• Journal of Digital Contents Society
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• v.19 no.8
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• pp.1481-1488
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• 2018

Recently, the terms of the fourth industrial revolution, deep running, artificial intelligence, post-human, and trans-human are frequently heard. These terms suggest that the rapid development of science and technology will make the future different from the present. However, rather than giving priority to striking a different future phenomenon, I think it is first of all to understand what kind of future technology or phenomenon is in the present stage. Therefore, in this study, in particular, the actual cases of linking or combining science and technology to the human body are increasing. So if you want to call this human being what kind of characteristics you have. To do this, I first looked at the meaning of trance, transformative, and trans humanism. Next, I looked at the relationship between science and technology and transhumanism. Next, we analyzed four transformative characteristics in the film Elysium and examined how it affects the understanding of transhumanism. This process will be the starting point for understanding post-human and post-humanism in the future.

• Journal of the Society of Naval Architects of Korea
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• v.50 no.3
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• pp.145-152
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• 2013

Recently, with oil price jumping and environmental issues, Green ship is paid deep attention to by ship owner, operator, builder, class and government. Fuel efficiency and reduction of $CO_2$ emissions are expected to have a strong influence on the design and operation of merchant ships. Many ship owners and operators are seeking the more economic method by the best operating route and the application of reliable and effective energy saving devices. With the Energy Efficiency Design Index (EEDI) in 2013 attention will more than ever be focused at achieving maximum fuel economy in the hydrodynamic design of hull forms, their appendages and propellers. IMO requirements for $CO_2$ emission for ships will now be implemented for vessels ordered from 1st January 2013. So far, a lot of new idea and patents have been proposed, tested, claimed and applied for various kinds of ship type. This paper shows numerical and experimental work related to a study on a energy saving devices particularly for fuller ship such as merchant vessel of Tanker and Bulker. From the bare hull wake measurements, typical upper/lower asymmetry of hull wake at the propeller disk was found. The pre-swirl duct have been designed and reviewed to recover the loss of propeller running in that condition. The general function of the pre-swirl duct was set to work against this asymmetry of wake and generate pre-swirled flow into the propeller against the propeller rotating direction.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.567-572
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• 2019

In this paper, we propose an algorithm to perform convolution, pooling, and ReLU operations in CNN using binary image and binary kernel. It decomposes 256 gray-scale images into 8 bit planes and uses a binary kernel consisting of -1 and 1. The convolution operation of binary image and binary kernel is performed by addition and subtraction. Logically, it is a binary operation algorithm using the XNOR and comparator. ReLU and pooling operations are performed by using XNOR and OR logic operations, respectively. Through the experiments to verify the usefulness of the proposed algorithm, We confirm that the CNN operation can be performed by converting it to binary logic operation. It is an algorithm that can implement deep running even in a system with weak computing power. It can be applied to a variety of embedded systems such as smart phones, intelligent CCTV, IoT system, and autonomous car.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.975-977
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• 2013

Arduino is for design based on open source prototyping platform, artist, designer, hobby activists, etc, i has been designed for all those who are interested in the environment construct. Arduino adventage you can easily create applications hardware, without deep knowledge about the hardware. Configuration of arduino using AVR microcontroller ATmage 168, software to action arduino using arduino program, MATLAB, Processing. Arduino is open source base, you can hardware production directly and using shield additionally, the arduino can be combined. Processing iis open source. You can 2D, 3D, PDF output, using P3D and OpenGL graphics. Also you can check by running a stand-alone application. Through a combination of Arduino, library support, such as sound, video, and computer vision can be expanded, this program is the Android phone and iPhone programming. In this paper, sortware was used for Processing, hardware was used for arduino MegaADK board, After making easy 2axis game, using the software and hardware verification.

