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This study proposes a multi-ship collision avoidance and route generating algorithm based on the general requirements of the International Regulations for Preventing Collisions at Sea (COLREGs) and the artificial potential field (APF) method. The velocity potential field is used as the field function of APF method. The algorithm consists of two modes, the course-changing and the track-keeping modes based on the velocity potential of vortex and dipole, respectively. The course-changing mode guides the ship to turn away from the obstacles according to the vector field of vortex potential. The track-keeping mode steers the ship back to and along on a pre-designed track in accordance with the vector field of dipole potential. The data of distance to the closest point of approach (DCPA), time to the closest point of approach (TCPA) and bearing angle evaluated from a maneuvering simulation are the acquired parameters of the proposed collision avoidance algorithm. The algorithm is straightforward and very simple to implement, and is suitable for the real time and distributed intelligent collision avoidance system. Simulation results indicate that the anti-collision formulation can avoid collision safely with the desired distance and indicate that the algorithm proposed can work effectively.

Two approaches, namely, the crossing-line and the Monte Carlo methods, for navigation safety assessment in approaching channel based on Automatic Identification System (AIS) are proposed. The crossing-line method evaluated the ship grounding probability using the probability density functions of the ship track parameters from single-crossing-line or multi-crossing-lines. The Monte Carlo method considers the causation of the track parameters. Such a causation was realized using a regression algorithm by which the relationships among the ship track parameters can be established through a crossing-line with the factors corresponding to the previous crossing-lines. With the relationships, Monte Carlo simulation of ship trajectory evaluation was then applied to assess the grounding frequency. The inbound ship behaviors of Kaohsiung Harbor are analyzed to quantitatively assess the grounding risk on entrance and inner traffic lane. The results of the grounding probability acquired from both methods are consistent with each other. The grounding probability of the starboard boundary is higher than the port one. The proposed methods could be further applied to risk assessment and will facilitate navigational safety in congested waterways and ports.

In this paper, we develop a novel algorithm that determines the overall best parameter setting in the design of experiments. The algorithm begins with successive orthogonal array experiments and ends with a full factorial experiment. The setup for the next orthogonal array experiment is obtained from the previous experiment by either fixing a factor at a given level or by reducing the number of levels considered for all currently non-fixed factors. We illustrate this method using a light-gauge steel wall sound isolation system with four parameters, each with three levels. In a previous study, the full factorial of 81 experiments was conducted, and the optimal parameter settings were determined. With the proposed algorithm, we found the same result using 15 experiments. As a further comparison, we obtained the optimal settings using a traditional Taguchi method and found that they correspond to the 23rd experiment out of the 81 experiments when sorted by the objective (or quality) function. We conclude that the proposed algorithm can provide an accurate, fast, and economic tool for the global optimization of design of experiments.

By successively employing the interval search method, we developed the proposed algorithm MPSO, introducing three creative position vectors to replace the three worst fitness particles among the population in the PSO, to overcome the premature convergence situation that occurs when a problem with a large number of variables and (or) multiple optima is solved. The results obtained by applying the MPSO and the PSO on 6 benchmark functions show that, except for the randomly shifted Rosenbrock functions, the MPSO can successfully secure a solution that is close to the exact solution for each of the remaining five functions. We also showed that all benchmark functions are solvable by the MPSO if the maximum number of generations is raised to be as high as possible. With regard to the PSOs performance for the three different numbers of variables, it fails to obtain a solution that is close to the exact solution for all of the tested functions except for the Sphere function with 30 variables.

In this paper, an intelligent garbage can model-based differential evolution algorithm (IGCMDE) is proposed to simulate human social organization by its population system, based on the differential evolution algorithms (DEs) and the logical framework of the garbage can decision model with group meeting. When faced with issues such as unclear goals and technologies, participators turnover, etc., representatives of all participating parties will communicate, argue, compromise and adapt with each other, in order to find a solution to the problems. Group meetings are conducted to choose the best solution in a more objective, reasonable and efficient way. At last, we used IGCMDE to optimize the fuzzy controller with fuzzy logic control theory. We present a method for daylight blending control with two novel contributions: 1) smart luminance sensing; 2) daylight luminance control. The proposed method was implemented as an intelligent lighting system in a parking tower environment. The result demonstrated that IGCMDE possesses an excellent search performance and the intelligent lighting system showed significant energy savings.

