• Journal of Information Processing Systems
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• v.16 no.1
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• pp.145-154
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• 2020

Maximum likelihood (ML) is the best estimator asymptotically as the number of training samples approaches infinity. This paper deduces an adaptive algorithm for blind signal processing problem based on gradient optimization criterion. A parametric density model is introduced through a parameterized generalized distribution family in ML framework. After specifying a limited number of parameters, the density of specific original signal can be approximated automatically by the constructed density function. Consequently, signal separation can be conducted without any prior information about the probability density of the desired original signal. Simulations on classical biomedical signals confirm the performance of the deduced technique.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.321-323
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• 2004

We developed the monte-carlo simulation code for analysis of the On signal in dental medium. In calculation, we obtain the two different propagation signals as a function of the probing depth. Signal 2 begins to exceed the signal 1 at a very small probing depth(=60${\mu}{\textrm}{m}$). For reduce the signal, detection area is limited to radius and detection angle. As numerical result, probing depth becomes appoximately 500${\mu}{\textrm}{m}$.

• Journal of Korean Port Research
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• v.8 no.2
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• pp.1-35
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• 1994

The main arterial which runs through the in City of Pusan, carries about 60% of downtown traffic or more, maintains about 20% yearly increase in traffic is severely suffering from the traffic congestion because of concentrated traffic volumes regardless of peak-time periods. The purpose of this study was to grasp the traffic, geometric, and signal conditions of the main arterial through the Videologging System Techniques, perform the transportation system analyses, and finally suggest the improvements which could increase the travel capacity, reduce the average delay and fuel consumption with the optimal conditions of signal system. The following conclusions were drawn : firstly the traffic system should be shifted for the travel distribution on the arterial during the peak time periods, secondly the roadway system of the arterial reviewed for left-turn traffic during the peak time periods, and thirdly the signal system of intersection reconstructed for signal optimization or progression within the range of cycle length suggested.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.436-439
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• 2007

This paper describes the optimization of PGS(Patterned Ground Shield) of 5.5 turns rectangular spiral inductor using Taguchi's method. PGS is decrease method of parasite component by silicon substrate among dielectric loss reduction method. By using the taguchi's method, each parameter is fixed upon that PGS high poison(A), slot spacing(B), strip width(C) and overlap turn number(D) of PGS design parameter. Then we verified that percentage contribution and design sensitivity analysis of each parameter and level by signal to noise ratio of larger-the-better type. We consider percentage contribution and design sensitivity of each parameter and level, and then verify that model of optimization for PGS is lower inductance decreasing ratio and higher Q-factor increasing ratio by EM simulation.

• International Journal of Contents
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• v.12 no.3
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• pp.22-28
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• 2016

Tensor with missing or incomplete values is a ubiquitous problem in various fields such as biomedical signal processing, image processing, and social network analysis. In this paper, we considered how to reconstruct a dataset with missing values by using tensor form which is called tensor completion process. We applied Tucker factorization to solve tensor completion which was built base on optimization problem. We formulated the optimization objective function using components of Tucker model after decomposing. The weighted least square matric contained only known values of the tensor with low rank in its modes. A first order optimization method, namely Nonlinear Conjugated Gradient, was applied to solve the optimization problem. We demonstrated the effectiveness of the proposed method in EEG signals with about 70% missing entries compared to other algorithms. The relative error was proposed to compare the difference between original tensor and the process output.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5015-5027
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• 2015

Transceiver optimization in multiple input multiple output (MIMO) cognitive systems is studied in this paper. The joint transceiver beamformer design is introduced to minimize the transmit power at secondary base station (SBS) while simultaneously controlling the interference to primary users (PUs) and satisfying the secondary users (SUs) signal-to-interference-plus-noise ratio (SINR) based on the convex optimization method. Due to the limited cooperation between SBS and PUs, the channel state information (CSI) usually cannot be obtained perfectly at the SBS in cognitive system. In this study, both perfect and imperfect CSI scenarios are considered in the beamformer design, and the proposed method is robust to CSI error. Numerical results validate the effectiveness of the proposed algorithm.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.170-177
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• 2014

This paper presents a new method of classification of the induction machine faults using Time Frequency Representation, Particle Swarm Optimization and artificial neural network. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). The classifier is designed using an artificial neural network. This method allows an accurate classification independently of load level. The introduction of the PSO in the classification procedure has given good results using the reduced size of the feature vectors obtained by the optimization process. These results are validated on a 5.5-kW induction motor test bench.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.554-558
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• 2002

A magnetostrictive sensor is used to measure stress waves propagating in a ferromagnetic cylinder without physical contact. The performance of a magnetostrictive sensor is affected most significantly by the bias magnetic field applied around the measurement location. The goal of this paper is to carry out the topology optimization of the bias magnet and yoke assembly to maximize the sensor output for traveling bending waves. We will use the multi-resolution topology optimization strategy to find the assembly of the bias magnet and the yoke that is easy to realize. The effectiveness of the present design is confirmed by an actual measurement of the sensor signal with the proposed bias magnet and yoke configuration.

• Journal of information and communication convergence engineering
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• v.9 no.1
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• pp.27-31
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• 2011

This paper describes the optimization of spectrum sensing in terms of the throughput of a cognitive radio (CR) system. Dealing with the optimization problem of spectrum sensing, this paper evaluates the throughput of a CR system by considering such situations as the penalty time of a channel search and incumbent user (IU) detection delay caused by a missed detection of an incumbent signal. Also, this paper suggests a serial channel search scheme as the search method for a vacant channel, and derives its mean channel search time by considering the penalty time due to the false alarm of a vacant channel search. The numerical results suggest the optimum sensing time of the channel search process using the derived mean channel search time of a serial channel search in the case of a sensing hardware structure with single radio frequency (RF) path. It also demonstrates that the average throughput is improved by two separate RF paths in spite of the hardware complexity of an RF receiver.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.2
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• pp.216-233
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• 2013

Conventional optimization designs of wireless networks mainly focus on spectral efficiency (SE) as a performance metric. However, as diverse media services are emerging, a green wireless network, which not only meets the quality of experience (QoE) requirements for users and also improves energy efficiency (EE), is the most appropriate solution. In this paper, we firstly propose the unit QoE per Watt, which is termed QoE efficiency (QEE), as a user-oriented metric to evaluate EE for wireless networks. We then analyze which is the kind of wireless resource given priority to use under different scenarios to obtain an acceptable QEE. Particularly, power, delay and data-rate related to QoE are separately addressed for several typical services, such as file download, video stream and web browsing services. Next, the fundamental tradeoffs are investigated between QEE and SE for wireless networks. Our analytical results are helpful for network design and optimization to strike a good balance between the users perceived QoE and energy consumption.