• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.248-248
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• 2020

토양수분은 증발산, 유출, 침투 등 물수지 요소들과 밀접한 연관이 있는 주요한 변수 중에 하나이다. 토양수분의 정도는 토양의 특성, 토지이용 형태, 기상 상태 등에 따라 공간적으로 상이하며, 특히 기상 상태에 따라 시간적 변동성을 보이고 있다. 기존 토양수분 측정은 토양시료 채취를 통한 실내 실험 측정과 측정 장비를 통한 현장 조사 방법이 있으나 시간적, 경제적 한계점이 있으며, 원격탐사 기법은 공간적으로 넓은 범위를 포함하지만 시간 해상도가 낮은 단점이 있다. 또한, 모델링을 통한 토양수분 예측 기술은 전문적인 지식이 요구되며, 복잡한 입력자료의 구축이 요구된다. 최근 머신러닝 기법은 수많은 자료 학습을 통해 사용자가 원하는 출력값을 도출하는데 널리 활용되고 있다. 이에 본 연구에서는 토양수분과 연관된 다양한 기상 인자들(강수량, 풍속, 습도 등)을 활용하여 머신러닝기법의 반복학습을 통한 토양수분의 예측 가능성을 분석하고자 한다. 이를 위해 시공간적으로 토양수분 실측 자료가 잘 구축되어 있는 청미천과 설마천 유역을 대상으로 머신러닝 기법을 적용하였다. 두 대상지에서 2008년~2012년 수문자료를 확보하였으며, 기상자료는 기상자료개방포털과 WAMIS를 통해 자료를 확보하였다. 토양수분 자료와 기상자료를 머신러닝 알고리즘을 통해 학습하고 2012년 기상 자료를 바탕으로 토양수분을 예측하였다. 사용되는 머신러닝 기법은 의사결정 나무(Decision Tree), 신경망(Multi Layer Perceptron, MLP), K-최근접 이웃(K-Nearest Neighbors, KNN), 서포트 벡터 머신(Support Vector Machine, SVM), 랜덤 포레스트(Random Forest), 그래디언트 부스팅 (Gradient Boosting)이다. 토양수분과 기상인자 간의 상관관계를 분석하기 위해 히트맵(Heat Map)을 이용하였다. 히트맵 분석 결과 토양수분의 시간적 변동은 다양한 기상 자료 중 강수량과 상대습도가 가장 큰 영향력을 보여주었다. 또한 다양한 기상 인자 기반 머신러닝 기법 적용 결과에서는 두 지역 모두 신경망(MLP) 기법을 제외한 모든 기법이 전반적으로 실측값과 유사한 형태를 보였으며 비교 그래프에서도 실측값과 예측 값이 유사한 추세를 나타냈다. 따라서 상관관계있는 과거 기상자료를 통해 머신러닝 기법 기반 토양수분의 시간적 변동 예측이 가능할 것으로 판단된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.456-477
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• 2024

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.3973-3982
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• 2023

As for two-phase flow in rectangular channels, the flow regimes especially like churn-turbulent and annular flow are significant for the physical problem like Countercurrent Flow Limitation (CCFL). In this study, the rectangular channels with cross-sections of 4 × 66 mm, 6 × 66 mm, 8 × 66 mm are adopted to investigate the flow regimes of air-water vertical upward two phase flow under adiabatic condition. The gas and liquid superficial velocities are 0 ≤ j_g ≤ 20m/s and 0.25 ≤ j_f ≤ 3m/s respectively which covering bubbly to annular flow. The flow regimes are identified by random forest algorithm and the flow regime maps are obtained. As the results, the transitional void fraction from slug to churn turbulent flow fluctuate from 0.47 to 0.58 which is significantly affected by the dimensional size of channel and flow rate. Besides, the void fraction at transitional points from churn-turbulent (slug) to annular flow are 0.66-0.67, which are independent with the gap size. Furthermore, a new criteria of slug to churn-turbulent flow is established in this study. In addition, by introducing the interfacial force model, the criteria of churn-turbulent (slug) flow to annular flow is verified.

• Korean Journal of Environment and Ecology
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• v.32 no.6
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• pp.686-697
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• 2018

Due to the advance in remote sensing technology, it has become easier to more frequently obtain high resolution imagery to detect delicate changes in an extensive area, particularly including forest which is not readily sub-classified. Time-series analysis on high resolution images requires to collect extensive amount of ground truth data. In this study, the potential of land coverage mapas ground truth data was tested in classifying high-resolution imagery. The study site was Wonju-si at Gangwon-do, South Korea, having a mix of urban and natural areas. KOMPSAT-3A imagery taken on March 2015 and land coverage map published in 2017 were used as source data. Two pixel-based classification algorithms, Support Vector Machine (SVM) and Random Forest (RF), were selected for the analysis. Forest only classification was compared with that of the whole study area except wetland. Confusion matrixes from the classification presented that overall accuracies for both the targets were higher in RF algorithm than in SVM. While the overall accuracy in the forest only analysis by RF algorithm was higher by 18.3% than SVM, in the case of the whole region analysis, the difference was relatively smaller by 5.5%. For the SVM algorithm, adding the Majority analysis process indicated a marginal improvement of about 1% than the normal SVM analysis. It was found that the RF algorithm was more effective to identify the broad-leaved forest within the forest, but for the other classes the SVM algorithm was more effective. As the two pixel-based classification algorithms were tested here, it is expected that future classification will improve the overall accuracy and the reliability by introducing a time-series analysis and an object-based algorithm. It is considered that this approach will contribute to improving a large-scale land planning by providing an effective land classification method on higher spatial and temporal scales.

