• Journal of Ocean Engineering and Technology
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• v.33 no.2
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• pp.168-177
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• 2019

KRISO (Korea Research Institute of Ship & Ocean Engineering) started a project to develop the core algorithms for autonomous intervention using an underwater robot in 2017. This paper introduces the development of the robot platform for the core algorithms, which is an ROV (Remotely Operated Vehicle) type with one 7-function manipulator. Before the detailed design of the robot platform, the 7E-MINI arm of the ECA Group was selected as the manipulator. It is an electrical type, with a weight of 51 kg in air (30 kg in water) and a full reach of 1.4 m. To design a platform with a small size and light weight to fit in a water tank, the medium-size manipulator was placed on the center of platform, and the structural analysis of the body frame was conducted by ABAQUS. The robot had an IMU (Inertial Measurement Unit), a DVL (Doppler Velocity Log), and a depth sensor for measuring the underwater position and attitude. To control the robot motion, eight thrusters were installed, four for vertical and the rest for horizontal motion. The operation system was composed of an on-board control station and operation S/W. The former included devices such as a 300 VDC power supplier, Fiber-Optic (F/O) to Ethernet communication converter, and main control PC. The latter was developed using an ROS (Robot Operation System) based on Linux. The basic performance of the manufactured robot platform was verified through a water tank test, where the robot was manually operated using a joystick, and the robot motion and attitude variation that resulted from the manipulator movement were closely observed.

• Journal of the Korea Organic Resources Recycling Association
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• v.30 no.4
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• pp.59-66
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• 2022

Lithium-ion batteries have greatly expanded along with the mobile phone market, and as the electric vehicle business is activated in earnest, they will attract many people's attention even afterwards. Until now, many people have attracted attention to the recovery of valuable metals inside lithium-ion batteries, but graphite, which is mainly used as an anode material, is also worth recycling. Therefore, in order to recover graphite with high purity and valuable metals, graphite that can be used as an anode material of a secondary battery may be generated again through a regeneration process of purifying and separating graphite from a waste lithium-ion battery and recovering electrical characteristics of graphite. This paper describes the process of converting waste graphite into regenerated graphite and the environmental and economic effects of regenerated graphite.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.185-197
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• 2022

Recently, deep learning networks with high performance for object recognition are emerging. In the case of object recognition using deep learning, it is important to build a training data set to improve performance. To build a data set, we need to collect and label the images. This process requires a lot of time and manpower. For this reason, open data sets are used. However, there are objects that do not have large open data sets. One of them is data required for license plate detection and recognition. Therefore, in this paper, we propose an artificial license plate generator system that can create large data sets by minimizing images. In addition, the detection rate according to the artificial license plate arrangement structure was analyzed. As a result of the analysis, the best layout structure was FVC_III and B, and the most suitable network was D2Det. Although the artificial data set performance was 2-3% lower than that of the actual data set, the time to build the artificial data was about 11 times faster than the time to build the actual data set, proving that it is a time-efficient data set building system.

• Journal of IKEEE
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• v.26 no.2
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• pp.141-149
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• 2022

With the development of artificial intelligence and unmanned technologies, the remote surveillance/autonomous driving systems have been actively researched. For an effective performance analysis of the developed remote control system, it is important to record the data of it in real time. In addition, in order to analyze the performance between the control system and the remote system, the recorded data from them should be synchronized with time. In this paper we proposed a novel time synchronization method for the remote control system. The proposed remote control system satisfies the time difference of the recorded data within 1 ms, and we can reduce the time difference by using a CPU shielding and affinity setting. The performance of the proposed method was proved through various network data storage experiments. And the experiments confirmed that the proposed method can be applied to recording devices of unmanned ground vehicles and control vehicles. The proposed method will be used as a method for analyzing network data of UGV-R (Unmanned Ground Vehicle - Reconnaissance).

