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For precisely presenting the swimming behavior of fish robots underwater and the practical implementation purpose, this paper aims to investigate a well-formulated fish robot model which integrates the nonlinear rigid body dynamics, kinematics and models of actuators.

This fish robot model is mainly built up by three basic parts: a balance mechanism, a four-links vibrator and a caudal fin. In the fish robot’s head, there is a balance mechanism used to control the rotations in pitch and roll directions of the fish robot by moving two movable masses. The four-links vibrator with three active joints actuated by DC motors is designed to vibrate the fish’s body. In the end of the fish robot body, a caudal fin which connects with the passive joint is developed to generate hydrodynamic thrust forces to propel the fish robot.

From the real stability tests and control verification, it is obvious that this proposed model can precisely present the swimming behavior of fish robots and possesses the potential to develop a fish-like robotic prototype.

A well-formulated model with dynamics of actuators is integrated for presenting the swimming behavior of carangiform locomotion type fish robots in this investigation. From the simulation results and the practical test of a real fish robot, the feasibility of this proposed model for building up real fish robots can be proven, and this proposed model is accurate enough to effectively present the swimming behavior of fish robots.

The purpose of this paper is to develop the multi‐degree‐of‐freedom measurement system to test, verify, and control the nano‐measuring machine.

A generic differential model approach is constructed to numerically describe the hysteresis effects of piezoelectric actuators. Based on the generic differential model, a feedforward compensator with a proportional integral (PI) type controller is designed to compensate for the hysteresis nonlinearity of a piezoelectric actuated three degree‐of‐freedom coplanar nanostage which can provide high‐precision applications.

The Z‐tilts (z, pitch, and roll motion) error compensation stage of the nano‐measuring machine is accomplished. Moreover, a high‐resolution laser interferometer is used to measure position accurately.

This paper contributes to develop a tracking control design method for the piezoelectric motion platform which combines a closed‐loop feedforward compensator with a PI type controller.

The purpose of this paper is to examine the impact of the customer- and firm-focused driving factors, relationship quality (RQ), and identification on customer citizenship behaviors (CCB) on corporate social networking sites (SNS), as well as the impact of service attribute in such relationship.

A survey was conducted among the Facebook members of 7-Eleven (318) and Starbucks (316) in Taiwan to test the proposed framework. A structural equation modeling was used to test the validity of the research model and hypotheses.

The results reveal that SNS RQ and SNS identification are key factors affecting CCB, whereas financial bond (firm-focused) and consumer-company identification (customer-focused) are critical initiators. The findings reveal contingencies across service attributes for such effects. Experienced service firms can better encourage CCB by intimating SNS relationships with followers. Search firms should secure online identification to enable customers to perform CCB. The results also confirm the mediating effects of SNS RQ and SNS identification.

This study contributes to the literature by simultaneously examining the firm (external)- and individual (internal)-level of incentives, and to further reveal the main drivers encouraging CCB on corporate SNS. This study also belongs to the limited studies that discuss consumer voluntary behaviors on the corporate SNS. The results shed light on the existence of a contingency role for service attribute on SNS, and further suggest how firms with distinct attributes can effectively allocate their limited resources when encouraging CCB on SNS.

The purpose of this paper is to introduce a novel personal scientific document retrieval system. The most common approach taken for the storage of personal documents is to construct a hierarchical folder structure. Most users prefer searching for documents by manually traversing their organizational hierarchy until reaching the location where the target item is stored, then locating the specific documents within its directory or folder. However, this is very time-consuming, especially when the number of personal scientific documents is very large. Unfortunately, related personal information management (PIM) systems, which provide solutions for managing various types of personal information, have thus far made little progress at managing personal scientific documents.

In this paper, we introduce the design of a personal scientific document retrieval system, namely, LocalContent. It is composed of database indexing and retrieval stages. During indexing, term feature extraction from scientific documents is performed by the natural language processing technique. The extracted terms are stored in the inverted index for later retrieval. For retrieval, a graphical user interface is provided by LocalContent, which allows users to search their personal scientific documents.

The evaluation results based on 20 different personal archives taken from 20 graduate students show that LocalContent is simple to use and can facilitate the search for relevant scientific documents. Moreover, these users were willing to have a system which provides specialized search functions like LocalContent to explore their personal scientific documents in the future.

LocalContent is a novel scientific document retrieval system and provides several particular functions of LocalContent including displaying the content summary of the query term frequency in each specific section of the retrieved documents, querying by local section specification and providing a number of recommended keywords related to the query terms.

Customer Support Knowledge of Customer Support Organization is one of the important assets of enterprises and “Customer Support Knowledge Management” is also the critical aspect of Business Knowledge Management; however, the attributes of Customer Support Knowledge are complicated, diverse, renewed rapidly and difficult to be managed. Thus, in order to design a successful Customer Support Knowledge Management System, apart from the consideration of “human” and “information technology” aspects, the concerns of attributes and Customer Support Knowledge and industry characteristics should be involved for meeting the requirements of Customer Support Organization and allowing the organization to acquire the competitive advantage of “Differentiation Service”. This research used the “Customer Support Knowledge Management System” in a high‐tech industry as an example and treated the end users of medical instruments in different types of hospitals in Taiwan which have received the support service of our company in recent six months as the population. The end users were mostly the nursing executives or ultrasonic wave technical personnel in intensive care unit and they had similar educational background and incomes and adopted the medical instruments such as physical supervision system, ultrasonic wave system, heart start or ECG machine produced by our company; the research method was to randomly treat the investigation results of the telephone customers’ satisfaction from respective 30 end users in the population three months before and after this system execution as the samples and use hypotheses to validate if the end users’ customer satisfaction significantly improved in terms of “Remote Support,” “On‐site Support,” “Service Turn Around time,” “Technical Competence” and “Service Manner” in order to understand the influence and managerial significance of execution of “Customer Support Knowledge Management System” on Customer Support Organization.

