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Automatic gasometer reading system using selective optical character recognition (관심 문자열 인식 기술을 이용한 가스계량기 자동 검침 시스템)

  • Lee, Kyohyuk;Kim, Taeyeon;Kim, Wooju
    • Journal of Intelligence and Information Systems
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    • v.26 no.2
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    • pp.1-25
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    • 2020
  • In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

A Ranking Algorithm for Semantic Web Resources: A Class-oriented Approach (시맨틱 웹 자원의 랭킹을 위한 알고리즘: 클래스중심 접근방법)

  • Rho, Sang-Kyu;Park, Hyun-Jung;Park, Jin-Soo
    • Asia pacific journal of information systems
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    • v.17 no.4
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    • pp.31-59
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    • 2007
  • We frequently use search engines to find relevant information in the Web but still end up with too much information. In order to solve this problem of information overload, ranking algorithms have been applied to various domains. As more information will be available in the future, effectively and efficiently ranking search results will become more critical. In this paper, we propose a ranking algorithm for the Semantic Web resources, specifically RDF resources. Traditionally, the importance of a particular Web page is estimated based on the number of key words found in the page, which is subject to manipulation. In contrast, link analysis methods such as Google's PageRank capitalize on the information which is inherent in the link structure of the Web graph. PageRank considers a certain page highly important if it is referred to by many other pages. The degree of the importance also increases if the importance of the referring pages is high. Kleinberg's algorithm is another link-structure based ranking algorithm for Web pages. Unlike PageRank, Kleinberg's algorithm utilizes two kinds of scores: the authority score and the hub score. If a page has a high authority score, it is an authority on a given topic and many pages refer to it. A page with a high hub score links to many authoritative pages. As mentioned above, the link-structure based ranking method has been playing an essential role in World Wide Web(WWW), and nowadays, many people recognize the effectiveness and efficiency of it. On the other hand, as Resource Description Framework(RDF) data model forms the foundation of the Semantic Web, any information in the Semantic Web can be expressed with RDF graph, making the ranking algorithm for RDF knowledge bases greatly important. The RDF graph consists of nodes and directional links similar to the Web graph. As a result, the link-structure based ranking method seems to be highly applicable to ranking the Semantic Web resources. However, the information space of the Semantic Web is more complex than that of WWW. For instance, WWW can be considered as one huge class, i.e., a collection of Web pages, which has only a recursive property, i.e., a 'refers to' property corresponding to the hyperlinks. However, the Semantic Web encompasses various kinds of classes and properties, and consequently, ranking methods used in WWW should be modified to reflect the complexity of the information space in the Semantic Web. Previous research addressed the ranking problem of query results retrieved from RDF knowledge bases. Mukherjea and Bamba modified Kleinberg's algorithm in order to apply their algorithm to rank the Semantic Web resources. They defined the objectivity score and the subjectivity score of a resource, which correspond to the authority score and the hub score of Kleinberg's, respectively. They concentrated on the diversity of properties and introduced property weights to control the influence of a resource on another resource depending on the characteristic of the property linking the two resources. A node with a high objectivity score becomes the object of many RDF triples, and a node with a high subjectivity score becomes the subject of many RDF triples. They developed several kinds of Semantic Web systems in order to validate their technique and showed some experimental results verifying the applicability of their method to the Semantic Web. Despite their efforts, however, there remained some limitations which they reported in their paper. First, their algorithm is useful only when a Semantic Web system represents most of the knowledge pertaining to a certain domain. In other words, the ratio of links to nodes should be high, or overall resources should be described in detail, to a certain degree for their algorithm to properly work. Second, a Tightly-Knit Community(TKC) effect, the phenomenon that pages which are less important but yet densely connected have higher scores than the ones that are more important but sparsely connected, remains as problematic. Third, a resource may have a high score, not because it is actually important, but simply because it is very common and as a consequence it has many links pointing to it. In this paper, we examine such ranking problems from a novel perspective and propose a new algorithm which can solve the problems under the previous studies. Our proposed method is based on a class-oriented approach. In contrast to the predicate-oriented approach entertained by the previous research, a user, under our approach, determines the weights of a property by comparing its relative significance to the other properties when evaluating the importance of resources in a specific class. This approach stems from the idea that most queries are supposed to find resources belonging to the same class in the Semantic Web, which consists of many heterogeneous classes in RDF Schema. This approach closely reflects the way that people, in the real world, evaluate something, and will turn out to be superior to the predicate-oriented approach for the Semantic Web. Our proposed algorithm can resolve the TKC(Tightly Knit Community) effect, and further can shed lights on other limitations posed by the previous research. In addition, we propose two ways to incorporate data-type properties which have not been employed even in the case when they have some significance on the resource importance. We designed an experiment to show the effectiveness of our proposed algorithm and the validity of ranking results, which was not tried ever in previous research. We also conducted a comprehensive mathematical analysis, which was overlooked in previous research. The mathematical analysis enabled us to simplify the calculation procedure. Finally, we summarize our experimental results and discuss further research issues.