A new algorithm based orthogonal arrays was developed to determine the optimal solution amid all the optional settings for a new initiated device. The proposed algorithm begins with several successive orthogonal arrays and ends with a full factorial array. After each orthogonal array is accomplished, the algorithm employs variance analyses to screen the dominating variable from among all the current variables and then determines the best level of the dominating variable. Here, after successive orthogonal arrays were completed, the number of unfixed variables was gradually reduced to a number that was small enough to feasibly conduct a full factorial array, resulting in the final selection of the problem’s optimum setting. We verified the proposed algorithm by first applying it to the Rosenbrock function.To distinguish the superior performance of the proposed algorithm in comparison with other design of experiments approaches, we experimented with three different problems with non-interactive or interactive variables. The results show that the proposed method not only provides a final solution that is identical to the problem’s exact solution but also that the computation time in comparison with that required by a full factorial array drops drastically as the number of variables increases.

The course of socio-cultural transition is not a phenomenon of aimless, arbitrary development, but towards a clear goal. Such process of moving towards higher-level soul experience and mental state is a common goal of social species' evolution. In this paper, the model of cultural development is imitated to be the system thinking frame for developing an evolution algorithm, namely cultural evolution algorithm. It consists of several search methods with similar thinking and then proposes four strategies of the cultural evolution algorithm. Seven benchmark functions are utilized to validate the search performance of the proposed algorithm. The results show that all of the four strategies of cultural evolution algorithm have better performance when compared with relevant literatures.

In this paper, we develop a novel algorithm that determines the overall best parameter setting in design of experiments. The algorithm begins with successive orthogonal array experiments and ends with a full factorial experiment. The setup for the next orthogonal-array experiment is obtained from the previous one by either fixing a factor at a given level or by reducing the number of levels considered for all currently non-fixed factors. We illustrate this method using a light-gauge steel wall sound isolation system with four parameters, each with three levels. In previous work, the full factorial of =81 experiments was conducted and the best parameter settings was found. With the proposed algorithm, we found the same result using 15 experiments. As a further comparison, we obtained the optimum using a traditional Taguchi method, and found it corresponded to the of the 81experiments when sorted by the objective (or quality) function. We conclude the proposed algorithm would provide an accurate, fast, and economic tool for global optimization of design of experiments.

A new algorithm based on orthogonal arrays is developed to determine the optimal solution among the optional settings using the design of experiment approach for newly designed devices. The proposed algorithm begins with several successive orthogonal arrays and ends with a full factorial array. In the proposed algorithm, we suggest a mandatory verification of two additional settings at each orthogonal array to avoid errors in identifying the best level of the dominant factor, which often occur when a non-separable problem is solved. We initially verify the proposed algorithm by applying it to a well-known benchmark problem, the Rosenbrock function. The proposed procedure is described in detail in this paper. To distinguish the superior performance of the proposed algorithm relative to that of other design of experiment approaches, we study three types of problems with non-interactive or interactive variables. Finally, we applied the algorithm on the optimization design of an M-type spring used in a slider cell-phone. With minimizing the maximum stress as the objective function of the designing product, the results show that the proposed method not only provides a final solution that is identical to the problem’s exact solution but also requires much less computation time than a full factorial method.

A new algorithm based on orthogonal arrays is developed to determine the optimal solution among the optional settings using the design of experiment approach for newly designed devices. The proposed algorithm begins with several successive orthogonal arrays and ends with a full factorial array. In the proposed algorithm, we suggest a mandatory verification of two additional settings at each orthogonal array to avoid errors in identifying the best level of the dominant factor, which often occur when a non-separable problem is solved. We initially verify the proposed algorithm by applying it to a well-known benchmark problem, the Rosenbrock function. The proposed procedure is described in detail in this paper. To distinguish the superior performance of the proposed algorithm relative to that of other design of experiment approaches, we study three types of problems with non-interactive or interactive variables. Finally, a light-gauge steel wall sound isolation system design is used to demonstrate the robust performance of the proposed algorithm. The results show that the proposed method not only provides a final solution that is identical to the problem’s exact solution but also requires much less computation time than a full factorial method.

