Earticle

컴퓨터과학의 원리를 교육의 기반이 되는 알고리즘 교육이 초등학교에서부터 강조되고 있다. 그러나 초등학생에 적합한 알고리즘을 교육시키고 초등학생들이 이해하기는 어렵다. 본 연구에서는 초등학생들이 알고리즘에 대해 쉽게 배울 수 있도록 검증되어진 컴퓨터과학 언플러그드의 프로그램을 기반으로 교육과정 및 학습자료를 개발하였다. 학습방법은 활동중심학습으로 구성 하였으며, 학습내용은 해싱기법을 사용하여 주어진 자료를 정렬하고, 요구되는 자료를 찾는 교육과정을 제시하였다. 본 연구에서 제시한 교육과정과 학습자료는 전문가 집단의 검증 및 정보담당 선생님의 설문 분석을 통해 적절하다는 결론을 얻었다.

Algorithm education that become at the base of computational thinking is emphasized as an instrument for teaching the basic principles of Computer Science. We’ll present ‘the sorting teaching contents for algorithm in the elementary student. And they will successfully guide the student to understand sorting using algorithm. The activity-based learning is provided for the contents, and the data will be found out in everyday life. To check the adequacy of these materials they were tested to the elementary classroom, and the results can help to enhance the problem solving ability and the creativity.

본 연구에서는 실행 기능 평가를 위해 널리 쓰이고 있는 신경심리검사인 위스콘신 카드 분류 검사(Wisconsin Card Sorting Test-64 card version: WCST-64)에 대한 한국 정상 노인 규준을 산출하고자 하였다. 연구 참여자는 경기도 성남시에 거주 중인 468명(남:251명, 여:217명)의 정상 노인으로 연령 범위는 65-94세이며 교육연한 범위는 0-22년이다. 수행 분석 결과, 성별은 검사 점수에 유의미한 영향을 주지 않는 것으로 나타나 규준에는 제외되었으며 연령과 교육연한에 따라 규준을 세분화하였다. 연령은 중복 연령 규준표를 적용하여 70-74세, 75-79세, 80-84세, 85-90세 노인에게 사용할 수 있도록 구분하였으며, 교육연한은 0-6년, 7년 이상으로 층화하였다. 연령과 교육연한에 따라 8개로 세분화된 규준 단위에는 각각의 평균, 표준편차, 5백분위값, 중앙치, 95백분위값을 제시하였다.

Wisconsin Card Sorting Test-64 card version(WCST-64) is a neuropsychological assessment instrument, that is widely used to evaluate executive function. The aim of this study is to provide normative data of the WCST-64 performance in the Korean elderly. The WCST-64 was administered to 468 elders (male: 251, female: 217), aged 65-94 years, who were recruited from participants in the Korean Longitudinal Study on Health and Aging(KLOSHA). Age and educational level had a significant effect on the WCST-64 score, but gender did not. The norms are stratified by age (70-74yrs, 75-79yrs, 80-84yrs, 85-90yrs) and education (0-6yrs, above 7yrs). For the resulting 8 normative units, mean, standard deviation, 5%ile, median and 95%ile were calculated.

CD133, originally classified as a neural stem cell marker, could be used for enrichment of cancer stem cells (CSCs). In the current study, we analyzed the immunophenotypic characteristics of cancer cell lines derived from brain tumor lesions of 5 meningioma patients. Interestingly, tumor spheres only generated from the specimen of high-grade meningioma cell line (atypical meningioma cell line, WHO grade II), whereas there was no sphere formation in typical meningioma cell line (WHO grade I). The self-renewing capacity of these tumor sphere was assayed by dissociation of primary tumor spheres, and plating of single cells and maintained in the conditional medium. These single cells produced secondary sphere formation after another 7 days, indicating that these meningioma cell lines in spheres were capable of self-renewal and proliferation. Using flow cytometry, we observed the pattern of expression of dual markers CD44/CD133 in atypical meningioma cell line. The cytometry dot plot of CD133 expression in atypical meningioma cells with about 20.84% CD133− (gate Q4) and 79.15% CD133+ cells (gate Q2) before sorting. The CD44+CD133+ subpopulations showed stem cell properties, including extensive proliferation, instead of their CD44+CD- 133- counterparts. A primary culture of brain cancer cells and isolation of CD133+ CSCs might be used as a patient-specific, biology-driven tool to predict the outcome at diagnosis and to study the mechanism of the brain tumor.

