Solving the Matrix Differential Riccati Equation: A Lyapunov Equation Approach

In this technical note, we investigate a solution of the matrix differential Riccati equation that plays an important role in the linear quadratic optimal control problem. Unlike many methods in the literature, the approach that we propose employs the negative definite anti-stabilizing solution of the matrix algebraic Riccati equation and the solution of the matrix differential Lyapunov equation. An illustrative numerical example is provided to show the efficiency of our approach.      

A framework for linguistic logic programming

Lawry's label semantics for modeling and computing with linguistic information in natural language provides a clear interpretation of linguistic expressions and thus a transparent model for real‐world applications. Meanwhile, annotated logic programs (ALPs) and its fuzzy extension AFLPs have been developed as an extension of classical logic programs offering a powerful computational framework for handling uncertain and imprecise data within logic programs. This paper proposes annotated linguistic logic programs (ALLPs) that embed Lawry's label semantics into the ALP/AFLP syntax, providing a linguistic logic programming formalism for development of automated reasoning systems involving soft data as vague and imprecise concepts occurring frequently in natural language. The syntax of ALLPs is introduced, and their declarative semantics is studied. The ALLP SLD‐style proof procedure is then defined and proved to be sound and complete with respect to the declarative semantics of ALLPs. © 2010 Wiley Periodicals, Inc.     

Design and implementation of look-ahead linear jerk filter for a computerized numerical controlled machine

In this study, an open programmable logic controller (PLC) from Fuji electric prescribed in the Structured Text program was applied to develop a look-ahead linear jerk filter (LALJF) for a computerized numerically controlled (CNC) machine. To ensure the smooth and accurate motion of a tool with a linear change in jerk during real-time machining, the proposed filter was formed by combining a look-ahead algorithm with three modified moving average filters (3MMAF). The look-ahead algorithm performed a single look-ahead step-change in the speed of the speed curve. Based on a step-changing speed profile, given maximal acceleration/deceleration and maximum jerk, the speed curve was modified before it was passed through a linear jerk filter to reduce machining time. The speed commands filtered by the proposed filter stabilize machine table at the beginning and end of its motion, and at any point at which its speed exhibits a step-change. The theoretical and computational aspects of the LALJF are presented together with experimental results of its implementation on an X–Y table. The multiple-step-changing speed curve of a CNC machine and the speed curve of a high-speed measurement system were constructed in order to verify the feasibility and precision of the proposed method.     

Development of a sequential robust–tolerance design model for a destructive quality characteristic

Robust design (RD) and tolerance design (TD) have received much attention from researchers and practitioners for more than two decades, and a number of methodologies for modeling and optimizing the RD and TD processes have been studied. However, there is ample room for improvement. Because most existing research considers RD and TD as separate research fields, the primary objective of this paper is to develop a sequential robust–tolerance design method to jointly determine the best factor settings and the closed-form solutions for the optimal specification limits. We then apply the proposed method to a destructive quality characteristic. Finally, a case study and sensitivity analyses are performed for verification purposes, and further studies are discussed.     

Robust self-stabilizing weight-based clustering algorithm

Ad hoc networks consist of wireless hosts that communicate with each other in the absence of a fixed infrastructure. Such networks cannot rely on centralized and organized network management. The clustering problem consists of partitioning network nodes into non-overlapping groups called clusters. Clusters give a hierarchical organization to the network that facilitates network management and that increases its scalability.In a weight-based clustering algorithm, the clusterheads are selected according to their weight (a node’s parameter). The higher the weight of a node, the more suitable this node is for the role of clusterhead. In ad hoc networks, the amount of bandwidth, memory space or battery power of a node could be used to determine weight values.A self-stabilizing algorithm, regardless of the initial system configuration, converges to legitimate configurations without external intervention. Due to this property, self-stabilizing algorithms tolerate transient faults and they are adaptive to any topology change.In this paper, we present a robust self-stabilizing weight-based clustering algorithm for ad hoc networks. The robustness property guarantees that, starting from an arbitrary configuration, after one asynchronous round, the network is partitioned into clusters. After that, the network stays partitioned during the convergence phase toward a legitimate configuration where the clusters verify the “ad hoc clustering properties”.     

A dispatching method for automated lifting vehicles in automated port container terminals

In automated container terminals, containers are transported from the marshalling yard to a ship and vice versa by automated vehicles. The automated vehicle type studied in this paper is an automated lifting vehicle (ALV) that is capable of lifting a container from the ground by itself. This study discusses how to dispatch ALVs by utilizing information about pickup and delivery locations and time in future delivery tasks. A mixed-integer programming model is provided for assigning optimal delivery tasks to ALVs. A procedure for converting buffer constraints into time window constraints and a heuristic algorithm for overcoming the excessive computational time required for solving the mathematical model are suggested. Numerical experiments are reported to compare the objective values and computational times by a heuristic algorithm with those by an optimizing method and to analyze the effects of dual cycle operation, number of ALVs, and buffer capacity on the performance of ALVs.     

A combined work optimization technology under resource constraints with an application to road repair

We propose an approach for solving the task prioritization problem in road surface repair under bounded resources; the idea is to use a combination of defect recognition and classification methods based on statistical analysis and machine learning (random forests) with original methods for solving infinite-dimensional optimization problems (optical-geometric analogy). We show the results of a computational experiment that indicate high performance of the developed algorithms, and the resulting solutions were evaluated highly by experts in road facilities management. Our results may encourage more efficient use of resources to improve the quality of motorways.     

SVM for English semantic classification in parallel environment

Semantic analysis is very important and very helpful for many researches and many applications for a long time. SVM is a famous algorithm which is used in the researches and applications in many different fields. In this study, we propose a new model using a SVM algorithm with Hadoop Map (M)/Reduce (R) for English document-level emotional classification in the Cloudera parallel network environment. Cloudera is also a distributed system. Our English testing data set has 25,000 English documents, including 12,500 English positive reviews and 12,500 English negative reviews. Our English training data set has 90,000 English sentences, including 45,000 English positive sentences and 45,000 English negative sentences. Our new model is tested on the English testing data set and we achieve 63.7% accuracy of sentiment classification on this English testing data set.     

A decision tree using ID3 algorithm for English semantic analysis

Natural language processing has been studied for many years, and it has been applied to many researches and commercial applications. A new model is proposed in this paper, and is used in the English document-level emotional classification. In this survey, we proposed a new model by using an ID3 algorithm of a decision tree to classify semantics (positive, negative, and neutral) for the English documents. The semantic classification of our model is based on many rules which are generated by applying the ID3 algorithm to 115,000 English sentences of our English training data set. We test our new model on the English testing data set including 25,000 English documents, and achieve 63.6% accuracy of sentiment classification results.