Miracle-dz/UniFAR-FTUs huggingface dataset:
A Vascular Interventional Surgical Robotic System Based on Force-Visual Feedback

In this paper, based on the previous research of our research team. We proposed a Vascular Interventional Surgical Robotic System based on Force-Visual feedback. In the process of vascular interventional surgery, it is very important to accurately detect the resistance force of surgical catheter, which can improve the safety of operation. Therefore, we developed a force sensor for the vascular interventional surgery robotic system based on the principle of resistance strain gauge bridge circuit. In addition, a complete robotic system with only force feedback is not enough. Therefore, it is necessary to combine the visual feedback interface to complete the operation. Finally, some verification experiments have been completed, including the evaluation experiment of the internal fixture of the force sensor, the calibration experiment of the force sensor, the evaluation experiment of the force sensor, the evaluation experiment of the force feedback, The axial and radial synchronous motion experiment between the master and slave side. Experimental results showed that the feasibility of the force sensor. Vascular Interventional Surgical Robotic System based on Force-Visual feedback has good performance of the force feedback and the synchronous motion between the master and slave side. There is a certain error in the synchronous motion between the master and slave side and the force feedback of the vascular interventional surgery robotic system based on Force-Visual feedback, the error is within the allowable range.
Building a Filipino Colloquialism Translator Using Sequence-to-Sequence Model

Colloquialism in the Philippines has been prominently used in day-to-day conversations. Its vast emergence is evident especially on social media platforms but poses issues in terms of understandability to certain groups. For this research, machine translators have been implemented to fill in that gap. The translators cover Filipino Textspeak or Shortcuts, Swardspeak or Gay-lingo, Conyo, and Datkilab – implemented on Tensorflow library and Moses tool. Implementing in Tensorflow achieved 85.88 BLEU score when evaluated to the training data and 14.67 to the test data, while Moses garnered 95.27 BLEU score on training data and 79.91 on test data. Analyses on both implementations include advantages and disadvantages in using each one. Through the analyses and development of this research, it is recommended to implement the following in the future: addition of colloquialism samples, experimentation on sequence-to-sequence configurations, applying Graphical User Interface (GUI) to the translators, implementing the translators to Natural Language Processing (NLP) tools, and to deploy the translators as a web application.
Fuzzy approach for production planning by using a three-dimensional printing-based ubiquitous manufacturing system

Abstract A ubiquitous manufacturing (UM) system is used in manufacturing for obtaining the Internet of things solutions and provides location-based manufacturing services. Human-induced uncertainty and early termination are two complications that hamper the effectiveness of an UM system based on three-dimensional (3D) printing. To resolve these complications, several solutions were considered in this study. First, fuzzy-valued parameters were defined to determine uncertainty. Subsequently, slack was derived to determine whether to restart an early terminated 3D printing process in the same 3D printing facility. Consequently, two optimization models – a fuzzy mixed-integer linear programming model and a fuzzy mixed-integer quadratic programming model – were developed in this study. Based on the two optimization models, a fuzzy 3D printing-based UM system that considers uncertainty and early termination was developed. The effectiveness of the proposed methodology was tested by conducting a regional experiment. The experimental results revealed that the proposed methodology could shorten the average cycle time by 9% and could enable 3D printing facilities to make real-time, online reprinting decisions.
A novel face recognition algorithm based on the combination of LBP and CNN

In order to overcome the effects of posture, illumination, expression and other factors on face recognition, this paper proposes an algorithm which is based on local binary pattern (LBP) and convolutional neural network (CNN). LBP is a texture description method which describes the local texture features of an image. It has good robustness of illumination and posture. CNN can effectively extract the spatial features of images and reduce the dimensions of features. This paper combines the advantages of LBP and CNN to improve the accuracy of face recognition. The CNN in this algorithm has four convolution layers, two max-pooling layers, one activation layer, one fully connected layer, and one output layer. In order to optimize the network structure, batch normalization layer is added after the convolution layer. We get the local binary pattern coded images and put the images as the input of the CNN and train the network. Hence, we can use the well-trained CNN for classification and identification. The Experiments on the CMU-PIE face database show that our algorithm can effectively improve the rate of face recognition.
Stabilizing Flow Boiling Operation of a Microchannel Heat Sink using a Hybrid Geometric Configuration

