• Journal of IKEEE
• /
• v.23 no.4
• /
• pp.1381-1386
• /
• 2019

As traffic congestion in cities becomes more serious, intelligent traffic control is actively being researched. Reinforcement learning is the most actively used algorithm for traffic signal control, and recently Deep reinforcement learning has attracted attention of researchers. Extended versions of deep reinforcement learning have been emerged as deep reinforcement learning algorithm showed high performance in various fields. However, most of the existing traffic signal control were studied in a single intersection environment, and there is a limitation that the method at a single intersection does not consider the traffic conditions of the entire city. In this paper, we propose a cooperative traffic control at multi-intersection environment. The traffic signal control algorithm is based on a combination of extended versions of deep reinforcement learning and we considers traffic conditions of adjacent intersections. In the experiment, we compare the proposed algorithm with the existing deep reinforcement learning algorithm, and further demonstrate the high performance of our model with and without cooperative method.

• Journal of the Ergonomics Society of Korea
• /
• v.26 no.4
• /
• pp.25-31
• /
• 2007

The purpose of this study was to investigate the strength and endurance of the deep neck flexor muscles in individuals with work-related neck pain. Subjects consisted of two groups: twenty industrial workers with neck pain and twenty age-matched healthy subjects. To evaluate the strength and endurance of deep cervical flexors, maximum voluntary contractile strength (MVCS) and a sustained time at sub-maximal voluntary contractile strength (SMVCS) (80% and 50% of MVCS) were measured using a pressure biofeedback unit and a stop watch in supine. The MVCS of deep neck flexor muscles was 29.67${\pm}$4.56 in neck pain group and 54.27${\pm}$6.78㎜Hg in normal group. The sustained time at 80% SMVCS was 12.42${\pm}$2.64 seconds and 55.12${\pm}$12.76 seconds in the groups with and without neck pain. The sustained time at 50% SMVCS was 25.40±5.88 seconds and 109.70${\pm}$31.50 seconds in the groups with and without neck pain. The difference of the lower jaw position was 16.75${\pm}$3.57㎜ and 23.03${\pm}$2.51㎜. The MVCS, endurance at the two sub-maximal levels and the difference of the lower jaw position were significantly greater in the group without neck pain than with neck pain (p$<$0.05). The findings indicate that the maximal strength and endurance of the deep neck flexors were decreased in the workers with neck pain compared to those without neck pain. Therefore, it is necessary to include strengthening and endurance exercises of the deep neck flexor muscles in therapeutic program of work-related musculoskeletal disorders involving neck pain.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.11
• /
• pp.3099-3120
• /
• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.9
• /
• pp.3663-3679
• /
• 2020

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.

• The Journal of Korea Robotics Society
• /
• v.15 no.4
• /
• pp.398-403
• /
• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• Nuclear Engineering and Technology
• /
• v.33 no.2
• /
• pp.231-240
• /
• 2001

When it is assumed that PWR, CANDU and DUPIC spent fuels are disposed of in deep geological repository, consequent annual individual doses are calculated, and it is shown that doses meet the regulatory limit. From these results, the hazardous radionuclides applicable to partitioning and transmutation are selected. These selected radionuclides such as Tc-99, Ⅰ-129, Cs-135 and Np-237 are then reviewed in terms of partitioning and transmutation. Separation of I-129, Np-237 and Tc-99 from spent fuels is considered desirable, and transmutation of these radionuclides results in remarkable hazard reduction. However, it is concluded that separation and transmutation of Cs-135 may be ineffective although it is classified into a hazardous radionuclide.