The purpose of this research is to find the optimal design for biodegradable polymer microneedle patches. Based on the mechanical properties of different skin layers and the failure criterion of the material, this research designs a microneedle of four types and three sizes, then discusses the insertion force and the variation of stress during the process of PLA microneedle insertion into skin by numerical simulation. This research uses the dynamic finite element software ANSYS / LS-DYNA to simulate the processing for PLA microneedle inserts into skin. The master microneedle array was fabricated by the MEMS process. This research uses PDMS to fabricate the mould for microneedles. Finally, a biodegradable polymer polylactic acid (PLA) microneedle patch was fabricated using a PDMS mould micro hot embossing method.

This paper presents an irregular stock cutting system (NEST) developed on AutoCAD software platform. A detail description to its mechanism is included. This development is based on two known techniques: the bitmap representation to transform all shapes drawn by AutoCAD into pixel matrices before nesting, and the bottom-left-fill algorithm for nesting. The system NEST is developed for further research works on practical stock cutting problems. It provides two important parameters: one is the precision level which decides how the bitmap representation can precisely describe a shape and therefore affects the time taken by nesting procedure; the other is the unit rotation angle which make a shape self-rotates in steps with small angle and to be finally placed on sheet material with its best self-rotation angle. NEST can tightly position every shape on a sheet material and performs satisfyingly from practical view point. The nesting result is demonstrated by the original drawings of all shapes and the sheet material instead of their graphics transformed from pixel matrices. Several tests are carried out to show its performance.

In this paper, we develop an approach that determines the overall best parameter setting in design of experiments. The approach starts with successive orthogonal array experiments and ends with a full factorial experiment. The setup for the next orthogonal-array experiment is obtained from the previous one by either fixing a factor at a given level or by reducing the number of levels considered for all currently non-fixed factors. We illustrate this method using an industrial problem with seven parameters, each with three levels. In previous work, the full factorial of 37 = 2,187 points was evaluated and the best point was found. With the new method, we found the same point using 3% of these evaluations. As a further comparison, we obtained the optimum using a traditional Taguchi approach, and found it corresponded to the 366th of the 2,187 possibilities when sorted by the objective function. We conclude the proposed approach would provide an accurate, fast, and economic tool for optimization using design of experiments.

This study fabricated a novel biodegradable polymer microneedle patch. First, a master microneedle array was fabricated using the micro electro-mechainal system (MEMS) process. Polydimethylsiloxane (PDMS) was then utilized to fabricate the microneedle mold. Finally, the biodegradable polymer polylactic acid (PLA) microneedle patch was fabricated by micro hot embossing. The Taguchi method was applied to identify the optimal process parameters for micro hot embossing the PLA microneedle. The sizes of the original microneedle and that of the PLA microneedle were compared, through the transfor by PDMS mold. Next, a numerical simulation and the skin puncture test were used to confirm the PLA microneedle patch fabrication using the optimal process parameters. During numerical simulations, this study used dynamic finite element software (ANSYS/LS-DYNA) to simulate the process of the PLA microneedle being inserted into skin. Based on the mechanical properties of different skin layers and the material failure criterion, insertion force and stress variation during PLA microneedle insertion into skin were determined. In a skin puncture experiment, transepidermal water loss (TEWL) was measured to determine whether the PLA microneedle damaged the stratum corneum and decreased the water protection capacity of skin.

In this paper, the particle swarm optimization (PSO), originated as a simulation of a simplified social system with swarm intelligence and having exploring and exploiting characteristics of the particle swarm, is adopted to deal with the global optimization problems. To begin with, four non-linear two-dimensional functions are adopted as benchmark examples for determining the suitable ranges of parameters in the PSO. It is found that uses of the PSO can make the search quickly converge to the global optimum. Then, several benchmark functions below ten-dimensions are adopted especially for comparisons of search efficiency on the PSO and other algorithms. Results show that the proposed approach is not only superior to other algorithms but also has the higher success rate. Finally, the PSO is also applied to deal with the optimization problem of a grillage structure. Optimization solutions of the success rate and the used average generation show that the PSO also has the better performance for the optimal design of structures with constraints.