Two-phase cooling using microchannel heat sinks is a promising solution for the thermal management of microelectronic devices. While conventional straight microchannel heat sinks can dissipate large quantities of heat, they suffer from flow instabilities that result in premature critical heat flux conditions. It therefore becomes necessary to develop heat sinks that offer a stabilized boiling process over a wider range of operating heat flux. In this work, a novel hybrid heat sink developed to attain this objective is first presented. The heat sink involves a hybrid geometric configuration that integrates a $300\mu \mathrm {m} \mathrm {x} 600\mu \mathrm {m}$ microchannel array in the upstream region with a $25\mathrm {m}\mathrm {m} \mathrm {x} 600\mu \mathrm {m}$ microgap in the downstream region of a $25\mathrm {m}\mathrm {m}\mathrm {x}25\mathrm {m}\mathrm {m}$ copper heat sink. Flow boiling experiments are conducted with deionized water at 3 mass fluxes in the range of $100-399\mathrm {k}\mathrm {g}/\mathrm {m}^{2}\mathrm {s}$ and at heat fluxes ranging $\mathrm {f}_{\mathrm {T}}\mathrm {o}\mathrm {m}0-128\mathrm {W}/\mathrm {c}\mathrm {m}^{2}$. The boiling operation is found to be stable across a wide range of moderate to high heat flux. Instabilities are experienced only at low heat flux close to the onset of nucleate boiling. The effect of the downstream microgap surface roughness on the hybrid heat sink flow boiling performance is then investigated. Hybrid heat sinks with microgap surface roughness values of $1.2\mu \mathrm {m}$ and $1.9\mu \mathrm {m}$ are compared with the baseline hybrid heat sink having microgap surface roughness of $0.27\mu \mathrm {m}$. The two-phase heat transfer performance shows a highest improvement of 14.5% with the hybrid heat sinks having roughened microgaps while the pressure drop penalty remained relatively unaffected.
Object-in-Place Memory Predicted by Anterolateral Entorhinal Cortex and Parahippocampal Cortex Volume in Older Adults

The lateral portion of the entorhinal cortex is one of the first brain regions affected by tau pathology, an important biomarker for Alzheimer’s disease (AD). Improving our understanding of this region’s cognitive role may help identify better cognitive tests for early detection of AD. Based on its functional connections, we tested the idea that the human anterolateral entorhinal cortex (alERC) may play a role in integrating spatial information into object representations. We recently demonstrated that the volume of the alERC was related to processing the spatial relationships of the features within an object (Yeung et al., 2017). In the present study, we investigated whether the human alERC might also play a role in processing the spatial relationships between an object and its environment using an eyetracking task that assessed visual fixations to a critical object within a scene. Guided by rodent work, we measured both object-in-place memory, the association of an object with a given context (Wilson et al., 2013), and object-trace memory, the memory for the former location of objects (Tsao, Moser, & Moser, 2013). In a group of older adults with varying stages of brain atrophy and cognitive decline, we found that the volume of the alERC and the volume of the parahippocampal cortex (PHC) selectively predicted object-in-place memory, but not object-trace memory. These results provide support for the notion that the alERC may integrate spatial information into object representations.
Binary Diagnosis of Hall Effect Sensors in Brushless DC Motor Drives

