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57 results about "Dual model" patented technology

The "Dual Sector Model" is a theory of development in which surplus labor from traditional agricultural sector is transferred to the modern industrial sector whose growth over time absorbs the surplus labour, promotes industrialization and stimulates sustained development.

Mesoporous X-type molecular sieve, adsorbent based on molecular sieve, and preparation and application thereof

The invention relates to a preparation method of a mesoporous X-type molecular sieve and a method for preparing an adsorbent by using the molecular sieve as an active component. The prepared adsorbent based on the mesoporous X-type molecular sieve is used as a paraxylene adsorptive separation adsorbent. The method for preparing the mesoporous X-type molecular sieve comprises the following steps: by using water glass as a silicon source and aluminum hydroxide as an aluminum source, adding a template, and carrying out hydrothermal synthesis to obtain the mesoporous X-type molecular sieve. The mesoporous molecular sieve and kaolin are proportionally molded to obtain 0.3-0.8mm granules, and the granules are subjected to barium ion or (and) potassium ion exchange until the exchange degree is greater than 99%, thereby obtaining the adsorbent which has excellent adsorptive separation capacity for paraxylene in C8 aromatic hydrocarbons. Compared with the prior art, the active component mesoporous X-type molecular sieve of the adsorbent, which is prepared by using the template, has the crystal form structure of the X-type molecular sieve and the pore distribution of the mesoporous and microporous dual models; the mesoporous pore size distribution is 2nm or so; and thus, the problem of overlow mass transfer rate in the adsorbent can be solved.
Owner:SHANGHAI LVQIANG NEW MATERIALS CO LTD +1

Indoor position service-oriented navigation network construction method

The invention provides an indoor position service-oriented navigation network construction method, which comprises the following steps of S1, importing a BIM building model in a target building IFC format, and obtaining an original model capable of being applied to indoor position service through three-dimensional visualization; S2, extracting semantic information in IFC through semantic filtering, and constructing an ontology model in a formalized mode; S3, in the form of a dual graph, converting the topological relation in the original model into a graph model; S4, extracting geometric information of structural components such as rooms, columns, walls and stairs and indoor facilities such as furniture in the BIM model, constructing constraint boundaries, performing spatial subdivision through a limited Delaunay triangulation refinement algorithm, and constructing a geometric network model; and S5, integrating the ontology model, the graph model and the geometric model data to form anavigation network for indoor position service. Based on BIM model data, a dual model is established by extracting semantic information, geometric information and a topological relation, and a construction method considering a real indoor environment navigation geometric network is explored.
Owner:NANJING FORESTRY UNIV

Auto-fluorescence tomography re-establishing method based on multiplier method

ActiveCN102988026AImprove robustnessAccurate and reliable light source distribution informationDiagnostic recording/measuringSensorsSteep descentDescent direction
The invention discloses an auto-fluorescence tomography re-establishing method based on a multiplier method. The auto-fluorescence tomography re-establishing method based on the multiplier method comprises the following steps of: carrying out discretization by using a finite element method diffusion equation, and establishing an optimization problem model without constraint conditions based on a penalty term of an L1 norm; obtaining a dual model of the optimization problem model without constraint conditions; establishing an augmentation lagrange function of the dual model; simplifying the maximum function of the augmentation lagrange function; solving the maximum value of the augmentation lagrange function by using a truncated-Newton algorithm; upgrading the target vector by using the gradient of the augmentation lagrange function as the steepest descent direction of a target vector; upgrading a penalty vector; and calculating an objective function value J(w), calculating k=k+1 if the ratio of the norm of J(w)k-J(w)(k-1) to the norm of Pai m being not smaller than t0l is real, and jumping to the step S4, otherwise, ending the calculation, wherein t0l is the convergence efficiency threshold value of the target function. The auto-fluorescence tomography re-establishing method provided by the invention can quickly obtain accurate and reliable light source distribution information within a large image-forming region, so that other parameters except from the regularization parameter can realize self-adaptive adjustment for improving the image-forming robustness.
Owner:INST OF AUTOMATION CHINESE ACAD OF SCI

Dual-model image decision fusion tracking method based on mutual updating of models

The invention discloses a dual-model image decision fusion tracking method based on mutual updating of models. The dual-model image decision fusion tracking method based on the mutual updating of the models comprises the steps that according to the characteristics of infrared images and visible images, firstly characteristic description vectors of the infrared images and the characteristic description vectors of the visible images are extracted, so that complementary information can be provided by the characteristic description vectors, and description of the amount of the information of the images can be increased; then, an infrared classifier model and a visible classifier model are established respectively by adopting a GentleAdaboost learning algorithm, and a tracking problem is converted into two classification problems of a target and a background; cooperative training is carried out in a semi-supervised learning frame, mutual model updating is carried out at the same time, and thus the problem of accumulation of model errors is solved effectively; final likelihood images are obtained by using training results and respective confidence coefficients of the training results for carrying out decision fusion, and the position of the target is located in the final likelihood images through a mean value drifting algorithm. The dual-model image decision fusion tracking method based on the mutual updating of the models can effectively solve the problem of tracking missing caused by model error accumulation and the limitation of single-model images in describing the information of the target, and improves tracking robustness.
Owner:HANGZHOU DIANZI UNIV