The objective of this study was to establish a selection process for high quality sperm in bovine semen using sperm separation by magnetic activation (MACS). For this, semen from 21 Nellore bulls was collected using an artificial vagina. To guarantee the presence of pathologies in the ejaculate, animals previously declassified in four consecutive spermiogram were used. Semen was analyzed in five statuses: (1) fresh semen (fresh); (2) density gradient centrifugation (DGC), percoll column; (3) non-apoptotic fraction after separation by MACS (MAC); (4) apoptotic fraction from the separation (MACPOOR); and (5) MAC followed by DGC (MACDGC). Using a computerized analysis system (CASA), motility was measured. The sperm morphology was evaluated by phase contrast, and the supravital test was completed with eosin/nigrosin staining. For DGC, 20 × 106 cells were used in a gradient of 90% and 45% percoll. MACS used 10 × 106 cells with 20 μL of nanoparticles attached to annexin V, and filtered through the MiniMACS magnetic separation column. Membrane integrity was assessed with SYBR-14/IP and mitochondrial potential with JC-1 by flow cytometry. Processing sperm by MACDGC, was more effective in obtaining samples with high quality sperm, verified by the total of abnormalities in the samples: 35.04 ± 2.29%, 21.50 ± 1.47%, 17.30 ± 1.10%, 30.68 ± 1.94% and 10.50 ± 1.46%, respectively for fresh, DGC, MAC, MACPOOR, and MACDGC. The subpopulation of non-apoptotic sperm had a high number of live cells (82.65%), membrane integrity (56.60%) and mitochondrial potential (83.98%) (p < 0.05). These findings suggest that this nanotechnological method, that uses nanoparticles, is efficient in the production of high-quality semen samples for assisted reproduction procedures in cattle.

In 2013, highly concentrated rare-earth-rich mud (REE-rich mud) was discovered around Minami-Torishima Island, presenting an opportunity to develop a new source of rare-earth elements (REEs). This study proposes a new method for selectively removing non-REE components using magnetic separation. First, we calculated the theoretical levitation positions of the target minerals using the magneto-Archimedes method, considering the mineral density, magnetic susceptibility, and magnetic field distribution of a high-temperature superconducting (HTS) bulk magnet. Next, we conducted separation experiments to selectively isolate REEadsorbed apatite from a simulated mud sample consisting of a mixture of apatite, montmorillonite, quartz, and zeolite. The results demonstrated that non-apatite minerals were successfully removed using magnetic levitation method. Based on these results, we applied the method to actual core samples (REE-rich mud) fromthe coast of Minami-Torishima Island. These experiments showed that phillipsite could be selectively separated from the core sample using magnetic levitation. However, the overall weight reduction was less significant than that achieved with the simulated REE-rich mud. This discrepancy was attributed to the inability to separate the paramagnetic mineral montmorillonite into the levitated fraction, because of its swelling property and cation exchange capacity, and location-dependent variability in the mineral composition of the core samples. Possible solutions to this problem include improving the sample dispersion and combining this method with other separation techniques.

Spermatogonial stem cells (SSCs) can produce sperms transferring genetic information into the next generation in seminiferous tubule of testes. Accordingly, an efficient isolation technique of SSCs with extremely low numbers from testes is required for successful downstream researches related to maintenance, differentiation and cryopreservation of SSCs. To date, a variety of isolation techniques in a variety of species have been used for retrieving SSCs from testicular tissue. However, comparison of their efficiency has been not revealed clearly. Accordingly, among isolation methods described previously, we tried to elucidate a technique showing the best isolation efficiency in the retrieval of SSCs from testes derived from mice. For these, SSCs were isolated from mouse testis as follows: differential plating (DP), EpCAM, Thy1 or GFRα1 antibody-based magnetic-activating cell sorting (MACS) post-DP, EpCAM, Thy1 or GFR α1 antibody-based MACS, EpCAM, Thy1 or GFRα1 antibody-based MACS post-MACS based on GFRα1 antibody (double MACS for positive selection), and CD34 or α-SMA antibody-based MACS post-MACS based on GFRα1 antibody (double MACS for negative selection). Subsequently, SSCs isolated from each method were stained by alkaline phosphatase (AP) staining and percentage of AP positive SSCs was compared to find an optimal method among isolation method candidates. As the results, SSCs isolated by DP for 8 h showed numerically the highest percentage of AP positive SSCs. In case of MACS post-DP, SSCs isolated by MACS based on Thy1 antibody post-DP (MACSThy1 post-DP) revealed numerically higher percentage of AP positive cells than those on EpCAM and GFRα1 antibodies post-DP. Moreover, numerically the highest percentage of AP positive SSCs was detected when SSCs were isolated from mouse testis by MACS based on GFRα1 (MACSGFRα1) compared to EpCAM and Thy1 antibodies. In case of double MACS for positive selection, the usage of EpCAM antibody in the second MACS post-GFRα1 antibody-based MACS (double MACSGFRα1/EpCAM) showed numerically the highest percentage of AP positive SSCs compared to Thy1 and GFRα1 antibodies. On the other hands, after GFRα1 antibody-based MACS sorting, the usage of α-SMA antibody in the second MACS (double MACSGFRα1/α-SMA) for negative selection showed numerically higher percentage of AP positive SSCs than CD34 antibody. The subsequent comparison of isolation efficiency derived from each method demonstrated that MACSGFRα1 and double MACSGFRα1/EpCAM resulted in significantly the best isolation efficiency. Accordingly, we could elucidate that MACSGFRα1 and double MACSGFRα1/EpCAM were techniques sorting effectively SSCs from mouse testes.

