• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4C
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• pp.329-337
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• 2012

Capsule Endoscope(CE) is a capsule-shaped electronic device which can examine the lesions in digestive tract of human body. Recently the medical procedure using capsule endoscope is receiving great attention to both doctors and patients, since the conventional push-typed endoscope using cables brings great pain and fear to the patients. The technique was firstly available in 2000 and is based on a convergence techniques among BT(Bio Technology), IT(Information Technology), and NT(Nano Technology). The device consists of an optical parts including LEDs(Light Emitting Diodes), an image sensor, a communication module and a power module. Capsule endoscope is the embodiment of the state-of-the art technology and requires key technologies in the various engineering fields. Therefore, in this paper, we introduce the composition of the capsule endoscope system, and compare the communication method between RF(Radio Frequency) communication and HBC(Human Body Communication), which are typically used for data transmission in the capsule endoscope. Furthermore, we analyze the specification of commercialized capsule endoscopes and present the future developments and technical challenges.

• Korean Journal of Clinical Laboratory Science
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• v.41 no.4
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• pp.173-179
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• 2009

The analysis of serum free light chains (sFLCs) can improve the diagnosis and monitoring of multiple myeloma and other plasma cell dyscrasias. As with other immunoassays, sFLCstests are subject to potential antigen excess and heterophilic antibody interference. We describe 9 cases of sFLCs antigen excess in patients with multiple myeloma using the FreeliteTM Human Kappa and Lambda Free Kits (The Binding Site ltd., Birmingham, UK) and the Hitachi7600 P module turbidimetric system. A total of 1,247 consecutive samples from 250 patients with multiple myeloma were assayed for sFLCs from April to September, 2009. The samples were assayed using an initial dilution of 1 :5and subsequent dilutions of 1 :50 and 1: 100. The same samples were analyzed for the presence of monoclonal gammopathies using serum protein electrophoresis (SPE) and immunofixation electrophoresis (IFE). There were 9 samples (0.72%) of antigen excess with 3 cases of kappa (0.24%) and 6 cases of lambda (0.48%). These cases represents an example of antigen excess or "hook effect" using the serum free light chain assays and mandates high level of attention to falsely low sFLC levels due to antigen excess, especially when it is disaccordant to other assay results or clinical manifestations.

• Archives of design research
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• v.19 no.2 s.64
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• pp.333-342
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• 2006

Consumers have paid attention to various functions of PC's nowadays from its efficiency in the past. This study set limits to adapted computers by consumer needs for their residence facilities and ubiquitous environment in our society. The purpose of this study is to suggest high potentialities of adapted computers and it's design for materializing house ubiquitous system. It has come out from analyzing PC market. The result is that Barebone PC, a kind of set PC, could be one of the most actual way for materializing house ubiquitous system. This study suggests many potentialities of Barebone PC which invites modular system. The existing Barebone PC is a kind of semimanufactures, however the new Barebone PC in this study is a expanded concept of modular system that connected to the area of the electric home appliances. This is inspected by analyzing the relation among potentialities and impotance of set PC design and module.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.7
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• pp.1740-1749
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• 2009

As mobile devices evolve and digital convergence trend is here to stay, mobile phones are built with multiple functions including cameras, MP3s, TVs and game consoles. As a consequence, such multi-functional mobile phones come to spend more power, facilitating development of next-generation ultra-capacity lithium ion battery. In addition, environmental regulations and rising oil prices cause demand for hybrid cars to keep rising. Accordingly, more and more attention is being paid to medium and large batteries and more efforts are being made to realize lower battery prices, higher outputs and stability. This study presented a patent technology related to the lithium ion battery packing that allows reducing processes related, increasing productivity and recycling parts other than the body. The lithium ion battery pack to which protection circuits are elastically attached provides short circuit protection for the circuit and the body and makes electric connection of the circuit and the body easier.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1433-1449
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• 2023

In recent years, the rapid development of social networks has led to a rapid increase in the amount of information available on the Internet, which contains a large amount of sensitive information related to pornography, politics, and terrorism. In the aspect of sensitive image detection, the existing machine learning algorithms are confronted with problems such as large model size, long training time, and slow detection speed when auditing and supervising. In order to detect sensitive images more accurately and quickly, this paper proposes a multiclassification sensitive image detection method based on lightweight Convolutional Neural Network. On the basis of the EfficientNet model, this method combines the Ghost Module idea of the GhostNet model and adds the SE channel attention mechanism in the Ghost Module for feature extraction training. The experimental results on the sensitive image data set constructed in this paper show that the accuracy of the proposed method in sensitive information detection is 94.46% higher than that of the similar methods. Then, the model is pruned through an ablation experiment, and the activation function is replaced by Hard-Swish, which reduces the parameters of the original model by 54.67%. Under the condition of ensuring accuracy, the detection time of a single image is reduced from 8.88ms to 6.37ms. The results of the experiment demonstrate that the method put forward has successfully enhanced the precision of identifying multi-class sensitive images, significantly decreased the number of parameters in the model, and achieved higher accuracy than comparable algorithms while using a more lightweight model design.