Commonly, three Hall effect sensors are used to detect the rotor position in brushless motors. Since one of these sensors may fail and lead to unstable operation, fault-tolerant control (FTC) of Hall sensor drives has attracted renewed research attention recently to ensure very high reliability. This article presents simple combinatory functions to detect fails in Hall effect sensors based only on Hall sensors signals, which require just a memory of 3 b. The proposed method guarantees more reliability and overcomes erroneous alarms that can be triggered by previous fault diagnosis methods using the information on the observed acceleration, which may be the subject of unexpected variations and lead consequently to false diagnosis. In addition, the article opens the discussion about the BLDC startup with a faulty sensor, in order to perform FTC algorithms ensuring a long use of the motor given the difficulty to repair Hall effect sensors inserted between the coils inside the stator. To confirm the theoretical analysis, experimental results are reported and limitations on the performances of the faulty system in terms of fault detection and signal reconstruction algorithm are discussed. The proposed schemes circuits can be inserted in conventional drive systems or by being placed between the existing driver and the motor.
Mask R-CNN with Feature Pyramid Attention for Instance Segmentation

In this paper, a new network for instance semantic segmentation is proposed, which is combined with an attention mechanism and is theoretically effective and simple. In order to perform spatial pyramid attention on high-level output, the proposed network extends Mask R-CNN by exploiting a Feature Pyramid Attention module. The proposed network is simple to be trained and extended. Moreover, the new network is easy to be generalized to other tasks. For example, the new network is suitable for bounding-box detection, person keypoint detection and instance segmentation in the same framework. Compared with the other methods, including MNC, FCIS and Mask R-CNN, which are the champion of the COCO 2015-2017 semantic segmentation challenges respectively, the newly proposed network is superior to them on small scale, and the APs of the result is 14.7. On other scales and for AR metric, the proposed network achieves the comparable performance with the state-of-the-art.
Import Competition and Internal Migration

Abstract We examine the U.S. internal migration response to increased import competition following the granting of Permanent Normal Trade Relations to China in 2001. Using a variety of data sets and empirical approaches, we find that local labor markets most exposed to the policy change experienced a relative reduction in population growth over the following decade. The majority of the effect occurs at a lag of seven to ten years and is most pronounced among young individuals and low-education groups. Such population adjustments should influence the interpretation of evidence in the growing literature on import competition and local labor markets.
Study of the die position accuracy in the fabrication process of a die first type FO-PLP

Die position accuracy is one of the important topics of the fabrication processes of die first fan out packages (FO-PKG). In this research, the die position accuracy and the compensation were studied by the combined approach of the sample preparation and the numerical calculation. As a result, the flow didn’t influence on the die shift significantly and the die shift could be estimated by the component material properties, which were the curing shrinkage of the EMC (epoxy molding compound), and the CTE (coefficient of thermal expansions) and the elastic moduli of the EMC, the TBA (temporary bonding adhesive) and the support. The value of the thermal die shift can be predicted by the developed formulasm. The pre-shift die mouting using the predicted value singnificanltly can suppress the thermal die shift.
A Review of the Hydraulic Transient and Dynamic Behavior of Hydropower Plants with Sloping Ceiling Tailrace Tunnels

The sloping ceiling tailrace tunnel is a novel tailrace tunnel system for hydropower plants. The design, operation, and maintenance of hydropower plants with sloping ceiling tailrace tunnels are based on the calculation and analysis of hydraulic transients and dynamic behavior. Research achievements have provided guidance and a basis for the safe, stable, and efficient operation of hydropower plants with sloping ceiling tailrace tunnels. Based on research achievements, sloping ceiling tailrace tunnels have been applied to more and more hydropower plants. This review paper gives a systematic literature investigation on the hydraulic transient and dynamic behaviors of hydropower plants with sloping ceiling tailrace tunnels. First, the appearance and development of sloping ceiling tailrace tunnels are stated. Key issues in the hydraulic transient and dynamic behaviors of hydropower plants with sloping ceiling tailrace tunnels are illuminated. Then, research achievements on six issues (i.e., the working principles of sloping ceiling tailrace tunnels, the shape design of sloping ceiling tailrace tunnels, the free surface pressurized flow characteristics in sloping ceiling tailrace tunnels, numerical simulations of transient processes for hydro-turbine governing systems with sloping ceiling tailrace tunnels, the stability of hydro-turbine governing systems with sloping ceiling tailrace tunnels, and the transient process control of hydro-turbine governing systems with sloping ceiling tailrace tunnels) are elaborated. Finally, future research trends are presented. In future research, fluid–solid coupling of the tunnel wall and free surface pressurized flow in sloping ceiling tailrace tunnels is worth studying. For hydropower plants with sloping ceiling tailrace tunnels, a combined operating scheme with thermal power and wind power should be explored.
Efficient Image Interpolation via Anchored Neighborhood Weighted Nonlinear Regression

