• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.597-603
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• 2000

A versatile dimensionality assessment index DETECT has been developed for binary item response data by Kim (1994). The present paper extends the use of DETECT to the polytomously scored item data. A simulation study shows DETECT performs well in differentiating multidimensional data from unidimensional one by yielding a greater value of DETECT in the case of multidimensionality. An additional investigation is necessary for the dimensionally meaningful clustering methods, such as HAC for binary data, particularly sensitive to the polytomous data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2543-2548
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• 2011

The Smoke Detect System is setup to extinguish early a fire on the large ship like as the cargo ship and Bulk Carrier at sea. The Addressable Type Smoke Detect System that keeps the advantages and dispenses with the disadvantages of the existing conventional type system is composed of only one electrical cords. In this paper, we make the Smoke Detect System of an addressable type and implement to control it using ATmega Micro-controller and evaluate it.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.467-477
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• 2006

A multidimensionality detecting procedure DETECT, based on conditional covariances between items, is extended and refined to deal with polytomous item data as well as binary one. A large body of simulation study shows extraordinary performance of DETECT in both enumerating degrees of multidimensionality in a test and discovering dimensionally distinctive item clusters. Real data study also provides very meaningful results, making DETECT a strong dimensionality assessment tool for the test data analysis.

• The Pure and Applied Mathematics
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• v.3 no.2
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• pp.141-154
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• 1996

A data-driven index of dimensionality for an educational or psychological test - DETECT, short for Dimensionality Evaluation To Enumerate Contributing Traits, is proposed in this paper. It is based on estimated conditional covariances of item pairs, given score on remaining test items. Its purpose is to detect whatever multidimensionality structure exists, especially in the case of approximate simple structure. It does so by assigning items to relatively dimensionally homogeneous clusters via attempted maximization of the DETECT over all possible item cluster partitions. The performance of DETECT is studied through real and simulated data analyses.

• Journal of Biosystems Engineering
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• v.25 no.6
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• pp.517-526
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• 2000

A series of experiments was conducted to evaluate the capability of detecting nutrimental deficient stress of N, P and Ca of a tomato plant using several fast and intact type physiological properties measuring devices - a chlorophyll content meter an infra-red thermometer to measure leaf temperature a chlorophyll fluorescence meter a porometer an optical spectrometer a multi-scan radiometer and a canopy analyzer. to detect N deficient stress a chlorophyll content meter a spectrometer and a multi-scan radiometer were useful and their possibility to detect was estimated as about 50%, 100% and 100% respectively. To detect P deficient stress the infra-red thermometer the porometer and the spectrometer proved their usefulness an their possibility to detect was estimated as about 70%, 70% and 70% respectively. To detect Ca deficient stress an thermometer a porometer a spectrometer and a multi-scan radiometer were useful and their possibility to detect was estimated as about 60%, 70%,80% and 100% respectively. The experiments resulted that use of a spectrometer and a multi-scan radiometer in combination with a chlorophyll content meter an infra-red thermometer and a porometer were desirable to distinguish the nutrimental stress tested in the study.

• Journal of the Korean Society of Radiology
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• v.13 no.1
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• pp.9-14
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• 2019

This study aimed to investigate the performance of the S-detect method in breast ultrasonography and to determine how to reduce unnecessary biopsy by comparing the results of the S-detect method and biopsy. Thirty patients who had undergone breast ultrasonography between August and October 2018 and were scheduled to undergo biopsy because of the presence of breast nodules were retrospectively analyzed. The McNemar test was performed to determine whether detection of a malignant breast mass significantly differed between the S-detect method and biopsy. The following results were obtained from the analysis of the S-detect method: sensitivity, 90.9%; specificity, 84.21%; validity, 86.66%; positive predictive value, 76.92%; and negative predictive value, 94.11%. Analysis of the degree of agreement between the S-detect method and biopsy revealed a kappa value as high as 0.724 (p < 0.05), exhibiting good agreement between the two methods. The S-detect method in breast ultrasonography is diagnostically valuable in terms of distinguishing between malignant and benign breast masses, and if used properly before breast biopsy, unnecessary biopsy can be reduced.

• Journal of the Korean Society of Radiology
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• v.18 no.5
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• pp.551-556
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• 2024

This study aims to evaluate the diagnostic concordance and accuracy by comparing thyroid lesions diagnosed with the artificial intelligence-based computer-aided diagnosis (CAD) system, S-Detect^TM, to the results of fine-needle aspiration biopsy(FNAB). A retrospective study was conducted involving 60 patients at N Hospital in Gyeongnam from May 2023 to September 2023. The study used S-Detect^TM to analyze ultrasound findings and malignancy risk of thyroid nodules and compared these findings with FNAB results to determine accuracy. The study assessed the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of S-Detect^TM and evaluated the diagnostic concordance between the two methods using Kappa analysis. S-Detect^TM demonstrated a sensitivity of 90.5%, specificity of 83.2%, accuracy of 88.3%, PPV of 80.7%, and NPV of 92.7%. The Kappa value for diagnostic agreement between S-Detect^TM and FN AB was 0.719 (p<0.05), indicating a high level of agreement between the methods. Therefore, the CAD system S-Detect^TM proves valuable in distinguishing between malignant and benign thyroid lesions and could reduce unnecessary tissue examinations when used appropriately before thyroid fine-needle aspiration.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.959-966
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• 2000

A dimensionality assessment procedure DETECT uses the property of being near zero of conditional covariances as an indication of unidimensionality .This study provides the convergent properties to zero of conditional covariances when the dta is unidimensional, with which DETECT extends its theoretical grounds.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.1-6
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• 2012

We suggested a microwave sensor structure and fabricated experimentally to detect particular particles in the air which are difficult to detect using conventional UWB sensors because their reaction frequencies to electromagnetic waves are too high. In the experiment to detect water particles in the air with up-conversion frequency of 10GHz, we verified detect signal amplitude variation of 75% depending on presence or absence of water particles. The suggested structure can be useful to detect particular materials at the high frequencies more than a few 10th GHz, because the same method can be applied to detect other materials.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.57-65
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• 2020

At present, the existing virus recognition systems usually use signature approach to detect malicious executable files, but these methods often fail to detect new and invisible malware. At the same time, some methods try to use more general features to detect malware, and achieve some success. Moreover, machine learning-based approaches are applied to detect malware, which depend on features extracted from malicious codes. However, the different distribution of features oftraining and testing datasets also impacts the effectiveness of the detection models. And the generation oflabeled datasets need to spend a significant amount time, which degrades the performance of the learning method. In this paper, we use transfer learning to detect new and previously unseen malware. We first extract the features of Portable Executable (PE) files, then combine transfer learning training model with KNN approachto detect the new and unseen malware. We also evaluate the detection performance of a classifier in terms of precision, recall, F1, and so on. The experimental results demonstrate that proposed method with high detection rates andcan be anticipated to carry out as well in the real-world environment.