해를 탐색한 결과를 나타냈으며 다른 목적함수를 사용하는 경우에는 최적해에 가까운 결과를 나타냈다. Downhill Simplex법은 초기값에 따라 다른 결과를 나타냈으며, 최적해에 가까운 초기값을 사용할 경우 전역 최적해에

With increasing demand for gas sensors in mobile devices, research on developing an electronic nose (E-nose) is actively conducted. However, conventional E-nose systems based on von Neumann computing have encountered challenges such as high hardware costs and power consumption because of the necessity of hardware-intensive circuits and processors. This work implements low-power artificial olfactory neuron modules within a spiking neural network (SNN) to address this issue. The artificial olfacto

요인분석은 시스템이나 현상에 대한 정보를 요약하고 관련 자료를 축약하는데 사용되는 다변량 분석기법의 하나이다. 이 기법은 변수들을 통계적 특성을 고려하여 몇 개의 요인들로 그룹화하고 분석결과는 중요도가 상대적으로 낮은 변수를 제거하는데 이용이 된다. 본 연구에서는 요인분석을 한강수계의 댐 군 연계운영에 대한 영향요인 추출에 적용하였다. 한강 수계에는 소양강댐, 충주댐과 같은 2개의 다목적 댐이 있으며, 이들은 이수기에 연계 운영되어 하류로 용수를 공급하고 발전을 실시하고 있다. 요인분석을 실시하기 위하여, 먼저 댐 운영에 관련된 자료(변수)들을 5개의 대분류(유입량, 용수수요량, 발전량, 저류량, 과거 운영 실적)로 나누어 수집하였다. 통계적 특성을 고려하여 수집된 변수 중에서 13개와 15개의 변수가 각각 선정되어 5개의 요인(소양강댐: 수문조건, 과거 댐 운영, 평수기 댐 운영, 저류량 관리, 하류 댐 운영; 충주댐: 수문조건, 과거 댐 운영, 평수기 댐 운영, 용수수요, 하류

SP is significantly associated with depressive symptoms in Korean workers across employment and occupational types, with a prominent association in service workers.

Here, we investigate the effects of interface defects on the electrical characteristics of amorphous indium-tin-gallium-zinc oxide (a-ITGZO) thin-film transistors (TFTs) utilizing bottom, top, and dual gatings. The field-effect mobility (27.3 cm²/V∙s) and subthreshold swing (222 mV/decade) under a dual gating is substantially better than those under top (12.6 cm²/V∙s, 301 mV/decade) and bottom (11.1 cm²/V∙s, 487 mV/decade) gatings. For an a-ITGZO TFT, oxygen defi

본 연구에서는 2차원 부정류 해석을 위한 유한체적 수치모형을 개발하였다. 개발된 모형에서는 천수방정식의 수치해를 구하기 위하여 Lax-Friedrichs 해법에 근간을 둔 ENO (essentially non-oscillatory) 기법을 적용하였으며, 공간적으로 2차 정도 모의를 위하여 수정 MUSCL 기법을 적용하였다. 개발된 모형의 적용성을 평가하기 위하여 해석해가 있는 1차원 댐 붕괴파 모의에 적용한 결과, 해석해와 유사한 결과를 나타냈으며, 수정 MUSCL 기법의 모의결과를 minmod 경사제한자를 사용한 모의결과와 비교한 결과, 수정 MUSCL 기법을 사용하는 경우 해석해와 더 근접한 결과를 나타냈다. 댐붕괴파의 2차원 해석에 대한 모형의 적용성을 평가하기 위하여 대칭형, 비대칭형 댐 붕괴에 적용한 결과, 저수지에서 수로 구간으로 충격파 전파를 잘 모의했으며, 기존의 연구결과와 유사한 모의결과를 나타냈다. 개수로의 부정류 해석에 대한 모형의 적용성을 평가하기 위하여 말단이

Blue spaces, water-based open spaces, are becoming focal points for urban vitalization. While previous studies have explored waterfronts’ various effects, little research has focused on their influence on actual visitation and vitality. This study addresses this gap by analyzing the effect of riverfronts on mobility using de facto population data, which tracks citizen activity by location through mobile information. The study focuses on two major rivers in northwestern Seoul, covering nine major

In this research, we propose a humidity compensation method using a deep learning network for minimizing the effect of humidity on the semiconductor metal oxide (SMO) gas sensors. The SMO gas sensors were fabricated by depositing In<inf>2</inf>O<inf>3</inf> on a suspended microheater platform, and the gas tests were conducted under various humidity conditions using NO<inf>2</inf> gas. The gas sensing data and humidity data were simultaneously used as input data of the deep learning network for r

In this study, we investigate the impact of rising time on alternating current (AC) stress-induced degradation in amorphous indium-tin-gallium-zinc oxide (a-ITGZO) TFTs through both experiments and simulations. When AC bias stresses with rising and falling times (<i>t</i>_r-f) of 400 ns, 200 ns, and 100 ns were applied to the a-ITGZO TFTs, the threshold voltage (<i>V</i>_TH) shifted positively by 0.97 V, 2.68 V, and 2.83 V, respectively. These experimental results align with