We present an efficient and effective learning-based nonlinear image interpolation method in this paper. Our proposed method utilizes a single-hidden layer feed-forward neural network to mapping the local nonlinear relationship between high resolution patches and low resolution patches directly. In training phase, the training samples are firstly divide into 4 groups based on different known pixels pattern. For each group, a low resolution dictionary is trained and neighborhood are searched in the whole group data set for each atom which is regarded as an anchor. The searched neighborhoods for each anchor especially are then used for learning nonlinear regression model. In the interpolation phase, in order to further improve the interpolation performance, we weight the regression models of related anchors according to the similarity between input patch and anchors. With the off-line learned regression models, our method can achieve high computational efficiency. Extensive experimental results demonstrate the superior performance of our interpolation method compared with the state-of-the-art interpolation methods.
Non-modal vibration-based methods for bridge damage identification

Abstract Many methods of damage identification in bridge structures have focused on the use of numerical models, modal parameters or non-destructive damage tests as a means of condition assessment. These techniques can often be very effective but can also suffer from specific pitfalls such as, numerical model calibration issues for non-linear and inelastic behaviour, modal parameter sensitivity to environmental and operational conditions and bridge usage restrictions for non-destructive testing. This paper covers alternative approaches to damage identification of bridge structures using empirical parameters applied to measured vibration response data obtained from two field experiments of progressively damaged bridges subjected to ambient and vehicle-induced excitation, respectively. Numerous non-modal vibration-based damage features are detailed and selected for the assessment of either the ambient or vehicle-induced excitation data based on their inherent properties. The results of the application to two real bridges, one under ambient vibration and the other of forced vibration, demonstrate the robustness of the proposed damage features for damage identification using measurements of ambient and vehicle excitations. Moreover, this investigation has demonstrated that the novel empirical vibration parameters assessed are suitable for damage detection, localisation and quantification. AbbreviationsCAV cumulative absolute velocityCAD cumulative absolute displacementDVI distributed vibration intensityMCVI mean cumulative vibration intensityIVI instantaneous vibration intensityAIVI Amalgamated instantaneous vibration intensityEMD empirical mode decompositionICEEMDAN improved complete ensemble empirical mode decomposition with adaptive noiseHHT Hilbert–Huang TransformIMF intrinsic mode functionsMCD minimum covariance determinateMSD mahalanobis squared distanceMTS Mahalanobis Taguchi system
How Functions Evolve in Deep Convolutional Neural Network

Deep Convolutional Neural Network (CNN) has been successful in various visual applications. Unfortunately, the mechanism to explain how CNN actually learns and how it works is not clearly revealed and understood. In this paper, we propose analytics for CNN from the functional perspective by constructing three simple yet effective measurements on the convolutional filters. It quantitatively measures the change of convolutional filters in training process, which can be used to explain the learning mechanism of different CNN architectures. By experimental facts on representative VGGNet and ResNet, we find that 1) the change magnitude of lower layer parameters is greater than that of upper layers; 2) lower layers are closer to raw data and wash out redundancy, and higher layers learn more useful information of the data; 3) redundant filters do exist in typical CNN; and 4) the functional behaviors of VGGNet and ResNet can explain the intrinsic difference of plain CNN and residual CNN architectures. Our analytical framework and observations can facilitate future research to understand the learning mechanisms of a wider range of CNN family comprehensively.
A Textural Layer L0 Gradient Minimization based De-noise Method for Scene Text Images

