• Title/Summary/Keyword: size metrics

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Analysis of a Queueing Model with a Two-stage Group-testing Policy (이단계 그룹검사를 갖는 대기행렬모형의 분석)

  • Won Seok Yang
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.45 no.4
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    • pp.53-60
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    • 2022
  • In a group-testing method, instead of testing a sample, for example, blood individually, a batch of samples are pooled and tested simultaneously. If the pooled test is positive (or defective), each sample is tested individually. However, if negative (or good), the test is terminated at one pooled test because all samples in the batch are negative. This paper considers a queueing system with a two-stage group-testing policy. Samples arrive at the system according to a Poisson process. The system has a single server which starts a two-stage group test in a batch whenever the number of samples in the system reaches exactly a predetermined size. In the first stage, samples are pooled and tested simultaneously. If the pooled test is negative, the test is terminated. However, if positive, the samples are divided into two equally sized subgroups and each subgroup is applied to a group test in the second stage, respectively. The server performs pooled tests and individual tests sequentially. The testing time of a sample and a batch follow general distributions, respectively. In this paper, we derive the steady-state probability generating function of the system size at an arbitrary time, applying a bulk queuing model. In addition, we present queuing performance metrics such as the offered load, output rate, allowable input rate, and mean waiting time. In numerical examples with various prevalence rates, we show that the second-stage group-testing system can be more efficient than a one-stage group-testing system or an individual-testing system in terms of the allowable input rates and the waiting time. The two-stage group-testing system considered in this paper is very simple, so it is expected to be applicable in the field of COVID-19.

Application of Text-Classification Based Machine Learning in Predicting Psychiatric Diagnosis (텍스트 분류 기반 기계학습의 정신과 진단 예측 적용)

  • Pak, Doohyun;Hwang, Mingyu;Lee, Minji;Woo, Sung-Il;Hahn, Sang-Woo;Lee, Yeon Jung;Hwang, Jaeuk
    • Korean Journal of Biological Psychiatry
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    • v.27 no.1
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    • pp.18-26
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    • 2020
  • Objectives The aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-based medical records. Methods Electronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes with three diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independent validation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF) and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vector classification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find an effective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results Five-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis (accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final working DL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showed slightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions The current results suggest that the vectorization may have more impact on the performance of classification than the machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category, and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machine learning models.

Progressive occupancy network for 3D reconstruction (3차원 형상 복원을 위한 점진적 점유 예측 네트워크)

  • Kim, Yonggyu;Kim, Duksu
    • Journal of the Korea Computer Graphics Society
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    • v.27 no.3
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    • pp.65-74
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    • 2021
  • 3D reconstruction means that reconstructing the 3D shape of the object in an image and a video. We proposed a progressive occupancy network architecture that can recover not only the overall shape of the object but also the local details. Unlike the original occupancy network, which uses a feature vector embedding information of the whole image, we extract and utilize the different levels of image features depending on the receptive field size. We also propose a novel network architecture that applies the image features sequentially to the decoder blocks in the decoder and improves the quality of the reconstructed 3D shape progressively. In addition, we design a novel decoder block structure that combines the different levels of image features properly and uses them for updating the input point feature. We trained our progressive occupancy network with ShapeNet. We compare its representation power with two prior methods, including prior occupancy network(ONet) and the recent work(DISN) that used different levels of image features like ours. From the perspective of evaluation metrics, our network shows better performance than ONet for all the metrics, and it achieved a little better or a compatible score with DISN. For visualization results, we found that our method successfully reconstructs the local details that ONet misses. Also, compare with DISN that fails to reconstruct the thin parts or occluded parts of the object, our progressive occupancy network successfully catches the parts. These results validate the usefulness of the proposed network architecture.