No-reference objective quality assessment of image using blur and blocking metric (블러링과 블록킹 수치를 이용한 영상의 무기준법 객관적 화질 평가)

  • Jeong, Tae-Uk;Kim, Young-Hie;Lee, Chul-Hee
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.46 no.3
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    • pp.96-104
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    • 2009
  • In this paper, we propose a no-reference objective Quality assessment metrics of image. The blockiness and blurring of edge areas which are sensitive to the human visual system are modeled as step functions. Blocking and blur metrics are obtained by estimating local visibility of blockiness and edge width, For the blocking metric, horizontal and vertical blocking lines are first determined by accumulating weighted differences of adjacent pixels and then the local visibility of blockiness at the intersection of blocking lines is obtained from the total difference of amplitudes of the 2-D step function which is modelled as a blocking region. The blurred input image is first re-blurred by a Gaussian blur kernel and an edge mask image is generated. In edge blocks, the local edge width is calculated from four directional projections (horizontal, vertical and two diagonal directions) using local extrema positions. In addition, the kurtosis and SSIM are used to compute the blur metric. The final no-reference objective metric is computed after those values are combined using an appropriate function. Experimental results show that the proposed objective metrics are highly correlated to the subjective data.

Suggestions on the Types of the Distribution of Gardens for the Overseas Establishment of Traditional Korean Gardens - Oriented the Garden which is Applicable to the Open Space - (한국전통정원 해외조성을 위한 정원보급 유형 제안 - 공공 공간에 적용될 정원을 대상으로 -)

  • Kwon, Jin-Wook;Park, Eun-Yeong;Hong, Kwang-Pyo;Hwang, Min-Ha
    • Journal of the Korean Institute of Traditional Landscape Architecture
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    • v.31 no.3
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    • pp.106-113
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    • 2013
  • This study aims to establish the identity of traditional Korean gardens and develop a universal way for overseas Koreans and foreigners to have an appropriate understanding of traditional Korean gardens, as part of efforts to distribute and promote the overseas establishment of traditional Korean gardens. The focus of this study is on developing planning and design guidelines to ensure that traditional Korean gardens have individuality when they are established overseas and on establishing directional rules for planners. Although traditional Korean gardens may vary in form according to their purposes and spatial scales, the most important thing is that they should incorporate emotions that are well-matched with Korean landscapes and that their design language should be easily recognizable and understandable to everyone. The basic spatial types of traditional Korean gardens for overseas establishment, which are presented in this study, include the exhibition(fair) type, the garden type and the park type. These basic types serve as prototypes that correspond to the purposes of the gardens. In consideration of the spatial scale, the exhibition(fair) type is set as the minimum unit for composition, and suggested basic facilities include trees, a well, a pond and an island in the pond, flower beds and fences. The results of this study have significance as basic information for planning and designing traditional Korean gardens for overseas establishment.

