• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.245-254
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• 2022

Sensor dataset for autonomous driving is one of the essential components as the deep learning approaches are widely used. However, most driving datasets are focused on typical environments such as sunny or cloudy. In addition, most datasets deal with color images and lidar. In this paper, we propose a driving dataset with multi-spectral images and lidar in adverse weather conditions such as snowy, rainy, smoky, and dusty. The proposed data acquisition system has 4 types of cameras (color, near-infrared, shortwave, thermal), 1 lidar, 2 radars, and a navigation sensor. Our dataset is the first dataset that handles multi-spectral cameras in adverse weather conditions. The Proposed dataset is annotated as 2D semantic labels, 3D semantic labels, and 2D/3D bounding boxes. Many tasks are available on our dataset, for example, object detection and driveable region detection. We also present some experimental results on the adverse weather dataset.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.521-523
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• 2021

As the invention of automobiles and construction of roads for vehicles began, the occurrence of traffic accidents began to increase. Accordingly, efforts were made to prevent traffic accidents by changing the road construction method and using signal systems such as traffic lights, but even today, numerous human and property damages have occurred due to traffic accidents caused by freezing of the road due to bad weather. In this paper, in order to reduce traffic accidents due to road freezing, we propose a method of transferring the ice detection information obtained by deep learning of infrared wavelength data obtained using an infrared camera to the vehicle's navigation.

• Journal of Information Technology Applications and Management
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• v.30 no.6
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• pp.69-80
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• 2023

Ship collision accidents not only cause loss of life and property damage, but also cause marine pollution and can become national disasters, so prevention is very important. Most of these ship collision accidents are caused by human factors due to the navigation officer's lack of vigilance and carelessness, and in many cases, they can be prevented through the support of a system that helps with situation awareness. Recently, artificial intelligence has been used to develop systems that help navigators recognize the situation, but the sea is very wide and deep, so it is difficult to secure maritime traffic datasets, which also makes it difficult to develop artificial intelligence models. In this paper, to solve these difficulties, we propose a method to build a dataset with characteristics similar to actual maritime traffic datasets. The proposed method uses segmentation and inpainting technologies to build a foreground and background dataset, and then applies compositing technology to create a synthetic dataset. Through prototype implementation and result analysis of the proposed method, it was confirmed that the proposed method is effective in overcoming the difficulties of dataset construction and complementing various scenes similar to reality.

• Journal of The Korean Association For Science Education
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• v.22 no.2
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• pp.299-313
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• 2002

A web-based instruction program for the enriched course under the 7th Revised National Curriculum of Biology in Korea was developed and the application effects to learners were analyzed. For the development of the web-based instruction program, five topics of biology from the enriched courses through 7th to 10th grades in the middle and high school science textbooks were selected and modulated with interrogative sentences. Each topic of programs was divided into four activity sections according to the learners' activity procedures supplemented with explanations and evaluations. Each activity was hyper-linked to multi-layers and animations. Further, a virtual experiment was also developed and an evaluation section designed by Java Script was attached. Among five topics, one topic of 'Reproduction and development' at 9th grade level was selected to examine the effects on students' learning. Among 247 9th grade students in the research subject school, only 67 students were able to accessible to ultra-thin Internet cables with their computers at home and they became an experimental group. A control group was assigned to those who are similar level of school science achievement to the experiment group and did not use the web-based program. It was found that most of 9th grade students are able to use Internet at home, however, they do not prefer to use Internet for homework or task project. Rather, most of students used Internet for e-mail or information navigation. Students used internet to solve problems of science and perceived the benefits of Internet for science learning. However, there are not many students to utilize Internet for science homework or task project. Students expressed that they do not prefer to use a web-based learning program for science learning due to lack of interests in science. The effects on students who studied with this program appeared to be significantly high compared to those who did not study with this program. Students who studied with this program positively evaluated this program, in particular, they enjoyed animation effect and virtual experiments. It was concluded that a web-based program for science learning should be developed and distributed through Internet in an attractive and interesting format for students. It was also concluded that various web-based programs for science learning with animation effect and virtual experiments should be developed to increase students' interests in science as well as to improve students' science achievements.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.417-420
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• 2002