Sources of Carbonaceous Materials in the Airborne Particulate Matter of Dhaka

  • Begum, Bilkis A.;Hossain, Anwar;Saroar, Golam;Biswas, Swapan K.;Nasiruddin, Md.;Nahar, Nurun;Chowdury, Zohir;Hopke, Philip K.
    • Asian Journal of Atmospheric Environment
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    • v.5 no.4
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    • pp.237-246
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    • 2011
  • To explore the sources of carbonaceous material in the airborne particulate matter (PM), comprehensive PM sampling was performed (3 to 14 January 2010) at a traffic hot spot site (HS), Farm Gate, Dhaka using several samplers: AirMetrics MiniVol (for $PM_{10}$ and $PM_{2.5}$) and MOUDI (for size fractionated submicron PM). Long-term PM data (April 2000 to March 2006 and April 2000 to March 2010 in two size fractions ($PM_{2.2}$ and $PM_{2.2-10}$) obtained from two air quality-monitoring stations, one at Farm Gate (HS) and another at a semi-residential (SR) area (Atomic Energy Centre, Dhaka Campus, (AECD)), respectively were also analyzed. The long-term PM trend shows that fine particulate matter concentrations have decreased over time as a result of government policy interventions even with increasing vehicles on the road. The ratio of $PM_{2.5}/PM_{10}$ showed that the average $PM_{2.5}$ mass was about 78% of the $PM_{10}$ mass. It was also found that about 63% of $PM_{2.5}$ mass is $PM_1$. The total contribution of BC to $PM_{2.5}$ is about 16% and showed a decreasing trend over the years. It was observed that $PM_1$ fractions contained the major amount of carbonaceous materials, which mainly originated from high temperature combustion process in the $PM_{2.5}$. From the IMPROVE TOR protocol carbon fraction analysis, it was observed that emissions from gasoline vehicles contributed to $PM_1$ given the high abundance of EC1 and OC2 and the contribution of diesel to $PM_1$ is minimal as indicated by the low abundance of OC1 and EC2. Source apportionment results also show that vehicular exhaust is the largest contributors to PM in Dhaka. There is also transported $PM_{2.2}$from regional sources. With the increasing economic activities and recent GDP growth, the number of vehicles and brick kilns has significantly increased in and around Dhaka. Further action will be required to further reduce PM-related air pollution in Dhaka.

Prefetching Mechanism using the User's File Access Pattern Profile in Mobile Computing Environment (이동 컴퓨팅 환경에서 사용자의 FAP 프로파일을 이용한 선인출 메커니즘)

  • Choi, Chang-Ho;Kim, Myung-Il;Kim, Sung-Jo
    • Journal of KIISE:Information Networking
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    • v.27 no.2
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    • pp.138-148
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    • 2000
  • In the mobile computing environment, in order to make copies of important files available when being disconnected the mobile host(client) must store them in its local cache while the connection is maintained. In this paper, we propose the prefetching mechanism for the client to save files which may be accessed in the near future. Our mechanism utilizes analyzer, prefetch-list producer, and prefetch manager. The analyzer records file access patterns of the user in a FAP(File Access Patterns) profile. Using the profile, the prefetch-list producer creates the prefetch-list. The prefetch manager requests a file server to return this list. We set the parameter TRP(Threshold of Reference Probability) to ensure that only reasonably related files can be prefetched. The prefetch-list producer adds the files to a prefetch-list if their reference probability is greater than the TRP. We also use the parameter TACP(Threshold of Access Counter Probability) to reduce the hoarding size required to store a prefetch-list. Finally, we measure the metrics such as the cache hit ratio, the number of files referenced by the client after disconnection and the hoarding size. The simulation results show that the performance of our mechanism is superior to that of the LRU caching mechanism. Our results also show that prefetching with the TACP can reduce the hoard size while maintaining similar performance of prefetching without TACP.

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The Analysis on the Relationship between Firms' Exposures to SNS and Stock Prices in Korea (기업의 SNS 노출과 주식 수익률간의 관계 분석)

  • Kim, Taehwan;Jung, Woo-Jin;Lee, Sang-Yong Tom
    • Asia pacific journal of information systems
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    • v.24 no.2
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    • pp.233-253
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    • 2014
  • Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.

Effective Detective Quantum Efficiency (eDQE) Evaluation for the Influence of Focal Spot Size and Magnification on the Digital Radiography System (X-선관 초점 크기와 확대도에 따른 디지털 일반촬영 시스템의 유효검출양자효율 평가)

  • Kim, Ye-Seul;Park, Hye-Suk;Park, Su-Jin;Kim, Hee-Joung
    • Progress in Medical Physics
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    • v.23 no.1
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    • pp.26-32
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    • 2012
  • The magnification technique has recently become popular in bone radiography, mammography and other diagnostic examination. However, because of the finite size of X-ray focal spot, the magnification influences various imaging properties with resolution, noise and contrast. The purpose of study is to investigate the influence of magnification and focal spot size on digital imaging system using eDQE (effective detective quantum efficiency). Effective DQE is a metric reflecting overall system response including focal spot blur, magnification, scatter and grid response. The adult chest phantom employed in the Food and Drug Administration (FDA) was used to derive eDQE from eMTF (effective modulation transfer function), eNPS (effective noise power spectrum), scatter fraction and transmission fraction. According to results, spatial frequencies that eMTF is 10% with the magnification factor of 1.2, 1.4, 1.6, 1.8 and 2.0 are 2.76, 2.21, 1.78, 1.49 and 1.26 lp/mm respectively using small focal spot. The spatial frequencies that eMTF is 10% with the magnification factor of 1.2, 1.4, 1.6, 1.8 and 2.0 are 2.21, 1.66, 1.25, 0.93 and 0.73 lp/mm respectively using large focal spot. The eMTFs and eDQEs decreases with increasing magnification factor. Although there are no significant differences with focal spot size on eDQE (0), the eDQEs drops more sharply with large focal spot than small focal spot. The magnification imaging can enlarge the small size lesion and improve the contrast due to decrease of effective noise and scatter with air-gap effect. The enlargement of the image size can be helpful for visual detection of small image. However, focal spot blurring caused by finite size of focal spot shows more significant impact on spatial resolution than the improvement of other metrics resulted by magnification effect. Based on these results, appropriate magnification factor and focal spot size should be established to perform magnification imaging with digital radiography system.

