Static output-feedback control under information structure constraints

An important challenge in the static output-feedback control context is to provide an isolated gain matrix possessing a zero–nonzero structure, mainly in problems presenting information structure constraints. Although some previous works have contributed some relevant results to this issue, a fully satisfactory solution has not yet been achieved up to now. In this note, by using a Linear Matrix Inequality approach and based on previous results given in the literature, we present an efficient methodology which permits us to obtain an isolated static output-feedback gain matrix having, simultaneously, a zero–nonzero structure imposed a priori.     

Pulse-modulation eddy current probes for imaging of external corrosion in nonmagnetic pipes

Since the nonmagnetic pipe is normally utilized in corrosive and hostile environment, it is prone to the external corrosion which occurs on the outer surface of the pipe and severely undermines the structural integrity and safety. Although Pulsed Eddy Current technique (PEC) is currently preferred for detection and evaluation of subsurface defects in tubular conductors, it is subject to technical drawbacks. In light of this, Pulse-modulation Eddy Current technique (PMEC) is intensively investigated in the paper for enhancement of the evaluation sensitivity to external corrosion and accuracy of corrosion imaging. Closed-form expressions of the PMEC response and its sensitivity to external corrosion in tubular conductors are formulated via the Extended Truncated Region Eigenfunction Expansion (ETREE) modeling. Following simulations for analysis and comparison of field signals and evaluation sensitivities of PMEC and PEC, experiments of PMEC for evaluation and imaging of external corrosion are carried out. Through theoretical and experimental investigation, it has been found that regarding the evaluation and imaging of external corrosion in nonmagnetic pipes, the PMEC-based probe have higher sensitivity and imaging accuracy than that based on PEC. The superiority of PMEC to PEC in inspection of tubular conductors is further identified.     

利用慧鱼创意组合模型实现数控设备教具推广教学的探讨与实践

分析了目前数控技术教学过程中存在的传统教学方式表现力不够、加工现场教学效果欠佳等问题,提出了一种通过共享慧鱼创意组合模型的连接方式及相关程序,以实现数控设备教具在讲解相关理论知识院校之间的普及,并且介绍了一种基于该种分享模式的新的制造业相关学科的教学体系。     

Efficient algorithms based on the k-means and Chaotic League Championship Algorithm for numeric,categorical, and mixed-type data clustering

The success rates of the expert or intelligent systems depend on the selection of the correct data clusters. The k-means algorithm is a well-known method in solving data clustering problems. It suffers not only from a high dependency on the algorithm's initial solution but also from the used distance function. A number of algorithms have been proposed to address the centroid initialization problem, but the produced solution does not produce optimum clusters. This paper proposes three algorithms (i) the search algorithm C-LCA that is an improved League Championship Algorithm (LCA), (ii) a search clustering using C-LCA (SC-LCA), and (iii) a hybrid-clustering algorithm called the hybrid of k-means and Chaotic League Championship Algorithm (KSC-LCA) and this algorithm has of two computation stages. The C-LCA employs chaotic adaptation for the retreat and approach parameters, rather than constants, which can enhance the search capability. Furthermore, to overcome the limitation of the original k-means algorithm using the Euclidean distance that cannot handle the categorical attribute type properly, we adopt the Gower distance and the mechanism for handling a discrete value requirement of the categorical value attribute. The proposed algorithms can handle not only the pure numeric data but also the mixed-type data and can find the best centroids containing categorical values. Experiments were conducted on 14 datasets from the UCI repository. The SC-LCA and KSC-LCA competed with 16 established algorithms including the k-means, k-means++, global k-means algorithms, four search clustering algorithms and nine hybrids of k-means algorithm with several state-of-the-art evolutionary algorithms. The experimental results show that the SC-LCA produces the cluster with the highest F-Measure on the pure categorical dataset and the KSC-LCA produces the cluster with the highest F-Measure for the pure numeric and mixed-type tested datasets. Out of 14 datasets, there were 13 centroids produced by the SC-LCA that had better F-Measures than that of the k-means algorithm. On the Tic-Tac-Toe dataset containing only categorical attributes, the SC-LCA can achieve an F-Measure of 66.61 that is 21.74 points over that of the k-means algorithm (44.87). The KSC-LCA produced better centroids than k-means algorithm in all 14 datasets; the maximum F-Measure improvement was 11.59 points. However, in terms of the computational time, the SC-LCA and KSC-LCA took more NFEs than the k-means and its variants but the KSC-LCA ranks first and SC-LCA ranks fourth among the hybrid clustering and the search clustering algorithms that we tested. Therefore, the SC-LCA and KSC-LCA are general and effective clustering algorithms that could be used when an expert or intelligent system requires an accurate high-speed cluster selection.     

