• 제목/요약/키워드: Compression classes

검색결과 37건 처리시간 0.027초

퍼지시스템에 의한 부영상의 적응분류와 영상데이타 압축에의 적용 (Adaptive Classification of Subimages by the Fuzzy System for Image Data Compression)

  • Kong, Seong-Gon
    • 대한전기학회논문지
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    • 제43권7호
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    • pp.1193-1205
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    • 1994
  • This paper presents a fuzzy system that adaptively classifies subimages to four classes according to image activity distribution. In adaptive transform image coding, subimage classification improves the compression performance by assigning different bit maps to different classes. A conventional classification method sorts subimages by their AC energy and divides them to classes with equal number of subimages. The fuzzy system provides more flexible classification to natural images with various distribution of image details than does the conventional method. Clustering of training data in the input-output product space generated the fuzzy rules for subimage classification. The fuzzy system of small number of fuzzy rules successfully classified subimages to improve the compression performance of the transform image coding without sorting of AC energies.

수입 의료용 압박스타킹의 제조국가별 비교 (A Comparison of Imported Medical Compression Stockings by Manufacturing Country)

  • 도월희;김남순
    • 한국의류학회지
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    • 제36권3호
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    • pp.335-345
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    • 2012
  • This study provides product survey data for the development of medical compression stockings. An investigation analyzed imported medical compression stockings of 40 brands from 6 countries sold in the Korean market, such as Jobst$^{(R)}$, Therafirm$^{(R)}$, Rxtar$^{(R)}$, Varicoin$^{(R)}$, and Sheer&Soft (USA), Sigvaris Venosan$^{(R)}$ 4000 (Switzerland), Best$^{(R)}$ and Segreta$^{(R)}$ (Italy), Venex$^{(R)}$, Star cotton, Doktus$^{(R)}$, Maxis$^{(R)}$, Maxis$^{(R)}$ Cotton, Lastofa$^{(R)}$, and Memory Aloe Vera (Germany), and Gunze (Japan), Venos and Yolanda (Taiwan). The main fibers of compression stockings were nylon and spandex; in addition, the fiber content was different by country and brand. The number of compression classes of imported products was USA (5), Italy (5), Germany (4), Switzerland (3), Japan (3), and Taiwan (3). For basic body measurements, USA and Swiss brands used ankle circumference, calf circumference, thigh circumference, calf length, and thigh length. Italian brands used height and weight, and Japanese brands used height and hip circumference. German brands used subdivided circumferences such as ankle circumference, calf circumference, knee circumference, middle thigh circumference, and thigh circumference.

슬래그모래를 사용한 모르터의 압축강도특성 비교에 관한 실험적 연구 (A Experimental Study on the Comparison of the Compression Strength Characteristics of Mortar using the Blast-Furnace Slag Sand)

  • 김종락;김성식;이복만;임남기;정상진
    • 한국콘크리트학회:학술대회논문집
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    • 한국콘크리트학회 1999년도 봄 학술발표회 논문집(I)
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    • pp.40-45
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    • 1999
  • This experimental study presents the strength properties of mortar using the blast-furnace slag sand. The mix disign of this study is based on the each three classes of unit water; (250, 275, 300)kg/㎥ and four classes of W/C; (45, 50, 55, 60)% and substitution rate(0, 25, 50, 75, 100)%. It gives following result. As W/C ratio increase, the strength is decrease. In case of mortar using air-cooled blast-furnace slag sand, the 3-days and 7-days compression strength is increase as substitution rate is higher. But in case of the mortar using the quenched blast-furnace slag sand, the compression strength is decrease as substitution rate is higher.