The purpose of this paper is to research the influence of inflation and delay in payment on the inventory model on Weibull distribution and present a new method of inventory control model for fresh agricultural products.

The previous researches deal with the inventory problems. However, these researches are based on deteriorating industrial products, so that the method for inventory model of agricultural products should be employed. According to agricultural products characteristics, the model assumes that deterioration is defined as fixed and demand is taken as Weibull distribution. An optimal order quantity and the minimum inventory total cost are formulated and solved under inflation and delay in payment.

The minimum inventory total cost and the optimal ordering quantity are convex with the length of the period with positive stock. When the delay period is longer than the length of the period with positive stock, the minimum inventory total cost and the optimal ordering quantity increase with permissible delay in payment increase and decrease with inflation rate increase, changing steeply with inflation rate and increasing permissible delay.

The market demand of fresh agricultural products is on two‐parameter Weibull distribution.

The paper gives useful advice for agricultural supply chain managers.

The paper presents a new method of inventory control for fresh agricultural products.

The purpose of this paper is to provide numerical solutions of the time‐dependent advection‐diffusion problem by using B‐spline finite element methods in which Taylor series expansion is used for the related time discretization.

The solution domain is partitioned into uniform mesh. The collocation and the Galerkin methods where B‐spline functions are used as base functions are applied to advection‐diffusion equation.

Given methods are unconditionally stable and the obtained results are comparable with some earlier studies in terms of accuracy.

Quadratic and cubic B‐spline base functions are used with Taylor series expansion for the discretization of the equation.

The purpose of this paper is to find a practical active sliding mode control approach for synchronization of two uncertain chaotic systems.

Sliding mode control approach is known to be an efficient alternative way to implement synchronization for uncertain chaotic systems. However, design of traditional sliding mode controller usually needs complex state transformation. Owing to a novel idea of virtual state feedback, a control strategy for synchronization of uncertain chaotic systems is presented, which does not need any complex state transformation. Furthermore, based on Lyapunov stability theory, a sufficient condition is drawn for the robust stability of the error dynamics of synchronization for uncertain chaotic systems.

A novel active sliding mode control approach is proposed to achieve the synchronization of two uncertain chaotic systems.

The main limitation is that uncertainties must meet matched conditions.

The paper presents a useful control approach for synchronization of two uncertain chaotic systems.

The proposed sliding mode control approach based on novel virtual state feedback does not need any complex state transformation, unlike the traditional sliding mode control.

The purpose of this paper is to seek an approach to study decision making and optimization analyzing of enterprises with multi‐factors.

In this paper, a new grey decision dynamic model was set up; it integrates with modified GM model, the transfer function and response characteristic of cybernetics, and other knowledge. The building steps of this integrated model and its application method in a certain enterprise were presented.

Until recently, there have been many references studying grey decision or grey relational analysis of factors, but it was found that dynamic affecting of multi‐factors for enterprise production and their affecting levels were not studied synthetically in these references, and by this new dynamic model, these useful conclusions can be gotten.

The built time response equation and dynamic model in this paper can be only used for whole regularity analysis and not suited to daily one‐to‐one analyzing; otherwise the error of the reductive values must be tested.

This new grey decision dynamic model can be used widely in decision making and optimization analyzing of enterprises with multi‐factors. Practical applying results show that the proposed method can instruct effectively actual production.

This paper offers a new grey decision dynamic model that can be used in decision making and optimization analyzing of enterprises with multi‐factors. By applying this new dynamic model in practice, some useful conclusions are drawn; some dynamic factors affecting production capacity of enterprises and their affecting levels can be found.

Motivated by recent research indicating the significant challenges posed by imbalanced datasets in industrial settings, this paper presents a novel framework for Industrial Data-driven Modeling for Imbalanced Fault Diagnosis, aiming to improve fault detection accuracy and reliability.

This study addressing the challenge of imbalanced datasets in predicting hard drive failures is both innovative and comprehensive. By integrating data enhancement techniques with cost-sensitive methods, the research pioneers a solution that directly targets the intrinsic issues posed by imbalanced data, a common obstacle in predictive maintenance and reliability analysis.

In real industrial environments, there is a critical demand for addressing the issue of imbalanced datasets. When faced with limited data for rare events or a heavily skewed distribution of categories, it becomes essential for models to effectively mine insights from the original imbalanced dataset. This involves employing techniques like data augmentation to generate new insights and rules, enhancing the model’s ability to accurately identify and predict failures.

Previous research has highlighted the complexity of diagnosing faults within imbalanced industrial datasets, often leading to suboptimal predictive accuracy. This paper bridges this gap by introducing a robust framework for Industrial Data-driven Modeling for Imbalanced Fault Diagnosis. It combines data enhancement and cost-sensitive methods to effectively manage the challenges posed by imbalanced datasets, further innovating with a bagging method to refine model optimization. The validation of the proposed approach demonstrates superior accuracy compared to existing methods, showcasing its potential to significantly improve fault diagnosis in industrial applications.