Inverse random under sampling for class imbalance problem and its application to multi-label classification

In this paper, a novel inverse random under sampling (IRUS) method is proposed for the class imbalance problem. The main idea is to severely under sample the majority class thus creating a large number of distinct training sets. For each training set we then find a decision boundary which separates the minority class from the majority class. By combining the multiple designs through fusion, we construct a composite boundary between the majority class and the minority class. The proposed methodology is applied on 22 UCI data sets and experimental results indicate a significant increase in performance when compared with many existing class-imbalance learning methods. We also present promising results for multi-label classification, a challenging research problem in many modern applications such as music, text and image categorization.     

Study on the common criteria methodology for secure ubiquitous environment construction

The ubiquitous environment has been developed since the extension of IT technology. The term ‘Ubiquitous’ is referred firstly by Mark Weiser, which means the user based environment that provides network service ‘any time, anywhere’. In the ubiquitous environment, the computing environment should have the device that recognizes the user and the environment. But there is not a verification process when dealing in sensitive information, and it may cause serious errors or malfunction. In order to complement the problem, members of the CCRA utilized CC(Common Criteria) to build up an assessment system and create a secured ubiquitous environment.     

Topological evolution of laminar juncture flows under different critical parameters

Horseshoe vortex topological structure has been studied extensively in the past, traditional “saddle of separation” and new “attachment saddle point” topologies found in literature both have theoretical basis and experimental and computational evidences for support. The laminar incompressible juncture flows at low Reynolds numbers especially are observed to have new topology. Studies concerning the existence of the new topology though found in literature, the topological evolution and its dependency on various critical flow parameters require further investigation. A Particle Image Velocimetry based analysis is carried out to observe the effect of aspect ratio, δ*/D and shape of the obstacle on laminar horseshoe vortex topology for small obstacles. Rise in aspect ratio evolves the topology from the traditional to new for all the cases observed. The circular cross section obstacles are found more apt to having the new topology compared to square cross sections. It is noted that the sweeping effect of the fluid above the vortex system in which horseshoe vortex is immersed plays a critical role in this evolution. Topological evolution is observed not only in the most upstream singular point region of horseshoe vortex system but also in the corner region. The corner vortex topology evolves from the traditional type to new one before the topological evolution of the most upstream singular point, resulting in a new topological pattern of the laminar juncture flows “separation-attachment combination”. The study may help extend the understanding of the three-dimensional boundary layer separation phenomenon.     

Electric-field induced fluctuations in laser generated plasma plume

The effect of an external electric field on laser-generated plasma has been studied. It is observed that the laser-generated plasma can be used for the ignition of a spark in the presence of a low voltage external electric field. An eight-fold emission intensity enhancement in Cu Ⅰ spectral lines are measured as compared to the signal intensity in the absence of an external electric field.The plasma parameters remain the same initially, up to a few microseconds after the generation of plasma, and this feature makes it more interesting for the quantitative analysis of any sample using laser induced breakdown spectroscopy(LIBS). In the presence of an external electric field,fluctuations(contraction and expansion) in the laser-generated plasma are observed which increase the plasma decay time and consequently result in enhanced signal intensity.     

Highly Functional TNTs with Superb Photocatalytic,Optical, and Electronic Performance Achieving Record PV Efficiency of 10.1% for 1D‐Based DSSCs

Different nanostructures of TiO₂ play an important role in the photocatalytic and photoelectronic applications. TiO₂ nanotubes (TNTs) have received increasing attention for these applications due to their unique physicochemical properties. Focusing on highly functional TNTs (HF‐TNTs) for photocatalytic and photoelectronic applications, this study describes the facile hydrothermal synthesis of HF‐TNTs by using commercial and cheaper materials for cost‐effective manufacturing. To prove the functionality and applicability, these TNTs are used as scattering structure in dye‐sensitized solar cells (DSSCs). Photocatalytic, optical, Brunauer‐Emmett‐Teller (BET), electrochemical impedance spectrum, incident‐photon‐to‐current efficiency, and intensity‐modulated photocurrent spectroscopy/intensity‐modulated photovoltage spectroscopy characterizations are proving the functionality of HF‐TNTs for DSSCs. HF‐TNTs show 50% higher photocatalytic degradation rate and also 68% higher dye loading ability than conventional TNTs (C‐TNTs). The DSSCs having HF‐TNT and its composite‐based multifunctional overlayer show effective light absorption, outstanding light scattering, lower interfacial resistance, longer electron lifetime, rapid electron transfer, and improved diffusion length, and consequently, J _SC, quantum efficiency, and record photoconversion efficiency of 10.1% using commercial N‐719 dye is achieved, for 1D‐based DSSCs. These new and highly functional TNTs will be a concrete fundamental background toward the development of more functional applications in fuel cells, dye‐sensitized solar cells, Li‐ion batteries, photocatalysis process, ion‐exchange/adsorption process, and photoelectrochemical devices.     

Adsorption of dyes from aqueous solutions on activated charcoal

Adsorption of industrially important dyes namely bromophenol blue, alizarine red-S, methyl blue, methylene blue, eriochrome black-T, malachite green, phenol red and methyl violet from aqueous media on activated charcoal has been investigated. The effect of shaking time, pH and temperature on the adsorption behaviour of these dyes has been studied. It was noted that adsorption of all the dyes on activated charcoal decreases with an increase in the pH and the temperature. The adsorption isotherms at different temperatures were found to be of L-type. Adsorption data was fitted to Freundlich, BET and Langmuir isotherms and various adsorption parameters have been calculated. The thermodynamic parameters such as DeltaG, DeltaH and DeltaS were calculated from the slopes and intercepts of the linear variation of lnK against 1/T, where K is the adsorption coefficient obtained from Langmuir equation, was used. The calculated values for the heat of adsorption and the free energy indicate that adsorption of dyes is favored at low temperatures and the dyes are chemisorbed on activated charcoal.     

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.     

A Single Injection of NTG-101 Reduces the Expression of Pain-Related Neurotrophins in a Canine Model of Degenerative Disc Disease

Background: Tissue sources of pain emanating from degenerative discs remains incompletely understood. Canine intervertebral discs (IVDs) were needle puncture injured, 4-weeks later injected with either phosphate-buffered saline (PBS) or NTG-101, harvested after an additional fourteen weeks and then histologically evaluated for the expression of NGFr, BDNF, TrkB and CALCRL proteins. Quantification was performed using the HALO automated cell-counting scoring platform. Immunohistochemical analysis was also performed on human IVD tissue samples obtained from spinal surgery. Immunohistochemical analysis and quantification of neurotrophins and neuropeptides was performed using an in vivo canine model of degenerative disc disease and human degenerative disc tissue sections. Discs injected with NTG-101 showed significantly lower levels of Nerve Growth Factor receptor (NGFr/TrkA, p = 0.0001), BDNF (p = 0.009), TrkB (p = 0.002) and CALCRL (p = 0.008) relative to PBS injections. Human IVD tissue obtained from spinal surgery due to painful DDD show robust expression of NGFr, BDNF, TrkB and CALCRL proteins. A single intradiscal injection of NTG-101 significantly inhibits the expression of NGFr, BDNF, TrkB and CALCRL proteins in degenerative canine IVDs. These results strongly suggest that NTG-101 inhibits the development of neurotrophins that are strongly associated with painful degenerative disc disease and may have profound effects upon the management of patients living with discogenic pain.