• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1996.04a
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• pp.59-76
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• 1996

Vessels in the world merchant fleet generally operate in either liner or bulk trade. The supply and the demand trend of general cargo ship are both on the ebb however those trend of tankers and containers are ins light ascension. Oil tankers are so far the largest single vessel type in the world fleet and the tanker market is often cited as a texbook example of perfect competition. Some shipping statistics in recent years show that there has been a radical fluctuation in spot charter rate under easy charter's market. This implies that the proper scheduling of tankers under spot market fluctuation has the great potential of improving the owner's profit and economic performance of shipping. This paper aims at developing the TS-DSS(Decision Support System for Tanker Scheduling) in the context of the importance of scheduling decisions. TS-DSS is defined as a DSS based on the optimization models for tanker scheduling. The system has been developed through the life cycle of systems analysis design and implementation to be user-friendly system. The performance of the system has been tested and examined by using the data edited under several tanker scheduling has been tested and examined by using the data edited under several tanker scheduling scenarios and thereby the effectiveness of TS-DSS is validated satisfactorily. The authors conclude the paper with the comments of the need of appropriate support environment such as data-based DSS and network system for successful implementatio of the TS-DSS.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.116-119
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• 2015

In this paper we propose an algorithm for environment monitoring using multiple mobile sensor (MS) nodes. Our focus is on maximizing sensing coverage of a group of MS nodes for monitoring a phenomenon in an unknown and open area over time. In the proposed algorithm, MS nodes are iteratively relocated to new positions at which a higher sensing coverage can be obtained. We formulated an integer linear programming (ILP) optimization problem to find the optimal positions for MS nodes with the objective of coverage maximization. The performance evaluation was performed to confirm that the proposed algorithm can enable MS nodes to relocate to high interest positions, and obtain a maximum sensing coverage.

• Journal of Hydrogen and New Energy
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• v.29 no.1
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• pp.19-24
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• 2018

Fuel cells are used to generate electricity with a reformer. In particular, methanol has various advantages among the fuels for reformer. Methanol steam reformer devices can efficiently supply hydrogen to PEM fuel cell. This study investigated the optimal operation conditions of a methanol steam reforming process. For this purpose, aspen HYSYS was used for the optimization of reforming process. The optimal operating condition could be designed by setting independent variables such as temperature, pressure and steam to carbon ratio (SCR). The optimal temperature and steam to carbon ratio were $250-270^{\circ}C$ and 1.3-1.5, respectively. It is advantageous to operate at a pressure of 15-20 barg, considering the performance of the hydrogen purifier. In addition, a heat exchange network was designed to supply heat constantly to reformer through the latent heat of steam.

• Journal of Korea Game Society
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• v.20 no.4
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• pp.77-88
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• 2020

Recently, interest in game streaming is high in cloud-based gaming. In game streaming, remote game servers perform graphics rendering and stream the resulting scene images to clients' device on the Internet. We model the client-server allocation (CSA) problem for balancing the GPU load between servers in a game streaming environment as an optimization problem, and propose a simulated annealing-based method. The features of our method are that the method takes into account the constraints on network delay and has the ability to automatically adjust the frame rate of game sessions if necessary.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.302-307
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• 2001

This research is concerned with the development of a real-time adaptive PPF controller for the active vibration suppression of smart structure. In general, the tuning of the PPF controller is carried out off-line. In this research, the real-time learning algorithm is developed to find the optimal filter frequency of the PPF controller in real time and the efficacy of the algorithm is proved by implementing it in real time. To this end, the adaptive algorithm is developed by applying the gradient descent method to the predefined performance index, which is similar to the method used popularly in the optimization and neural network controller design. The experiment was carried out to verify the validity of the adaptive PPF controller developed in this research. The experimental results showed that adaptive PPF controller is effective for active vibration control of the structure which is excited by either impact or harmonic disturbance. The filter frequency of the PPF controller can be tuned in a very short period of time thus proving the efficiency of the adaptive PPF controller.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3519-3533
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• 2020

