ENERGY CONSUMPTION OF A WALKING MACHINE. MODEL ESTIMATIONS AND OPTIMIZATION

This article describes how with the continuous expansion on the volume of data produced by sensors in Cyber Physical Systems, the scale of the cloud storage system has become larger. This will lead to the problems of a high energy consumption rate and a low utilization becoming a serious issue. In order to enhance the effective energy consumption, reduce the invalid energy consumption, and supply more flexible QoS for users in CPS, this article proposes an automatic energy gear-shifting mechanism with flexible QoS constraints (QGLG). The QGLG predicts system load of the follow-up period through a support vector machine model. According to the current system load, the predicted load, and the flexible QoS, QGLG automatically up-shifts and down-shifts among nodes. Substantive results from the simulation experiments done on GridSim show that the QGLG can achieve energy consumption reduction while satisfying the user's flexible QoS requirements. Compared with a similar energy-reducing mechanism, QGLG has its obvious advantage when considering the requirements of user with energy saved notwithstanding.

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WALKING MOTION OPTIMIZATION OF THE WALKING MACHINE WITH DYNAMIC STABILITY

Vestnik of Kazan state agrarin university ◽

10.12737/2073-0462-2021-66-70 ◽

2021 ◽

Vol 16 (1) ◽

pp. 66-70

Author(s):

Yuriy Aleynikov Yuriy Georgievich

Keyword(s):

Energy Consumption ◽

Dynamic Change ◽

Ground Surface ◽

Electrical Energy ◽

False Alarms ◽

Time Interval ◽

Shock Loads ◽

Factors Affecting ◽

Walking Machine ◽

Rigid Contact

The movement of machines with a walking legs is accompanied by dynamic vibrations of its body and a large energy consumption for the reciprocating movements of the legs. The greatest influence on the smoothness of the course is exerted by the alternating accelerations of the moving masses and the rigid contact of the leg with the ground surface. To reduce the negative factors affecting the smoothness of movement and energy consumption, are proposed to optimize the trajectory of movement of the legs. The optimized trajectory of the legs movement made it possible to reduce the consumption of electrical energy by 12 ... 18% per cycle of movement compared to the trajectory lying in the same geometric plane. In order to reduce shock loads, the time interval between the triggering of the shock sensor and the load sensor when lowering the support to the surface was experimentally determined, which was about 100 ms at a lowering speed of 20 mm/s. Reducing the speed of the leg at the moment of triggering the shock sensor after contact with the surface and its subsequent smooth loading made it possible to reduce shock loads and reduce body vibrations caused by sharp impacts of the feet on the surface. Support acceleration on impact decreased from 6g to 1.5g. The triggering of the shock sensor located on the support foot when it touches hard and soft surfaces requires adjusting the sensor's sensitivity while the machine is moving. Optimization of the algorithm for filtering false alarms and dynamic change in sensitivity did not give a satisfactory result, therefore, it is necessary to equip the design with additional load sensors that react to contact with the surface when lowering the support

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PREDICTION OF GOVERNMENT-OWNED BUILDING ENERGY CONSUMPTION BASED ON AN RRELIEFF AND SUPPORT VECTOR MACHINE MODEL

Journal of Civil Engineering and Management ◽

10.3846/13923730.2014.893908 ◽

2015 ◽

Vol 21 (6) ◽

pp. 748-760 ◽

Cited By ~ 4

Author(s):

Hyojoo Son ◽

Changmin Kim ◽

Changwan Kim ◽

Youngcheol Kang

Keyword(s):

Support Vector Machine ◽

Energy Consumption ◽

Environmental Impact ◽

Building Energy ◽

Support Vector ◽

Government Agencies ◽

Design Phase ◽

Building Energy Consumption ◽

Machine Model ◽

Proposed Model

Accurate prediction of the energy consumption of government-owned buildings in the design phase is vital for government agencies, as it enables formulation of the early phases of development of such buildings with a view to reducing their environmental impact. The aim of this study was to identify the variables that are associated with energy consumption in government-owned buildings and to propose a predictive model based on those variables. The proposed approach selects relevant variables using the RReliefF variable selection algorithm. The support vector machine (SVM) method is used to develop a model of energy consumption based on the identified variables. The proposed approach was analyzed and validated on data for 175 government-owned buildings derived from the 2003 Commercial Building Energy Consumption Survey (CBECS) database. The experimental results revealed that the proposed model is able to predict the energy consumption of government-owned buildings in the design phase with a reasonable level of accuracy. The proposed model could be beneficial in guiding government agencies in developing early strategies and proactively reducing the environmental impact of a building, thereby achieving a high degree of sustainability of buildings constructed for government agencies.

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