scholarly journals A Case Study of “ Phantom Genji Scrolls ” Painter School Identification by means of Deep Learning Technology

2021 ◽  
Vol 36 (6) ◽  
pp. F-L12_1-16
Author(s):  
Mariko Inamoto ◽  
Takuya Kato ◽  
Akihiko Konagaya
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
School Identification

scholarly journals AUTOMATIC DETECTION OF TIMBER-CRACKS IN WOODEN ARCHITECTURAL HERITAGE USING YOLOv3 ALGORITHM

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1471-1476
Author(s):  
Y. Liu ◽  
M. Hou ◽  
A. Li ◽  
Y. Dong ◽  
L. Xie ◽  
...  
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Gpu Computing ◽  
Point Clouds ◽  
Learning Technology ◽  
Architectural Heritage ◽  
Detection Model ◽  
The Status ◽  
Computing Performance

Abstract. As there usually exist widespread crack, decay, deformation and other damages in the wooden architectural heritage (WAH). It is of great significance to detect the damages automatically and rapidly in order to grasp the status for daily repairs. Traditional methods use artificial feature-driven point clouds and image processing technology for object detection. With the development of big data and GPU computing performance, data-driven deep learning technology has been widely used for monitoring WAH. Deep learning technology is more accurate, faster, and more robust than traditional methods.In this paper, we conducted a case study to detect timber-crack damages in WAH, and selected the YOLOv3 algorithm with DarkNet-53 as the backbone network in the deep learning technology according to the characteristics of the crack. A large timber-crack dataset was first constructed, based on which the timber-crack detection model was trained and tested. The results were analyzed both qualitatively and quantitatively, showing that our proposed method was able to reach an accuracy of more than 90% through processing each image for less than 0.1s. The promising results illustrate the validity of our self-constructed dataset as well as the reliability of YOLOv3 algorithm for the crack detection of wooden heritage.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

FAKTOR PENDUKUNG DAN PENGHAMBAT DALAM PROSES PERUBAHAN ORGANISASI PADA POLITEKNIK KESEHATAN KEMENKES PALANGKA RAYA

2017 ◽  
Vol 13 (2) ◽  
pp. 119-128
Author(s):  
Nang Randu Utama
Keyword(s):  
Mental Model ◽  
Teaching And Learning ◽  
Learning Technology ◽  
Teaching Aids ◽  
Inhibiting Factors ◽  
Inhibiting Factor ◽  
Management Perspective ◽  
The Republic ◽  
Technical Guidance

This study aims to obtain a description of the supporting and inhibiting factors in the process of organizational change of education based on management perspective that occurs in the scope of higher health education of the Ministry of Health of the Republic of Indonesia. This study used a qualitative approach by conducting case study at Palangka Raya Health Polytechnic. The research results are as follows: (a) Supporting factor that must be there is the existence of a manual or technical guidance in organizing the organization; (b) Whereas the inhibiting factor is the old habits, the mindset, the mental model is still inhibiting from the organizers and members of the organization; (c) The inhibiting factor is the existence of selfishness of each highly visible party; (d) Inhibitors may also occur if there are still "little kings" and selfishness from each of the former institutions; (e) Other issues that support in this process of change are in terms of facilities and infrastructure, namely the availability of buildings and land; (f) Another inhibiting factor is that in terms of educational qualifications, there are departments that do not meet, for example in the midwifery department there are still many average teachers with Diploma IV education background and non-linear education; (g) Inhibiting factors may also occur if the reason of seniority is always carried around; (h) The inhibiting factor is lack of human resources in using modern health equipment, including the use of teaching aids in accordance with the progress of science and teaching and learning technology.   Penelitian ini bertujuan untuk memperolah gambaran mengenai faktor pendukung dan penghambat dalam proses perubahan organisasi pendidikan yang ditinjau dari perspektif manajemen yang terjadi di lingkup organisasi pendidikan tinggi kesehatan Kementerian Kesehatan Republik Indonesia. Penelitian ini menggunakan pendekatan kualitatif dengan melakukan studi kasus pada institusi Politeknik Kesehatan Kemenkes Palangka Raya. Hasil penelitian adalah sebagai berikut: (a) Faktor pendukung yang harus ada yaitu adanya buku pedoman atau petunjuk teknis dalam penyelenggaraan organisasi; (b) Sedangkan yang menjadi faktor penghambat itu adalah kebiasaan lama, mindset-nya, mental model-nya masih bersifat menghambat dari para pengelola dan anggota organisasi; (c) Faktor penghambat yaitu adanya keegoisan masing-masing pihak yang sangat tampak; (d) Penghambat juga dapat terjadi apabila masih ada “raja-raja kecil” dan keegoisan dari masing-masing institusi yang dulu; (e) Perihal lain yang mendukung dalam proses perubahan ini adalah dari sisi sarana dan prasarana, yaitu tersedianya gedung dan tanah; (f) Faktor penghambat lain yaitu dari sisi kualifikasi pendidikan ternyata ada jurusan yang tidak memenuhi, misalnya di jurusan kebidanan masih banyak rata-rata tenaga pengajar dengan latar pendidikan Diploma IV dan pendidikannya tidak linear; (g) Faktor penghambat juga dapat terjadi apabila alasan senioritas selalu dibawa-bawa; (h) Faktor penghambat yaitu masih kurang kesiapan sumber daya manusia dalam menggunakan alat-alat kesehatan modern termasuk penggunaan alat bantu belajar mengajar yang sesuai dengan kemajuan ilmu pengetahuan dan teknologi pengajaran dan pembelajaran.

scholarly journals Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1579
Author(s):  
Dongqi Wang ◽  
Qinghua Meng ◽  
Dongming Chen ◽  
Hupo Zhang ◽  
Lisheng Xu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Channel Model ◽  
Expert Knowledge ◽  
Automatic Detection ◽  
Data Representation ◽  
Learning Technology ◽  
Arrhythmia Detection ◽  
Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

scholarly journals Modeling vegetation greenness and its climate sensitivity with deep‐learning technology

2021 ◽  
Author(s):  
Zhiting Chen ◽  
Hongyan Liu ◽  
Chongyang Xu ◽  
Xiuchen Wu ◽  
Boyi Liang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Climate Sensitivity ◽  
Learning Technology ◽  
Vegetation Greenness
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