• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.133-142
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• 2021

With the recent advancements of deep learning, companies such as smart home, healthcare, and intelligent transportation systems are utilizing its functionality to provide high-quality services for vehicle detection, emergency situation detection, and controlling energy consumption. To provide reliable services in such sensitive systems, deep learning models are required to have high accuracy. In order to develop a deep learning model for analyzing previously mentioned services, developers should utilize the state of the art deep learning models that have already been verified for higher accuracy. The developers can verify the accuracy of the referenced model by validating the model on the dataset. For this validation, the developer needs structural information to document and apply deep learning models, including metadata such as learning dataset, network architecture, and development environments. In this paper, we propose a description language that represents the network architecture of the deep learning model along with its metadata that are necessary to develop a deep learning model. Through the proposed description language, developers can easily verify the accuracy of the referenced deep learning model. Our experiments demonstrate the application scenario of a deep learning description document that focuses on the license plate recognition for the detection of illegally parked vehicles.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.4
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• pp.1811-1815
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• 2016

Wetland geographical areas have a higher incidence of Opisthorchis viverrini-associated cholangiocarcinoma (CCA), confirmed by data from geographic information systems, than other areas. Behavioral data also indicate that people in these areas traditionally eat uncooked freshwater fish dishes, a vehicle for O. viverrini infection. The best approach to reducing CCA incidence is decreasing risk factors together with behavior alteration. Evaluation of CCA risk and its related factors are first needed for planning the prevention and control programs in the future. We therefore aimed to evaluate the CCA risk and explore its related factors among people in wetland communities of Ubon Ratchathani, Thailand. A cross-sectional study was conducted between July and August 2014. In total 906 participants, with informed consent, completed questionnaires. Overall risk of CCA was determined by multiplying odds ratios (ORs) of the risk factors for CCA from literature reviews. A mean score of 5.95 was applied as the cut-off point. Assessment of factors related to overall risk of CCA was accomplished using conditional logistic regression. Of all participants, 60.15% had a high level of the overall risk of CCA. Factors related to the overall risk of CCA were gender (p<0.001), marital status (p<0.001), perceived susceptibility (p=0.043) and prevention behavior for CCA (p<0.001). In conclusion, most participants in this community had a high level of overall risk of CCA. Therefore, integrated prevention and control programs continue to be urgently required.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.674-684
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• 2023

In this paper, a real-time prediction technique for gravity disturbances is proposed using a multi-layer perceptron (MLP) model. To select a suitable MLP model, 4 models with different network sizes were designed to compare the training accuracy and execution time. The MLP models were trained using the data of vehicle moving along the surface of the sea or land, including their positions and gravity disturbance. The gravity disturbances were calculated using the 2160^th degree and order EGM2008 with SHM. Among the models, MLP4 demonstrated the highest training accuracy. After training, the weights and biases of the 4 models were stored in the embedded computer of the INS to implement the MLP network. MLP4 was found to have the shortest execution time among the 4 models. These research results are expected to contribute to improving the navigation accuracy of INS through gravity disturbance compensation in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8940-8947
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• 2015

Mixed-model assembly line has been widely used in automotive assembly industry to quickly respond the diverse product demands. But, this model can lead to part confusion, which is a source for assembly errors when parts are physically interchangeable in a mixed-model assembly line. With the recent application of new technologies such as radio frequency identification (RFID) and ZigBee wireless sensor network (WSN) to the assembly process, real-time information has become available in this manufacturing systems through IT infrastructures. At first, this paper presents an RFID application for assembly processes, specifically, for a mixed-model assembly line. Thus, to ensure that parts be picked accurately, each cockpit module on the assembly line is attached with a RFID tag and the tag is scanned using a RFID reader and recognizes the vehicle, and each part of the cockpit module is attached with a barcode and the barcode is scanned by a barcode reader and each part is identified correctly for the vehicle. Second, this paper presents a ZigBee wireless sensor network (WSN) protocol-based application for a reconfigurable mixed-model assembly line of cockpit module to reduce the assembly errors and the cost of the change/reconfiguration on the assembly lines due to the various orders and new models from the motor company, avoiding the wiring efforts and inconvenience by wiring between the several RFID devices and the IT server system. Finally, we presents the operation results for several years using this RFID/ZigBee wireless sensor network (WSN) protocol-based cockpit module assembly line.

