• Journal of Navigation and Port Research
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• v.33 no.2
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• pp.143-151
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• 2009

The field of vehicle routing is currently growing rapidly because of many actual applications in truckload and less than truckload trucking, courier services, door to door services, and many other problems that generally hinder the optimization of transportation costs in a logistics network. The rapidly increasing number of customers in such a network has caused problems such as difficulty in cost optimization in terms of getting a global optimum solution in an acceptable time. Fast algorithms are needed to find sufficient solutions in a limited time that can be used for real time scheduling. In this paper, the nearest L-method (NLNM) is proposed to obtain a vehicle routing solution. String neighbors of different lengths were chosen, tested and compared. The applied de crossing procedure is meant to solve the routes by NLNM by giving a better solution and shorter computation time than that of NLNM with long string neighbors.

• Journal of the Semiconductor & Display Technology
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• v.18 no.3
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• pp.127-132
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• 2019

This paper presents an overview of automotive communication technologies, including related technology developments. We describe the latency of Audio Video Bridge (AVB) network as well as purpose the optimized design of the Ethernet network system for automotive. Our design plays a significant role in reducing the delay between components. The proposed approach on realistic test cases showed that there was a delay reduction, approximately 49.4%. It is expected that the optimization method for the actual automotive environment can greatly shorten the time period in the design and development process. The results obtained from the experiments on the delay time present in each function are reliable because average values are obtained through repeated actual tests for several months. It will greatly benefit the industry since analyzing the latency between each function in a short period of time is very important.

• ETRI Journal
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• v.36 no.4
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• pp.625-634
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• 2014

This paper studies a dual-hop multiple-access relay network where two independent source nodes transmit information to a common destination node with the aid of multiple single-antenna amplify-and-forward relays. Each relay node is subject to an individual power constraint. We focus on the design of distributed beamforming schemes for the relays to support the transmission rate requirements of the two sources. To this end, we first characterize the achievable rate region for this network via solving a sequence of corner point optimization problems proposed in this paper. We also develop several low-complexity suboptimal schemes in closed form. Two inner bounds of the achievable rate region are theoretically shown to be approximately optimal in two special scenarios. Finally, numerical results demonstrate the effectiveness of our proposed approaches.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9A
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• pp.916-922
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• 2007

Wireless networks (WNs) are developed and applied widely in a lot of areas. Now, a new generation of wireless networks is coming, and that is Wireless Mesh Network (WMN). At present, there are not so many researches which deal on this area. Most researches are derived from Mobile Ad hoc Networks (MANET) and WNs. In WMNs, there are some applications that require real-time delivery. To guarantee this, rate control and congestion control are needed. This problem leads to optimization issue in transport layer. In this paper, we propose a mathematical model which is applied in rate and congestion control in WNMs. From this model, we optimize rate and congestion control in WMNs by maximizing network utility. The proposed algorithm is implemented in distributed way both in links and sources.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.989-995
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• 2017

As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which can hardly describe its uncertainty, leading to the fact that its application in practice is low. In this paper, a wind power range prediction model based on the multiple output property of BP neural network is built, and the optimization criterion considering the information of predicted intervals is proposed. Then, improved Particle Swarm Optimization (PSO) algorithm is used to optimize the model. The simulation results of a practical example show that the proposed wind power range prediction model can effectively forecast the output power interval, and provide power grid dispatcher with decision.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1344-1346
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• 1996

In this paper, we discuss an informal analysis of diffusion, global optimization properties of Langevine competitive learning neural network. In the view of the stochastic process, it is important that competitive learning gurantee an optimal solution for pattern recognition. We show that the binary reinforcement function in Langevine competitive learning is a brownian motion as Gaussian process, and construct the Fokker-Plank equation for the proposed neural network. Finally, we show that the informal analysis of the proposed algorithm has a possiblity of globally optimal. solution with the proper initial condition.

• Annual Conference of KIPS
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• 2012.11a
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• pp.42-45
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• 2012

A network processor (NP) is an application-specific instruction-set processor for fast and efficient packet processing. There are many issues in compiler's code generation and optimization due to NP's hardware constraints and special hardware support. In this paper, we describe in detail how to resolve the issues. Our compiler was developed on LLVM 3.0 and the NP target was our in-house network processor which consists of 32 64-bit RISC processors and supports multi-context with special hardware structures. Our compiler incurs only 9.36% code size overhead over hand-written code while satisfying QoS, and the generated code was tested on a real packet processing hardware, called S20 for code verification and performance evaluation.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.2066-2073
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• 2021

The use of nuclear reactors is a large studied possible solution for thermochemical water splitting cycles. Nevertheless, there are several problems that have to be solved. One of them is to increase the efficiency of the cycles. Hence, in this paper, a thermal energy optimization of a Sulfur-Iodine nuclear hydrogen production cycle was performed by means a heuristic method with the aim of minimizing the energy targets of the heat exchanger network at different minimum temperature differences. With this method, four different heat exchanger networks are proposed. A reduction of the energy requirements for cooling ranges between 58.9-59.8% and 52.6-53.3% heating, compared to the reference design with no heat exchanger network. With this reduction, the thermal efficiency of the cycle increased in about 10% in average compared to the reference efficiency. This improves the use of thermal energy of the cycle.

• ETRI Journal
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• v.45 no.4
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• pp.594-602
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• 2023

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.

• Journal of Drive and Control
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• v.20 no.4
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• pp.54-63
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• 2023

High and intermittent loading cycles induce fatigue damage to transmission components, resulting in premature gearbox failure. To identify gearbox defects, numerous vibration-based diagnostics techniques, using several artificial intelligence (AI) algorithms, have recently been presented. In this paper, an optimized deep belief network (DBN) model for gearbox problem diagnosis was designed based on time-frequency visual pattern identification. To optimize the hyperparameters of the model, a particle swarm optimization (PSO) approach was integrated into the DBN. The proposed model was tested on two gearbox datasets: a wind turbine gearbox and an experimental gearbox. The optimized DBN model demonstrated strong and robust performance in classification accuracy. In addition, the accuracy of the generated datasets was compared using traditional ML and DL algorithms. Furthermore, the proposed model was evaluated on different partitions of the dataset. The results showed that, even with a small amount of sample data, the optimized DBN model achieved high accuracy in diagnosis.