• Archives of Plastic Surgery
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• v.40 no.5
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• pp.505-509
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• 2013

Background The aim of this study was to collect important data on the time of oxygen saturation change in relation to skin flap color alteration using non-invasive pulse oximetry to evaluate its ability to provide continuous monitoring of skin flap perfusion. Methods An experimental study on the monitoring of blood perfusion of 20 tube-island groin flaps of 10 male New Zealand rabbits was performed using pulse oximetry. The animals were randomly assigned to one of two groups representing a blockage of either arterial or venous blood flow. The oxygen saturation change and clinical color alteration were monitored from the beginning of vessel clamping until the saturation became undetectable. The result was analyzed by the t-test using SSPS ver. 10.0. Results The mean times from the vessel clamping until the saturation became undetectable were $20.19{\pm}2.13$ seconds and $74.91{\pm}10.57$ seconds for the artery and vein clamping groups, respectively. The mean time of the clinical alteration from the beginning of vein clamping was $34.5{\pm}11.72$ minutes, while the alteration in flaps with artery clamping could not be detected until 2.5 hours after clamping. Conclusions The use of neonate-type reusable flex sensor-pulse oximetry is objective and effective in early detection of arterial and vein blockage. It provides real-time data on vessel occlusion, which in turn will allow for early salvaging. The detection periods of both arterial occlusion and venous congestion are much earlier than the color alteration one may encounter clinically.

• Current Optics and Photonics
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• v.6 no.2
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• pp.171-182
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• 2022

A single-photon laser and mid-wave infrared (MWIR) common aperture optical system was designed and developed to detect and range a long-distance civil aviation aircraft. The secondary mirror of the Ritchey-Chretien (R-C) optical system was chosen as a dichroic lens to realize the design of a common aperture system for the laser and MWIR. Point spread function (PSF) ellipticity was introduced to evaluate the coupling efficiency of the laser receiving system. A small aperture stop and narrow filter were set in the secondary image plane and an afocal light path of the laser system, respectively, and the stray light suppression ability of the small aperture stop was verified by modeling and simulation. With high-precision manufacturing technology by single point diamond turning (SPDT) and a high-efficiency dichroic coating, the laser/MWIR common aperture optical system with a 𝜑300 mm aluminum alloy mirror obtained images of buildings at a distance of 5 km with great quality. A civil aviation aircraft detection experiment was conducted. The results show that the common aperture system could detect and track long-distance civil aviation aircraft effectively, and the coverage was more than 450 km (signal-to-noise ratio = 6.3). It satisfied the application requirements for earlier warning and ranging of long-range targets in the area of aviation, aerospace and ground detection systems.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.151-160
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• 2023

Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. Automatic segmentation of the breast part using Mammography pictures can help reduce the area available for cancer search while also saving time and effort compared to manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (CN-DCNN) were utilised in previous studies to automatically segment the breast area in Mammography pictures. We present Automatic SegmenAN, a unique end-to-end adversarial neural network for the job of medical image segmentation, in this paper. Because image segmentation necessitates extensive, pixel-level labelling, a standard GAN's discriminator's single scalar real/fake output may be inefficient in providing steady and appropriate gradient feedback to the networks. Instead of utilising a fully convolutional neural network as the segmentor, we suggested a new adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local attributes that collect long- and short-range spatial relations among pixels. We demonstrate that an Automatic SegmenAN perspective is more up to date and reliable for segmentation tasks than the state-of-the-art U-net segmentation technique.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.26 no.5
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• pp.285-291
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• 2014

The statistical information of the coastal water temperature monitoring data can be biased because of outliers and missing intervals. Though a number of outlier detection methods have been developed, their applications are very limited to the in-situ monitoring data because of the assumptions of the a prior information of the outliers and no-missing condition, and the excessive computational time for some methods. In this study, the practical robust method is developed that can be efficiently and effectively detect the outliers in case of the big-data. This model is composed of these two parts, one part is the construction part of the approximate components of the monitoring data using the robust smoothing and data re-sampling method, and the other part is the main iterative outlier detection part using the detailed components of the data estimated by the approximate components. This model is tested using the two-years 5-minute interval water temperature data in Lake Saemangeum. It can be estimated that the outlier proportion of the data is about 1.6-3.7%. It shows that most of the outliers in the data are detected and removed with satisfaction by the model. In order to effectively detect and remove the outliers, the outlier detection using the long-span smoothing should be applied earlier than that using the short-span smoothing.

• Journal of Radiation Protection and Research
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• v.45 no.2
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• pp.76-80
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• 2020

Background: To improve the measurement accuracy of liquid-scintillation counting for activity standardization, it is necessary to significantly reduce the background caused by thermal noise or after-pulses. We have therefore improved a movable 3 photomultiplier (3PM)-γ coincidence-counting method using the logical sum of three double coincidences for β events. Materials and Methods: We designed a new data-acquisition system in which β events are obtained by counting the logical sum of three double coincidences. The change in β-detection efficiency can be derived by moving three photomultiplier tubes sequentially from the liquid-scintillation vial. The validity of the method was investigated by activity measurement of ¹³⁴Cs calibrated at the Korea Research Institute of Standards and Science (KRISS) with 4π(PC)β-γ(NaI(Tl)) coincidence counting using a proportional counter (PC) for the β detector. Results and Discussion: Measurements were taken over 14 counting intervals for each liquidscintillation sample by displacing three photomultiplier tubes up to 45 mm from the sample. The dead time in each β- and γ-counting channel was adjusted to be a non-extending type of 20 ㎲. The background ranged about 1.2-3.3 s^-1, such that the contributions of thermal noise or after-pulses were negligible. As the β-detection unit was moved away from the sample, the β-detection efficiencies varied between 0.54 and 0.81. The result obtained by the method at the reference date was 396.3 ± 1.7 kBq/g. This is consistent with the KRISS-certified value of 396.0 ± 2.0 kBq/g within the uncertainty range. Conclusion: The movable 3PM-γ method developed in the present work not only succeeded in reducing background counts to negligible levels but enabled β-detection efficiency to be varied by a geometrical method to apply the efficiency extrapolation method. Compared with our earlier work shown in the study of Hwang et al. [2], the measurement accuracy has much improved. Consequently, the method developed in this study is an improved method suitable for activity standardization of β-γ emitters.