유사성행렬은 MDS 분석 등 다양한 다차원적 분석의 기초자료로 활용된다. 유사성행렬을 얻는 방법으로 항목의 수가 상대적으로 적을 때는 쌍대비교법이 사용될 수 있으나 항목의 수가 많을 경우 분류과제가 권장된다. 본 연구에서는 분류과제의 한 형태로서 지정분류과제의 특성과 제약을 두 개의 연구를 통해 알아보았다. 실험 1에서는 분류 순서가 MDS의 차원구조에 영향을 주는지를 알아보기 위해 분류 순서를 달리 한 두 종류의 지시문에서 얻은 MDS 차원구조를 비교하였는데 MDS 평면상에서 항목들의 상대적 위치는 서로 다르지 않았다. 목록 구성을 달리해서 목록구성의 영향을 분석한 실험 2의 결과는 각각의 목록구성에 합당한 차원구조를 산출하였다. 두 연구의 결과는 지정분류과제가 분류순서에 영향을 받지 않으면서 안정된 차원구조를 보여주며, 항목집합에 내재된 차원구조를 잘 드러낸다는 것이다. 이 결과를 근거로 지정분류과제의 몇 가지 특성과 제약에 관하여 논의하였다.

A similarity matrix provides basic data for multidimensional scaling analysis such as MDS and HCA. A similarity matrix can be obtained through paired comparison methods or sorting tasks. A sorting task is preferred when the number of item is large. We investigated the traits and constraints of a bounded sorting task, which is a subcategory of sorting task, in two studies. In Exp. 1, we compared two MDS planes from different sorting orders, but found no difference in the axis values. In Exp. 2, we compared two MDS planes from the different item constructions, and found a difference in the interpretation of axis meaning inherent in item construction. The results of the two studies showed that bounded sorting tasks produce stable dimensions free from sorting order, and sensible dimensions sensitive to the item construction. On the basis of these findings some traits and constraints were discussed.

스크랩금속의 재활용 향상을 위하여 Al, Mg과 같은 경금속 스크랩의 자동분류에 관한 새로운 기술개발이 진행되고 있다. 자동분류시스템은 스크랩 금속의 겉보기밀도와 3차원 형상의 차이에 의해 비교적 큰 벌크상태의 스크랩의 선별이 가능하며, 선형레이져 투영과 광학기능을 동시에 갖춘 3차원 화상카메라를 스크랩 이동이 이루어지는 콘베이어 위에 설치함으로써 이에 의해 스크랩의 직경, 높이, 부피와 불규칙 형상의 투영면적과 같은 형상지수를 측정하게 된다. 측정된 형상지수와 무게를 다중변수 분석 프로그램에 의해 처리, 변환시킴으로써, 폐차 슈레더시설로부터 발생되는 알루미늄주조재, 전신재, 마그네슘의 혼합시편을 분류하는데 사용할 수 있다. 이번에 발표하는 개발기술은 경금속 스크랩에 한정되지 않고 이 후 현재 일반적으로 실시하고 있는 중금속스크랩선별 공정을 대체할 수 있는 기반기술이라고 판단된다.

A new method for the automatic sorting of lightweight metals (Al, Mg) scrap has been developed to aid the recycling of scrap metal. The sorting system enables us to separate large metal pieces according to the differences in their apparent density and three-dimensional (3D) shape. Shape parameters such as width, height, volume, and projected area of irregular-shaped metal pieces moving along a conveyer are measured by means of a 3D imaging camera system consisting of a linear laser and camera with associated optics. The measured values of the weight and shape parameters are transferred to our own data-processing software that uses multi-variate analysis. Mixed fragments of cast aluminum, wrought aluminum, and magnesium from an end-of-life vehicles shredder facility were sorted based on the output of the data-processing software. The results show that the developed automatic sorting system is a highly viable method that could replace conventional dense medium separation and manual sorting.

