• The Journal of the Acoustical Society of Korea
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• v.39 no.2
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• pp.120-130
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• 2020

This paper solves the problem in underdetermined convolutive mixture by improving the disadvantages of the multichannel nonnegative matrix factorization technique widely used in blind source separation. In conventional researches based on Spatial Covariance Matrix (SCM), each element composed of values such as power gain of single channel and correlation tends to degrade the quality of the separated sources due to high variance. In this paper, level and frequency normalization is performed to effectively cluster the estimated sources. Therefore, we propose a novel SCM and an effective distance function for cluster pairs. In this paper, the proposed SCM is used for the initialization of the spatial model and used for hierarchical agglomerative clustering in the bottom-up approach. The proposed algorithm was experimented using the 'Signal Separation Evaluation Campaign 2008 development dataset'. As a result, the improvement in most of the performance indicators was confirmed by utilizing the 'Blind Source Separation Eval toolbox', an objective source separation quality verification tool, and especially the performance superiority of the typical SDR of 1 dB to 3.5 dB was verified.

• Journal of Korea Spatial Information System Society
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• v.7 no.3 s.15
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• pp.119-130
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• 2005

A spatial data warehouse is a system that stores geographical information as a subject oriented, integrated, time-variant, non-volatile collection for efficiently supporting decision. This system consists of a builder and a spatial data warehouse server. A spatial data warehouse server suspends user services, stores transferred data in the data repository and constructs index using stored data for short response time. Existing methods that construct index are bulk-insertion and index transfer methods. The Bulk-insertion method has high clustering cost for constructing index and searching cost. The Index transfer method has improper for the index reconstruction method of a spatial data warehouse where periodic source data are inserted. In this paper, the efficient index reconstruction method using a partial index in a spatial data warehouse is proposed. This method is an efficient reconstruction method that transfers a partial index and stores a partial index with expecting physical location. This method clusters a spatial data making it suitable to construct index and change treated clusters to a partial index and transfers pages that store a partial index. A spatial data warehouse server reserves sequent physical space of a disk and stores a partial index in the reserved space. Through inserting a partial index into constructed index in a spatial data warehouse server, searching, splitting, remodifing costs are reduced to the minimum.

• Journal of the Korean association of regional geographers
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• v.22 no.1
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• pp.1-24
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• 2016

This paper is to consider trends and issues of regional research in major sub-fields of human geography in Korea, following the previous one which dealt with contexts and general trends of new regional geography in Korea since the 2000s. They include historical and cultural geography on place and landscape, economic geography on industrial districts or agglomerated regions (i.e. clusters) and urban (and social) geography on urban networks and differentiation. Even though researchers in sub-fields have used different terms and concepts to identify region, they are in common to relate specificities of region to general processes such as (de)modernization, (de)industrialization, and globalization, to understand region as social and discursive constitution as well as substantive reality, and to give more attention to socio-spatial networks and relationality than territoriality of regions. These common points seem to reflect the emerging trend of new regional geography, and to get rid of existing traditional concept of region. It is suggested that major tasks for conceptualization of region in future research are to overcome dichotomy of speciality and generality, of substantive reality and discursive constitution, and of territoriality and relationality, and that important issues for empirical research on region include regional synthesis from new perspectives, uneven regional development as relational process in and between regions, and producing practice for alternative regions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.377-382
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• 2005