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다층 신경회로망 학습에 의한 정지 영상의 벡터 (Vector Quantization Compression of the Still Image by Multilayer Perceptron)

  • 이상찬;최태완;김지홍
    • 한국정보처리학회논문지
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    • 제3권2호
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    • pp.390-398
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    • 1996
  • 본 논문에서는 다층 신경회로망의 일반화 특성을 이용한 새로운 영상 압축 알 고리즘을 제안한다. 제안 알고리즘은 벡터 양자화방식을 이용하여 영상을 몇 개의 클래스로 분류하고 이들을 다층 신경회로망으로 학습한다. 이렇게 학습된 다층신경회 로망은 일반화 특성에 의하여 무 학습의 영상에 대해서도 압축과 복원을 수행 한다. 아울러 벡터 양자화방식에 있어서 벡터 양자화 오차와 수신측에서의 메모리를 감소시 킨다. 본 논문에서는 Lena 영상을 학습 영상으로 하여 이를 16개의 클래스로 나누고 각 클래스를 1개의 다층 신경회로망으로 학습하였다. 그리고 학습에 사용된 Lean 영상 및 무 학습 영상들에 대하여 압축과 복원을 수행하여 우수한 화질의 영상이 복원 되어 짐이 보인다.

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의료용 화상환자 압박복의 제조 국가별 비교 (Comparison of Medical Compression Garments by Manufacturing Country)

  • 조신현
    • 한국의상디자인학회지
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    • 제17권4호
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    • pp.31-39
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    • 2015
  • A special medical compression garment has been developed to be worn after surgery or treatment using an elastic fiber in which the amount of compression can be adjusted to a specified targeted area to reduce the formation of hypertrophic scarring. In order to develop this medical compression garments, specialized technical skill in fiber, compression class and body measurements are needed. This study provides product survey data for the development of medical compression garments. An investigation analyzed medical compression garments of 16 brands from 6 countries sold in the Korean market & online, such as Make Me Heal, Jobst, Bio Concepts, Design Veronique$^{(R)}$ Nouvelle and Leonisa$^{(R)}$ (USA), Respire(Germany), Malcom$^{(R)}$, Holistic garments and Jobskin(UK), Technomed, kanav Healthcare and Sindhoori surgicals(India), Soo medical and C&C medical(Korea), Secondskin(Australia). The main fibers of compression garments were nylon and spandex: in addition, the fiber content was different by country and brand. The number of compression classes of products was USA(4), UK(4), and India(4). For body measurements, USA and many brands used (bust, under bust, waist, hip, thigh, knee, calf, ankle, upper arm, elbow, wrist, armhole bicep) circumference and length.

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웨이브릿 영역에서의 영역분류와 대역간 예측 및 선택적 벡터 양자화를 이용한 다분광 화상데이타의 압축 (Multispectral Image Compression Using Classification in Wavelet Domain and Classified Inter-channel Prediction and Selective Vector Quantization in Wavelet Domain)

  • 석정엽;반성원;김병주;박경남;김영춘;이건일
    • 대한전자공학회:학술대회논문집
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    • 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
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    • pp.31-34
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    • 2000
  • In this paper, we proposed multispectral image compression method using CIP (classified inter-channel prediction) and SVQ (selective vector quantization) in wavelet domain. First, multispectral image is wavelet transformed and classified into one of three classes considering reflection characteristics of the subband with the lowest resolution. Then, for a reference channel which has the highest correlation with other channels, the variable VQ is performed in the classified intra-channel to remove spatial redundancy. For other channels, the CIP is performed to remove spectral redundancy. Finally, the prediction error is reduced by performing SVQ. Experiments are carried out on a multispectral image. The results show that the proposed method reduce the bit rate at higher reconstructed image quality and improve the compression efficiency compared to conventional method.

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On-Board Satellite MSS Image Compression

  • Ghassemian, Hassan;Amidian, Asghar
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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    • pp.645-647
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    • 2003
  • In this work a new method for on-line scene segmentation is developed. In remote sensing a scene is represented by the pixel-oriented features. It is possible to reduce data redundancy by an unsupervised segment-feature extraction process, where the segment-features, rather than the pixelfeatures, are used for multispectral scene representation. The algorithm partitions the observation space into exhaustive set of disjoint segments. Then, pixels belonging to each segment are characterized by segment features. Illustrative examples are presented, and the performance of features is investigated. Results show an average compression more than 25, the classification performance is improved for all classes, and the CPU time required for classification is reduced by the same factor.