• Journal of Korean Home Economics Education Association
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• v.23 no.4
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• pp.49-65
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• 2011

The survey method was used in this descriptive study. The purpose in this study was to investigate middle school home economics(HE) teachers' stages of concern, levels of use, and perception about career education, based on the Concerns Based Adoption Model(CBAM). Questionnaire was administrated to middle school HE teachers over the whole country except Jeju Island through mail or e-mail by systematic random sampling. 118 data collected from the responses were finally analyzed statistically with mean, standard deviation, frequencies, percentage, and independent-sample t-test by using SPSS/WIN 12.0 program. The results of the study were as follows: First, most-HE teachers were in stages of the personal concern (86.56) and the informational concern(85.42) about HE career education. Second, the highest number of teachers was in level of refinement(33.1%) use. Third, teachers recognized important goal of HE career education as 'understanding of diverse careers and formating of positive values and attitude about work'. Also they had generally conducted HE career education through 'Understanding Teenager' chapter of a first-year middle school. However they had struggled with difficulty such as lack of data, time, and expertise in conducting HE career education. Therefore, it is necessary that support such as development, supplies, and training opportunities of career education should be provided.

• Journal of Korean Society of Forest Science
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• v.102 no.4
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• pp.608-616
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• 2013

The objective of this study was to suggest contingent valuation method, simple random sampling method, systematic sampling method, maximum practice area method, and overlay practice area method as the estimation methods for forest management area to estimate carbon dioxide sink by forest management as stated in Article 3.4 of the Kyoto Protocol. A case study for Chungcheongbuk-do was performed using maximum practice area method which was thought of as the most rational method within MRV(Measurable, reportable and verifiable). The forest management ratio was defined as forest management area over the total forest area. Based on the definition of forest management activity, forest area was divided into practice forest land and non-practice forest land. The area of forest mangement for practices was based on the 'National Forest Management Information System' and the history of practices from the 'Proposals on Private Management Information Database'. The area of forest management for non-practices was based on the public used forest land from 'Categorial map of forest land'. It was shown that the forest management area of Chuncheongbuk-do was 115,566 ha for practice forest land, and 131,008 ha for non-practice forest land. The forest managent ratio was 49.7% of the total forest area of 495,806 ha. This study made possible to the estimation of the forest management ratio under Kyoto Protocol, and had an advantage in searching for a practical method of forest management plan in which the forest management ratio could be enhanced.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1179-1194
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• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.5
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• pp.981-986
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• 2020

The Internet of Things refers to a space-of-things connection network configured to allow things with built-in sensors and communication functions to interact with people and other things, regardless of the restriction of place or time.IoT is a network developed for the purpose of services for human convenience, but the scope of its use is expanding across industries such as power transmission, energy management, and factory automation. However, the communication protocol of IoT, MQTT, is a lightweight message transmission protocol based on the push technology and has a security vulnerability, and this suggests that there are risks such as personal information infringement or industrial information leakage. To solve this problem, we designed a synchronous MQTT security channel that creates a secure channel by using the characteristic that different chaotic dynamical systems are synchronized with arbitrary values in the lightweight message transmission MQTT protocol. The communication channel we designed is a method of transmitting information to the noise channel by using characteristics such as random number similarity of chaotic signals, sensitivity to initial value, and reproducibility of signals. The encryption method synchronized with the proposed key value is a method optimized for the lightweight message transmission protocol, and if applied to the MQTT of IoT, it is believed to be effective in creating a secure channel.

• Journal of Microbiology and Biotechnology
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• v.28 no.11
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• pp.1916-1927
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• 2018

Corynebacterium glutamicum is an excellent platform for the production of amino acids, and is widely used in the fermentation industry. Most industrial strains are traditionally obtained by repeated processes of random mutation and selection, but the genotype of these strains is often unclear owing to the absence of genomic information. As such, it is difficult to improve the growth and amino acid production of these strains via metabolic engineering. In this study, we generated a complete genome map of an industrial L-valine-producing strain, C. glutamicum XV. In order to establish the relationship between genotypes and physiological characteristics, a comparative genomic analysis was performed to explore the core genome, structural variations, and gene mutations referring to an industrial L-leucine-producing strain, C. glutamicum CP, and the widely used C. glutamicum ATCC 13032. The results indicate that a 36,349 bp repeat sequence in the CP genome contained an additional copy each of lrp and brnFE genes, which benefited the export of L-leucine. However, in XV, the kgd and panB genes were disrupted by nucleotide insertion, which increase the availability of precursors to synthesize L-valine. Moreover, the specific amino acid substitutions in key enzymes increased their activities. Additionally, a novel strategy is proposed to remodel central carbon metabolism and reduce pyruvate consumption without having a negative impact on cell growth by introducing the CP-derived mutant $H^+$/citrate symporter. These results further our understanding regarding the metabolic networks in these strains and help to elucidate the influence of different genotypes on these processes.

• Journal of the Korean Magnetics Society
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• v.17 no.5
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• pp.188-193
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• 2007

We simulated the magnetization reversal behavior of submicron-thickness magnetic films by applying pulses of sub-ns-long durations and amplitudes along the easy axis. The films were rectangular and elliptical $Ni_{80}Fe_{20}$, and their thickness was 2 nm and 4 nm. We observed different behaviors depending upon the shape and thickness of the films and found a normal non-switching in regions in which we expected complete switching after relaxation. In the elliptical film, the non-switching regions were found to be random and to be widely distributed throughout the switching map. The strong demagnetization field along the z-axis, the film thickness direction, is likely responsible for this abnormal behavior. In the rectangular film, the abnormal non-switching regions were less distributed than they were in the elliptical film due to edge domains resulting from the small $M_z$ or demagnetization field during the switching. Our simulation confirms that large demagnetization is detrimental to the ultra-fast magnetization reversal of magnetic ultra-thin films.