• Journal of Fisheries and Marine Sciences Education
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• v.2 no.1
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• pp.1-14
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• 1989

The authors studied on the modification of the curriculum for the department of fishing technology in Fisheries junior college, by collecting and analyaing the references, questionaires and consultation according to the trends in industry and employment. Especially, rapid develop of the fishing technology with the modernization and automatization of fishing vessels, the present curriculum formulated in 1983 is required to be rearranged. Accordingly, the authors studied on the development of characteristic curriculum which could cultivate the professional fishing engineers. The results obtained can be summarized as follows : 1. Total open credits should be 160 credits. 2. Cultural subjects are set 32credits, 20% of total open credits, but the subjects may be choosen by the situation of each fisheries junior college. 3. Major subjects are set 128 credits with 36 subjects including the 3rd year course. 4. The proportion of the compulsory to the elective credits in the major subjects are made 1 to 2. 5. Compulsory major subjects are set 29 credits with 11 subjects during the 1st and 2nd year course. Training on the board (I) and (II) are set 40 credits during 3rd year course and evaluated as pass or not. 6. The proportion of the credits of subjects concerning to fishery and navigation is made 1 to 1. 7. In order to strengthen the computation education, the E.D.P.S. is sets 3 credits in the cultural subjects.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.26 no.1
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• pp.14-19
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• 1990

This paper describes on the characteristics of the acoustic echoes from the scattering layers by the aggregation of marine organisms which was measured by using a 50kHz echo sounder on board of the training ship SAEBADA belong to National Fisheries University of Pusan during the summer, 1989 in the East China Sea. A data acquisition system was used to record digitally the envelope of the echoes and the frequency distribution of echo amplitudes on the whole water column was examined as a function of time during the sunrise and sunset periods. Acoustic data showed that the abundance distribution of marine organisms according to depth in the servery area changed with time and that the organisms were most active during the twilight of morning, and the echo strength on the water column or scattering layer was also strongest during this time. Additional results of this survey suggest that other interest, such as the relationship between environmental conditions and biomass depth distribution, could be also investigated in hydroacoustic method.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.425-433
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• 2020

This paper aims to develop a conceptual framework for evaluating the impact of motivational factors on the work results of lecturers at Vietnam National University, Hanoi (VNU), one of two leading multidisciplinary and multi-sectoral national universities in Vietnam. This study has considered wages and other benefits (WB), training and development (TD), working environment (WE) and working motivation (WM) as motivational factors, and proposed a structural model of the impact of motivational factors on the work results of lecturers at VNU. The empirical analysis used data from the survey data of 321 university lecturers. Comprehensive, valid, and reliable tools (SPSS 26 and SmartPLS 3.0 software) are used to evaluate rigorous statistical tests including convergence validity, discriminatory validity, reliability, and average variance extracted to analyze and verify the gathered data, and the hypotheses developed. The result of path analysis shows that four motivational factors constitute a structured system with different degrees of influence on the work results of lecturers. There is also a positive relationship between the motivational factors and the work results of lecturers. As a result, it can be concluded that all hypotheses developed are supported. Several recommendations are further suggested to improve the performance of lecturers at VNU.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.10a
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• pp.51-52
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• 2012

Safety at sea continues to be an important topic for research. The factors that contribute to safety issues are often complex and unclear. Lack of non-technical skills in such areas as communication, teamworking, situational awareness, decision making etc. are well documented as directly affecting safety in high risk work settings such as on board merchant vessels. Competence in non-technical skills can be affected by various habits and circumstances that influence a worker and result in either positive thinking and behaviour or negative thinking and behaviour. Negative thinking can cause stress, anger, frustration; loneliness etc. and this can possibly result in behaviour or actions that undermine the safety of crew and ship. Factors that may contribute to these negative thoughts and behaviours are many and varied and this paper will be looking at one aspect: mobile phone use. In the Republic of Korea, dependency on mobile phones is uniquely high. Maritime students are also susceptible to such dependency and this paper will demonstrate that the symptoms associated with mobile phone addiction and dependency, and in particular the symptoms of withdrawal, are important factors that need to be mitigated in order to improve safety at sea. Although the benefits of mobile phones are many it will be suggested that over-reliance, dependency or addiction can result in negative thinking or behaviour that may directly or indirectly contribute to the factors that cause incidents and accidents at sea. It will be suggested that the most appropriate method of dealing with this problem is twofold: firstly, through training at the educational level and secondly, by encouraging the shipping industry and government to invest more into providing seafarers with better access to the communication methods that they are used to on shore in order to improve work conditions.

• Korean Medical Education Review
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• v.19 no.1
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• pp.18-24
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• 2017

This study examined the perception and readiness of nursing educators regarding interprofessional education (IPE), and discussed the validity and application of IPE in nursing. From December 2016 to January 2017, 239 nursing professors and nurses completed a structured questionnaire consisting of general characteristics, the Interdisciplinary Education Perception Scale, the Readiness for Interprofessional Learning Survey (RIPLS), and an IPE action plan. The collected data were analyzed by descriptive statistics and t-test using the IBM SPSS ver. 23.0 program (IBM Corp., Armonk, NY, USA). The analysis revealed that 91.6% of the participants had not experienced IPE, and only 11.7% knew about IPE. However, approximately 80.0% answered that IPE is necessary. The results of this study showed that the score of the perceived need for cooperation was higher in nurses than it was in professors, while the score on competency and autonomy was higher in professors than it was in nurses. With reference to the scores on the RIPLS, those of professors were high on the sub-scales of teamwork and collaboration, professional identity, and roles and responsibility. The results revealed that participants considered the upper-grade undergraduate years as the ideal time for imparting IPE, and it was deemed suitable to include communication, simulation, and clinical practice in IPE. Doctors, pharmacists, and physiotherapists were thought to require cooperation for IPE the most. Despite the presence of several barriers to IPE, the participants thought that IPE can achieve learning outcomes such as interprofessional communication and cooperation, conflict resolution, and teamwork. It is necessary to cooperate with professionals in the complex clinical environment as professional areas are specialized and subdivided. Therefore, it is necessary to examine the application of IPE in undergraduate education and in on-the-job training.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.226-234
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• 2024