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영상압축을 위한 코넨네트워크 (KOHONEN NETWORK FOR ADAPTIVE IMAGE COMPRESSION)

  • 손형경;이영식;배철수
    • 한국정보통신학회:학술대회논문집
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    • 한국해양정보통신학회 2001년도 추계종합학술대회
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    • pp.571-574
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    • 2001
  • 본 논문에서는 코호넨 네트워크를 이용한 효과적인 적응 코딩 방법을 제안한다. 신경망을 응용한 압축법 분석을 통해 설명되는 코딩방법은 압축률을 높이기 위해서 우선 영상을 8$\times$8 부영상으로 나누고, 나눠진 모든 부영상은 DCT로 변형한다. 이들 DCT 부블럭들은 코호넨 네트워크로 N(4) 등급으로 나누어지게 되고, 비트들은 DCT 부블럭의 변수에 따라 분류된다. 그래서 N(4)비트 할당 행렬을 얻었다. 실험 결과는 시뮬레이션으로 나타내었고, 제안한 방법이 신경네트워크에서의 AC 에너지에 의해 부영상을 분류하는 것보다 우수하다는 결론을 얻을 수 있었다.

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Material Properties and Compressibility Using Heckel and Kawakita Equation with Commonly Used Pharmaceutical Excipients

  • Choi, Du-Hyung;Kim, Nam-Ah;Chu, Kyung-Rok;Jung, Youn-Jung;Yoon, Jeong-Hyun;Jeong, Seong-Hoon
    • Journal of Pharmaceutical Investigation
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    • 제40권4호
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    • pp.237-244
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    • 2010
  • This study investigated basic material properties and compressibility of commonly used pharmaceutical excipients. Five classes of excipients are selected including starch, lactose, calcium phosphate, microcrystalline cellulose (MCC), and povidone. The compressibility was evaluated using compression parameters derived from Heckel and Kawakita equation. The Heckel plot for lactose and dicalcium phosphate showed almost linear relationship. However, for MCC and povidone, curves in the initial phase of compression were observed followed by linear regions. The initial curve was considered as particle rearrangement and fragmentation and then plastic deformation at the later stages of the compression cycle. The Kawakita equation showed MCC exhibited higher compressibility, followed by povidone, lactose, and calcium phosphate. MCC undergoes significant plastic deformation during compression bringing an extremely large surface area into close contact and facilitating hydrogen bond formation between the plastically deformed, adjacent cellulose particles. Lactose compacts are consolidated by both plastic deformation and fragmentation, but to a larger extent by fragmentation. Calcium phosphate has poor binding properties because of its brittle nature. When formulating tablets, selection of suitable pharmaceutical excipients is very important and they need to have good compression properties with decent powder flowability. Material properties tested in this study might give a good guide how to select excipients for tablet formulations and help the formulation scientists design the optimum ones.

Adaptive Transform Image Coding by Fuzzy Subimage Classification

  • Kong, Seong-Gon
    • 한국지능시스템학회논문지
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    • 제2권2호
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    • pp.42-60
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    • 1992
  • An adaptive fuzzy system can efficiently classify subimages into four categories according to image activity level for image data compression. The system estimates fuzzy rules by clustering input-output data generated from a given adaptive transform image coding process. The system encodes different images without modification and reduces side information when encoding multiple images. In the second part, a fuzzy system estimates optimal bit maps for the four subimage classes in noisy channels assuming a Gauss-Markov image model. The fuzzy systems respectively estimate the sampled subimage classification and the bit-allocation processes without a mathematical model of how outputs depend on inputs and without rules articulated by experts.

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