Tuberculosis is a chronic and delayed infection which is easily experienced by young people. According to the statistics of the World Health Organization (WHO), there are nearly ten million fell ill with tuberculosis and a total of 1.5 million people died from tuberculosis in 2018 (including 251000 people with HIV). Tuberculosis is the largest single infectious pathogen that leads to death. In order to help doctors with tuberculosis diagnosis, we compare the tuberculosis classification abilities of six popular convolutional neural network (CNN) models in the same data set to find the best model. Before training, we optimize three parts of CNN to achieve better results. We employ sigmoid function to replace the step function as the activation function. What's more, we use binary cross entropy function as the cost function to replace traditional quadratic cost function. Finally, we choose stochastic gradient descent (SGD) as gradient descent algorithm. From the results of our experiments, we find that Densenet121 is most suitable for tuberculosis diagnosis and achieve a highest accuracy of 0.835. The optimization and expansion depend on the increase of data set and the improvements of Densenet121.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2686-2708
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• 2020

This paper investigates the energy efficiency of energy harvesting (EH) bidirectional cooperative sensor networks, in which the considered system model enables the uplink information transmission from the sensor (SN) to access point (AP) and the energy supply for the amplify-and-forward (AF) relay and SN using power-splitting (PS) or time-switching (TS) protocol. Considering the minimum EH activation constraint and quality of service (QoS) requirement, energy efficiency is maximized by jointly optimizing the resource division ratio and transmission power. To cope with the non-convexity of the optimizations, we propose the low complexity iterative algorithm based on fractional programming and alternative search method (FAS). The key idea of the proposed algorithm first transforms the objective function into the parameterized polynomial subtractive form. Then we decompose the optimization into two convex sub-problems, which can be solved by conventional convex programming. Simulation results validate that the proposed schemes have better output performance and the iterative algorithm has a fast convergence rate.

• Smart Structures and Systems
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• v.26 no.1
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• pp.117-134
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• 2020

Over recent years, the interest for vegetables and fruits in all seasons and places has much increased, from where diverse countries have directed to the commercial production in greenhouse. In this article, we propose an algorithm based on wireless sensor network technologies that monitor the microclimate inside a greenhouse and linear equations model for optimization plant production and material cost. Moreover, we also suggest a novel design of an intelligent greenhouse. We validate our algorithms with simulations on a benchmark based on experimental data made at lNRA of Montfavet in France. Finally, we calculate the statistical estimators RMSE, TSSE, MAPE, EF and R². The results obtained are promising, which shows the efficiency of our proposed system.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.9-21
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• 2013

Tracking is a process which explores user equipment (UE) in the area of tracking in terms of cells. In this paper, two tracking schemes based on macrocell-microcell tiers in hierarchical LTE networks, PMMT and IMMT, are evaluated. In this network, UE can receive a signal from macrocells and overlapping microcells, and can be called from each macrocell or microcell-tier in the PMMT. Also, the UE can be called from the combined macrocell-tier and microcell-tier in the IMMT. Finally, we analyze the optimization of load balance between marcocell-tier and microcell-tier, and an analytical model is developed to evaluate those two arrangements.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.990-995
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• 1990

A minimum-time trajectory planning for two robot arms with designated paths and coordination is proposed. The problem considered in this paper is a subproblem of hierarchically decomposed trajectory planning approach for multiple robots : i) path planning, ii) coordination planning, iii) velocity planning. In coordination planning stage, coordination space, a specific form of configuration space, is constructed to determine collision region and collision-free region, and a collision-free coordination curve (CFCC) passing collision-free region is selected. In velocity planning stage, normal dynamic equations of the robots, described by joint angles, velocities and accelerations, are converted into simpler forms which are described by traveling distance along collision-free coordination curve. By utilizing maximum allowable torques and joint velocity limits, admissible range of velocity and acceleration along CFCC is derived, and a minimum-time velocity planning is calculated in phase plane. Also the planning algorithm itself is converted to simple numerical iterative calculation form based on the concept of neural optimization network, which gives a feasible approximate solution to this planning problem. To show the usefulness of proposed method, an example of trajectory planning for 2 SCARA type robots in common workspace is illustrated.