본 논문에서는 출입국자 관리의 효율성과 체계적인 출입국 관리를 위하여 여권 코드를 자동으로 인식하는 방법을 제안한다. 여권 이미지는 기울어진 상태로 스캔 되어 획득되어질 수도 있으므로 기울기 보정은 문자 분할 및 인식에 있어 매우 중요하다. 따라서 본 논문에서는 여권 영상을 스미어링한 후, 추출된 문자열 중에서 가장 긴 문자열을 선택하고 이 문자열의 좌측과 우측 부분의 두께 중심을 연결하는 직선과 수평선과의 기울기를 이용하여 여권 영상에 대한 각도 보정을 수행한다. 여권 코드 추출은 소벨 연산자와 수평 스미어링, 8방향 윤관선 추적 알고리즘을 적용하여 여권 코드의 문자열 영역을 추출하고, 추출된 여권 코드 문자열 영역에 대해 반복 이진화 방법을 적용하여 코드의 문자열 영역을 이진화 한다, 이진화된 문자열 영역에 대해 여권 코드의 인식율을 높이기 위하여 잡영 가지 제거 알고리즘을 적용하여 개별 문자의 잡영을 제거한 후에 개별 코드를 추출하며, CDM 마스크를 적용하여 추출된 개별코드를 복원한다. 추출된 개별코드는 개선된 퍼지 ART 알고리즘을 제안하여 인식에 적용한다. 실제 여권 영상을 대상으로 실험한 결과, CDM 마스크를 이용하여 추출된 개별 코드를 개선된 퍼지 ART 알고리즘을 적용하여 인식한 방법보다 잡영 제거 알고리즘과 CDM 마스크를 적용하여 개선된 퍼지 ART 알고리즘으로 개별 코드를 인식하는 것이 효율적인 것을 확인하였다. 그리고 기존의 퍼지 ART 알고리즘을 이용하여 개별 코드를 인식하는 경우보다 본 논문에서 제안한 개선된 퍼지 ART 알고리즘을 이용하여 개별 코드를 인식하는 경우가 서로 다른 패턴들이 같은 클러스터로 분류되지 않아 인식 성능이 개선되었다.생산하고 있다. 또한 이러한 자료를 바탕으로 지역통계 수요에 즉각 대처할 수 있다. 더 나아가 이와 같은 통계는 전 국민에 대한 패널자료이기 때문에 통계적 활용의 범위가 방대하다. 특히 개인, 가구, 사업체 등 사회 활동의 주체들이 어떻게 변화하는지를 추적할 수 있는 자료를 생산함으로써 다양한 인과적 통계분석을 할 수 있다. 행정자료를 활용한 인구센서스의 이러한 특징은 국가의 교육정책, 노동정책, 복지정책 등 다양한 정책을 정확한 자료를 근거로 수립할 수 있는 기반을 제공한다(Gaasemyr, 1999). 이와 더불어 행정자료 기반의 인구센서스는 비용이 적게 드는 장점이 있다. 예를 들어 덴마크나 핀란드에서는 조사로 자료를 생산하던 때의 1/20 정도 비용으로 행정자료로 인구센서스의 모든 자료를 생산하고 있다. 특히, 최근 모든 행정자료들이 정보통신기술에 의해 데이터베이스 형태로 바뀌고, 인터넷을 근간으로 한 컴퓨터네트워크가 발달함에 따라 각 부처별로 행정을 위해 축적한 자료를 정보통신기술로 연계${cdot}$통합하면 막대한 조사비용을 들이지 않더라도 인구센서스자료를 적은 비용으로 생산할 수 있는 근간이 마련되었다. 이렇듯 행정자료 기반의 인구센서스가 많은 장점을 가졌지만, 그렇다고 모든 국가가 당장 행정자료로 인구센서스를 대체할 수 있는 것은 아니다. 행정자료로 인구센서스통계를 생산하기 위해서는 각 행정부서별로 사용하는 행정자료들을 연계${cdot}$통합할 수 있도록 국가사회전반에 걸쳐 행정 체제가 갖추어져야 하기 때문이다. 특히 모든 국민 개개인에 관한 기본정보, 개인들이 거주하며 생활하는 단위인 개별 주거단위에 관한 정보가 행정부에 등록되어

• Korean Journal of Food Science and Technology
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• v.54 no.1
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• pp.80-87
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• 2022

We investigated the effects of organic acid and ultrasonication treatment in maintaining the quality of garlic during storage. Samples were exposed for 5 min to either ultrasonication at 60℃, 1% citric acid, or 0.5% fumaric acid. Presence of microorganisms and minerals, hardness, and color were compared during storage at 4℃ for 28 days. The total aerobic bacterial count remained low. No proliferation of Escherichia coli was observed after treatment with fumaric acid or ultrasonication, and mold proliferation was inhibited by ultrasonication. The mineral content of the northern type garlic was higher than that of the southern type. Exposure to fumaric acid did not result in a substantial difference in hardness until 21 days of storage, at which time there was a decrease in the L-value in each cultivation type. Our results indicate that treatment with 0.5% fumaric acid for 5 min was effective in reducing the abundance of microorganisms during storage without affecting the hardness or color in garlic.

