• Archives of Plastic Surgery
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• v.48 no.3
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• pp.295-304
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• 2021

Clinical photography is an essential component of patient care in plastic surgery. The use of unsecured smartphone cameras, digital cameras, social media, instant messaging, and commercially available cloud-based storage devices threatens patients' data safety. This paper Identifies potential risks of clinical photography and heightens awareness of safe clinical photography. Specifically, we evaluated existing risk-mitigation strategies globally, comparing them to industry standards in similar settings, and formulated a framework for developing a risk-mitigation plan for avoiding data breaches by identifying the safest methods of picture taking, transfer to storage, retrieval, and use, both within and outside the organization. Since threats evolve constantly, the framework must evolve too. Based on a literature search of both PubMed and the web (via Google) with key phrases and child terms (for PubMed), the risks and consequences of data breaches in individual processes in clinical photography are identified. Current clinical-photography practices are described. Lastly, we evaluate current risk mitigation strategies for clinical photography by examining guidelines from professional organizations, governmental agencies, and non-healthcare industries. Combining lessons learned from the steps above into a comprehensive framework that could contribute to national/international guidelines on safe clinical photography, we provide recommendations for best practice guidelines. It is imperative that best practice guidelines for the simple, safe, and secure capture, transfer, storage, and retrieval of clinical photographs be co-developed through cooperative efforts between providers, hospital administrators, clinical informaticians, IT governance structures, and national professional organizations. This would significantly safeguard patient data security and provide the privacy that patients deserve and expect.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.75-84
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• 2017

It becomes more and more more important for the storage that supports high speed recording and stable access from network environment. As one field of basic science which produces massive astronomical data, VLBI(: Very Long Baseline Interferometer) is now demanding more data writing performance and which is directly related to astronomical observation with high resolution and sensitivity. But most of existing storage are cloud model based for the high throughput of general IT, finance, and administrative service, and therefore it not the best choice for recording of big stream data. Therefore, in this study, we design storage system optimized for high performance of I/O and concurrency. To solve this problem, we implement packet read and writing module through the use of libpcap and pf_ring API on the multi core CPU environment, and build a scalable storage based on software RAID(: Redundant Array of Inexpensive Disks) for the efficient process of incoming data from external network.

• Imaging Science in Dentistry
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• v.30 no.1
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• pp.55-62
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• 2000

Purpose: This study was performed to develop the electronic textbook (CD-rom title) about preclinical practice of oral and maxillofacial radiology, using multimedia technology with interactive environment. Materials and Methods: After comparing the three authoring methods of multimedia, i.e. programming language, multimedia authoring tool and web authoring tool, we determined the web authoring tool as an authoring method of our electronic textbook. Intel Pentium II 350 MHz IBM-compatible personal computer with 128 Megabyte RAM, Umax Powerlook flatbed scanner with transparency unit, Olympus Camedia l400L digital camera, ESS 1686 sound card, Sony 8 mm Handycam, PC Vision 97 pro capture board, Namo web editor 3.0, Photoshop 3.0, ThumbNailer, RealPlayer 7 basic and RealProducer G2 were used for creating the text document, diagram, figure, X-ray image, video and sound files. We made use of javascripts for tree menu structure, moving text bar, link button and spread list menu and image map etc. After creating all files and hyperlinking them, we burned out the CD-rom title with all of the above multimedia data, Netscape communicator and plug in program as a prototype. Results and Conclusions : We developed the dental radiology electronic textbook which has 9 chapters and consists of 155 text documents, 26 figures, 150 X-ray image files, 20 video files, 20 sound files and 50 questions with answers. We expect that this CD-rom title can be used at the intranet and internet environments and continuous updates will be performed easily.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.7
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• pp.624-630
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• 2006

ZnO deposition parameters are not independent and have a nonlinear and complex property. To propose a method that could verify and predict the relations of process variables, neural network was used. At first, ZnO thin films were deposited by using RF magnetron sputtering process with various conditions. Si, GaAs, and Glass were used as substrates. The temperature, work pressure, and RF power of the substrate were $50\sim500^{\circ}C$, 15 mTorr, and $180\sim210W$, respectively : the purity of the target was ZnO 4 N. Structural properties of ZnO thin films were estimated by using XRD (0002) peak intensity. The structure of neural network was a form of 4-7-1 that have one hidden layer. In training a network, learning rate and momentum were selected as 0.2, 0.6 respectively. A backpropagation neural network were performed with XRD (0002) peak data. After training a network, the temperature of substrate was evaluated as the most important parameter by sensitivity analysis and response surface. As a result, neural network could capture nonlinear and complex relationships between process parameters and predict structural properties of ZnO thin films with a limited set of experiments.

