Fundamental characteristics of printed gelatin utilizing micro 3D printer

Gelatin is useful for biofabrication, because it can be used for cell scaffolds and it has unique properties. Therefore, we attempted to fabricate biodevices of gelatin utilizing micro 3D printer which is able to print with high precision. However, it has been difficult to fabricate 3D structure of gelatin utilizing 3D printer, because a printed gelatin droplet on the metal plate electrode would spread before solidification. To clear this problem, we developed a new experimental set-up with a peltier device that can control temperature of the impact point. At an impact point temperature of 80 °C, the spreading of printed gelatin droplets was prevented. Therefore, we were able to print a ball gelatin. In addition, we were able to print a narrower gelatin line than at an impact point temperature of 20 °C.     

A learning-based colour image segmentation with extended and compact structural tensor feature representation

In this paper a novel Tensor-Based Image Segmentation Algorithm (TBISA) is presented, which is dedicated for segmentation of colour images. A purpose of TBISA is to distinguish specific objects based on their characteristics, i.e. shape, colour, texture, or a mixture of these features. All of those information are available in colour channel data. Nonetheless, performing image analysis on the pixel level using RGB values, does not allow to access information on texture which is hidden in relation between neighbouring pixels. Therefore, to take full advantage of all available information, we propose to incorporate the Structural Tensors as a feature extraction method. It forms enriched feature set which, apart from colour and intensity, conveys also information of texture. This set is next processed by different classification algorithms for image segmentation. Quality of TBISA is evaluated in a series of experiments carried on benchmark images. Obtained results prove that the proposed method allows accurate and fast image segmentation.     

A deep learning scheme for mental workload classification based on restricted Boltzmann machines

The mental workload (MWL) classification is a critical problem for quantitative assessment and analysis of operator functional state in many safety-critical situations with indispensable human–machine cooperation. The MWL can be measured by psychophysiological signals. In this work, we propose a novel restricted Boltzmann machine (RBM) architecture for MWL classification. In relation to this architecture, we examine two main issues: the optimal structure of RBM and selection of the most important EEG channels (electrodes) for MWL classification. The trial-and-error and entropy-based pruning methods are compared for the RBM structure identification. The degree of importance of EEG channels is calculated from the weights in a well-trained network in order to select the most relevant channels for classification task. Extensive comparative results showed that the selected EEG channels lead to accurate MWL classification across subjects.     

Development of an internet and fuzzy based control system of manufacturing process

The aim of this work is to develop an Internet and fuzzy based control and data acquisition system for an industrial process plant which can ensure remote running and fuzzy control of a cement factory. Cases studies of the proposed system application in three cement factories in Algeria, SCAEK (Setif), SCIMAT (Batna), and SCT (Tebessa), are discussed. The remote process control consists of alarms generated during running of the processes while maintaining and synchronizing different regulation loops thus ensuring automatic running of processes smoothly. In addition, fuzzy control of the kiln and the other two mills ensures that the system is operational at all times with minimal downtime. The process control system contains different operator station (OP), alarms table and a provision to monitor trends analysis. The operator can execute any operation according to his authorised access assigned by the system administrator using user administration tool. The Internet technology is used for human security by avoiding all times presence of operators at site for maintenance. Further, in case of a breakdown, the problem would be remotely diagnosed and resolved avoiding requirement of an expert on site thus eliminating traveling cost, security risks, visa formalities, etc. These trips are difficult to organize (costs, visas, risks). So the enterprise can reduce downtimes and travel costs. In order to realize a process control system guided by operators in the main control room or through Internet, the process control is based on programming in PCS 7 utilizing Cemat library and Fuzzy Control++ Siemens tools.     

Refined entanglement concentration for electron-spin entangled cluster states with quantum-dot spins in optical microcavities

We present a refined entanglement concentration protocol (ECP) for an arbitrary unknown less-entangled four-electron-spin cluster state by exploring the optical selection rules derived from the quantum-dot spins in one-sided optical microcavities. In our ECP, the parties obtain not only the four-electron-spin systems in the partial entanglement with two unknown parameters, but also the less-entangled two-electron-spin systems in the first step. Utilizing the above preserved systems as the resource for the second step of our ECP, the parties can obtain a standard cluster state by keeping the robust odd-parity instances with two parity-check gates. Meanwhile, the systems in the rest three instances can be used as the resource in the next round of our ECP. The success probability of our ECP is largely increased by iteration of the ECP process. Moreover, all the coefficients of our ECP are unknown for the parties without assistance of extra single electron-spin, so our ECP maybe has good applications in quantum communication network in the future.     

