• Journal of Korea Multimedia Society
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• v.3 no.2
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• pp.192-200
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• 2000

8 bit and 16 bit microprocessors are widely used in the small sited control machine. The embedded microprocessors which is integrated on a single chip with the memory and I/O circuit must have simple hardware circuit and high code density. This paper proposes a 16 bit high code density EISC(Extendable Instruction Set Computer) microprocessor. SE1608 has 8 general purpose registers and 16 bit fixed length instruction set which has the short length offset and small immediate operand. By using an extend register and extend flag, the offset and immediate operand in instruction could be extended. SE1608 is implemented with 12,000 gate FPGA and all of its functions have been tested and verified at 8MHz. And the cross assembler, the cross C/C++compiler and the instruction simulator of the SE1608 have been designed and verified. This paper also proves that the code density$.$ of SE1608 shows 140% and 115% higher code density than 16 bit microprocessor H-8300 and MN10200 respectively, which is much higher than traditional microprocessors. As a consequence, the SE1608 is suitable for the embedded microprocessor since it requires less program memory to any other ones, and simple hardware circuit.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.395-397
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• 2019

Despite technical advance, human error is the main reason for maritime accidents. To ensure a safety of maritime transporting environment, technical and methodological improvement to react to various types of maritime accidents should be developed instead of ambiguously anticipating maritime accidents due to human errors. Survey, questionnaires, and interview have been routinely applied to understand objective human lookout pattern differences in various navigational situations. Although the descriptive methodology helps systematically categorizing different patterns of human behavior to avoid accidents, the subjective methods limit to objectively recognize physical behavior patterns during navigation. The purpose of the study is to develop an objective lookout pattern detection system using wearable sensors in the simulated navigation environment. In the simulated maritime navigation environment, each participant performed a given navigational situation by wearing the wearable sensors on the wrist, trunk, and head. Activity classification algorithm that was developed in the previous navigation activity classification research was applied. The physical lookout behavior patterns before and after situation-aware showed distinctive patterns, and the results are expected to reduce human errors of navigators.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.792-798
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• 2021

In this paper, an algorithm for determining the coagulant input rate in the drug-injection tank during the process of the water purification plant was derived through big data analysis and prediction based on artificial intelligence. In addition, analysis of big data technology and AI algorithm application methods and existing academic and technical data were reviewed to analyze and review application cases in similar fields. Through this, the goal was to develop an algorithm for determining the coagulant input rate and to present the optimal input rate through autonomous driving simulator and pilot operation of the coagulant input process. Through this study, the coagulant injection rate, which is an output variable, is determined based on various input variables, and it is developed to simulate the relationship pattern between the input variable and the output variable and apply the learned pattern to the decision-making pattern of water plant operating workers.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.287-295
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• 2021

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.

• Journal of Biomedical Engineering Research
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• v.43 no.1
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• pp.27-34
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• 2022

Drunk driving defines a driver as unable to drive a vehicle safely due to drinking. To crack down on drunk driving, alcohol concentration evaluates through breathing and crack down on drinking using S-shaped courses. A method for assessing drunk driving without using BAC or BrAC is measurement via biosignal. Depending on the individual specificity of drinking, alcohol evaluation studies through various biosignals need to be conducted. In this study, we measure biosignals that are related to alcohol concentration, predict BrAC through SVM, and verify the effectiveness of the S-shaped course. Participants were 8 men who have a driving license. Subjects conducted a d2 test and a scenario evaluation of driving an S-shaped course when they attained BrAC's certain criteria. We utilized SVR to predict BrAC via biosignals. Statistical analysis used a one-way Anova test. Depending on the amount of drinking, there was a tendency to increase pupil size, HR, normLF, skin conductivity, body temperature, SE, and speed, while normHF tended to decrease. There was no apparent change in the respiratory rate and TN-E. The result of the D2 test tended to increase from 0.03% and decrease from 0.08%. Measured biosignals have enabled BrAC predictions using SVR models to obtain high Figs in primary and secondary cross-validations. In this study, we were able to predict BrAC through changes in biosignals and SVMs depending on alcohol concentration and verified the effectiveness of the S-shaped course drinking control method.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.

• Proceedings of the ESK Conference
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• 1996.10a
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• pp.47-55
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• 1996

This study developed a comprehensive and easily applicable nuclear reactor control system evaluation method using reactor operators behavioral and mental workload database. A proposed control panel design cycle consists of the 5 steps: (1) finding out inconvenient, erroneous, and mentally stressful factors for the proposed design through evaluative experiments, (2) drafting improved design alternatives considering detective factors found out in the step (1), (3) comparative experiements for the design alternatives, (4) selecting a best design alternative, (5) returning to the step (1) and repeating the design cycle. Reactor operators behavioral and mental workload database collected from evaluative experiments in the step (1) and comparative experiments in the step (3) of the design cycle have a key roll in finding out defective factors and yielding the criteria for selection of the proposed reactor control systems. The behavioral database was designed to include the major informations about reactor operators' control behaviors: beginning time of operations, involved displays, classification of observational behaviors, dehaviors, decisions, involved control devices, classification of control behaviors, communications, emotional status, opinions for man-machine interface, and system event log. The database for mental workload scored from various physiological variables-EEG, EOG, ECG, and respir- ation pattern-was developed to indicate the most stressful situation during reactor control operations and to give hints for defective design factors. An experimental test for the evaluation method applied to the Compact Nuclear Simulator (CNS) installed in Korea Atomic Energy Research Institute (KAERI) suggested that some defective design factors of analog indicators should be improved and that automatization of power control to a target level would give relaxation to the subject operators in stressful situation.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.623-628
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• 2023

