This paper proposes a relative attitude and distance estimation algorithm based on pairwise range measurements between vehicles as well as inertial measurement of each platform. The solution of Wahba＇s Problem is int...
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This paper proposes a relative attitude and distance estimation algorithm based on pairwise range measurements between vehicles as well as inertial measurement of each platform. The solution of Wahba＇s Problem is introduced to compute the relative attitude between multi-platforms with the sampled pairwise ranges, in which the relative distance estimation is derived and the estimation error distributions are analyzed. An extended Kalman filter is designed to fuse the estimated attitude and distance with the inertial measurement of each platform. The relative poses between platforms are determined without any external aided measurement. To show this novelty, a real testbed is constructed by our research lab. And the experiment results are positive.
At present, most fault diagnosis for grinding system is based on artificial judgments, which is inefficient, low accurate, high cost and easy to cause casualties. The traditional neural network has an unsatisfying per...
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ISBN:
(纸本)9781509046584
At present, most fault diagnosis for grinding system is based on artificial judgments, which is inefficient, low accurate, high cost and easy to cause casualties. The traditional neural network has an unsatisfying performance to predict on high dimensional dataset, and is hard to extract crucial features, which brings about terrible classification results. To solve the above problems, the paper present a deep learning based on autoencoder to realize the intelligent diagnosis for grinding system. The algorithm applies autoencoder to extract features from fault dataset, and transit the non-linearized features to Softmax classification to recognize the fault category. This paper compares autoencoder-based deep learning networks and the traditional BP neural networks in experiments, and it is concluded that the autoencoderbased deep learning outperforms BP networks in the unbalanced classification. The classification precision is up to 92.4% by using the proposed method.
To implement demand response in residential sector and facilitate the integration of renewable resources and plug-in electric vehicles in future smart grid, this paper proposes a framework of home energy management sy...
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ISBN:
(纸本)9781479987313
To implement demand response in residential sector and facilitate the integration of renewable resources and plug-in electric vehicles in future smart grid, this paper proposes a framework of home energy management system (HEMS) and a optimization algorithm for it based on improved artificial bee colony. The algorithm schedules the operations of schedulable home appliances according to electricity price, forecasted outdoor temperature and renewable power output, and user preferences to minimize user's electricity cost. The effectiveness of the algorithm is verified by simulations, and the electricity cost can be reduced by 47.76%.
At present, the fault diagnosis of grinding system is evaluated by human being, which causes low efficiency, low accuracy, high cost and casualties easily. The traditional method has the unsatisfied performance on the...
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Sulfur corrosion induced by active sulfur has been proved to significantly decrease the insulation performance of oil-immersed equipment. In order to protect the oil-paper insulation from sulfur corrosion, active sulf...
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Motor systems are critical equipment in modern *** make the equipment maintenance more efficient,this paper proposes an online and remote machine condition monitoring and fault diagnosis system for motor system based ...
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ISBN:
(纸本)9781479900763
Motor systems are critical equipment in modern *** make the equipment maintenance more efficient,this paper proposes an online and remote machine condition monitoring and fault diagnosis system for motor system based on WIA-PA(Wireless network for Industry Automation Process Automation).The system continuously monitors the status of the target motor systems using wireless vibration *** extracting the feature of vibration signals,the wireless vibration transmitters send feature data to host computer through WIA-PA wireless sensor *** data are processed further in host computer to make diagnosis for the motor system using the customized expert *** paper describes the structure of the system,vibration transmitter,WIA-PA wireless network and host computer system in *** proposed system was tested in field,and the results are presented.
At present, the fault diagnosis of grinding system is evaluated by human being, which causes low efficiency, low accuracy, high cost and casualties easily. The traditional method has the unsatisfied performance on the...
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ISBN:
(纸本)9781538604915
At present, the fault diagnosis of grinding system is evaluated by human being, which causes low efficiency, low accuracy, high cost and casualties easily. The traditional method has the unsatisfied performance on the classification ability of sample dataset with high dimension and temporal correlation. Aiming at the above problems, a RNN-LSTM based deep learning method is proposed in the paper, which realizes the intelligent fault diagnosis of grinding system. The dataset is batched for inputs of LSTM networks, and the temporal correlation of dataset is extracted, which is used to analyze the fault classification of feature vectors input of before and after time. We conduct the comparison of RNN-LSTM based networks and autoencoder based networks via simulation. It is concluded that the RNN-LSTM based method is obviously superior to the method with autoencoder for the dataset with high dimension and high temporal correlation, and the result of the error rate is less than 3%.
Residential sector is the biggest potential field of reducing peak demand through demand response (DR) in smart grid. Heating, ventilating, and air conditioning (HVAC) is the largest residential electricity user in ho...
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ISBN:
(纸本)9781479936519
Residential sector is the biggest potential field of reducing peak demand through demand response (DR) in smart grid. Heating, ventilating, and air conditioning (HVAC) is the largest residential electricity user in house. Therefore, controlling the operation of HVAC is an effective method to implement DR in residential sector. The algorithms proposed in literature are single objective optimization algorithms that only minimize the electricity cost and could not quantify the user's comfort level. To tackle this problem, this paper proposes a comfort level indicator, builds a multi-objective scheduling model, and presents a multi-objective optimal control algorithm for HVAC based on particle swarm optimization (PSO). The algorithm controls the operation of HVAC according to electricity price, outdoor temperature forecast, and user preferences to minimize the electricity cost and maximize the user comfort level simultaneously. The proposed algorithm is verified by simulations, and the results demonstrate that it can decrease the electricity cost significantly and maintain the user comfort level effectively.
The traditional 3D human motion capture methods require operators to wear tracking sensors on their body. However, these devices would bring operators much inconvenience. Currently, vision based motion capture systems...
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ISBN:
(纸本)9781509041039
The traditional 3D human motion capture methods require operators to wear tracking sensors on their body. However, these devices would bring operators much inconvenience. Currently, vision based motion capture systems provide us an alternative approach for motion capture technology. In this paper, based on human kinematics and function approximation technique (FAT), a novel method is presented for the trajectory control of Baxter robot. Each arm of the Baxter robot has seven degrees of freedom. The geometry vector approach is applied to capture human motion trajectory by using Microsoft Kinect sensor. A FAT controlsystem is employed to make the robot follow the trajectory of human motion. The UDP communication protocol is employed to send the reference human joint angle data to the robot. We carry out preliminary experiments on Baxter robot to verify the validity of the control approach and the results demonstrate that the method has achieved satisfactory performance.
This paper studies how to optimize the energy usage of home appliances in the demand response framework from the consumer's perspective. The loads of major home appliances are divided into three categories: fixed ...
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This paper studies how to optimize the energy usage of home appliances in the demand response framework from the consumer's perspective. The loads of major home appliances are divided into three categories: fixed loads, regulate-able loads, and deferrable loads. For efficient usage of the home appliances, an integrative optimization of the three category loads is needed. The paper investigates the relation of the integrative optimization and individual optimization of each category load. A regression-based learning strategy is employed to learn HAVC energy consumption model for development of more efficient DR policy. The study is conducted through an integrative computational experiment approach that combines a home energy simulator and MATlab together for demand response development and evaluation. The paper examines how the integrative demand response of the residential home appliances are affected by dynamic pricing tariffs, seasons, and weather. Case studies are conducted by considering home energy consumption, dynamic electricity pricing schemes, and demand response methods.
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