Continuous-variable quantum homomorphic signature

Quantum cryptography is believed to be unconditionally secure because its security is ensured by physical laws rather than computational complexity. According to spectrum characteristic, quantum information can be classified into two categories, namely discrete variables and continuous variables. Continuous-variable quantum protocols have gained much attention for their ability to transmit more information with lower cost. To verify the identities of different data sources in a quantum network, we propose a continuous-variable quantum homomorphic signature scheme. It is based on continuous-variable entanglement swapping and provides additive and subtractive homomorphism. Security analysis shows the proposed scheme is secure against replay, forgery and repudiation. Even under nonideal conditions, it supports effective verification within a certain verification threshold.     

Integrated design technique for materials and structures of vehicle body under crash safety considerations

With the rapid development of the vehicle industry, crashworthiness has become a crucial aspect in vehicle body design. In fact, crashworthiness is a multivariable optimization design problem for a vehicle body, regardless of structure or material. However, when crashworthiness involves a large number of design variables, including both material and structure variables, it is more difficult to deal with. In this paper, an integrated design technique for materials and structures of vehicle body under crash safety consideration is suggested. First, a finite element model of the vehicle body is established according to relevant vehicle safety standards. Then, the material parameters of the vehicle body are set as analytical factors for factor screening. Next, significant factors are obtained using a three-level saturated design integrated with multi-index comprehensive balance analysis and the MaxUr ⁽³⁾ method, with an improved evaluation method. These screened material parameters along with the corresponding continuous variables of the structure, are considered as the design variables of the integrated design of the vehicle body. Both the weight and the crashworthiness properties are set as the design objectives. Optimal Latin hypercube sampling and radius basis functions are utilized to construct highly accurate surrogate models. Furthermore, the non-dominated sorting genetic algorithm II is implemented to seek the optimal solutions. Finally, two cases considering the roof module and the frontal module of a vehicle body are analyzed to verify the proposed method.     

Microstructural topology optimization of structural-acoustic coupled systems for minimizing sound pressure level

The aim of this paper is to present a microstructural topology optimization methodology for the structural-acoustic coupled system. In the structural-acoustic system, the structure is considered to be a thin composite plate composed of periodic uniform microstructures. The discrete design variables are used in the microstructural topology optimization, and the constitutive matrix is interpolated by the power-law scheme at the micro scale. The equivalent macro material properties of the microstructure are computed through the homogenization method. The design objective is to minimize the sound pressure level (SPL) in an interior acoustic medium. The sensitivities of the SPL with respect to design variables are derived. The bi-directional evolutionary structural optimization (BESO) method is extended to solve the structural-acoustic coupled optimization problem to find the optimal material distribution of the microstructure. Numerical examples of a hexahedral box and an automobile passenger compartment are given to demonstrate the efficiency of the presented microstructural topology optimization method.     

Promoting traits into model-driven development

Traits, as sets of behaviors, can provide a good mechanism for reusability. However, they are limited in important ways and are not present in widely used programming and modeling languages and hence are not readily available for use by mainstream developers. In this paper, we add UML associations and other modeling concepts to traits and apply them to Java and C++ through model-driven development. We also extend traits with required interfaces so dependencies at the semantics level become part of their usage, rather than simple syntactic capture. All this is accomplished in Umple, a textual modeling language based upon UML that allows adding programming constructs to the model. We applied the work to two case studies. The results show that we can promote traits to the modeling level along with the improvement in flexibility and reusability.     

An Efficient Scaling Algorithm for the Minimum Weight Bibranching Problem

Let G=(VG,AG) be a digraph and let S ⊔ T be a bipartition of VG. A bibranching is a subset B⊆AG such that for each node s∈S there exists a directed s–T path in B and, vice versa, for each node t∈T there exists a directed S–t path in B.