Postprocessing of Inter-Frame Coded Images Based on Convex Projection and Regularization (POCS와 정규화를 기반으로한 프레임간 압출 영사의 후처리)

  • Kim, Seong-Jin;Jeong, Si-Chang;Hwang, In-Gyeong;Baek, Jun-Gi
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.39 no.3
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    • pp.58-65
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    • 2002
  • In order to reduce blocking artifacts in inter-frame coded images, we propose a new image restoration algorithm, which directly processes differential images before reconstruction. We note that blocking artifact in inter-frame coded images is caused by both 8×8 DCT and 16×16 macroblock based motion compensation, while that of intra-coded images is caused by 8×8 DCT only. According to the observation, we Propose a new degradation model for differential images and the corresponding restoration algorithm that utilizes additional constraints and convex sets for discontinuity inside blocks. The proposed restoration algorithm is a modified version of standard regularization that incorporate!; spatially adaptive lowpass filtering with consideration of edge directions by utilizing a part of DCT coefficients. Most of video coding standard adopt a hybrid structure of block-based motion compensation and block discrete cosine transform (BDCT). By this reason, blocking artifacts are occurred on both block boundary and block interior For more complete removal of both kinds of blocking artifacts, the restored differential image must satisfy two constraints, such as, directional discontinuities on block boundary and block interior Those constraints have been used for defining convex sets for restoring differential images.

Carotid Artery Intima-Media Thickness Measured by Iterated Layer-cluster Discrimination (순차적 층위군집(層位群集)판별에 의한 경동맥 내중막 두께 측정)

  • Hwang Jae-Ho;Kim Wuon-Shik
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.43 no.5 s.311
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    • pp.89-100
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    • 2006
  • The carotid intima-media thickness (IMT) is very important, because the severity of it is an independent predictor of transient cerebral ischemia, stroke, and coronary events such as myocardial infarction. The conventional image processing to measure the IMT has not been satisfactory, because the methods have relied on the manual section drawing and a regional segmentation by differential estimation. We propose a new image processing technology effective to extract features from the carotid artery image whose pixels have the directional vector properties with composed color distribution. The technique we presented here is not by differential variation but by verification of the layer properties of carotid artery image. Iterated vertical and horizontal analysis and segmentation of the IMT image show the vector characteristics. This new technique makes it possible to cluster the layers statistically, and to classify mathematical correlation between regions and resulting in correct measurements of thickness and its variation. The advantages and effectiveness of this approach are applicable to region process and character extraction of such a vector image.

Design of a Planar LPDA Antenna with Light-Weight Supporting Structure for Installing on an Aircraft (항공기 탑재용 경량화 지지 구조를 갖는 평면 LPDA 안테나 설계)

  • Park, Young-Ju;Park, Dong-Chul
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.27 no.3
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    • pp.253-260
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    • 2016
  • This paper proposes a planar Log-Periodic Dipole Array(LPDA) antenna with light-weight supporting structure for installing on an aircraft. The proposed antenna is designed by applying a planar skeleton supporting structure that has light-weight for an aircraft and is capable of withstanding structural vibration. The material of the planar skeleton supporting structure is a Polyether ether ketone(Peek) which has excellent characteristics on strength and temperature. The proposed antenna is fabricated by attaching the radiating elements of the LPDA on both sides of the supporting structure. The changed input impedance due to the dielectric material of the supporting structure was compensated for by controlling the distance and length of several radiating elements. The 10-dB return loss bandwidths of the designed planar LPDA antenna with light-weight supporting structure are obtained as 0.4~3.1 GHz(7.3:1) in the simulation and 0.41~3.5 GHz(8.2:1) in the measurement. The average gains in 0.5~3 GHz band are 6.77 dBi in the simulation and 6.55 dBi in the measurement. Therefore, we confirm that the designed antenna is appropriate to be installed on an aircraft due to its light-weight structure and wideband directional radiation characteristics.