The frequent handover problem and playing ping-pong effects in 5G (5th Generation) ultra-dense networking cannot be effectively resolved by the conventional handover decision methods, which rely on the handover thresholds and measurement reports. For instance, millimetre-wave LANs, broadband remote association techniques, and 5G/6G organizations are instances of group of people yet to come frameworks that request greater security, lower idleness, and dependable principles and correspondence limit. One of the critical parts of 5G and 6G innovation is believed to be successful blockage the board. With further developed help quality, it empowers administrator to run many systems administration recreations on a solitary association. To guarantee load adjusting, forestall network cut disappointment, and give substitute cuts in case of blockage or cut frustration, a modern pursuing choices framework to deal with showing up network information is require. Our goal is to balance the strain on BSs while optimizing the value of the information that is transferred from satellites to BSs. Nevertheless, due to their irregular flight characteristic, some satellites frequently cannot establish a connection with Base Stations (BSs), which further complicates the joint satellite-BS connection and channel allocation. SF redistribution techniques based on Deep Reinforcement Learning (DRL) have been devised, taking into account the randomness of the data received by the terminal. In order to predict the best capacity improvements in the wireless instruments of 5G and 6G IoT networks, a hybrid algorithm for deep learning is being used in this study. To control the level of congestion within a 5G/6G network, the suggested approach is put into effect to a training set. With 0.933 accuracy and 0.067 miss rate, the suggested method produced encouraging results.

• Proceedings of KOSOMES biannual meeting
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• 2007.05a
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• pp.1-7
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• 2007

OPRC-HNS Protocol places the responsibility for having on-board a pollution incident emergency plan, notifying other States the incident facts in case of being affected by that incident, development of national contingency plan, international co-operation in pollution response, research and development, technical co-operation, promotion of bilateral and multilateral co-operation in preparedness and response, providing of information service, promotion of education and training, providing of technical services and technical assistance, etc. on parties to pursue their goals. After joining OPRC-HNS protocol, the government is required to inspect current status of matters related to HNS for its relevance on any laws. Additional items including establishment of component organization playing a role of managing response institution, manpower and resources, purchase response resource, development of response technique, and those also to enforce education and training to promote the party's duty. The facts described above drive up to analyze the benefits and burdens of relevant industry which appears in those procedure.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.491-501
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• 2019

In side-channel analysis, which exploit physical leakage from a cryptographic device, deep learning based attack has been significantly interested in recent years. However, most of the state-of-the-art methods have been focused on classifying side-channel information in a profiled scenario where attackers can obtain label of training data. In this paper, we propose a new method based on deep learning to improve non-profiling side-channel attack such as Differential Power Analysis and Correlation Power Analysis. The proposed method is a signal preprocessing technique that reduces the noise in a trace by modifying Auto-Encoder framework to the context of side-channel analysis. Previous work on Denoising Auto-Encoder was trained through randomly added noise by an attacker. In this paper, the proposed model trains Auto-Encoder through the noise from real data using the noise-reduced-label. Also, the proposed method permits to perform non-profiled attack by training only a single neural network. We validate the performance of the noise reduction of the proposed method on real traces collected from ChipWhisperer board. We demonstrate that the proposed method outperforms classic preprocessing methods such as Principal Component Analysis and Linear Discriminant Analysis.

• Journal of Navigation and Port Research
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• v.32 no.1
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• pp.89-95
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• 2008

Foreign maritime officers were employed in Korean flag ocean-going merchant ships since the year of 2005. And the number of foreign maritime officers in Korean ship has been rapidly increased, and it approaches 700 recently. The shortage of Korean maritime officers was mainly claimed the reason of employing foreign maritime officers in Korean flag ship. In this paper, we surveyed the number of newly supplied officers from every Korean training institution, the number of officers on-board, on-leave, on-job hunting, and the number of officers leaving their job every year. And then we analysed supply-demand situation of Korean maritime officers for Korean flag ocean-going ships for reasonable decision-making in relation with employing foreign maritime officers.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1165-1173
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• 2017

Deep learning method, which is one of the most famous machine learning algorithms, has proven its applicability in various applications and is widely used in digital signal processing. However, it is difficult to apply deep learning technology to IoT devices with limited CPU performance and memory capacity, because a large number of training samples requires a lot of memory and computation time. In particular, if the Arduino with a very small memory capacity of 2K to 8K, is used, there are many limitations in implementing the algorithm. In this paper, we propose a method to optimize the ELM algorithm, which is proved to be accurate and efficient in various fields, on Arduino board. Experiments have shown that multi-class learning is possible up to 15-dimensional data on Arduino UNO with memory capacity of 2KB and possible up to 42-dimensional data on Arduino MEGA with memory capacity of 8KB. To evaluate the experiment, we proved the effectiveness of the proposed algorithm using the data sets generated using gaussian mixture modeling and the public UCI data sets.