• The Journal of Korea Robotics Society
• /
• v.17 no.1
• /
• pp.58-67
• /
• 2022

In this paper, we propose an approach resolving inaccuracy of the low-cost redundant manipulator workspace with low encoder and low stiffness. When the manipulators are manufactured with low-cost encoders and low-cost links, the robots can run into workspace inaccuracy issues. Furthermore, trajectory generation based on conventional forward/inverse kinematics without taking into account inaccuracy issues will introduce the risk of end-effector fluctuations. Hence, we propose an optimization for the trajectory generation method based on the DDPG (Deep Deterministic Policy Gradient) algorithm for the low-cost redundant manipulators reaching the target position in Euclidean space. We designed the DDPG algorithm minimizing the distance along with the jacobian condition number. The training environment is selected with an error rate of randomly generated joint spaces in a simulator that implemented real-world physics, the test environment is a real robotic experiment and demonstrated our approach.

• Geomechanics and Engineering
• /
• v.15 no.2
• /
• pp.721-728
• /
• 2018

The surrounding rock mass contains cracks and joints which are distributed randomly around tunnels, and in the process of tunnel blasting excavation, radial cracks could also be induced in the surrounding rock mass. In order to clearly understand the impact of radial cracks on tunnel stability, tunnel model tests and finite element numerical analysis were implemented in this paper. Two kinds of materials: cement mortar and sandstone, were used to make tunnel models, which were loaded vertically and confined horizontally. The tunnel failure pattern was simulated by using RFPA2D code, and the Tresca stresses and the stress intensity factors were calculated by using ABAQUS code, which were applied to the analysis of tunnel model test results. The numerical results generally agree with the model test results, and the mode II stress intensity factors calculated by ABAQUS code can well explain the model test results. It can be seen that for tunnels with a radial crack emanating from three points on tunnel edge, i.e., the middle point between tunnel spandrel and its top with a dip angle $45^{\circ}$, the tunnel foot with a dip angle $127^{\circ}$, and the tunnel spandrel with $135^{\circ}$ with tunnel wall, the tunnel model strength is about a half of the regular tunnel model strength, and the corresponding tunnel stability decreases largely.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.16 no.4
• /
• pp.491-505
• /
• 2018

As an alternative to deep geological disposal technology, which is considered as a reference concept, the domestic applicability of deep borehole disposal technology for high level radioactive waste, including spent fuel, has been preliminarily evaluated. Usually, the environment of deep borehole disposal, at a depth of 3 to 5 km, has more stable geological and geo-hydrological conditions. For this purpose, the characteristics of rock distribution in the domestic area were analyzed and drilling and investigation technologies for deep boreholes with large diameter were evaluated. Based on the results of these analyses, design criteria and requirements for the deep borehole disposal system were reviewed, and preliminary reference concept for a deep borehole disposal system, including disposal container and sealing system meeting the criteria and requirements, was developed. Subsequently, various performance assessments, including thermal stability analysis of the system and simulation of the disposal process, were performed in a 3D graphic disposal environment. With these analysis results, the preliminary evaluation of the domestic applicability of the deep borehole disposal system was performed from various points of view. In summary, due to disposal depth and simplicity, the deep borehole disposal system should bring many safety and economic benefits. However, to reduce uncertainty and to obtain the assent of the regulatory authority, an in-situ demonstration of this technology should be carried out. The current results can be used as input to establish a national high-level radioactive waste management policy. In addition, they may be provided as basic information necessary for stakeholders interested in deep borehole disposal technology.

• Journal of Korea Multimedia Society
• /
• v.23 no.8
• /
• pp.1075-1087
• /
• 2020

Aviation ICT technology is a convergence technology between aviation and electronics, and has a wide variety of applications, including navigation and education. Among them, in the field of aerial pilot training, there are many problems such as the possibility of accidents during training and the lack of coping skills for various situations. This raises the need for a simulated pilot training system similar to actual training. In this paper, pilot training data were collected in pilot training system using VR/AR to increase immersion in flight training, and Customized Pilot Training Platform with Collaborative Deep Learning in VR/AR Environment that can recommend effective training courses to pilots is proposed. To verify the accuracy of the recommendation, the performance of the proposed collaborative deep learning algorithm with the existing recommendation algorithm was evaluated, and the flight test score was measured based on the pilot's training data base, and the deviations of each result were compared. The proposed service platform can expect more reliable recommendation results than previous studies, and the user survey for verification showed high satisfaction.