SSQUSAR : A Large-Scale Qualitative Spatial Reasoner Using Apache Spark SQL (SSQUSAR : Apache Spark SQL을 이용한 대용량 정성 공간 추론기)

  • Kim, Jonghoon;Kim, Incheol
    • KIPS Transactions on Software and Data Engineering
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    • v.6 no.2
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    • pp.103-116
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    • 2017
  • In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner, which can derive new qualitative spatial knowledge representing both topological and directional relationships between two arbitrary spatial objects in efficient way using Aparch Spark SQL. Apache Spark SQL is well known as a distributed parallel programming environment which provides both efficient join operations and query processing functions over a variety of data in Hadoop cluster computer systems. In our spatial reasoner, the overall reasoning process is divided into 6 jobs such as knowledge encoding, inverse reasoning, equal reasoning, transitive reasoning, relation refining, knowledge decoding, and then the execution order over the reasoning jobs is determined in consideration of both logical causal relationships and computational efficiency. The knowledge encoding job reduces the size of knowledge base to reason over by transforming the input knowledge of XML/RDF form into one of more precise form. Repeat of the transitive reasoning job and the relation refining job usually consumes most of computational time and storage for the overall reasoning process. In order to improve the jobs, our reasoner finds out the minimal disjunctive relations for qualitative spatial reasoning, and then, based upon them, it not only reduces the composition table to be used for the transitive reasoning job, but also optimizes the relation refining job. Through experiments using a large-scale benchmarking spatial knowledge base, the proposed reasoner showed high performance and scalability.

Motion Linearity-based Frame Rate Up Conversion Method (선형 움직임 기반 프레임률 향상 기법)

  • Kim, Donghyung
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.18 no.7
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    • pp.734-740
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    • 2017
  • A frame rate up-conversion scheme is needed when moving pictures with a low frame rate is played on appliances with a high frame rate. Frame rate up-conversion methods interpolate the frame with two consecutive frames of the original source. This can be divided into the frame repetition method and motion estimation-based the frame interpolation one. Frame repetition has very low complexity, but it can yield jerky artifacts. The interpolation method based on a motion estimation and compensation can be divided into pixel or block interpolation methods. In the case of pixel interpolation, the interpolated frame was classified into four areas, which were interpolated using different methods. The block interpolation method has relatively low complexity, but it can yield blocking artifacts. The proposed method is the frame rate up-conversion method based on a block motion estimation and compensation using the linearity of motion. This method uses two previous frames and one next frame for motion estimation and compensation. The simulation results show that the proposed algorithm effectively enhances the objective quality, particularly in a high resolution image. In addition, the proposed method has similar or higher subjective quality than other conventional approaches.

T-Commerce Sale Prediction Using Deep Learning and Statistical Model (딥러닝과 통계 모델을 이용한 T-커머스 매출 예측)

  • Kim, Injung;Na, Kihyun;Yang, Sohee;Jang, Jaemin;Kim, Yunjong;Shin, Wonyoung;Kim, Deokjung
    • Journal of KIISE
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    • v.44 no.8
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    • pp.803-812
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    • 2017
  • T-commerce is technology-fusion service on which the user can purchase using data broadcasting technology based on bi-directional digital TVs. To achieve the best revenue under a limited environment in regard to the channel number and the variety of sales goods, organizing broadcast programs to maximize the expected sales considering the selling power of each product at each time slot. For this, this paper proposes a method to predict the sales of goods when it is assigned to each time slot. The proposed method predicts the sales of product at a time slot given the week-in-year and weather of the target day. Additionally, it combines a statistical predict model applying SVD (Singular Value Decomposition) to mitigate the sparsity problem caused by the bias in sales record. In experiments on the sales data of W-shopping, a T-commerce company, the proposed method showed NMAE (Normalized Mean Absolute Error) of 0.12 between the prediction and the actual sales, which confirms the effectiveness of the proposed method. The proposed method is practically applied to the T-commerce system of W-shopping and used for broadcasting organization.