• Journal of the Microelectronics and Packaging Society
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• v.29 no.4
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• pp.41-47
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• 2022

Ag sintering technologies have received great attention as it was applied to the inverter of Tesla's electric vehicle Model III. Ag sinter bonding technology has advantages in heat dissipation design as well as high-temperature stability due to the intrinsic properties of the material, so it is useful for practical use of SiC and GaN devices. This study was carried out to understand the sinter joining temperature effect on the robust Ag sintered joints in air without pressure within 10 min. Electroplated Ag finished Cu dies (3 mm × 3 mm × 2 mm) and substrates (10 mm × 10 mm × 2 mm) were introduced, respectively, and nano Ag paste was applied as a bonding material. The sinter joining process was performed without pressure in air with the bonding temperature as a variable of 175 ℃, 200 ℃, 225 ℃, and 250 ℃. As results, the bonding temperature of 175 ℃ caused 13.21 MPa of die shear strength, and when the bonding temperature was raised to 200 ℃, the bonding strength increased by 157% to 33.99 MPa. When the bonding temperature was increased to 225 ℃, the bonding strength of 46.54 MPa increased by about 37% compared to that of 200 ℃, and even at a bonding temperature of 250 ℃, the bonding strength exceeded 50 MPa. The bonding strength of Ag sinter joints was directly influenced by changes in the necking thickness and interfacial connection ratio. In addition, developments in the morphologies of the joint interface and porous structure have a significant effect on displacement. This study is systematically discussed on the relationship between processing temperatures and bonding strength of Ag sinter joints.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.11 no.2
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• pp.13-26
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• 2007

Background: Neurofeedback is an electroencephalographic biofeedback technique for training individuals to alter their brain activity via operant conditioning. Also neurofeedback is a form of behavioural training aimed at developing skills for brain activity. Within the past decade, several neurofeedback studies have been published that tend to overcome the methodological shortcomings of earlier studies. This research describes the methodical basis of neurofeedback and reviews the evidence base for its clinical efficacy and effectiveness in attention-deficit hyperactivity disorder (ADHD). Methods: In neurofeedback training, self-regulation of specific aspects of electrical brain activity is acquired by means of immediate feedback and positive reinforcement. In frequency training, activity in different EEG frequency bands has to be decreased or increased. Slow cortical potentials (SCPs) training is focused on the regulation of cortical excitability. Results: Neurofeedback studies revealed training-specific effects on, for example, attention and memory processes and performance improvements in real-life conditions, in healthy subjects as well as in patients. In several studies it was shown that ADHD symptomatology was reduced after frequency training or SCP(Slow cortical potentials) training. Moreover a decrease of impulsivity errors and an increase of the contingent negative variation. Conclusions: This research provides evidence for both positive behavioural and specific neurophysiological effects of neurofeedback training. Also there is growing evidence for neurofeedback as a valuable module in neuropsychiatric disorders. Further, controlled studies are warranted.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.9
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• pp.379-390
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• 2019

In this paper, we propose a novel deep neural network model for Vision-and-Language Navigation (VLN) named LVLN (Landmark-based VLN). In addition to both visual features extracted from input images and linguistic features extracted from the natural language instructions, this model makes use of information about places and landmark objects detected from images. The model also applies a context-based attention mechanism in order to associate each entity mentioned in the instruction, the corresponding region of interest (ROI) in the image, and the corresponding place and landmark object detected from the image with each other. Moreover, in order to improve the success rate of arriving the target goal, the model adopts a progress monitor module for checking substantial approach to the target goal. Conducting experiments with the Matterport3D simulator and the Room-to-Room (R2R) benchmark dataset, we demonstrate high performance of the proposed model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.389-395
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• 2022

Because of improvement of deep learning techniques, deep learning using computer vision such as classification, detection and segmentation has also been used widely at many fields. Expecially, automatic driving is one of the major fields that applies computer vision systems. Also there are a lot of works and researches to combine multiple tasks in a single network. In this study, we propose the network that predicts the individual depth of pedestrians and vehicles. Proposed model is constructed based on YOLOv3 for object detection and Monodepth for depth estimation, and it process object detection and depth estimation consequently using encoder and decoder based on hard parameter sharing. We also used attention module to improve the accuracy of both object detection and depth estimation. Depth is predicted with monocular image, and is trained using self-supervised training method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4065-4083
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• 2021

Super-resolution can improve the clarity of low-resolution (LR) images, which can increase the accuracy of high-level compute vision tasks. Portable devices have low computing power and storage performance. Large-scale neural network super-resolution methods are not suitable for portable devices. In order to save the computational cost and the number of parameters, Lightweight image processing method can improve the processing speed of portable devices. Therefore, we propose the Enhanced Information Multiple Distillation Network (EIMDN) to adapt lower delay and cost. The EIMDN takes feedback mechanism as the framework and obtains low level features through high level features. Further, we replace the feature extraction convolution operation in Information Multiple Distillation Block (IMDB), with Ghost module, and propose the Enhanced Information Multiple Distillation Block (EIMDB) to reduce the amount of calculation and the number of parameters. Finally, coordinate attention (CA) is used at the end of IMDB and EIMDB to enhance the important information extraction from Spaces and channels. Experimental results show that our proposed can achieve convergence faster with fewer parameters and computation, compared with other lightweight super-resolution methods. Under the condition of higher peak signal-to-noise ratio (PSNR) and higher structural similarity (SSIM), the performance of network reconstruction image texture and target contour is significantly improved.