Knowledge based cluster ensemble for cancer discovery from biomolecular data

The adoption of microarray techniques in biological and medical research provides a new way for cancer diagnosis and treatment. In order to perform successful diagnosis and treatment of cancer, discovering and classifying cancer types correctly is essential. Class discovery is one of the most important tasks in cancer classification using biomolecular data. Most of the existing works adopt single clustering algorithms to perform class discovery from biomolecular data. However, single clustering algorithms have limitations, which include a lack of robustness, stability, and accuracy. In this paper, we propose a new cluster ensemble approach called knowledge based cluster ensemble (KCE) which incorporates the prior knowledge of the data sets into the cluster ensemble framework. Specifically, KCE represents the prior knowledge of a data set in the form of pairwise constraints. Then, the spectral clustering algorithm (SC) is adopted to generate a set of clustering solutions. Next, KCE transforms pairwise constraints into confidence factors for these clustering solutions. After that, a consensus matrix is constructed by considering all the clustering solutions and their corresponding confidence factors. The final clustering result is obtained by partitioning the consensus matrix. Comparison with single clustering algorithms and conventional cluster ensemble approaches, knowledge based cluster ensemble approaches are more robust, stable and accurate. The experiments on cancer data sets show that: 1) KCE works well on these data sets; 2) KCE not only outperforms most of the state-of-the-art single clustering algorithms, but also outperforms most of the state-of-the-art cluster ensemble approaches.     

Knowledge discovery from observational data for process control using causal Bayesian networks

This paper investigates learning causal relationships from the extensive datasets that are becoming increasingly available in manufacturing systems. A causal modeling approach is proposed to improve an existing causal discovery algorithm by integrating manufacturing domain knowledge with the algorithm. The approach is demonstrated by discovering the causal relationships among the product quality and process variables in a rolling process. When allied with engineering interpretations, the results can be used to facilitate rolling process control.     

A method for identifying rear-end collision risks using inductive loop detectors

An innovative feature of this study is to firstly attempt to capture rear-end collision potentials from the analysis of inductive loop detector data. Signals collected from loops are applied for monitoring individual vehicle information on freeways to estimate safe stopping distances in car-following situations. An index to quantify the potential of rear-end collisions is derived, and further employed for developing criteria to evaluate levels of rear-end collision risks. The proposed methodology based on loop detector data enables to identify collision potentials in real time. It is believed that the index would be a valuable tool for operating agencies in developing various strategies and policies toward enhancements of traffic safety.     

A novel integer programming formulation with logic cuts for the U-shaped assembly line balancing problem

U-shaped assembly lines are regarded as an efficient configuration in Just-In-Time manufacturing. Balancing the workload in these lines is an unsolved problem that attracted significant research within the past two decades. We present a novel integer programming formulation for U-shaped line balancing problems, where cycle time, the interval between two consecutive outputs, is known and the aim is to minimize the number of workstations. To enhance the efficiency of the LP relaxation of the new formulation, we present three types of logic cuts (assignable-station-cuts, task-assignment-cuts and knapsack-cuts) that exploit the inherent logic of the problem structure. The new formulation and logic cuts are tested on an extensive set of benchmark problems to provide a comparative analysis with the existing models in the literature. The results show that our novel formulation augmented by assignable-station-cuts is significantly better than the previous formulations.     

A knowledge acquisition model for selecting coordinate measuring machines using inductive learning

The objective of this study is to use the knowledge acquisition model of inductive learning to establish a selection system for coordinate measuring machines. In this paper, an example knowledge base for the selection of a coordinate measuring machine was induced and analyzed from the diffusive information in the example. The goal is to reduce the amount of knowledge related to the event so as to represent the knowledge in the knowledge base with the fewest number of rules possible. Then the concise decision tree is established by means of the knowledge acquisition model to describe the entire selection problem of coordinate measuring machines.     

A mathematical programming model for production planning using CONWIP

Push-based MRP and pull-based Kanban systems are effective planning tools for a wide range of manufacturing production. Both of them, however, have certain limitations when they are implemented in different production environments. In recent years, CONWIP (CONstant Work-In-Process) has been proposed and studied to take advantage of MRP and Kanban systems for optimal work-inprocess (WIP) inventory control. In this paper, an integer nonlinear mathematical programming model was developed to determine an optimal production sequence and lot sizes in a CONWIP production line. The nonlinear programming model was linearized and solved directly for a number of illustrative example problems.     