The purpose of this study was to evaluate the fracture strength and removal torque value (RTV) of a conventional angled abutment and a newly developed angled abutment (Beauty up abutment) with an angulated screw access hole. Each abutment was divided into a control group and an experimental group (n = 20, respectively). To measure the fracture strength, the abutment was connected to the internal hex implant with 30 Ncm torque, and a load was applied at 30 degree angle with cross-head speed of 1 mm/min using a universal testing machine according to the ISO 14801:2016 standard. To measure RTV, each abutment was fastened to the implant with 30 Ncm torque. Retightening was performed after 10 minutes, and initial RTV was measured with a digital torque gauge. After retightening, a load of 250 N was applied to the abutment at a 30 degree angle using a chewing simulator. After a total of 100,000 repeated loads, RTV was measured. Statistical analysis was performed using Wilcoxon signed rank test and Mann-Whitney U test (α = .05). The fracture strength of the experimental group was statistically significantly lower than that of the control group (P = .009). There was no significant difference between initial RTV and post-loading RTV between the experimental group and the control group (P = .753, P = .527, respectively), and cyclic loading did not significantly affect RTV in both groups (P = .078).

• Proceedings of the ESK Conference
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• 1997.04a
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• pp.56-60
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• 1997

The study of human erroneous actions has traditionally taken place along two different lines of approach. One has been concerned with finding and explaining the causes of erroneous actions, such as studies in the psychology of "error". The other has been concerned with the qualitative and quantitative prediction of possible erroneous actions, exemplified by the field of human reliability analysis (HRA). Another distinction is also that the former approach has been dominated by an academic point of view, hence emphasising theories, models, and experiments, while the latter has been of a more pragmatic nature, hence putting greater emphasis on data and methods. We have been developing a method to make predictions about error modes. The input to the method is a detailed task description of a set of scenarios for an experiment. This description is then analysed to characterise thd nature of the individual task steps, as well as the conditions under which they must be carried out. The task steps are expressed in terms of a predefined set of cognitive activity types. Following that each task step is examined in terms of a systematic classification of possible error modes and the likely error modes are identified. This effectively constitutes a qualitative analysis of the possibilities for erroneous action in a given task. In order to evaluate the accuracy of the predictions, the data from a large scale experiment were analysed. The experiment used the full-scale nuclear power plant simulator in the Halden Man-Machine Systems Laboratory (HAMMLAB) and used six crews of systematic performance observations by experts using a pre-defined task description, as well as audio and video recordings. The purpose of the analysis was to determine how well the predictions matiched the actually observed performance failures. The results indicated a very acceptable rate of accuracy. The emphasis in this experiment has been to develop a practical method for qualitative performance prediction, i.e., a method that did not require too many resources or specialised human factors knowledge. If such methods are to become practical tools, it is important that they are valid, reliable, and robust.

• Radiation Oncology Journal
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• v.17 no.2
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• pp.172-178
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• 1999

Purpose : To measure the basic structural characteristics of radiation oncology facilities in Korea during 1997 and to compare personnel, equipments and patient loads between Korea and developed countries. Methods and Materials : Mail serveys we conducted in 1998 and data on treatment machines, personnel and peformed new patients were collected. Responses were obtained from the 100 percent of facilities. The consensus data of the whole country were summarized using Microsoft Excel program. Results: In Korea during 1997, 42 facilities delivered megavoltage radiation theraphy with 71 treatment machines, 100 radiation oncologists, 26 medical physicist, 205 technologists and 19,773 new patients. Eighty nine percent of facilities in Korea had linear accelators at least 6 MeV maximum photon energy. Ninety five percent of facilities had simulators while five percent of facilities had no simulator, Ninety one percent of facilities had computer planning systems and eighty three percent of facilities reported that they had a written quality assurance program. Thirty six percent of facilities had only one radiation oncologist and thirty eight percent of facilities had no medical physicists. The median of the distribution of annual patients load of a facility, patients load per a machine, patients load per a radiation oncologist, patients load per a therapist and therapists per a machine in Korea were 348 patients per a year, 263 patients per a machine, 171 patients per a radiation oncologist, 81 patients per a therapist, and 3 therapists per a machine respectively. Conclusions : The whole scale of the radiation oncology departments in Korea was smaller than Japan and USA in population ratio regard. In case of hardware level like linear accelerators, simulators and computer planning systems, there was no big differences between Korea and USA. The patients loads of radiation oncologists and therapists had no significant differences as compared with USA. However, it was desirable to consider the part time system in USA because there were a lot of hospitals which did not employ medical physicists.