본 연구에서는 라인스캔 카메라를 이용한 색도인식 스크랩 선별시스템을 설계 제작하고 제작한 시스템을 이용하여 철스크랩에 혼합되어 있는 Cu 스크랩을 자동으로 분리하는 연구를 수행하였다. 스크랩 자동선별 시스템은 크게 측정부, 이송부 그리고 이젝터로 구분되며 라인스캔 카메라, 광원 및 frame grabber로 구성된 측정부에서 스크랩 표면의 색도를 이메지 프로쎄싱 알고리즘에 의해 인식함으로써, 임의로 지정한 특정한 표면색상의 스크랩만에 에어노즐을 작동케하여 선별하도록 되어 있다. 본 연구에서는 선별처리의 고속화에 대응하기 위하여 주파수 가변 광원시스템을 제작하여 선별시스템에 적용하였으며, 최적실험조건으로 스크랩 이송속도 25 m/min.에서 철스크랩중에 포함되어 있는 Cu스크랩을 90%이상 인식하여 약 80%의 선별효율을 얻었다.

In this study, a scrap sorting system using a color recognition method has been developed to automatically sort out specified materials from a mixture, and its application has been examined in the separation of Cu and other non-ferrous metal parts from a mixture of iron scraps. The system is composed of three parts; measuring, conveying and ejecting parts. The color of scrap surface is recognized by the measuring part consisting of a line-scan camera, light sources and a frame grabber. The recognition is program-controlled by a image processing algorithms, and thus only the scrap part of designated color is separated by the use of air nozzles. In addition, the light system is designed to meet a high speed of sorting process with a frequency-variable inverter and the air nozzled ejectors are to be operated by an I/O interface communication with a hardware controller. In the functional tests of the system, its efficiency in the recognition of Cu scraps from its mixture with Fe ones reaches to more than 90%, and that in the separation more than 80% at a conveying speed of 25 m/min. Therefore, it is expected that the system can be commercialized in the industry of shredder makers if a high efficiency ejecting system is realized.

폐자동차 범퍼 파쇄물 재활용을 위하여 색채선별법을 도입하였다. 색채선별법은 기존의 비중, 입도 차이에 의한 선별이 어려운물질을 색상 차이를 이용한 카메라와 영상 분석기법으로 분리하는 선별법이다. 본 연구에서는 반응표면법 중 BBD (Box-Behnken Design)를 적용하여 실험을 계획하고 최적 조건을 도출하였다. 색채민감도, 피드투입량 및 입자크기의 영향을 분석하였으며, 회귀분석과 통계적인 방법에 기초하여 2차 반응 모델을 획득하였다. R 2 및 p-value는 각각 99.56%, < 0.001로 타당하였으며, 추정된 최적조건은 색채민감도 32%, 피드투입 200 kg/h, 입자크기 33 mm 조건에서 94.1%의 회수율이 나올 것으로 예측하였다. 실제 실험을통한 회수율은 93.8%로 나타나 해당 모델이 적절함을 확인하였다.

An color sorting technique was introduced to recycle End-of-life automobile shredded bumpers. The color sorting is a innovate method of separating the differences in the color of materials which are difficult to separate in gravity and size classification by using a camera and an image process technique. Experiments were planned and optimal conditions were derived by applying BBD (Box-Behnken Design) in the reaction surface method. The effects of color sensitivity, feed rate and sample size were analyzed, and a second-order reaction model was obtained based on the analysis of regression and statistical methods and R 2 and p-value were 99.56% and < 0.001. Optimum recovery was 94.1% under the conditions of color sensitivity, feed rate and particle size of 32%, 200 kg/h, and 33 mm respectively. The recovery of actual experiment was 93.8%. The experimental data agreed well with the predicted value and confirmed that the model was appropriate.

소형가전 제품은 종류가 다양할 뿐만 아니라 구성부품의 재질도 복잡하여 폐기시 재활용이 매우 어려운 실정이다. 특히, 폐소형가전의 경우 흑색 플라스틱의 함유량이 높을 뿐만 아니라 재질이 다양하여 재활용 공정에서 발생하는 플라스틱의 재질을 인식하여효율적으로 선별 회수하는 것이 매우 어렵다. 본 연구에서는 기존 선별기술이 가지고 있는 흑색 플라스틱의 재질별 선별에 대한 기술적 한계 및 단점을 보완하기 위하여 레이저유도붕괴분광법(Laser-Induced Breakdown Spectroscopy, LIBS)을 기반으로 하는 흑색플라스틱의 재질별 자동선별 시스템을 개발하였다. 본 시스템은 정량 공급장치, 위치 자동인식 장치, 레이저유도기반분광분석(LIBS) 장치, 선별분리장치 및 Control unit 등으로 구성되어 있다. 레이저유도붕괴분광법(LIBS)을 이용하여 흑색 플라스틱의 재질별 특성스펙트럼 데이터를 획득하고, 인공지능형 알고리즘을 적용한 분류기를 설계하여 적용함으로써 흑색 플라스틱의 재질을 효율적으로인식하고 분류할 수 있다. 본 연구에서 개발한 방사형기저함수신경회로망(RBFNNs) 분류기의 분류율은 약 97% 이상으로 나타났으며, 자동선별 시스템의 흑색 플라스틱의 재질별 인식률은 약 94.0% 이상, 선별효율은 80.0% 이상으로 조사되었다. 본 연구에서는실험실 규모의 자동선별장치를 개발하였으며, 본 장치에 대한 실험결과를 바탕으로 흑색 플라스틱 재질인식 및 선별효율 등을 분석하므로써 향후 폐소형가전의 재활용 현장에 적용할 예정이다.