RNA secondary structured logic gates for profiling the microRNA cancer biomarkers

Deregulation of microRNAs expression is symptomatic of cancer disease and occurs before the awareness of cancer signs. Early detection of cancer disease can improve or drop the disease entirely. DNA computing is an emerging field of detecting microRNAs based on toehold‐mediated strand displacement reactions, which is a more efficient method than the commonly used method like real‐time PCR. Accuracy and cost of diagnostic applications are essential criteria that are achieved by using the DNA logic gates based on the DNA computing method. In this study, the authors proposed the multi‐input liver cancer biosensor with the RNA secondary structure motifs as the computational module and two approaches are suggested.Inspec keywords: cancer, biocomputing, biochemistry, DNA, RNA, biosensors, logic gates, liver, macromolecules, genetics, molecular biophysics, diseasesOther keywords: RNA secondary structured logic gates, microRNA cancer biomarkers, microRNAs expression, cancer disease, cancer signs, detecting microRNAs, toehold‐mediated strand displacement reactions, DNA logic gates, DNA computing method, multiinput liver cancer biosensor, RNA secondary structure motifs     

Accelerating materials discovery using machine learning

The discovery of new materials is one of the driving forces to promote the development of modern society and technology innovation,the traditional materials research mainly depended on the trial-and-error method,which is time-consuming and laborious.Recently,machine learning (ML) methods have made great progress in the researches of materials science with the arrival of the big-data era,which gives a deep revolution in human society and advance science greatly.However,there exist few systematic generalization and summaries about the applications of ML methods in materials science.In this review,we first provide a brief account of the progress of researches on materials science with ML employed,the main ideas and basic procedures of this method are emphatically introduced.Then the algorithms of ML which were frequently used in the researches of materials science are classified and compared.Finally,the recent meaningful applications of ML in metal materials,battery materials,photovoltaic materials and metallic glass are reviewed.     

Control logic verification for an automotive body assembly line using simulation

In this paper, an approach is proposed that verifies the controller logic processes for the automobile industry via simulation. For this purpose, a state-based object model that creates a virtual car body assembly line is proposed and a verification methodology using observed signal sequences during the simulation is proposed. This approach was applied to an assembly line controlled by a PLC and the effectiveness of the proposed system was explained in a case study.     

Repeat inspection planning using dynamic programming

In this paper we propose a dynamic programming approach to the problem of determination of the inspection sequence of multi-characteristic critical components, and the number of repeat inspection for each characteristic. The model presented here considers the case of several classification of a product by an inspector. An inspector could classify a product as non-defective, to be reworked, or to be scrapped, with respect to a certain characteristic. The model accounts as well for possible misclassification by the inspector. The dynamic programming algorithm searches for a solution that minimizes the total cost of inspection per accepted component. The total cost includes the cost of false rejection of good items, the cost due to false acceptance of an item which is either reworkable or to be scrapped, the cost of inspection, and the cost of rework.     

QFD optimization using linear physical programming

A new approach to quality function deployment (QFD) optimization is presented. The approach uses the linear physical programming (LPP) technique to maximize overall customer satisfaction in product design. QFD is a customer-focused product design method which translates customer requirements into product engineering characteristics. Because market competition is multidimensional, companies must maximize overall customer satisfaction by optimizing the design of their products. At the same time, all constraints (e.g. product development time, development cost, manufacturing cost, human resource in design and production, etc.) must be taken into consideration. LPP avoids the need to specify an importance weight for each objective in advance. This is an effective way of obtaining optimal results. Following a brief introduction to LPP in QFD, the proposed approach is described. A numerical example is given to illustrate its application and a sensitivity analysis is carried out. Using LPP in QFD optimization provides a new direction for optimizing the product design process.     