• Parasites, Hosts and Diseases
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• v.21 no.1
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• pp.1-5
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• 1983

Thin layer immunoassay was carried out to demonstrate antibodies against Clcnorchis sinensis in sera from clonorchiasis patients. Saline extract of adult worm was used as antigen. TIA technique was performed as described earlier by Elwing et at. (1976), but agarose was used instead of agar. The antibody titres of sera in 60 clonorchiasis casts were higher than that of 10 healthy and 10 amoebiasis cases, but not different comparing with that of 10 paragonimiasis cases. Antibody litres in clonorchiasis gave no differences according to the age, sex, EPG in feces, eosinophilia degree of blood, level of alkaline phosphatase and transaminase (SGOT, SGPT) in sera. It is suggested that, after evaluation, the TIA might supplement or be used as an alternative to other immunodiagnostic tests already in use for the diagnosis of clonorchiasis.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1315-1318
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• 2003

In this paper, a neural network based approach using a self-organizing feature map is proposed for the segmentation of X ray images. A number of algorithms based on such approaches as histogram analysis, region growing, edge detection and pixel classification have been proposed for segmentation of general images. However, few approaches have been applied to X ray image segmentation because of blur of the X ray image and vagueness of its edge, which are inherent properties of X ray images. To this end, we develop a new model based on the neural network to detect objects in a given X ray image. The new model utilizes Mumford-Shah functional incorporating with a modified adaptive SOFM. Although Mumford-Shah model is an active contour model not based on the gradient of the image for finding edges in image, it has some limitation to accurately represent object images. To avoid this criticism, we utilize an adaptive self organizing feature map developed earlier by the authors.[1] It's learning rule is derived from Mumford-Shah energy function and the boundary of blurred and vague X ray image. The evolution of the neural network is shown to well segment and represent. To demonstrate the performance of the proposed method, segmentation of an industrial part is solved and the experimental results are discussed in detail.

• Annals of Clinical Neurophysiology
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• v.4 no.2
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• pp.137-139
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• 2002

Background : Subtle status epilepticus (SE) is an end-stage of convulsive SE. This phenomenon might be a clinical expression of neuronal exhaustion caused by sustained electrical discharges. As subtle SE may show diverse clinical features, early detection depends on clinical suspicion. Case : A 68-year-old woman was presented with repetitive involuntary movement of right limbs after two generalized tonic-clonic seizures. She experienced right middle cerebral artery infarction 4 months ago, and after the event, left side hemiplegia sustained. These seizures were first-ever after the cerebral infarction. Orientation and verbal responses were fairly preserved but general cognitive function was minimally slowed. During the video-EEG monitoring, repetitive sharp waves were noted in the right hemisphere and these sharp waves occasionally spread to the contralateral side. Her right side involuntary movement was identifiable when the epileptic discharges were found on her right hemisphere. Conclusion : We suggested that this unexpected convulsive movement is a reflection of earlier exhaustion in the right hemisphere or deefferentation of right hemisphere because of preexisting neuronal damage.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.6
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• pp.2771-2776
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• 2014

Background: Breast cancer screening and higher quality mammography have resulted in an increase in the diagnosis of ductal carcinoma in situ worldwide. We compared the incidence and other factors in our cases of ductal carcinoma in situ between two recent decades. Materials and Methods: Medical records of cases of ductal carcinoma in situ who had been admitted to the surgery wards of the Cancer Institute of Tehran, Iran were evaluated from March 1993 to March 2003 as phase 1, and from April 2003 to April 2013 as phase 2. Results: Ratio of ductal carcinoma in situ to overall breast cancer was 1.27 and 3.93 in phases 1 and 2, respectively. Rates of excisional or incisional biopsies versus core needle biopsies and clinically versus mammographically detected cases as well as median size of tumors dropped between the 2 phases while a substantial rise in the number of patients attending for screening was seen in this time period. Surgical treatments followed a trend from modified radical mastectomy and axillary lymphatic dissection toward breast conserving surgery and sentinel node dissection or no axillary intervention. Conclusions: Our study shows a considerable trend toward earlier detection of breast cancer and evolution of treatment strategies toward standard less invasive surgery of DCIS in Iran.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2479-2487
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• 2009

Earlier researches on Sensor Networks preferred symmetric key-based authentication schemes in consideration of limitations in network resources. However, recent advancements in cryptographic algorithms and sensor-node manufacturing techniques have opened suggestion to public key-based solutions such as Merkle tree-based schemes. This paper proposes a new concept of public key-based authentication using Polynomial Secret Sharing that can be effectively applied to sensor networks and a detection of malicious node using the hash function. This scheme is based on exponential distributed data concept, a derivative from Shamir's (t,n) threshold scheme, in which the authentication of neighbouring nodes are done simultaneously while minimising resources of sensor nodes and providing network scalability.