Low speed impact behaviour of adhesively bonded foam-core sandwich T-joints

This paper investigates the effects of foam core density and aluminum skin plates on the low speed impact behaviour of adhesively bonded sandwich T-joints having a PVC foam core and aluminum face-sheets. The dynamic response of adhesively bonded sandwich T-joints was analyzed by the explicit finite element method. Two different material models were implemented to the foam core material: a hyperelastic model and a crushable foam material with ductile damage whereas the aluminum face-sheets were modelled as an elasto-plastic material. The cohesive response of adhesive interfaces was included using three dimensional cohesive element based on cohesive zone model. Adhesively bonded sandwich T-joint specimens were manufactured and tested to validate the numerical model. A very good agreement between the experimental and FE results were achieved. The density of the foam core material of adhesively bonded sandwich T-joint played important role on the joint failure mechanism. The joint having a stiffer foam core experienced more damage in both stiffener panel and adhesive layers.     

Statistical Analysis and Multimodal Classification on Noisy Eye Tracker and Application Log Data of Children with Autism and ADHD

Emotion recognition behavior and performance may vary between people with major neurodevelopmental disorders such as Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and control groups. It is crucial to identify these differences for early diagnosis and individual treatment purposes. This study represents a methodology by using statistical data analysis and machine learning to provide help to psychiatrists and therapists on the diagnosis and individualized treatment of participants with ASD and ADHD. In this paper we propose an emotion recognition experiment environment and collect eye tracker fixation data together with the application log data (APL). In order to detect the diagnosis of the participant we used classification algorithms with the Tomek links noise removing method. The highest classification accuracy results were reported as 86.36% for ASD vs. Control, 81.82% for ADHD vs. Control and 70.83% for ASD vs. ADHD. This study provides evidence that fixation and APL data have distinguishing features for the diagnosis of ASD and ADHD.     

Robust watermarking of fingerprint images

This paper introduces two spatial methods in order to embed watermark data into fingerprint images, without corrupting their features. The first method inserts watermark data after feature extraction, thus preventing watermarking of regions used for fingerprint classification. The method utilizes an image adaptive strength adjustment technique which results in watermarks with low visibility. The second method introduces a feature adaptive watermarking technique for fingerprints, thus applicable before feature extraction. For both of the methods, decoding does not require original fingerprint image. Unlike most of the published spatial watermarking methods, the proposed methods provide high decoding accuracy for fingerprint images. High data hiding and decoding performance for color images is also observed.     

A new fault tolerant control approach for the three-tank system using data mining

In this study, we propose a knowledge-based approach for detection and isolation of sensor faults in fault tolerant control (FTC) of the three-tank system. Farthest first traversal algorithm (FFTA) of data mining is used first-time for the classification of faults in an FTC system. The sliding window is used to detect signal changes, which contain possible transients due to faults. The variance-changing ratio is calculated to extract the features of the sensor signal in each window. Then, FFTA is utilized for the isolation of sensor faults. In order to demonstrate the efficiency of the proposed method, seven types of artificial faults were applied to closed-loop fault tolerant control system in certain periods. All faults were detected and isolated immediately after they occurred. Moreover, fault isolation was achieved when multiple faults occurred simultaneously.     

XChange: A Universal Mechanism for Asset Exchange between Permissioned Blockchains