Used small household appliances have a wide variety of product types and component materials, and contain high percentage of black plastics. However, they are not being recycled efficiently as conventional sensors such as near-infrared ray (NIR), etc. are not able to detect black plastic by types. In the present study, an automatic sorting system was developed based on laserinduced breakdown spectroscopy (LIBS) to promote the recycling of waste plastics. The system we developed mainly consists of sample feeder, automatic position recognition system, LIBS device, separator and control unit. By applying laser pulse on the target sample, characteristic spectral data can be obtained and analyzed by using CCD detectors. The obtained data was then treated by using a classifier, which was developed based on artificial intelligent algorithm. The separation tests on waste plastics also were carried out by using a lab-scale automatic sorting system and the test results will be discussed. The classification rate of the radial basis neural network (RBFNNs) classifier developed in this study was about > 97%. The recognition rate of the black plastic by types with the automatic sorting system was more than 94.0% and the sorting efficiency was more than 80.0%. Automatic sorting system based on LIBS technology is in its infant stage and it has a high potential for utilization in and outside Korea due to its excellent economic efficiency.

본 연구에서는 레이저유도붕괴분광(Laser Induced Breakdown Spectroscopy, LIBS)을 이용하여 방사형 기저함수 신경회로망(Radial Basis Function Neural Networks, RBFNNs) 분류기 설계방법론을 개발하고 실제 폐소형가전제품의 플라스틱 분류 시스템에 적용하였다. ABS, PP, PS와 같은 검정색 플라스틱을 구별하기 위해, 지능형 알고리즘 중 하나인 방사형 기저함수 신경회로망 분류기를 설계하였다. 획득한 입력변수는 주성분 분석법(Principal Component Analysis, PCA)을 이용하여 축소시켰으며, 군집화기법 중 하나인K-means 클러스터링 방법을 이용해 여러 그룹으로 분할하였다. 전체 데이터는 학습 데이터와 테스트 데이터를 4:1의 비율로 나누었으며, 제안된 분류기의 성능 및 신뢰도를 평가하기 위하여 5-FCV(5-Fold Cross Validation) 기법을 사용하였다. 입력변수와 클러스터의 개수가 각각 5개인 경우, 제안된 분류기의 분류 성능은 96.78%로 나타났다. 또한, 제안된 분류기는 다른 분류기들과 비교하였을 경우 분류 성능의 관점에서 우수성을 보여주었다.

In this study, the design methodology of Radial Basis Function Neural Networks is developed with the aid of Laser Induced Breakdown Spectroscopy and also applied to the practical plastics sorting system. To identify black plastics such as ABS, PP, and PS, RBFNNs classifier as a kind of intelligent algorithms is designed. The dimensionality of the obtained input variables are reduced by using PCA and divided into several groups by using K-means clustering which is a kind of clustering techniques. The entire data is split into training data and test data according to the ratio of 4:1. The 5-fold cross validation method is used to evaluate the performance as well as reliability of the proposed classifier. In case of input variables and clusters equal to 5 respectively, the classification performance of the proposed classifier is obtained as 96.78%. Also, the proposed classifier showed superiority in the viewpoint of classification performance where compared to other classifiers.

폐소형가전의 재활용 공정에서 발생하는 플라스틱류 중 검정색 플라스틱의 경우 재질 선별이 어려워 혼합물의형태로 저급 재활용 되고 있다. 본 연구에서는 검정색 플라스틱의 재질별 선별을 위하여 비중선별, 정전선별, 근적외선 선별 및 IR/Raman 분광법 등 기존 선별 기술의 검토를 통하여, 현장 적용에 대한 문제점 및 제한사항을 확인하였다. 검정색 플라스틱의 재질선별에 대한 기술적 한계를 극복하기 위하여 레이저유도붕괴분광법(Laser Induced Breakdown Spectroscopy, LIBS)을 이용하여 검정색 플라스틱의 각 재질별 LIBS 스펙트럼을 분석하였으며, 정규화과정을 수행한 후 차원축소 알고리즘인 주성분 분석(Principal Component Analysis, PCA)을 수행하였다. 또한, 검정색 플라스틱의 각 재질별 LIBS 스펙트럼 분석 및 주성분 분석을 통하여 향후 재질별 인식 및 선별 기술의 현장적용을 위해서는 추가적으로 해결해야 할 문제점을 확인하였다. 검정색 플라스틱의 재질별 인식의 정확성 및 선별효율의 향상을 위하여 지능형 알고리즘 분야의 연구를 통하여 효율이 우수한 분류기를 설계하고 분류기의 성능 및신뢰도 향상을 위한 연구가 추가적으로 진행되어야 할 것으로 사료된다.