An innovative framework for designing genetic algorithm structures

Genetic Algorithms are popular optimization algorithms, often used to solve complex large scale optimization problems in many fields. Like other meta-heuristic algorithms, Genetic Algorithms can only provide a probabilistic guarantee of the global optimal solution. Having a Genetic Algorithm (GA) capable of finding the global optimal solution with high success probability is always desirable. In this article, an innovative framework for designing an effective GA structure that can enhance the GA's success probability of finding the global optimal solution is proposed. The GA designed with the proposed framework has three innovations. First, the GA is capable of restarting its search process, based on adaptive condition, to jump out of local optima, if being trapped, to enhance the GA's exploration. Second, the GA has a local solution generation module which is integrated in the GA loop to enhance the GA's exploitation. Third, a systematic method based on Taguchi Experimental Design is proposed to tune the GA parameter set to balance the exploration and exploitation to enhance the GA capability of finding the global optimal solution. Effectiveness of the proposed framework is validated in 20 large-scale case study problems in which the GA designed by the proposed framework always outperforms five other algorithms available in the global optimization literature.     

无缝钢管生产的新工艺——金属斜连轧

金属斜连轧工艺是一种新型的无缝钢管生产工艺,能够在1台设备上实现穿孔—轧管工艺。介绍了斜连轧设备的结构,斜连轧工艺的特征、流程以及与传统轧管工艺的区别,同时介绍了利用斜连轧试验机组展开试验研究的情况。该工艺的优势在于:只需1台设备就可完成无缝钢管的穿孔和轧管,工艺流程短,生产效率高,节能环保,投资少,成本低。经试验证明:该工艺切实可行,在生产难变形金属时具有一定的竞争优势。     

Power harmonics analysis of programmable AC power source via variation semi-sinusoidal window

Harmonic contents are employed for indicating the quality of the programmable AC power source (PACPS). However, spectrum leakage usually occurs. This paper employs a variation semi-sinusoidal window (VSSW) for performing the power harmonic analysis. Since the rolloff of the sidelobes of the VSSW is fast, experimental results show that the VSSW outperforms existing windows.     

Downstream regulation of CO2 emissions in California's electricity sector

This paper examines the implications of alternative forms of cap-and-trade regulations on the California electricity market. Specific focus is given to the implementation of a downstream form of regulation known as the first-deliverer policy. Under this policy, importers (i.e., first-deliverers) of electricity into California are responsible for the emissions associated with the power plants from which the power originated, even if those plants are physically located outside of California. We find that, absent strict non-economic barriers to changing import patterns, such policies are extremely vulnerable to reshuffling of import resources. The net impact implies that the first-deliverer policies will be only marginally more effective than a conventional source-based regulation.     

Lithography-based ceramic manufacturing (LCM) – Viscosity and cleaning as two quality influencing steps in the process chain of printing green parts

The advantages of suspension based Additive Manufacturing (AM), e.g. the lithography-based ceramic manufacturing (LCM), are high structural resolution, and compared to other available AM techniques, the manufacturing of dense (>99%) ceramic components with high performance. This novel manufacturing technique permits innovative designs, new types of ceramic components, and offers a range of new applications; like micro reactors, catalyst supporting structures or heat exchangers, as well as cutting edge biomedical devices and personalized medical products. Some examples of personalized medical products are customised ceramic knee implants or custom spinal fusion implants. Producing these implants using LCM would allow product offerings not possible before. However, the LCM process chain includes several open points, which have to be solved, in order to get high quality end results. In this paper, the development of suspensions (curable slurries) based on different binders, and the procedures for cleaning printed parts are thoroughly considered.     

Single transition metal atom catalysts on Ti2CN2 for efficient CO2 reduction reaction

Electrochemical CO₂ reduction reaction (CO₂RR) is an efficient way in the utilization of CO₂. In this work, single transition-metal (TM) atom (TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn) anchored on two-dimensional (2D) Ti₂CN₂ are designed for CO₂RR using density-functional-theory (DFT) calculation. We show that Ti₂CN₂ serves as an excellent substrate to support single atom catalysts (SACs), compared to Ti₂CO₂ and Ti₂CF₂. We find that the Sc, Ti and V supported on Ti₂CN₂ show high catalytic activities to produce CO with a low overpotential of 0.37, 0.27, and 0.23 eV, respectively. Differently, the Mn and Fe on Ti₂CN₂ are catalytically active for the production of HCOOH with a low overpotential of 0.32 and 0.43 eV, respectively. We further show that the negatively charged TM-Ti₂CN₂ can capture and activate CO₂ more effectively, and the catalytic activity and selectivity can be significantly tuned by injecting extra electrons.