Performance Enhancement and Evaluation of AES Cryptography using OpenCL on Embedded GPGPU (OpenCL을 이용한 임베디드 GPGPU환경에서의 AES 암호화 성능 개선과 평가)

  • Lee, Minhak;Kang, Woochul
    • KIISE Transactions on Computing Practices
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    • v.22 no.7
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    • pp.303-309
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    • 2016
  • Recently, an increasing number of embedded processors such as ARM Mali begin to support GPGPU programming frameworks, such as OpenCL. Thus, GPGPU technologies that have been used in PC and server environments are beginning to be applied to the embedded systems. However, many embedded systems have different architectural characteristics compare to traditional PCs and low-power consumption and real-time performance are also important performance metrics in these systems. In this paper, we implement a parallel AES cryptographic algorithm for a modern embedded GPU using OpenCL, a standard parallel computing framework, and compare performance against various baselines. Experimental results show that the parallel GPU AES implementation can reduce the response time by about 1/150 and the energy consumption by approximately 1/290 compare to OpenMP implementation when 1000KB input data is applied. Furthermore, an additional 100 % performance improvement of the parallel AES algorithm was achieved by exploiting the characteristics of embedded GPUs such as removing copying data between GPU and host memory. Our results also demonstrate that higher performance improvement can be achieved with larger size of input data.

Data Communication Prediction Model in Multiprocessors based on Robust Estimation (로버스트 추정을 이용한 다중 프로세서에서의 데이터 통신 예측 모델)

  • Jun Janghwan;Lee Kangwoo
    • The KIPS Transactions:PartA
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    • v.12A no.3 s.93
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    • pp.243-252
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    • 2005
  • This paper introduces a noble modeling technique to build data communication prediction models in multiprocessors, using Least-Squares and Robust Estimation methods. A set of sample communication rates are collected by using a few small input data sets into workload programs. By applying estimation methods to these samples, we can build analytic models that precisely estimate communication rates for huge input data sets. The primary advantage is that, since the models depend only on data set size not on the specifications of target systems or workloads, they can be utilized to various systems and applications. In addition, the fact that the algorithmic behavioral characteristics of workloads are reflected into the models entitles them to model diverse other performance metrics. In this paper, we built models for cache miss rates which are the main causes of data communication in shared memory multiprocessor systems. The results present excellent prediction error rates; below $1\%$ for five cases out of 12, and about $3\%$ for the rest cases.

Development of Fishway Assessment Model based on the Fishway Structure, Hydrology and Biological Characteristics in Lotic Ecosystem

  • Choi, Ji-Woong;Park, Chan-Seo;An, Kwang-Guk
    • Journal of Ecology and Environment
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    • v.39 no.1
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    • pp.71-80
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    • 2016
  • The main goal of this study is to develop a multi-metric fishway assessment model (Mm-FA) and evaluate the efficiency of fishway. The Mm-FA model has three major fishway components with nine metrics: structural characteristics, hydraulic/hydrologic features, and biological attributes. The model was developed for diagnosing and assessing fishway efficiency and tested to Juksan Weir at the Yeongsan River Watershed. Structural characteristics of fishway included slope of the fishway (M1), ratios of fishway width to stream width (M2), and the proportion of orifice clogging and orifice size (M3). Hydraulic/hydrologic characteristics included depth of fishway entrance head (M4), depth of exit tail (M5), and current velocity of inner fishway (M6). Biological characteristics included fish species ratio of inner fishway to upper-lower weir (M7), fish length distribution (M8), and the proportion of migratory fish species to the total number of species (M9). Overall, the assessment of fishway efficiency showed the total score of the Mm-FA model was 25 in the Juksan Weir, indicating "good condition" by the criteria of the five-level classification system. The Mm-FA model may be used as a key tool for the assessment of fishway efficiency, especially on the 16 weirs constructed for the "Four Rivers Restoration Project" after a partial calibration of Mm-FA model.