Used small household appliances(small e-waste) consists of a variety of complex materials and components. The small ewaste is mainly composed of plastics and an important potential source of waste plastic. The black plastics, particularly are very difficult to separate by resin type and therefore these are mainly recycled in the form of a mixtures. In the present study, the sorting technologies such as gravity and electro static separation, near-infrared ray(NIR) and IR/Raman optical sorting separation on mixture of black plastics were analyzed and their limitations on sorting process were also investigated. The Laser Induced Breakdown Spectroscopy(LIBS) spectrum of each black plastics was used for identification of black plastics by resin type, and after analyzing the normalization operation, Principal Component Analysis(PCA) was carried out. The spectrum data was optimized through PCA process. In order to improve the identification accuracy and sorting efficiency of black plastics, it is necessary to design a classifier with high efficiency and to improve the performance and reliability of the classifier by applying the field of intelligent algorithms.

Automatic tablet dispensing & packing system (ATDPS) is becoming essential in the drug management field of the hospital pharmacy and the local pharmacy. The ATDPS device automatically sorts, distributes, and packages in connection with the order communication system (OCS) or electronic medical record (EMR). In the process of using the ATDPS, some pre-packaged drugs cannot be applied to the patient. Such drugs are re-used after disassembling the packaging. However, many problems such as drug contamination, breakage, and mixing with other drugs can arise during the drug re-using process. Therefore, automation is urgently required in the drug re-using process. Automatic sorting device has not yet been introduced to the domestic pharmacy. The aim of this study was to develop a semi-automatic pills sorting device (SPSD) for the purpose of cost-saving, enhancing operation efficiency, and reducing medication errors. The proposed SPSD is affordable and efficient with potential for use in the pharmacy to help drug management.

본 연구에서는 폐소형가전 재활용을 위한 파쇄, 선별 및 회수 공정에 대해 전과정평가(Life Cycle Assessment, LCA) 기법을 활용하여 환경영향을 분석하였다. 폐소형가전 파쇄, 선별 및 회수 공정의 지구온난화(Global Warming Potential, GWP)에 대한 환경영향은 1.29E+02kg CO2-eq./kg으로 분석되었으며, 가중치 결과 1.17E-02Pt로 지구온난화(GWP)에 대한 환경영향의 경우 전체 환경영향의 약 57.1%, 자원고갈(ADP) 약 35.4%, 광화학산화물 형성(POCP) 약 4.8% 등이었다. 유용자원 1톤을 선별/회수하는 공정의 지구온난화(GWP)에 대한 환경영향은 플라스틱류 즉, ABS 33.7%, PP와 HIPS가 각각 약 16.8%, 철금속류 약 9.4% 등으로 플라스틱류의 선별/회수공정의 경우 지구온난화에 대한 환경영향이 가장 큰 것으로 분석되었다. 또한, 유용자원을 회수하여 재활용하므로서신재 생산보다 지구온난화(GWP) 환경영향에 대하여 약 2.73E + 04ton CO2-eq./ton의 환경회피 효과가 발생하였으며, 이는 연간 약48,236.4천원의 경제적 효과가 발생하는 것으로 조사되었다. 폐소형가전으로부터 유용자원을 효율적으로 재활용할 경우 지구온난화(GWP)에 대한 환경영향을 줄여 기후변화 대응에 효과적일 것으로 사료된다.

This study aims at evaluating environmental impacts on recycling process of used small household appliances. The recycling process mainly consists of manual dismantling, crushing and various sorting processes to effectively recover valuable resources and to minimize environmental impact. In this study, life-cycle assessment (LCA) methodology is applied to analyze major environmental parameters such as GWP, ADP, POCP, EP, etc. One of the major impact categories on the weight basis in the recycling process is global warming (GWP) 57.1%, next to ADP 35.4% and POCP 4.8%, respectively. As a result of environmental impact on recovery of valuable resources/ton, the GWP of plastics for ABS is highest (33.7%) compared to ferrous metals (9.4%). The effects of environmental and economical benefit are also analyzed to compare with the amount of virgin materials to be recycled by recycled materials. In addition, recycled materials are also more economical in comparison to virgin materials due to the envi-ronmental avoiding effect by recycling. In conclusion, the key environmental issues related to the recycling of e-wastes are analyzed and therefore, the effective recycling process will contribute to mitigate global warming potential in the near future.