• Journal of KIISE
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• v.43 no.6
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• pp.662-667
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• 2016

As electric and electronic control systems have sharply increased in vehicles, safety accident has emerged as an important issue. Therefore, in order to ensure safety of the vehicle, engineers are required to identify the hazards utilizing PHA and HAZOP, etc. in the early phase of development and implement safety mechanisms to prevent them. HAZOP has been widely used in a systematic manner based on guidewords. However, HAZOP identifies malfunctions from the top-level functionality provided by the system, so it cannot sufficiently identify hazards during the system operation. This leads to restrictions in designing testcases, because the safety requirements are derived from only some of the hazards. This research aimed to provide a hazard identification method utilizing Use case description, which defines operation procedure of the system and HAZOP and a testcase design method based on safety requirements. We introduced a case study on Smart Key Control System in vehicles and compared with hazards identification results based on HAZOP, to demonstrate the effectiveness of this study. The result of this study could potentially reduce development cost and increase system quality by adequately identifying hazards and safety requirements and designing the related testcase.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.137-146
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• 2024

drones are used in a variety of fields, including business, leisure, lifesaving, and war. Various research using drones is being conducted in the military. In particular, the use of drones in 『Manned-Unmanned Complex combat performance plan』, powered by various unmanned vehicles deployed in the Army TIGER system, is expected to be a major factor realizing the Army's future combat performance that minimizes damage to ally combat troops while causing maximum damage to the enemy. As the deployment of various systems progresses, combat performance methods utilizing each system are evolving, but there is a lack of research to identify and resolve limitations in the perspective of unmanned vehicle operators. Based on the Ukrainian military's FPV drone combat case, we would like to make suggestions from the operator's perspective on overcoming perspective limitations through the introduction of FPV and the designation of military drone frequency.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.64-73
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• 2015

Recently, the integrated utilization of technology with IT is in demand for the effectiveness of field management together with the prevention and prompt action on safety accident at construction site. In addition, the establishment of construction site support system is necessary to implement the securing of worker's safety, smooth work instruction, efficiency in construction, and others. Data standardization and network architecture were designed regarding data and sound information for data transmission between systems and management. These were to construct USFSS based on integrated VI-GNSS technology in this research. In the stability test of data for each system constructed through it, around 98% stability was secured between workers and for transfer vehicle system within underground structure and field server system in regards to the data transmission stability, around 100% stability was secured between field server system and control system, respectively. Also, in the sound transmission stability test, around 99% reliability could be secured with 1km distance as its standard in case of sound transmission from underground structure construction site to field office near the field through wireless FRS system.

• Journal of Korea Spatial Information System Society
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• v.4 no.2 s.8
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• pp.55-64
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• 2002

VEfficient generation of road data is one of the most important issues in GIS (Geographic Information System). In this paper, we propose a hybrid approach for automatic generation of road data by combining mobile mapping and image processing techniques. Mobile mapping systems have a form of vehicle equipped with CCD camera, GPS, and INS. They can calculate absolute position of objects that appear in acquired image by photogrammetry, but it is labor-intensive and time-consuming. Automatic road detection methods have been studied also by image processing technology. However, the methods are likely to fail because of obstacles and exceptive conditions in the real world. To overcome the problems, we suggest a hybrid method for automatic road generation, by exploiting both GPS/INS data acquired by mobile mapping system and image processing algorithms. We design an estimator to estimate 3-D coordinates of road line and corresponding location in an image. The estimation process reduces complicated image processing operations that find road line. The missing coordinates of road line due to failure of estimation are obtained by cubic spline interpolation. The interpolation is done piecewise, separated by rapid change such as road intersection. We present experimental results of the suggested estimation and interpolation methods with image sequences acquired by mobile mapping system, and show that the methods are effective in generation of road data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.3
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• pp.209-219
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• 2013

Epidemiological models on disease spread attempt to simulate disease transmission and associated control processes and such models contribute to greater understanding of disease spatial diffusion through of individual's contacts. The objective of this study is to develop an agent-based modeling(ABM) approach that integrates geographic information systems(GIS) to simulate the spread of FMD in spatial environment. This model considered three elements: population, time and space, and assumed that the disease would be transmitted between farms via vehicle along the roads. The model is implemented using FMD outbreak data in Andong city of South Korea in 2010 as a case study. In the model, FMD is described with the mathematical model of transmission probability, the distance of the two individuals, latent period, and other parameters. The results show that the GIS-agent based model designed for this study can be easily customized to study the spread dynamics of FMD by adjusting the disease parameters. In addition, the proposed model is used to measure the effectiveness of different control strategies to intervene the FMD spread.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.6
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• pp.619-623
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• 2008

A lane detection system is one of the major components of intelligent vehicle systems. Difficulties in lane detection mainly come from not only various weather conditions but also a variety of special environment. This paper describes a simple and stable method for the broken lane tracking in various environments. Probabilistic Hough Transform (PHT) and the Least-square method (LSM) are used to track and correct the lane orientation. For the efficiency of the proposed method, two regions of interest (ROIs) are placed in the lower part of each image, where lane marking areas usually appear with less intervention in our system view. By testing in both a set of static images and video sequences, the experiments showed that the proposed approach yielded robust and reliable results.