Homogeneous magnet design using linear programming

We introduce a technique for designing homogeneous magnets using linear programming, We first show that minimum-power homogeneous magnet design can be cast as a linear programming problem. We also show that the method is applicable to minimum conductor mass superconducting magnet design. The method has several advantages over existing techniques including: it allows complete flexibility in arbitrary geometric constraints on both the coil locations and the shape of the homogeneous volume; it guarantees a globally optimal solution; and it offers rapid computation speed (about 30 s). Three resistive magnet design examples and one shielded superconducting magnet design are presented to illustrate the flexibility of the method     

Frictional contact formulation using quadratic programming

A new solution procedure for contact problems in elasticity with prescribed normal tractions on contact surface has been proposed in this paper. The procedure is based on the boundary element method and quadratic programming. It is next used in a two step solution algorithm for the analysis of contact problems with friction. Several numerical examples are presented and compared with results obtained using alternative solution methods.This research has been supported in part of the National Science Foundation Presidental Young Investigator award MSS-9057055 with D. Oscar Dillon as the program manager, and by the U.S. Department of Energy, under contact DE-AC05-84OR21400 with Martin Marietta Energy Systems, Inc., by an appointment to the Oak Ridge National Laboratory Postdoctoral Research Associates Program administered jointly by the Oak Ridge Institute for Science and Education and Oak Ridge National Laboratory.     

High-precision inductive instruments for measuring dimensions

A new generation of inductive transducers is described which differ from the similar transducers made by foreign firms in having smaller overall dimensions, no sliding-friction pairs in the moving parts, a lower power consumption, and a higher sensitivity.Translated from Izmeritel'naya Tekhnika, No. 10, pp. 19–21, October, 1994.     

Resonant inductive systems for information capture

We discuss some types of resonant inductive systems for information capture, present the elements of their design, and discuss results of research on such instruments. Translated from Izmeritel'naya Tekhnika, No. 8, pp. 7–10, August, 1997.     

Thin-film inductive heads for perpendicular recording

A modified thin-film magnetic head for perpendicular recording in rigid disk drives with improved read/write characteristics, especially at high areal bit densities, is presented. The head on which the modified design is based is described. It combines the advantages of single-pole heads and thin-film heads, writing with the sharp field edge of the leading pole and reading like a thin-film head. To increase the writing efficiency and improve the yield, the sequence of magnetic layers in the head is changed; the second layer of the four-layer head is embedded in the substrate, where it can be placed much closer to the pole tip of the first layer. The improved write capability depends mainly on the position of the embedded layer. In addition, there results an improved magnetic flux guidance from the embedded layer into the pole tip layer, providing the potential for a significant improvement in fabrication yield. The embedded-layer approach also allows a further increase in areal density. The results of read/write tests and the write-wide and read-narrow characteristics are presented     

A new hybrid method for optimal circuit design using semi-definite programming

In this article a new method for yield optimization (design centring) is introduced. The method has a statistical-geometrical nature, hence it is called hybrid. The method exploits the semi-definite programming applications in approximating the feasible region with two bounding ellipsoids. These ellipsoids are obtained using a two phase algorithm. In the first phase, the minimum volume ellipsoid enclosing the feasible region is obtained. The largest ellipsoid that can be inscribed inside the feasible region is obtained in the second phase. The centres of these bounding ellipsoids are used as design centres. In the second phase, an additional polytopic region approximation is constructed. A comparison between the obtained region approximations is given. Saving in the number of circuit simulations needed for yield optimization is also considered. Practical examples are given to show the effectiveness of the new method.     

Redundancy allocation for multi-state systems using physical programming and genetic algorithms

This paper proposes a multi-objective optimization model for redundancy allocation for multi-state series–parallel systems. This model seeks to maximize system performance utility while minimizing system cost and system weight simultaneously. We use physical programming as an effective approach to optimize the system structure within this multi-objective optimization framework. The physical programming approach offers a flexible and effective way to address the conflicting nature of these different objectives. Genetic algorithm (GA) is used to solve the proposed physical programming-based optimization model due to the following three reasons: (1) the design variables, the number of components of each subsystems, are integer variables; (2) the objective functions in the physical programming-based optimization model do not have nice mathematical properties, and thus traditional optimization approaches are not suitable in this case; (3) GA has good global optimization performance. An example is used to illustrate the flexibility and effectiveness of the proposed physical programming approach over the single-objective method and the fuzzy optimization method.