• Electrical & Electronic Materials
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• v.12 no.7
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• pp.7-11
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• 1999

Fully sealed field emission display in size of 4.5 inch has been fabricated using single-wall carbon nanotubes-organic vehicle com-posite. The fabricated display were fully scalable at low temperature below 415$^{\circ}C$ and CNTs were vertically aligned using paste squeeze and surface rubbing techniques. The turn-on fields of 1V/${\mu}{\textrm}{m}$ and field emis-sion current of 1.5mA at 3V/${\mu}{\textrm}{m}$ (J=90${\mu}{\textrm}{m}$/$\textrm{cm}^2$)were observed. Brightness of 1800cd/$m^2$ at 3.7V/${\mu}{\textrm}{m}$ was observed on the entire area of 4.5-inch panel from the green phosphor-ITO glass. The fluctuation of the current was found to be about 7% over a 4.5-inch cath-ode area. This reliable result enables us to produce large area full-color flat panel dis-play in the near future. Carbon nanotubes (CNTs) have attracted much attention because of their unique elec-trical properties and their potential applica-tions [1, 2]. Large aspect ratio of CNTs together with high chemical stability. ther-mal conductivity, and high mechanical strength are advantageous for applications to the field emitter [3]. Several results have been reported on the field emissions from multi-walled nanotubes (MWNTs) and single-walled nanotubes (SWNTs) grown from arc discharge [4, 5]. De Heer et al. have reported the field emission from nan-otubes aligned by the suspension-filtering method. This approach is too difficult to be fully adopted in integration process. Recently, there have been efforts to make applications to field emission devices using nanotubes. Saito et al. demonstrated a car-bon nanotube-based lamp, which was oper-ated at high voltage (10KV) [8]. Aproto-type diode structure was tested by the size of 100mm $\times$ 10mm in vacuum chamber [9]. the difficulties arise from the arrangement of vertically aligned nanotubes after the growth. Recently vertically aligned carbon nanotubes have been synthesized using plasma-enhanced chemical vapor deposition(CVD) [6, 7]. Yet, control of a large area synthesis is still not easily accessible with such approaches. Here we report integra-tion processes of fully sealed 4.5-inch CNT-field emission displays (FEDs). Low turn-on voltage with high brightness, and stabili-ty clearly demonstrate the potential applica-bility of carbon nanotubes to full color dis-plays in near future. For flat panel display in a large area, car-bon nanotubes-based field emitters were fabricated by using nanotubes-organic vehi-cles. The purified SWNTs, which were syn-thesized by dc arc discharge, were dispersed in iso propyl alcohol, and then mixed with on organic binder. The paste of well-dis-persed carbon nanotubes was squeezed onto the metal-patterned sodalime glass throuhg the metal mesh of 20${\mu}{\textrm}{m}$ in size and subse-quently heat-treated in order to remove the organic binder. The insulating spacers in thickness of 200${\mu}{\textrm}{m}$ are inserted between the lower and upper glasses. The Y\ulcornerO\ulcornerS:Eu, ZnS:Cu, Al, and ZnS:Ag, Cl, phosphors are electrically deposited on the upper glass for red, green, and blue colors, respectively. The typical sizes of each phosphor are 2~3 micron. The assembled structure was sealed in an atmosphere of highly purified Ar gas by means of a glass frit. The display plate was evacuated down to the pressure level of 1$\times$10\ulcorner Torr. Three non-evaporable getters of Ti-Zr-V-Fe were activated during the final heat-exhausting procedure. Finally, the active area of 4.5-inch panel with fully sealed carbon nanotubes was pro-duced. Emission currents were character-ized by the DC-mode and pulse-modulating mode at the voltage up to 800 volts. The brightness of field emission was measured by the Luminance calorimeter (BM-7, Topcon).