본 연구는 학령기 정상 아동집단을 대상으로 위스콘신 카드분류검사(WCST)의 10개 채점지표에 나타나는 발달추세를 검증하고 이들 채점지표가 측정하는 인지요인을 파악하는 것을 목적으로 실시되었다. 컴퓨터 버전 WCST(Heaton, 1991)을 7세에서 11세 연령범위의 초등학생 160명에게 실시하였다. KEDI-WISC로 측정하였을 때 이들 아동의 전체지능은 80에서 138(M=112.02, SD=11.77)의 범위에 속하였으며 연령집단별 전체지능의 유의미한 차이는 나타나지 않았다. WCST의 10개 채점지표를 사용하여 주축요인화법(principal axis factoring)을 사교회전 방법(oblique rotation)을 사용하여 실시한 결과 아이겐값이 1이상인 3개의 요인이 추출되었으며 이들 요인은 ‘개념형성(요인 Ⅰ: 설명변량 60.24%)’, ‘보속성(요인 Ⅱ: 설명변량 18.52%)’, ‘주의유지능력(요인 Ⅲ: 설명변량 12.12%)으로 해석되었다. 7-11세까지 5개의 연령집단의 수행을 분석하였을 때 10개의 채점지표중 ‘지속실패’를 제외한 9개의 채점지표에서 연령집단의 주효과가 유의미한 것으로 나타났다. 그러나 성별 및 성별과 연령의 상호작용 효과는 유의미하지 않았다. 요인 Ⅰ에 가장 높은 요인 부하값을 나타낸 ‘전체 시행수’, ‘비보속오류수’, ‘개념수준반응수’, ‘완성범주수’와 ‘첫범주완성시행수’의 5개 채점지표는 모두 7세에서 8세 사이에 유의미하게 수행이 향상되는 것으로 나타났다. 반면 요인 Ⅱ에 높은 요인 값을 나타낸 ‘보속오류수’는 9세에 비하여 11세에서 유의미하게 감소하는 것으로 나타났다. 이러한 결과는 WCST가 단일 인지기능을 측정하기보다는 서로 다른 발달 경로를 가진 중다인지기능을 측정하는 도구임을 지지하는 것이다.

Although the Wisconsin Card Sorting Test (WCST) has been widely used to evaluate the frontal lobe functioning, there is no published data on normal children in Korea which results in limitation of its clinical validity in children. The present study was conducted to investigate developmental trends of normal children`s performance on the WCST. The computerized version of WCST (Heaton, 1991) was administered to 160 elementary school children aged 7 to 11 years. Their Full Scale IQs measured by the Korean version of WISC-R ranged from 80 to 138 (M=111.02, SD=11.77). Children did not differ in FSIQ, VIQ, and PIQ when compared based on age. However, very moderate correlations were found between FSIQ, VIQ, and PIQ scores and the scoring variables. Out of the 10 WCST scoring variables, 9 variables except Failure to Maintain Set showed age effect, but sex difference was not found in any variables. There was also no significant interaction effect between age and sex. Important differences in developmental trends were found among the 10 WCST scoring variables. The nature of these differences was effectively explained by the three factors. From the principal axis analysis with oblique rotation using the 10 WCST scoring variables, three factors with eigenvalue greater than 1 emerged, interpreted as conceptual formation (Factor Ⅰ: accounted for 60.24% of variance), perseveration (Factor Ⅱ: 18.52% of variance), and ability to sustain attention (Factor Ⅲ: 12.12% of variance). The growth curves for the 5 scoring variables constituting Factor Ⅰ(Total Number of Corrects, Nonperseverative Errors, Conceptual level Responses, Number of Categories Completed, and Trials to Complete First Category) showed significant improvement from age 7 to 8, suggesting a developmental spurt during that period. By contrast, Perseveration Errors included in Factor Ⅱ significantly decreased from age 9 to 11, while Failure to Maintain Set included in Factor Ⅲ showed no age effect. These results suggest that the WCST performance requires multiple cognitive functioning with different developmental trends.

