Effect of the green-emitting CaF2:Ce3+,Tb3+ phosphor particles’ size on color rendering index and color quality scale of the in-cup packaging multichip white LEDs

In this paper, we investigate the effect of the green-emitting CaF2:Ce (3+), Tb (3+) phosphor particle's size on the color rendering index (CRI) and the color quality scale (CQS) of the in-cup packaging multichip white LEDs (MCW-LEDs). For this purpose, 7000K and 8500K in-cup packaging MCW-LEDs is simulated by the commercial software Light Tools. Moreover, scattering process in the phosphor layers is investigated by using Mie Theory with Mat Lab software. Finally, the research results show that the green-emitting CaF2: Ce (3+), Tb (3+) phosphor's size crucially influences on the CRI and CQS. From that point of view, CaF2: Ce (3+), Tb (3+) can be proposed as a potential practical direction for manufacturing the in-cup packaging phosphor WLEDs.Web of Science13235134

Model Reduction for Vehicle Systems Modelling

The full model of a double-wishbone suspension has more than 30 differential-algebraic equations which takes a remarkably long time to simulate. By contrast, the look-up table for the same suspension is simulated much faster, but may not be very accurate. Therefore, developing reduced models that approximate complex systems is necessary because model reduction decreases the simulation time in comparison with the original model, enables real time applications, and produces acceptable accuracy. In this research, we focus on model reduction techniques for vehicle systems such as suspensions and how they are approximated by models having lower degrees of freedom. First, some existing model reduction techniques, such as irreducible realization procedures, balanced truncation, and activity-based reduction, are implemented to some vehicle suspensions. Based on the application of these techniques, their disadvantages are revealed. Then, two methods of model reduction for multi-body systems are proposed. The first proposed method is 2-norm power-based model reduction (2NPR) that combines 2-norm of power and genetic algorithms to derive reduced models having lower degrees of freedom and fewer number of components. In the 2NPR, some components such as mass, damper, and spring are removed from the original system. Afterward, the values of the remaining components are adjusted by the genetic algorithms. The most important advantage of the 2NPR is keeping the topology of multi-body systems which is useful for design purposes. The second method uses proper orthogonal decomposition. First, the equations of motion for a multi-body system are converted to explicit second-order differential equations. Second, the projection matrix is obtained from simulation or experimental data by proper orthogonal decomposition. Finally, the equations of motion are transferred to a lower-dimensional state coordinate system. The implementation of the 2NPR to two double-wishbone suspensions and the comparison with other techniques such as balanced truncation and activity-based model reduction also demonstrate the efficiency of the new reduction technique

AUTOMATIC DETECTION OF ACCESS POINT LOCATION USING MACHINE LEARNING AND PRIOR MODEL CALIBRATION

Knowledge of the correct location of an access point (AP) is of vital importance within a wireless ecosystem. Techniques are presented herein that support a new AP location identification method that uses prior model calibrations and machine learning (ML) techniques to detect an AP’s location using either received signal strength indicator (RSSI) data or fine time measurement (FTM) protocol data. Among other things, the new method is faster and more accurate than conventional trilateration methods

Defect-engineered metal-organic frameworks (MOF-808) towards the improved adsorptive removal of organic dyes and chromium (vi) species from water

In this work, two defective zirconium-based metal-organic frameworks (Zr-MOFs), MOF-808-OH and MOF-808-NH2, were synthesized by partially replacing the 1,3,5-benzenetricarboxylate building block with 5-hydroxyisophthalate and 5-aminoisophthalate, respectively. The structural features of the defective materials were analyzed by powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), nitrogen physisorption at 77 K, and thermogravimetric analysis (TGA). Importantly, the number of defect sites determined via proton nuclear magnetic resonance (1H-NMR) analysis of the digested materials was approximately 7 mol% for MOF-808-OH and 3 mol% for MOF-808-NH2. The presence of the defect sites increased the number of acidic centers on Zr-clusters originating from missing-linker nodes which accounted for a remarkable adsorption capacity towards various anionic organic dyes and chromium (vi) species. Compared to standard MOF-808, the defect-engineered ones showed significant increments by 30-60% in trapping capacity for anionic contaminants including sunset yellow, quinoline yellow, methyl orange, and potassium dichromate, while they exhibited modest improvements by 5-15% in the removal of cationic dyes, namely malachite green and methylene blue

MirrorNet: Bio-Inspired Camouflaged Object Segmentation

Camouflaged objects are generally difficult to be detected in their natural environment even for human beings. In this paper, we propose a novel bio-inspired network, named the MirrorNet, that leverages both instance segmentation and mirror stream for the camouflaged object segmentation. Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the mirror stream corresponding with the original image and its flipped image, respectively. The output from the mirror stream is then fused into the main stream's result for the final camouflage map to boost up the segmentation accuracy. Extensive experiments conducted on the public CAMO dataset demonstrate the effectiveness of our proposed network. Our proposed method achieves 89% in accuracy, outperforming the state-of-the-arts. Project Page: https://sites.google.com/view/ltnghia/research/camoComment: Under Revie

TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573