261 research outputs found
sj-docx-2-dhj-10.1177_20552076241249277 - Supplemental material for A mobile app to predict and manage behavioral and psychological symptoms of dementia: Development, usability, and users’ acceptability
No full textSupplemental material, sj-docx-2-dhj-10.1177_20552076241249277 for A mobile app to predict and manage behavioral and psychological symptoms of dementia: Development, usability, and users’ acceptability by Eunhee Cho, Minhee Yang, Jiyoon Jang, Jungwon Cho, Bada Kang, Yoonhyung Jang and Min Jung Kim in DIGITAL HEALTH</p
sj-docx-1-dhj-10.1177_20552076241249277 - Supplemental material for A mobile app to predict and manage behavioral and psychological symptoms of dementia: Development, usability, and users’ acceptability
No full textSupplemental material, sj-docx-1-dhj-10.1177_20552076241249277 for A mobile app to predict and manage behavioral and psychological symptoms of dementia: Development, usability, and users’ acceptability by Eunhee Cho, Minhee Yang, Jiyoon Jang, Jungwon Cho, Bada Kang, Yoonhyung Jang and Min Jung Kim in DIGITAL HEALTH</p
Fluence-map generation for prostate intensity-modulated radiotherapy planning using a deep-neural-network
Get PDFA deep-neural-network (DNN) was successfully used to predict clinically-acceptable dose distributions from organ contours for intensity-modulated radiotherapy (IMRT). To provide the next step in the DNN-based plan automation, we propose a DNN that directly generates beam fluence maps from the organ contours and volumetric dose distributions, without inverse planning. We collected 240 prostate IMRT plans and used to train a DNN using organ contours and dose distributions. After training was done, we made 45 synthetic plans (SPs) using the generated fluence-maps and compared them with clinical plans (CP) using various plan quality metrics including homogeneity and conformity indices for the target and dose constraints for organs at risk, including rectum, bladder, and bowel. The network was able to generate fluence maps with small errors. The qualities of the SPs were comparable to the corresponding CPs. The homogeneity index of the target was slightly worse in the SPs, but there was no difference in conformity index of the target, V-60Gy of rectum, the V-60Gy of bladder and the V-4(5G)y of bowel. The time taken for generating fluence maps and qualities of SPs demonstrated the proposed method will improve efficiency of the treatment planning and help maintain the quality of plans.
Non-destructive thickness characterisation of 3D multilayer semiconductor devices using optical spectral measurements and machine learning
No full textThree-dimensional (3D) semiconductor devices can address the limitations of traditional two-dimensional (2D) devices by expanding the integration space in the vertical direction. A 3D NOT-AND (NAND) flash memory device is presently the most commercially successful 3D semiconductor device. It vertically stacks more than 100 semiconductor material layers to provide more storage capacity and better energy efficiency than 2D NAND flash memory devices. In the manufacturing of 3D NAND, accurate characterisation of layer-by-layer thickness is critical to prevent the production of defective devices due to non-uniformly deposited layers. To date, electron microscopes have been used in production facilities to characterise multilayer semiconductor devices by imaging cross-sections of samples. However, this approach is not suitable for total inspection because of the wafer-cutting procedure. Here, we propose a non-destructive method for thickness characterisation of multilayer semiconductor devices using optical spectral measurements and machine learning. For > 200-layer oxide/nitride multilayer stacks, we show that each layer thickness can be non-destructively determined with an average of approximately 1.6 Å root-mean-square error. We also develop outlier detection models that can correctly classify normal and outlier devices. This is an important step towards the total inspection of ultra-high-density 3D NAND flash memory devices. It is expected to have a significant impact on the manufacturing of various multilayer and 3D devices.