본 연구에서는 레이저유도 플라즈마 분광법(Laser induced breakdown spectroscopy, LIBS) 기반의 금속 종류별 스펙트럼 데이터를 이용하여 연성정보(soft information)를 추정하고 최빈 클래스로 분류하는(most probable classification) 금속 분류 방법을 제안한다. 폐금속 자원과 같이 사전 정보가 없는 금속을 분류하는 경우 몇 가지 핵심 구성성분에 대한 정량 분석을 통해서 클래스를 추정하는 방법이 효율적이다. 이에 따라 부분 집합 기반의 부분최소제곱회귀법(Partial Least Square Regression, PLSR)을 이용하여LIBS 검출 스펙트럼으로부터 각 성분의 농도를 독립적으로 신뢰성 있게 추정하고, 인증 표준물질(CRM) 등 알려진 모집합의 농도정보에 기반하여 최고 확률을 갖도록 분류하는 기술을 제안한다. 샘플 스펙트럼들의 다변량 분석을 통해서 여러 성분의 추정 농도를 다변량 정규 분포를 갖는 것으로 가정하고 통합(Joint) 추정 연성정보를 구할 수 있으며, 이를 활용한 최빈 확률 검출이나 추가적인 사전 정보의 결합 등을 통해서 분류 성능을 향상시킬 수 있다. 제안된 기술의 평가를 위해서 9가지 종류의 CRM 금속시료의LIBS 스펙트럼 데이터를 사용하며, 부분 집합 기반의 PLSR 농도 추정 기술을 기반으로 단변량 혹은 다변량 정규 분포 연성 정보추정을 통해 미지 금속의 검출과 연성 정보의 검출 등을 테스트 하였다. 또한 방사형 차트(Radar chart)를 이용하여 추정된 농도와획득한 연성정보를 효과적으로 시각화함으로써 기존 라이브러리에 포함된 부분 집합의 금속과 비교하여 해당 금속과의 유사성을 그래프를 통해 추정할 수 있다.

In this study, a novel soft information based most probable classification scheme is proposed for sorting recyclable metal alloys with laser induced breakdown spectroscopy (LIBS). Regression analysis with LIBS captured spectrums for estimating concentrations of common elements can be efficient for classifying unknown arbitrary metal alloys, even when that particular alloy is not included for training. Therefore, partial least square regression (PLSR) is employed in the proposed scheme, where spectrums of the certified reference materials (CRMs) are used for training. With the PLSR model, the concentrations of the test spectrum are estimated independently and are compared to those of CRMs for finding out the most probable class. Then, joint soft information can be obtained by assuming multi-variate normal (MVN) distribution, which enables to account the probability measure or a prior information and improves classification performance. For evaluating the proposed schemes, MVN soft information is evaluated based on PLSR of LIBS captured spectrums of 9 metal CRMs, and tested for classifying unknown metal alloys. Furthermore, the likelihood is evaluated with the radar chart to effectively visualize and search the most probable class among the candidates. By the leave-one-out cross validation tests, the proposed scheme is not only showing improved classification accuracies but also helpful for adaptive post-processing to correct the mis-classifications.

국내의 경우 내화재는 일정 기간이 지나면 대부분 폐기 처분되고 있으며, 폐기되는 내화재의 50% 이상이 탄화되지 않은 상태이다. 본 연구에서는 내화재를 색상인식 방법으로 선별하여 재활용 가능성을 검토하였다. 선별기 광원의 색온도가 자연광에 가까운 6,500K에서 순도 97.2%의 회수물을 얻었으며, 자연광에 가까울수록 주변 광원의 간섭이 적어 선별효율이 우수한 것으로 조사되었다. 실험결과 투입 컨베이어 벨트의 속도 800mm/sec에서 순도 98.8%, 회수율 98.1%로 조사되었으며, 시료크기 20∼30mm에서 순도 및 회수율은 각각 98.2%, 98.4%로 조사되었다. 회수물의 순도 및 선별효율은 컨베이어 벨트의 속도가 느릴수록, 시료의 크기가 클수록 향상되는 것으로 조사되었다. 그러나, 본 색상 자동선별 시스템의 처리용량 및 폐내화재의 재활용성 등을 고려할 경우 최적의 선별조건은 색온도 6,500K, 컨베이어 벨트 속도 1,000mm/sec, 시료 크기 20mm이하인 것으로 판단된다. 카메라 해상도를 고정하고 인지 영역을 좁게 하여 시료 크기 -10mm, 벨트컨베이어 속도 1,000mm/sec에서 순도 97%이상, 회수율 및 선별효율이 각각 98%, 96%이상인 것으로 나타났으며, 동일해상도에서 인지영역이 좁을수록 순도 및 선별효율을 높일 수 있는 것으로 조사되었다.

More than 50% of refractory materials generated from high-temperature melting furnace was not carbonized and could be recycled by adopting proper separation process. In the present work, the separation of refractory materials has studied by adopting color sorting technology to promote the recycling of waste refractory. Purity of the refractory materials was obtained with at 97.2%, color temperature of sorter light source 6,500K, which gives less interference of surrounding light source. Purity and separation efficiency were improved as size is setting bigger and lower conveyer belt speed. It is revealed that optimum conditions were color temperature 6,500K, conveyer belt speed 1,000mm/sec, particle size -20mm, etc. To improve purity and separation efficiency on below 10mm size, the resolution of should be fixed camera and it narrow recognition range. As a result of the study, color sorting technology could be used for separation of waste refractory materials and will contribute to promote the waste recycling.