Permissioned blockchains are increasingly being used as a solution to record transactions between companies. Several use cases that leverage permissioned blockchains focus on the representation and management of real-world assets. Since the number of incompatible blockchains is quickly growing, there is an increasing need for a universal mechanism to exchange, or trade, digital assets between these isolated platforms. There currently is no universal mechanism for inter-blockchain asset exchange without a requirement for trusted authorities that coordinate the trade. We address this shortcoming and present XChange, a universal mechanism for asset exchange between permissioned blockchains. To achieve universality and to avoid trusted authorities that coordinate a trade, XChange does not provide atomic guarantees but leverages risk mitigation strategies to reduce value at stake. Our mechanism records the specifications and progression of each trade within records on a distributed log. XChange reduces the economic gains of adversaries by bounding the total amount of fraud they can commit at any time. After having committed fraud, an adversary is forced to finish its ongoing trades before it can engage in new trades. We first present a four-phased protocol that coordinates an asset exchange between two traders. We then outline how trade records can be stored on TrustChain, which is a lightweight distributed ledger specifically built for the tamper-proof storage of data elements. We implement XChange and conduct experiments. Our experiments demonstrate that XChange is capable of reducing the economic gains of adversaries by more than 99.9% when replaying a real-world trading dataset. A deployment on low-resource devices reveals that the latency added to a trade by XChange is only 493 milliseconds. Finally, our scalability evaluation shows that XChange achieves over 1’000 trades per second and that its throughput, in terms of trades per second, scales linearly with the system load.

     

Application of NSGA-II to feature selection for facial expression recognition

Facial expression recognition generally requires that faces be described in terms of a set of measurable features. The selection and quality of the features representing each face have a considerable bearing on the success of subsequent facial expression classification. Feature selection is the process of choosing a subset of features in order to increase classifier efficiency and allow higher classification accuracy. Many current dimensionality reduction techniques, used for facial expression recognition, involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. In this paper, we present a methodology for the selection of features that uses nondominated sorting genetic algorithm-II (NSGA-II), which is one of the latest genetic algorithms developed for resolving problems with multiobjective approach with high accuracy. In the proposed feature selection process, NSGA-II optimizes a vector of feature weights, which increases the discrimination, by means of class separation. The proposed methodology is evaluated using 3D facial expression database BU-3DFE. Classification results validates the effectiveness and the flexibility of the proposed approach when compared with results reported in the literature using the same experimental settings.     

Achieving zT > 1 in Inexpensive Zintl Phase Ca9Zn4+xSb9 by Phase Boundary Mapping

Complex multinary compounds (ternary, quaternary, and higher) offer countless opportunities for discovering new semiconductors for applications such as photovoltaics and thermoelectrics. However, controlling doping has been a major challenge in complex semiconductors as there are many possibilities for charged intrinsic defects (e.g., vacancies, interstitials, antisite defects) whose energy depends on competing impurity phases. Even in compounds with no apparent deviation from a stoichiometric nominal composition, such defects commonly lead to free carrier concentrations in excess of 10²⁰ cm^?3. Nevertheless, by slightly altering the nominal composition, these defect concentrations can be tuned with small variation of the chemical potentials (composition) of each element. While the variation of chemical composition is undetectable, it is shown that the changes can be inferred by mapping (in nominal composition space) the boundaries where different competing impurity phases form. In the inexpensive Zintl compound Ca₉Zn_4+x Sb₉, the carrier concentrations can be finely tuned within three different three‐phase regions by altering the nominal composition (x = 0.2–0.8), enabling the doubling of thermoelectric performance (zT). Because of the low thermal conductivity, the zT can reach as high as 1.1 at 875 K, which is one of the highest among the earth abundant p‐type thermoelectrics with no ion conducting.     

Left ventricular assist device patient maintained on home hemodialysis: A novel class of patients to the home dialysis population

Severe heart failure is increasingly being managed by cardiac transplantation, and in some cases mechanical support devices serve as destination therapies. Left ventricular assist devices (LVADs) were approved for destination therapy for end stage heart failure patients before the more advanced total artificial heart modality became available. One common complication of mechanical assist device placement is acute kidney injury. Historically, patients with mechanical support devices have had to have inpatient hemodialysis until combined heart kidney transplant. Though, some units have started accepting LVAD patients in outpatient dialysis clinics. The cost of in center hemodialysis remains high and home dialysis modalities are becoming increasingly popular. We report the first patient with an LVAD to undergo training and successful home hemodialysis while awaiting combined heart kidney transplantation.