• Geophysics and Geophysical Exploration
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• v.8 no.4
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• pp.270-279
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• 2005

Under the research project supported by Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), we have conducted the development of GPR systems for landmine detection. Until 2005, we have finished development of two prototype GPR systems, namely ALIS (Advanced Landmine Imaging System) and SAR-GPR (Synthetic Aperture Radar-Ground Penetrating Radar). ALIS is a novel landmine detection sensor system combined with a metal detector and GPR. This is a hand-held equipment, which has a sensor position tracking system, and can visualize the sensor output in real time. In order to achieve the sensor tracking system, ALIS needs only one CCD camera attached on the sensor handle. The CCD image is superimposed with the GPR and metal detector signal, and the detection and identification of buried targets is quite easy and reliable. Field evaluation test of ALIS was conducted in December 2004 in Afghanistan, and we demonstrated that it can detect buried antipersonnel landmines, and can also discriminate metal fragments from landmines. SAR-GPR (Synthetic Aperture Radar-Ground Penetrating Radar) is a machine mounted sensor system composed of B GPR and a metal detector. The GPR employs an array antenna for advanced signal processing for better subsurface imaging. SAR-GPR combined with synthetic aperture radar algorithm, can suppress clutter and can image buried objects in strongly inhomogeneous material. SAR-GPR is a stepped frequency radar system, whose RF component is a newly developed compact vector network analyzers. The size of the system is 30cm x 30cm x 30 cm, composed from six Vivaldi antennas and three vector network analyzers. The weight of the system is 17 kg, and it can be mounted on a robotic arm on a small unmanned vehicle. The field test of this system was carried out in March 2005 in Japan.

• Journal of IKEEE
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• v.23 no.4
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• pp.1461-1464
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• 2019

In this paper, we propose the development of embedded board for integrated radiation exposure protection fireman's life-saving alarm capable of location tracking and radiation measurement. The proposed techniques consist of signal processing unit, communication unit, power unit, main control unit. Signal processing units apply shielding design, noise reduction technology and electromagnetic wave subtraction technology. The communication unit is designed to communicate using the wifi method. In the main control unit, power consumption is reduced to a minimum, and a high performance system is formed through small, high density and low heat generation. The proposed techniques are equipment operated by exposure to poor conditions, such as disaster and fire sites, so they are designed and manufactured for external appearance considering waterproof and thermal endurance. The proposed techniques were tested by an authorized testing agency to determine the effectiveness of embedded board. The waterproof grade has achieved the IP67 rating, which can maintain stable performance even when flooded with water at the disaster site due to the nature of the fireman's equipment. The operating temperature was measured in the range of -10℃ to 50℃ to cope with a wide range of environmental changes at the disaster site. The battery life was measured to be available 144 hours after a single charge to cope with emergency disasters such as a collapse accident. The maximum communication distance, including the PCB, was measured to operate at 54.2 meters, a range wider than the existing 50 meters, at a straight line with the command-and-control vehicle in the event of a disaster. Therefore, the effectiveness of embedded board for embedded board for integrated radiation exposure protection fireman's life-saving alarm has been demonstrated.

• Korean Chemical Engineering Research
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• v.45 no.5
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• pp.466-472
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• 2007

A technology platform for wafer-level three-dimensional integration circuits (3D-ICs) is presented, and that uses wafer bonding with low-k polymeric adhesives and Cu damascene inter-wafer interconnects. In this work, one of such technical platforms is explained and characterized using a test vehicle of inter-wafer 3D via-chain structures. Electrical and mechanical characterizations of the structure are performed using continuously connected 3D via-chains. Evaluation results of the wafer bonding, which is a necessary process for stacking the wafers and uses low-k dielectrics as polymeric adhesive, are also presented through the wafer bonding between a glass wafer and a silicon wafer. After wafer bonding, three evaluations are conducted; (1) the fraction of bonded area is measured through the optical inspection, (2) the qualitative bond strength test to inspect the separation of the bonded wafers is taken by a razor blade, and (3) the quantitative bond strength is measured by a four point bending. To date, benzocyclobutene (BCB), $Flare^{TM}$, methylsilsesquioxane (MSSQ) and parylene-N were considered as bonding adhesives. Of the candidates, BCB and $Flare^{TM}$ were determined as adhesives after screening tests. By comparing BCB and $Flare^{TM}$, it was deduced that BCB is better as a baseline adhesive. It was because although wafer pairs bonded using $Flare^{TM}$ has a higher bond strength than those using BCB, wafer pairs bonded using BCB is still higher than that at the interface between Cu and porous low-k interlevel dielectrics (ILD), indicating almost 100% of bonded area routinely.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.99-107
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• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.