Syndrome is a crucial principle of Traditional Chinese Medicine. Formula classification is an effective approach to discover herb combinations for the clinical treatment of syndromes. In this study, a local search based firefly algorithm (LSFA) for parameter optimization and feature selection of support vector machines (SVMs) for formula classification is proposed. Parameters C and γ of SVMs are optimized by LSFA. Meanwhile, the effectiveness of herbs in formula classification is adopted as a feature. LSFA searches for well-performing subsets of features to maximize classification accuracy. In LSFA, a local search of fireflies is developed to improve FA. Simulations demonstrate that the proposed LSFA-SVM algorithm outperforms other classification algorithms on different datasets. Parameters C and γ and the features are optimized by LSFA to obtain better classification performance. The performance of FA is enhanced by the proposed local search mechanism.

Blind source separation (BSS) techniques are required for various signal decomposing issues. Independent component analysis (ICA), assuming only a statistical independence among stochastic source signals, is one of the most useful BSS tools because it does not need a priori information on each source. However, there are many requirements for decomposing multiple deterministic signals such as complex sinusoidal signals with different frequencies. These requirements may include pulse compression or clutter rejection. It has been theoretically shown that an ICA algorithm based on maximizing non-Gaussianity successfully decomposes such deterministic signals. However, this ICA algorithm does not maintain a sufficient separation performance when the frequency difference of the sinusoidal waves becomes less than a nominal frequency resolution. To solve this problem, this paper proposes a super-resolution algorithm for complex sinusoidal signals by extending the maximum likelihood ICA, where the probability density function (PDF) of a complex sinusoidal signal is exploited as a priori knowledge, in which the PDF of the signal amplitude is approximated as a Gaussian distribution with an extremely small standard deviation. Furthermore, we introduce an optimization process for this standard deviation to avoid divergence in updating the reconstruction matrix. Numerical simulations verify that our proposed algorithm remarkably enhances the separation performance compared to the conventional one, and accomplishes a super-resolution separation even in noisy situations.

ε-filter is a nonlinear filter for reducing noise and is applicable not only to speech signals but also to image signals. The filter design is simple and it can effectively reduce noise with an adequate filter parameter. This paper presents a method for estimating the optimal filter parameter of ε-filter based on signal-noise decorrelation and shows that it yields the optimal filter parameter concerning a wide range of noise levels. The proposed method is applicable where the noise to be removed is uncorrelated with signal, and it does not require any other knowledge such as noise variance and training data.

Programmable Logic Controller (PLC) has been widely used in the industrial control. Inherently, the PLC-based system is a class of Hybrid Dynamical System (HDS) in which continuous state of the plant is controlled by the discrete logic-based controller. This paper firstly presents the formal algebraic model of the PLC-based control systems which enable the designer to formulate the various kinds of optimization problem. Secondly, the optimization problem of the 'sensor parameters,' such as the location of the limit switch in the material handling system, the threshold temperature of the thermostat in the temperature control system, is addressed. Finally, we formulate this problem as Mixed Logical Dynamical Systems (MLDS) form which enables us to optimize the sensor parameters by applying the Mixed Integer Programming.

Utilizing a macromodel which calculates the floating gate potential by combining resistances and dependent voltage and current sources, DC transfer characteristics for multi-input neuron MOS inverters and for those in the neuron MOS full adder circuit are simulated both at room temperature and at 77 K. Based on the simulated results, low temperature circuit failures are discussed. Furthermore, circuit design parameter optimization both for low and room temperature operations is described.

We have designed the Half Adder (HA) circuit and the Carry Save Serial Adder (CSSA) circuit based on pipeline architecture. Our HA has the structure of a two-stage pipeline and consists of 160 Josephson Junctions (JJs). Our CSSA has the structure of a four-stage pipeline with a feedback loop and consists of 360 JJs. These circuits were fabricated by the NEC standard process. There are two issues which should be considered in the design. One is parameter spreads generated by the fabrication process and the other is leakage currents between the gates. We have introduced a parameter optimization method to deal with the parameter spreads. We have also inserted three stages of JTLs to reduce leakage currents. We have experimentally confirmed the correct operations of these circuits. The obtained bias margins were 33.1% for the HA and 24.6% for the CSSA.

This paper presents a new implementation of fuzzy filters for edge-preserving smoothing of an image corrupted by impulsive and white Gaussian noise. This filter structure is expressed as an adaptive weighted mean filter that uses fuzzy control. The parameters of this filter can be adjusted by learning. Finally, simulation results demonstrate the effectiveness of the proposed technique.

This paper presents a multi-thread evolutionary programming (MEP) technique that is composed of global, local, and minimal search units. An appropriate search routine is called depending on the current situation and the individuals are updated by using the selected routine. In each search routine, the individuals are updated with a normalized relative fitness function to improve the robustness of the algorithm. The proposed method is applied to the problem of backing up a truck-and-trailer system to a loading dock. A fuzzy logic controller is designed for a truck-and-trailer backer-upper system and the MEP algorithm is used to optimize the representative parameters of the fuzzy logic controller. The simulation results show that the proposed controller performs well even under a large variety of initial positions.

In this paper, we present an efficient approach for technology scaling of MOS analog circuits by using circuit optimization techniques. Our new method is based on matching equivalent circuit parameters between a previously designed circuit and the circuit undergoing redesign. This method has been applied to a MOS operational amplifier. We were able to produce a redesigned circuit with almost the same performance in under 4 hours, making this method 5 times more efficient than conventional methods