Corrections to “Dig-Grasping via Direct Quasistatic Interaction Using Asymmetric Fingers: An Approach to Effective Bin Picking†[Apr 21 3033-3040]
No full text1) The authors of [1] would like to gratefully acknowledge the support of Hong KongRGC26209319, in addition to HongKong ITF ITS/240/17FX, ITS/018/17FP, and ITS/104/19FP already acknowledged in [1]. 2) The corresponding author details in [1] need correction. The correct corresponding author is Jungwon Seo (email: [email protected]).</p
Angstrom-accuracy multilayer thickness determination using optical metrology and machine learning
No full textThe era of big data and cloud computing services has driven the demand for higher capacity and more compact semiconductor devices. As a result, semiconductor devices are moving from 2-D to 3-D. Most notably, three-dimensional (3D) NAND flash memory is the most successful 3D semiconductor device today. 3D NAND overcomes the spatial limitation of conventional planar NAND by stacking memory cells vertically. Since hundreds of vertically stacked semiconductor materials become the channel length in the final product, accurate thickness characterization is critical. In this paper, we propose a non-destructive multilayer thickness characterization method using optical measurements and machine learning. For a silicon oxide/nitride multilayer stack of >200 layers, we could predict the thickness of each layer with an average root-mean-square error (RMSE) of 1.6 Å. In addition, we could successfully classify normal and outlier devices using simulated data. We expect this method to be highly suitable for semiconductor fabrication processes
Selective Crystallization of Ferroelectric HfxZr1- xO2via Excimer Laser Annealing
No full textHerein, we report the ferroelectricity of HfxZr1-xO2 (HZO) thin films crystallized via excimer laser annealing at 25 °C under atmospheric conditions. We characterized the structure and antiferroelectric/ferroelectric properties of HZO thin films using X-ray diffraction and standard polarization-voltage hysteresis. The laser-annealed Hf0.5Zr0.5O2 thin film was gradually crystallized with increasing laser pulse number. The HZO thin film series exhibited systemic changes in antiferroelectric/ferroelectric properties with variations in Zr concentration. In addition, we constructed a phase diagram of laser-annealed HZO thin films. © 2023 American Chemical Society.11Nsciescopu
Fully Automated Valet Parking System Based on Infrastructure Sensing
No full textIn this paper, we propose a novel automated valet parking system exploiting infrastructure hardware for a more applicable, safe and efficient solution. The infrastructure equipped with camera and computing device-detect automobiles on IPM (Inverse Perspective Mapping) image, generate a path to the empty parking space and control the vehicle to track the path. In order to recognize empty parking spaces and vehicles, a deep learning-based recognition model was used to have a robust recognition rate in various lighting conditions and environments. In addition, a more efficient route was created by creating a parking path considering the vehicle model. Our system was tested in a 1/4-sized experimental environment; it showed a high parking success rate and confirmed that our system works well in various environments. Therefore, from our system proposed in this paper, we can see the possibility of the development of an efficient and highly intelligent free autonomous parking system based on infrastructure sensing. Our source code is publicly available at https://github.com/FVCD2019. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd
Malware Authorship Attribution Model using Runtime Modules based on Automated Analysis
Get PDFMalware authorship attribution is a research field that identifies the author of malware by extracting and analyzing features that relate the authors from the source code or binary code of malware. Currently, it is being used as one of the detection techniques based on malware forensics or identifying patterns of continuous attacks such as APT attacks. The analysis methods to identify the author are as follows. One is a source code-based analysis method that extracts features from the source code, and the other is a binary-based analysis method that extracts features from the binary. However, to handle the modularization and the increasing amount of malicious code with these methods, both time and manpower are insufficient to figure out the characteristics of the malware. Therefore, we propose the model for malware authorship attribution by rapidly extracting and analyzing features using automated analysis. Automated analysis uses a tool and can be analyzed through a file of malware and the specific hash values without experts. Furthermore, it is the fastest to figure out among other malware analysis methods. We have experimented by applying various machine learning classification algorithms to six malware author groups, and Runtime Modules and Kernel32.dll API extracted from the automated analysis were selected as features for author identification. The result shows more high accuracy than the previous studies. By using the automated analysis, it extracts features of malware faster than source code and binary-based analysis methods
Clinical implementation of a wide-field electron arc technique with a scatterer for widespread Kaposi's sarcoma in the distal extremities
No full textA novel wide-field electron arc technique with a scatterer is implemented for widespread Kaposi's sarcoma (KS) in the distal extremities. Monte Carlo beam modeling for electron arc beams was established to achieve <2% deviation from the measurements, and used for dose calculation. MC-based electron arc plan was performed using CT images of a foot and leg mimicking phantom and compared with in-vivo measurement data. We enrolled one patient with recurrent KS on the lower extremities who had been treated with photon radiation therapy. The 4- and 6-MeV electron arc plans were created, and then compared to two photon plans: two opposite photon beam and volumetric modulated arc with bolus. Compared to the two photon techniques, the electron arc plans resulted in superior dose saving to normal organs beneath the skin region, although it shows inferior coverage and homogeneity for PTV. The electron arc treatment technique with scatterer was successfully implemented for the treatment of widespread KS in the distal extremities with lower radiation exposure to the normal organs beyond the skin lesions, which could be a treatment option for recurrent skin cancer in the extremities.
- …