Dual-model dual-standby Big Dipper (GPS) positioning electronic lock system

The invention relates to a dual-model dual-standby satellite positioning electronic lock system and an oil tank truck. The system comprises a monitoring center, a vehicle-mounted terminal host and an electronic lead sealing control unit. When the oil tank truck arrives at the destination, a radio frequency read-write module reads an unsealing request of an unsealing card; the vehicle-mounted terminal host uploads the present geographical coordinates through a GPRS/CDMA communication module; and the monitoring center judges that whether the oil tank truck is at the preset geographical coordinates or not, and if yes, sends an unsealing command, that is, the vehicle-mounted terminal host controls an oil outlet opening and closing actuator to open an oil outlet valve after acquiring the unsealing command through the GPRS/CDMA communication module. According to the system, the oil tank truck is effectively monitored in the transport process by the monitoring center, the vehicle-mounted terminal host and the electronic lead sealing control unit, the phenomena of oil stealing and the like are effectively prevented, and the dual-model dual-standby satellite positioning electronic lock system works together with a vehicle stability system and integrates real-time monitoring and security.
Owner:江苏千里马科技有限公司

System for supporting mobile terminal to realize dual model switching and mobile terminal using same

InactiveCN104349433ARealize the purpose of power savingPower managementHigh level techniquesElectricityDual mode
The invention discloses a system for supporting a mobile terminal to realize dual mode switching. The system comprises an application processor and a baseband processor which are connected with each other, a first mode peripheral group connected with the application processor and a second mode peripheral group connected with the baseband processor; when the mobile terminal is in a first mode, the application processor operates, the baseband processor operates synergistically, the first mode peripheral group is powered on and the second mode peripheral group is powered off; when the mobile terminal is in a second mode, the application processor is dormant, the baseband processor operates independently, the first mode peripheral group is powered off, and the second mode peripheral group is powered on. By virtue of applying the system, a user can switch the first mode and the second mode; when the electric quantity of the mobile terminal is insufficient and basic functions such as communication need to be ensured, the user can switch to the second mode so as to realize the purpose of saving power; when needing to recover the first mode, the user switches to the first mode. The invention also discloses a mobile terminal comprising the system.
Owner:BEIJING SAMSUNG TELECOM R&D CENT +1

Sintering mixed water adding control method based on dual-model collaborative prediction

The invention relates to the technical field of sintering mixed water adding control, and provides a sintering mixed water adding control method based on dual-model collaborative prediction. The method comprises: firstly, collecting historical data of the sintering, mixing and water adding process to form a historical data set; secondly, preprocessing the historical data set; constructing and training a water adding amount regression prediction model based on the convolutional neural network by taking the blanking amount of each raw material as input and the water adding amount as output; constructing and training a moisture content classification prediction model based on a convolutional neural network by taking the blanking amount and the water adding amount of each raw material as inputand the moisture content category corresponding to the moisture content of the mixture as output; and finally, controlling the water adding amount based on dual-model collaborative prediction: predicting the water adding amount and the moisture content category in real time, and adjusting the next water adding amount according to the predicted value of the moisture content category. According tothe invention, the accuracy of water addition prediction and control can be improved, the moisture content of the mixture is stabilized in the optimal range, and the prediction control efficiency is high.
Owner:东北大学秦皇岛分校

Complex visual image reconstruction method based on depth encoding and decoding dual model

ActiveCN108573512AHigh image accuracyReduce noiseImage codingDecoding methodsVoxel
The invention discloses a complex visual image reconstruction method based on a depth encoding and decoding dual model and belongs to the visual scene reconstruction technology field in a biomedical image brain decoding. The method is characterized by firstly, collecting and watching functional magnetic resonance signals under a lot of natural images; and then establishing four network models: 1,a coding model, 2, a decoding model, 3, a natural image discrimination model and 4, a visual area response discrimination model, wherein in the coding model, a convolutional neural network is used tocode the natural images into the voxel signals of a visual area; in the decoding model, the convolutional neural network and a deconvolution neural network are used to decode the voxel signals of thevisual area into the natural images; in the natural image discrimination model, true images and false images are discriminated; and in the a visual area response discrimination model, true signals andfalse signals are discriminated. Through training the four designed models, visual scene images can be recovered from a brain signal. In the invention, for the first time, the problem of direct conversion between a natural scene and the brain signal is solved, and the practical application of a brain-computer interface scene can be realized.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
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