978-1-5386-4673-1/18/$31.00 ©2018 IEEEText images in complex scenes are often suffer from various noises, which result in quality degradation and further effect the performance of automatic analysis. A novel denoise method for scene text images is proposed in this paper. With an image layered decomposition model, text images can be decomposed into a structural layer and a textual layer. Text component can be represented by the structural layer, and the texture layer of the image is closely related to the fine details and random noises. Based on this observation, a L0 gradient minimization scheme is applied on the textual layer in our method. The L0 gradient minimization is used to control the number of nonzero gradient values in a textured layer image, which maintains important features in the image while effectively suppress the gradients with small magnitude, which usually associate with noise. Comprehensive experimental results show that the proposed method can effectively remove the noise in the images and improve the visual quality of the text images.
STASH: SDN-Based Trajectory-Aware Seamless Handover Scheme with Multiple Antennas in Ultra-Dense Smallcell Networks

Software-defined network (SDN) is a new networking paradigm, which separates the control plane and data plane, for the implementation of flexible networking. However, few papers focus on the mobility management issue in SDN networks. For filling this part, we propose a SDN-based trajectory-aware seamless handover (STASH) scheme in ultra-dense smallcell networks. In STASH, the SDN controller will preset the flow entries of switches according to UE’s trajectory information for reducing the signaling overhead and use a multiple pre-registration mechanism to mitigate the workload of the SDN controller. Moreover, our scheme improves the throughput and reduces the handover latency by multiple antennas. The simulation results show that our scheme outperforms other existing schemes in terms of handover latency and network throughput.
High bonding strength of silver sintered joints on non-precious metal surfaces by pressure sintering under air atmosphere using micro-silver sinter paste

Owing to its superb properties such as high melting temperature, high thermal and electrical conductivity, silver sintering is considered as a promising die attach technology in recent years for high power electronics packaging with demanding requirements such as high power density, high current capacity and high operating temperature. Our previous studies have demonstrated the feasibility of semiconductor devices attachment on silver, gold and copper surfaces by silver sintering. Eliminating precious metal finishing on substrate would represent significant compatibility to present supply chain and lower the entry barrier to adopt silver sinter solution. In this paper, we extended our study to semiconductor devices attachment on non-precious metal surfaces by pressure sintering process in air atmosphere. We attached Ag metallized Si dies on direct copper bonding substrates with nickel plated and without plating as well as on aluminum plate by silver sintering process at $250^{\circ}\mathrm{C}$ with a pressure of 10 MPa for 3 min using a newly developed silver sinter paste. We demonstrate that it is feasible to create high bonding strength of silver sintered joint on Ni, Al and Cu surfaces with an average die shear strength above 15 N/mm$^{2}$. The die shear failure mode shows that cohesive break in the sintered layer was obtained for all the samples. SEM-EDX results further confirmed that silver sintered joint was formed on Ni, Al and Cu surfaces with an interdiffusion between Ag and Ni, Ag and Al as well as Ag and Cu. SAM was performed on the samples after pressure sintering and the SAM images clearly illustrate that void, drying channel and delamination in the silver sintered layer were not observed.
Global optimum-based search differential evolution

In this paper, a global optimum-based search strategy is proposed to alleviate the situation that the differential evolution ( DE ) usually sticks into a stagnation, especially on complex problems. It aims to reconstruct the balance between exploration and exploitation, and improve the search efficiency and solution quality of DE. The proposed method is activated by recording the number of recently consecutive unsuccessful global optimum updates. It takes the feedback from the global optimum, which makes the search strategy not only refine the current solution quality, but also have a change to find other promising space with better individuals. This search strategy is incorporated with various DE mutation strategies and DE variations. The experimental results indicate that the proposed method has remarkable performance in enhancing search efficiency and improving solution quality.