scholarly journals Perancangan Sistem Deteksi Dini Lift Barang Berbasis Arduino di PT Dharma Electrindo Manufacturing

2019 ◽  
Vol 2 (1) ◽  
pp. 32
Author(s):  
Maharani Ongky Anggraini ◽  
Antonius Suhartomo
Keyword(s):  
Early Detection ◽  
Production Process ◽  
Detection System ◽  
Market Price ◽  
Automatic Guided Vehicle ◽  
Elevator Door

PT Dharma Electrindo Manufacturing is one of the compant in automotive manufacture. This company has two plants which are in Cikarang and Cirebon, with Cirebon focusing on the manufacturing side. PT Dharma Electrindo Manufacturing Cirebon branch has three floors for production. Thus it is necessary to have a freight elevator to ease production process on second and third floor with support from AGV (Automatic Guided Vehicle) with the market price of Rp 200.000.000 without humans directly controlling it. Several accidents could happen due to unsynchronized elevator door and AGV. In 2017, 15 accidents occurred on the elevator door on the second floor. Because of this, an early detection system has been implemented in the form of a crossbar on the door such that the AGV does not hit the acrelyte-made door which could cause the AGV to fall and caused the company loss about Rp 200.000.000 or the broken elevator door all through 2017 which caused Rp 202.250.000 loss. It is hoped that this crossbar implementation is developed using stabilizer.

scholarly journals Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3052
Author(s):  
Mas Ira Syafila Mohd Hilmi Tan ◽  
Mohd Faizal Jamlos ◽  
Ahmad Fairuz Omar ◽  
Fatimah Dzaharudin ◽  
Suramate Chalermwisutkul ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Early Detection ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Predictive Analytics ◽  
Detection System ◽  
Imaging Techniques ◽  
Ganoderma Boninense ◽  
Machine Learning Classification ◽  
Detection Techniques

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.

Research on the Key Technology of Polynaphthalene Sulphonate Preparation

2011 ◽  
Vol 217-218 ◽  
pp. 1361-1365
Author(s):  
Yun Li
Keyword(s):  
Production Process ◽  
Price Change ◽  
Market Price ◽  
Key Technology ◽  
Specific Research ◽  
Normal Production ◽  
Increasing Demand ◽  
A Current ◽  
Main Material ◽  
Overseas Market

Industrial naphthalene is the main material for the polynaphthalene sulphonate production, domestic industrial naphthalene supply is unable to meet increasing demand of the market, and has to be always relying on imports, the price is greatly impacted by overseas market price change, for recent years, industrial naphthalene price has been increasing all the time, therefore how to reduce the consumption of industrial naphthalene has be a current scientific and production subject. This project is based on the normal production process, given the key problems of low sulphonation level, poor polymerization of polycondensate and quick slump loss of polynaphthalene sulphonate during the polynaphthalene sulphonate production, provides some specific research and improvement and which proves to be satisfactory.

Early detection system using low-dose CT lung cancer screening and related SNP information

2021 ◽  
Author(s):  
Yuta Azuma ◽  
Yoshiki Kawata ◽  
Noboru Niki ◽  
Issei Imoto ◽  
Masahiko Kusumoto ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Early Detection ◽  
Low Dose ◽  
Detection System ◽  
Lung Cancer Screening ◽  
Low Dose Ct

scholarly journals An Investigation of Early Detection of Driver Drowsiness Using Ensemble Machine Learning Based on Hybrid Sensing

2020 ◽  
Vol 10 (8) ◽  
pp. 2890
Author(s):  
Jongseong Gwak ◽  
Akinari Hirao ◽  
Motoki Shino
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Driving Simulator ◽  
Detection System ◽  
Driving Performance ◽  
Machine Learning Algorithms ◽  
Drowsy Driving ◽  
Physiological Indicators ◽  
Driver Drowsiness ◽  
Behavioral Indices

Drowsy driving is one of the main causes of traffic accidents. To reduce such accidents, early detection of drowsy driving is needed. In previous studies, it was shown that driver drowsiness affected driving performance, behavioral indices, and physiological indices. The purpose of this study is to investigate the feasibility of classification of the alert states of drivers, particularly the slightly drowsy state, based on hybrid sensing of vehicle-based, behavioral, and physiological indicators with consideration for the implementation of these identifications into a detection system. First, we measured the drowsiness level, driving performance, physiological signals (from electroencephalogram and electrocardiogram results), and behavioral indices of a driver using a driving simulator and driver monitoring system. Next, driver alert and drowsy states were identified by machine learning algorithms, and a dataset was constructed from the extracted indices over a period of 10 s. Finally, ensemble algorithms were used for classification. The results showed that the ensemble algorithm can obtain 82.4% classification accuracy using hybrid methods to identify the alert and slightly drowsy states, and 95.4% accuracy classifying the alert and moderately drowsy states. Additionally, the results show that the random forest algorithm can obtain 78.7% accuracy when classifying the alert vs. slightly drowsy states if physiological indicators are excluded and can obtain 89.8% accuracy when classifying the alert vs. moderately drowsy states. These results represent the feasibility of highly accurate early detection of driver drowsiness and the feasibility of implementing a driver drowsiness detection system based on hybrid sensing using non-contact sensors.

scholarly journals Automated and High Accuracy Out-Of-Hospital Heart Diseases Early Detection System

2017 ◽  
Vol 249 ◽  
pp. S9-S10 ◽  
Author(s):  
A.F. Hussein ◽  
S.J. Hashim ◽  
A.F. Abdul Aziz ◽  
F.Z. Rokhani ◽  
W.A. Wan Adnan
Keyword(s):  
Early Detection ◽  
Detection System ◽  
Heart Diseases ◽  
High Accuracy
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