Recent works in the area of multimedia studies focus on a wide range of issues from the impact of multimedia on culture to its impact on economics and anything in between. The interconnectedness of the issues raised by this new practice is complicated by the fact that media are rapidly converging: in a very real way, multimedia is becoming a media prism that reflects the way in which media continually influence each other across disciplines and cultural borders. Thus, the impact of multimedia reflects a complicated crossroads where media, human experience, culture and technology converge. An effective design is generally based on shaping aesthetics for function and utility, with an emphasis on ease of use. However, in designing for cyberspace, it is possible to create narratives that challenge the interactor by encoding in the design an instructional aspect that teaches new approaches and forms. Such a design offers an equally aesthetic experience for the interactor as they explore the meaning of the work. This design approach has been used constructively in many applications. The crucial concern is to determine how little or how much information must be presented for the interactor to achieve a suitable level of cognition. This is always a balancing act: too much difficulty will result in interactor frustration and the abandonment of the activity and too little will result in boredom leading to the same negative result In addition, it can be anticipated that the interactor will bring her or his own level of experiential cognition and/or accretion, to the experience providing reflective cognition and/or restructure the learning curve. If the design of the application is outside their present experience, interactors will begin with established knowledge in order to explore the new work. Thus, it may be argued that the interactor explores, learns and cognates simultaneously based on primary experiential cognition. Learning is one of the most important keys to establishing a comfort level in a new media work. Once interactors have learned a new convention, they apply this cognitive knowledge to other new media experiences they may have. Pierre Levy would describe this process as a "new nomadism" that creates "an invisible space of understanding, knowledge, and intellectual power, within which new qualities of being and new ways of fashioning a society will flourish and mutate" (Levy xxv 1997). Thus, navigation itself of offers the interactors the opportunity to both apply and loam new cognitive skills. This suggests that new media narrative strategies are still in the process of developing unique conventions and, as a result, have not reached a level of coherent grammar. This paper intends to explore the cognitive aspects of new media design and in particular, will explore issues related to the design of new media interfaces. The paper will focus on the creation of narrative strategies that engage interactors through loaming curves thus enhancing interactivity.vity.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.339-349
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• 2017

The purpose of this study was to identify the effect of Career Education Program(CEP) on freshmen of nursing department. A non-equivalent pre-post test of quasi-experimental design was used. 44 freshmen were assigned to an intervention group and 36 freshmen to a control group. The intervention program was composed of introduction, self-navigation, explore the world of work, career design and termination during 12 weeks from March to June, 2014. Data were collected before-and after program, and analyzed using the SPSS 22.0 program. There were significant increases in learning motivation (t=8.92, p<.001) and college life adaptation (t=3.51, p<.001) in the experimental group compared to the control group. This study suggests that CEP would be an efficient way to adapt to school and take learning motivation for nursing students. It is necessary to develop a systematic and tailored career education program for nursing students from a freshmen to a senior.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.574-583
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• 2021

Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.

• Journal of Advanced Navigation Technology
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• v.15 no.2
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• pp.287-297
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• 2011

As electronic mails are being widely used for facility and speedness of information communication, as the amount of spam mails which have malice and advertisement increase and cause lots of social and economic problem. A number of approaches have been proposed to alleviate the impact of spam. These approaches can be categorized into pre-acceptance and post-acceptance methods. Post-acceptance methods include bayesian filters, collaborative filtering and e-mail prioritization which are based on words or sentances. But, spammers are changing those characteristics and sending to avoid filtering system. In the case of Korean, the abnormal usages can be much more than other languages because syllable is composed of chosung, jungsung, and jongsung. Existing formal expressions and learning algorithms have the limits to meet with those changes promptly and efficiently. So, we present an methods for recognizing Korean abnormal language(Koral) to improve accuracy and efficiency of filtering system. The method is based on syllabic than word and Smith-waterman algorithm. Through the experiment on filter keyword and e-mail extracted from mail server, we confirmed that Koral is recognized exactly according to similarity level. The required time and space costs are within the permitted limit.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.539-545
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• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.211-218
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• 2022

Continuous wave (CW) radar has the advantage of reliability and accuracy compared to other sensors such as camera and lidar. In addition, binarized neural network (BNN) has a characteristic that dramatically reduces memory usage and complexity compared to other deep learning networks. Therefore, this paper proposes binarized neural network based human identification and motion classification system using CW radar. After receiving a signal from CW radar, a spectrogram is generated through a short-time Fourier transform (STFT). Based on this spectrogram, we propose an algorithm that detects whether a person approaches a radar. Also, we designed an optimized BNN model that can support the accuracy of 90.0% for human identification and 98.3% for motion classification. In order to accelerate BNN operation, we designed BNN hardware accelerator on field programmable gate array (FPGA). The accelerator was implemented with 1,030 logics, 836 registers, and 334.904 Kbit block memory, and it was confirmed that the real-time operation was possible with a total calculation time of 6 ms from inference to transferring result.