• Korean Journal of Environmental Agriculture
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• v.20 no.4
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• pp.203-210
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• 2001

In order to environmentally use wood chips manufactured from low valued forest resources by forest tendering, wood chips were used for the evaluation on chips characteristics, decomposition capability of organic wastes, and field experiment and determination of conditions for decomposer. Bioclusters manufactured by Cryptomeria japonica, commercially available wood chips in Japan, showed higher pore ratio, water reservation and water resistance, and higher cellulose content with lower hot water solubles than domestic wood chips. The useful size of wood chips for swine manure decomposition was 10 (length) ${\times}$ 5 (width) ${\times}$ 2 (thickness) mm, and cellulose contents and alkali solubles of Pinus densiflora and Populus tomentiglandulosa were similar to those of bioclusters. According to the decomposition ratio depending on wood species, it was ordered as Pinus densiflora > Pinus koraiensis > Cryptomeria japonica. The swine manure decomposition ratio depending on treatment hours by Pinus koraiensis was constant with the ratio of 15 to 16 g per hour by 1 kg of chip, indicating of daily swine decomposition amount of 390 kg by 1 ton of chips which was equal to the amount of daily swine manure production by 70 swines. Analyzing by long term used wood chips during 40 days treatment, the treated wood chips characteristically showed stable total nitrogen content, suitable pH, high accumulation of inorganic contents such as calcium, phosphorus, potassium and sodium, and no odor. During winter, the inner temperature of decomposer was kept at $43^{\circ}C$, but air bubble was occurred due to high pH and viscosity of swine manure. The most appropriate mixing ratio between wood chips and swine manure was 1 versus 2 or 3, and at more than ratio 1 versus 3, ammonia gas was caused because of anaerobic fermentation status by high moisture content of wood chips. The mixing interval of decomposer was 3 mins. per hour for the best swine decomposition.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.293-302
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• 2016

Since the electricity is produced and consumed simultaneously, predicting the electric load and securing affordable electric power are necessary for reliable electric power supply. In particular, a university campus is one of the highest power consuming institutions and tends to have a wide variation of electric load depending on time and environment. For these reasons, an accurate electric load forecasting method that can predict power consumption in real-time is required for efficient power supply and management. Even though various influencing factors of power consumption have been discovered for the educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative prediction of electric load. In this paper, we build an electric load forecasting model by implementing and evaluating various machine learning algorithms. To do that, we consider three building clusters in a campus and collect their power consumption every 15 minutes for more than one year. In the preprocessing, features are represented by considering periodic characteristic of the data and principal component analysis is performed for the features. In order to train the electric load forecasting model, we employ both artificial neural network and support vector machine. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to real electric load.

• Journal of Service Research and Studies
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• v.3 no.2
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• pp.53-60
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• 2013

Datamining from the large data in the form of various techniques for obtaining information have been developed. In recent years one of the most sought areas of pattern recognition and machine learning method is created with most of existing learning algorithms based on categorical attributes to a rule or decision model. However, the real-world data, it may consist of numeric attributes in many cases. In addition it contains attributes with numerical values to the normal categorical attribute. In this case, therefore, it is required processes in order to use the data to learn an appropriate value for the type attribute. In this paper, the domain of the numeric attributes are divided into several segments using learning algorithm techniques of discritization. It is described Clustering with other data mining techniques. Large amount of first cluster with characteristics is similar records from the database into smaller groups that split multiple given finite patterns in the pattern space. It is close to each other of a set of patterns that together make up a bunch. Among the set without specifying a particular category in a given data by extracting a pattern. It will be described similar grouping of data clustering technique to classify the data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.239-242
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• 1996