• Membrane and Water Treatment
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• v.8 no.4
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• pp.311-321
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• 2017

Hydrophilic and high modulus PPTA molecules were incorporated into PVDF matrix via the in situ polymerization of PPD and TPC in PVDF solution. PPTA/PVDF/NWF blend membrane was prepared through the immersion precipitation phase inversion method and nonwoven coating technique. The membrane integrated technology including PPTA/PVDF/NWF blend membrane and reverse osmosis (RO) membrane was employed to treat the polyester/viscose spunlace nonwoven process wastewater. During the consecutive running of six months, the effects of membrane integrated technology on the COD, ammonia nitrogen, suspended substance and pH value of water were studied. The results showed that the removal rate of COD, ammonia nitrogen and suspended substance filtered by PPTA/PVDF blend membrane was kept above 90%. The pH value of the permeate water was about 7.1 and the relative water flux of blend membrane remained above 90%. After the deep treatment of RO membrane, the permeate water quality can meet the water circulation requirement of spunlace process.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.288-296
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• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.105-113
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• 2019

Physically-based fluid simulation takes a lot of time for high resolution. To solve this problem, there are studies that make up the limitation of low resolution fluid simulation by using deep running. Among them, Super-resolution, which converts low-resolution simulation data to high resolution is under way. However, traditional techniques require to the entire space where there are no density data, so there are problems that are inefficient in terms of the full simulation speed and that cannot be computed with the lack of GPU memory as input resolution increases. In this paper, we propose a new method that divides and classifies 2D smoke simulation data into the space using the quad tree, one of the spatial partitioning methods, and performs Super-resolution only required space. This technique accelerates the simulation speed by computing only necessary space. It also processes the divided input data, which can solve GPU memory problems.

• The Journal of Korean Institute of Information Technology
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• v.17 no.7
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• pp.31-45
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• 2019

The core technologies for intelligent services today are deep learning, that is neural networks, and parallel and distributed processing technologies such as GPU parallel computing and big data. However, for intelligent services and knowledge sharing services through globally shared ontologies in the future, there is a technology that is better than the neural networks for representing and reasoning knowledge. It is a knowledge representation of IF-THEN in RIF or SWRL, which is the standard rule language of the Semantic Web, and can be inferred efficiently using the rete algorithm. However, when the number of rules processed by the rete algorithm running on a single computer is 100,000, its performance becomes very poor with several tens of minutes, and there is an obvious limitation. Therefore, in this paper, we analyze the past and current studies on parallel and distributed processing of rete algorithm, and examine what aspects should be considered to implement an efficient rete algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.714-721
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• 2020

DNN is expanding its use in embedded systems such as robots and autonomous vehicles. For high recognition accuracy, computational complexity is greatly increased, and multiple DNNs are running aperiodically. Therefore, the ability processing multiple DNNs in embedded environments is a crucial issue. Accordingly, multicore based platforms are being released. However, most DNN models are operated in a batch process, and when multiple DNNs are operated in multicore together, the execution time deviation between each DNN may be large and the end-to-end execution time of the whole DNNs could be long depending on how they are allocated to the cores. In this paper, we solve these problems by providing a framework that decompose each DNN into individual layers and then distribute to multicores through a global queue. As a result of the experiment, the total DNN execution time was reduced by 31%, and when operating multiple identical DNNs, the deviation in execution time was reduced by up to 95.1%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.842-849
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• 2022

With the development of deep learning technology, there are many cases of using DNNs in embedded systems such as unmanned vehicles, drones, and robotics. Typically, in the case of an autonomous driving system, it is crucial to run several DNNs which have high accuracy results and large computation amount at the same time. However, running multiple DNNs simultaneously in an embedded system with relatively low performance increases the time required for the inference. This phenomenon may cause a problem of performing an abnormal function because the operation according to the inference result is not performed in time. To solve this problem, the solution proposed in this paper first reduces the computation by applying the Tucker decomposition to DNN models with big computation amount, and then, make DNN models run in parallel as much as possible in the unit of hidden layer inside the GPU. The experimental result shows that the DNN inference time decreases by up to 75.6% compared to the case before applying the proposed technique.