scholarly journals Subliminal Calibration for Machine Operation

10.5772/9243 ◽  
2010 ◽  
Author(s):  
Hiroshi Igarashi
Keyword(s):  
Machine Operation

Wet End Improvement Method by Correlation Analysis between Water Quality and Machine Operation by S.sensing

2016 ◽  
Vol 70 (10) ◽  
pp. 1044-1048
Author(s):  
Hiroki Katsura ◽  
Takashi Saigusa ◽  
Katsuhiko Hidaka ◽  
Kaname Harada ◽  
Shinichi Kurihara
Keyword(s):  
Water Quality ◽  
Correlation Analysis ◽  
Machine Operation ◽  
Improvement Method

Clean Paper Machine Operation by Proper Wet-end Chemistry

2008 ◽  
Vol 62 (8) ◽  
pp. 941-948
Author(s):  
Koichi Tadaki ◽  
Tomoko Asada ◽  
Hideaki Kawakami ◽  
Kazutaka Kasuga ◽  
Shigeru Kurose
Keyword(s):  
Paper Machine ◽  
Machine Operation

Eye Movements as a Means to Evaluate Operation Interface of Flat Knitting Machine

2014 ◽  
Vol 607 ◽  
pp. 664-668
Author(s):  
Zhi Hui Liu ◽  
Sheng Ze Wang ◽  
Qiong Shen ◽  
Jia Jun Feng
Keyword(s):  
Eye Movements ◽  
Eye Movement ◽  
Objective Evaluation ◽  
Layout Design ◽  
Eye Tracker ◽  
Heat Maps ◽  
Machine Operation

This study investigates the characteristics of eye movements by operating flat knitting machine. For the objective evaluation purpose of the flat knitting machine operation interface, we arrange participants finish operation tasks on the interface, then use eye tracker to analyze and evaluate the layout design. Through testing of the different layout designs, we get fixation sequences, the count of fixation, heat maps, and fixation length. The results showed that the layout design could significantly affect the eye-movement, especially the fixation sequences and the heat maps, the count of fixation and fixation length are always impacted by operation tasks. Overall, data obtained from eye movements can not only be used to evaluate the operation interface, but also significantly enhance the layout design of the flat knitting machine.

Influence of soil bio stimulators on the change in the quality of a soil tillage machine operation

2020 ◽  
Vol 24 (1) ◽  
pp. 22-26
Author(s):  
P. Findura ◽  
◽  
M. Prístavka ◽  
V. Hrdá ◽  
A. Szparaga ◽  
...  
Keyword(s):  
Soil Tillage ◽  
Machine Operation

Potenziale der Mehrmaschinenbedienung*/Potentials of multiple-machine operation

2016 ◽  
Vol 106 (04) ◽  
pp. 211-217
Author(s):  
M. Thurm ◽  
S. Horler ◽  
D. Oehme ◽  
A. Opitz ◽  
E. Prof. Müller
Keyword(s):  
Analytical Model ◽  
Production System ◽  
Analytical Approach ◽  
Software Tool ◽  
New Approach ◽  
Machine Operation ◽  
Resource Input

Die prozess- und kostenorientierte Auslegung der Mehrmaschinenbedienung soll mithilfe eines neuen analytischen Modellansatzes die Ressourcennutzung effizienter gestalten. Dabei steht die Integration von Mitarbeiterqualifikation und Maschinenpriorität im Fokus. Durch die später geplante Implementierung des neu entwickelten Ansatzes in das Softwaretool SmartPlanner der CAPPcore GmbH gelingt es, die Planung und Optimierung von Produktionssystemen zu verbessern.   A new approach for process and cost-related designing of multiplemachine operation is developed to optimize the resource input. Skills of employees and priority of machines have to be considered in the analytical model. The new analytical approach of multiple machine operation will be integrated in the software tool Smart Planner of the enterprise CAPPcore GmbH later on to improve the planning and optimization of the production system as a whole.

THE HEAP PARTS MOVEMENT ON THE SHAREBOARD SURFACE OF THE POTATO HARVESTING MACHINE

2021 ◽  
Vol 14(63) (1) ◽  
pp. 127-140
Author(s):  
Serhii HRUSHETSKYI ◽  
Vitaly YAROPUD ◽  
Ihor KUPCHUK ◽  
Ruslana SEMENYSHENA
Keyword(s):  
Mathematical Model ◽  
Experimental Studies ◽  
Separation Process ◽  
Kinematic Parameters ◽  
Quality Performance ◽  
Machine Operation ◽  
Degree Of Damage ◽  
Total Capacity ◽  
Comparison Of The Results

The article is devoted to the problem of the reduction of tubers mechanical damages while providing qualitative indicators of the potato heap separation process. Theoretical and experimental dependences of the influence of design and kinematic parameters of the machine operation on the quality performance are obtained. Within the field of experimental studies, a field installation was made to investigate the potato harvester as a whole on the efficiency of separation, the degree of damage, the magnitude of losses and the total capacity for aggregation. Comparison of the results of theoretical and experimental studies showed that the developed mathematical model of the process of separation of potato heap is adequate.

scholarly journals The Practical Examples of Paper Machine Operation Using “MOAS”

2003 ◽  
Vol 57 (1) ◽  
pp. 48-54,019
Author(s):  
Noboru Negishi ◽  
Youhei Shiokoshi
Keyword(s):  
Paper Machine ◽  
Machine Operation

scholarly journals Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5446
Author(s):  
Hyojung Ahn ◽  
Inchoon Yeo
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Short Term Memory ◽  
Detection System ◽  
Noisy Environments ◽  
Acoustic Data ◽  
Detection Techniques ◽  
Machine Operation ◽  
Detection Technology ◽  
Environment Noise

As the workforce shrinks, the demand for automatic, labor-saving, anomaly detection technology that can perform maintenance on advanced equipment such as vehicles has been increasing. In a vehicular environment, noise in the cabin, which directly affects users, is considered an important factor in lowering the emotional satisfaction of the driver and/or passengers in the vehicles. In this study, we provide an efficient method that can collect acoustic data, measured using a large number of microphones, in order to detect abnormal operations inside the machine via deep learning in a quick and highly accurate manner. Unlike most current approaches based on Long Short-Term Memory (LSTM) or autoencoders, we propose an anomaly detection (AD) algorithm that can overcome the limitations of noisy measurement and detection system anomalies via noise signals measured inside the mechanical system. These features are utilized to train a variety of anomaly detection models for demonstration in noisy environments with five different errors in machine operation, achieving an accuracy of approximately 90% or more.

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