• Journal of information and communication convergence engineering
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• v.8 no.4
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• pp.349-354
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• 2010

Moving pixel displacement detection algorithm using correlation functions for making panorama image on the continuous images is presented in this paper. The input images get from a CMOS image sensor (CIS). The camera is maintained by constant brightness and uniform sensing area in test input pattern. For simple navigation and capture image has to 70% overlapped region. A correlation rate in two image data is evaluated by using reference image with first captures, and compare image with next captures. The displacement of the two images are expressed to second order function of x, y and solved with finding the coefficient in second order function. That results in the change in the peak correlation displacement from the reference to the compare image, is moving to pixel length. The navigating error is reduced by varying the path because the error is shown in the difference of the positioning vector between the true pixel position and the navigated pixel position. The algorithm performance is evaluated to be different from the error vector to vary the navigating path grid.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.1-9
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• 2019

Single image rain removal is a typical inverse problem which decomposes the image into a background scene and a rain streak. Recent works have witnessed a substantial progress on the task due to the development of convolutional neural network (CNN). However, existing CNN-based approaches train the network with synthetically generated training examples. These data tend to make the network bias to the synthetic scenes. In this paper, we present an unsupervised framework for removing rain streaks from real-world rainy images. We focus on the natural phenomena that static rainy scenes capture a common background but different rain streak. From this observation, we train siamese network with the real rain image pairs, which outputs identical backgrounds from the pairs. To train our network, a real rainy dataset is constructed via web-crawling. We show that our unsupervised framework outperforms the recent CNN-based approaches, which are trained by supervised manner. Experimental results demonstrate that the effectiveness of our framework on both synthetic and real-world datasets, showing improved performance over previous approaches.

• Computers and Concrete
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• v.1 no.3
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• pp.227-248
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• 2004

A combined experimental-computational study of a double edge-notched stone specimen subjected to tensile loading is presented. In the experimental part, the load-deformation response and the displacement field around the crack tip are recorded. An Electronic Speckle Pattern Interferometer (ESPI) is used to obtain the local displacement field. The experimental results are used to validate a numerical model for the description of fracture using finite elements. The numerical model uses displacement discontinuities to model cracks. At the discontinuity, a plasticity-based cohesive zone model is applied for monotonic loading and a combined damage-plasticity cohesive zone model is used for cyclic loading. Both local and global results from the numerical simulations are compared with experimental data. It is shown that local measurements add important information for the validation of the numerical model. Consequently, the numerical models are enhanced in order to correctly capture the experimentally observed behaviour.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.333-339
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• 2021

The study seeks to identify the determinants of the adoption of e-commerce by small and medium-sized enterprises (SMEs) in developing countries, in our case, in Indonesia. The aim of this study is to examine the factors influencing e-commerce adoption. This study uses the method of quantitative data collection based on a questionnaire survey of SMEs in Indonesia. The research relies on Regional Project stipulations regarding Business Development in Indonesia, to capture businesses with a range of 5 to 100 employees that are classified as SMEs. This study randomly chose 100 SMEs in Indonesia from the IndoNetwork database. Partial least square (PLS) structural model data processing was used for path coefficients analysis. Structural equation modeling is applied in this study to analyze the determinant factors on the e-commerce adoption. The study findings reveal that four factors, namely, perceived benefits, compatibility, technology readiness, and government support, significantly influence the adoption of e-commerce, whereas customer/supplier pressure does not have influence. So, this study concludes that perceived benefits, compatibility, technology readiness, and government support had a significant and positive relationship with e-commerce adoption. Meanwhile, customer/supplier pressure had no effect on the e-commerce adoption of by Indonesia SMEs.

• Computers and Concrete
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• v.20 no.6
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• pp.635-643
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• 2017

The rehabilitation and strengthening of concrete structures using Fiber-Reinforced Polymer (FRP) materials have been widely investigated. As a priority issue, however, the effect of curing conditions on the bonding behavior between FRP and concrete structures is still elusive. This study aims at developing a prediction model to accurately capture the mode-II interfacial debonding between FRP strips and concrete under different curing conditions. Single shear debonding experiments were conducted on FRP-concrete samples with respect to different curing time t and temperatures T. The J-integral formulation and constrained least square minimization are carried out to calibrate the parameters, i.e., the maximum slip $\bar{s}$ and stretch factor n. The prediction model is developed based on the cohesive model and Arrhenius relationship. The experimental data are then analyzed using the proposed model to predict the debonding between FRP and concrete, i.e., the interfacial shear stress-slip relationship. A Finite Element (FE) model is developed to validate the theoretical predictions. Satisfactory agreements are obtained. The prediction model can be used to accurately capture the bonding performance of FRP-concrete structures.