본 논문에서는 BP(Back Propagation)에 비해서 빠른 학습시간과 다른 경쟁학습 신경회로망 알고리즘에 비해서 비교적 우수한 성능으로 패턴인식 등에 많이 이용되고 있는 LVQ(Learning Vector Quantization) 알고리즘의 성능을 향상시키기 위한 방법을 논의하고자 한다. 일반적으로 LVQ는 음(negative)의 학습을 하기 때문에 초기 가중치가 제대로 설정되지 않으면 발산할 수 있다는 단점이 있으며, 경쟁학습 계열의 신경망이기 때문에 출력 층의 뉴런 수에 따라 성능에 큰 영향을 받는다고 알려져 있다.[1]. 지도학습 형태를 지닌 LVQ의 경우에 학습패턴이 n개의 클래스를 가지고, 각 클래스 별로 학습패턴의 수가 같은 경우에 일반적으로 전체 출력뉴런에 대해서 (출력뉴런수/n)개의 뉴런을 각 클래스의 목표(desired) 클러스터로 할당하여 학습을 수행하는데, 본 논문에서는 각 클래스에 동일한 수의 출력뉴런을 할당하지 않고, 학습데이터에서 각 클래스의 분산을 추정하여 각 클래스의 분산을 추정분산에 비례하게 목표 출력뉴런을 할당하고, 초기 가중치도 추정분산에 비례하게 각 클래스의 초기 임의 위치 입력백터를 사용하여 학습을 수행하는 방법을 제안한다. 본 논문에서 제안하는 방법은 분류하고자 하는 데이터에 대해서 필요한 최적의 출력뉴런 수를 찾는 것이 아니라 이미 결정되어 있는 출력뉴런 수에 대해서 각 클래스에 할당할 출력 뉴런 수를 데이터의 추정분산에 의해서 결정하는 것으로, 추정분산이 크면 상대적으로 많은 출력 뉴런을 할당하고 작으면 상대적으로 적은 출력뉴런을 할당하고 초기 가중치도 마찬가지 방법으로 결정하며, 이렇게 하면 정해진 출력뉴런 개수 안에서 각 클래스 별로 분류의 어려움에 따라서 출력뉴런을 할당하기 때문에 미학습 뉴런이 줄어들게 되어 성능의 향상을 기대할 수 있으며, 실험적으로 제안된 방법이 더 나은 성능을 보임을 확인했다.initially they expected a more practical program about planting than programs that teach community design. Many people are active in their own towns to create better environments and communities. The network system "Alpha Green-Net" is functional to support graduates of the course. In the future these educational programs for citizens will becomes very important. Other cities are starting to have their own progrms, but they are still very short term. "Alpha Green-Net" is in the process of growing. Many members are very keen to develop their own abilities. In the future these NPOs should become independent. To help these NPOs become independent and active the educational programs should consider and teach about how to do this more in the future.단하였는데 그 결과, 좌측 촉각엽에서 제4형의 신경연접이 퇴행성 변화를 나타내었다. 그러므로 촉각의 지각신경세포는 뇌의 같은 족 촉각엽에 뻗어와 제4형 신경연접을 형성한다고 결론되었다.$/ 값이 210 $\mu\textrm{g}$/$m\ell$로서 효과적인 저해 활성을 나타내었다 따라서, 본 연구에서 빈

• Journal of the Microelectronics and Packaging Society
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• v.27 no.2
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• pp.33-38
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• 2020

In the photoelectrochemical (PEC) water splitting, GaN is one of the most promising photoanode materials due to high stability in electrolytes and adjustable energy band position. However, the application of GaN is limited because of low efficiency. To improve solar to hydrogen conversion efficiency, we introduce a Cobalt Phosphate (Co-pi) catalyst by photo-electrodeposition. The Co-pi deposition GaN were characterized by SEM, EDS, and XPS, respectively, which illustrated that Co-pi was successfully decorated on the surface of GaN. PEC measurement showed that photocurrent density of GaN was 0.5 mA/㎠ and that of Co-pi deposited GaN was 0.75 mA/㎠. Impedance and Mott-Schottky measurements were performed, and as a result of the measurement, polarization resistance (R_p) and increased donor concentration (N_D) values decreased from 50.35 Ω to 34.16 Ω were confirmed. As a result of analyzing the surface components before and after the water decomposition, it was confirmed that the Co-pi catalyst is stable because Co-pi remains even after the water decomposition. Through this, it was confirmed that Co-pi is effective as a catalyst for improving GaN efficiency, and when applied as a catalyst to other photoelectrodes, it is considered that the efficiency of the PEC system can be improved.