Showing papers in "Recent Patents on Engineering in 2020"

LIF Neuron with Hypo-exponential Distributed Delay: Emergence of Unimodal, Bimodal, Multimodal ISI Distribution with Long Tail

Abstract: Background: Distributed Delay Framework (DDF) has suggested a mechanism to incorporate the delay factor in the evolution of the membrane potential of a neuron model in terms of distributed delay kernel functions. Incorporation of delay in neural networks provide comparatively more efficient output. Depending on the parameter of investigation, there exist a number of choices of delay kernel function for a neuron model. Objective: We investigate the Leaky integrate-and-fire (LIF) neuron model in DDF with hypoexponential delay kernel. LIF neuron with hypo-exponential distributed delay (LIFH) model is capable to regenerate almost all possible empirically observed spiking patterns. Methods: In this article, we perform the detailed analytical and simulation based study of the LIFH model. We compute the explicit expressions for the membrane potential and its first two moment viz. mean and variance, in analytical study. Temporal information processing functionality of the LIFH model is investigated during simulation based study. Results: We find that the LIFH model is capable to reproduce unimodal, bimodal and multimodal inter-spike- interval distributions which are qualitatively similar with the experimentally observed ISI distributions. Conclusion: We also notice the neurotransmitter imbalance situation, where a noisy neuron exhibits long tail behavior in aforementioned ISI distributions which can be characterized by power law behavior.

Optimal Sensor Placement Algorithm for Structural Damage Identification

Abstract: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. It has been shown that the proposed method is simple to implement and effective for structural damage identification.

Reliability Estimation for Hybrid System under Constant-stress Partially Accelerated Life Test with Progressively Hybrid Censoring

Abstract: Reliability analysis for the systems with masked data had been studied by many scholars. However, most researches focused on a system that is either series or parallel only, and the component in the system is mainly exponential or Weibull. In engineering practice, it is often seen that the structure of a system is a combination of series and parallel system, and other types of components are also used in the system. So it is important to study the reliability analysis of hybrid systems with modified Weibull components. For the hybrid system with masked data, the constant stress partial accelerated life test is performed under type-II progressive hybrid censoring. These data from life test are used to estimate unknown parameters and reliability index of system. The research results will not only provide theoretical basis and reference for system reliability assessment but also favor the patents on partial accelerated life test. Maximum likelihood estimates of unknown parameters are investigated with the numerical method. The approximate confidence intervals, and bootstrap confidence intervals for parameters are constructed by the asymptotic theory and the bootstrap method, respectively. Maximum likelihood estimations of unknown parameters and reliability index of system are derived. The approximate confidence intervals and bootstrap confidence intervals for unknown parameters are proposed. The performance of estimation of unknown parameters and reliability index are evaluated numerically through Monte Carlo method. The performance on maximum likelihood estimation method is effective and satisfying. For the confidence intervals of parameters, bootstrap method outperforms the approximate method.

Power Management for Plug-in Hybrid Electric Vehicle with Automated Mechanical Transmission using Multiple Dimensional Scaling Method

Abstract: The study of kerosene fuel for gasoline engines is of great significance to the supply, management, storage and transportation of military fuel, as well as its safety. Small aviation two-stroke kerosene engine fuel injection controller is the key technology of kerosene engines. It is very important to improve the performance of kerosene engine by controlling the air-fuel ratio accurately. The initial injection pulse spectrum was firstly obtained by numerical calculation in the absence of kerosene injection pulse spectrum, and then the injection controller was designed based on the initial injection pulse spectrum. Firstly, a numerical model of the whole engine was established by using BOOST software. The air mass flow data of the inlet was obtained through numerical calculation. The amount of initial engine fuel injection was calculated according to the requirements of air-fuel ratios in each working condition, from which an initial injection pulse spectrum was obtained. Then, based on Free scale 16-bit embedded micro-controller MC9S12DP512, a kerosene engine fuel injection controller was developed, together with the circuit was also designed. According to the initial fuel injection pulse spectrum, a two-dimensional interpolation algorithm was developed by using assembly language and C language mixed programming, and the anti-electromagnetic interference ability of the controller was further enhanced. Finally, the accuracy of the initial injection pulse spectrum and the performance and reliability of the injection controller of the kerosene engine were verified by the kerosene engine bench test. The experimental results show that the numerical model was accurate, and the development time of the injection controller was shortened by using the numerical model to calculate the initial injection pulse spectra. The developed controller was stable and reliable, which can meet the control requirement.

PNN and BP Neural Network Fault Diagnosis Research on Electronic Accelerator Pedal Detection

Abstract: The working state of electronic accelerator pedal directly affects the safety of vehicles and drivers. Effective fault detection and judgment for the working state of the accelerator pedal can prevent accidents. Aiming at different working conditions of electronic accelerator pedal, this paper used PNN and BP diagnosis model to detect the state of electronic accelerator pedal according to the principle and characteristics of PNN and BP neural network. The fault diagnosis test experiment of electronic accelerator pedal was carried out to get the data acquisition. After the patents for electronic accelerator pedals are queried and used, the first measured voltage, the upper limit of first voltage, the first voltage lower limit, the second measured voltage, the upper limit of second voltage and the second voltage lower limit are tested to build up the data samples. Then the PNN and BP fault diagnosis models of electronic accelerator pedal are established. Six fault samples are defined through the design of electronic accelerator pedal fault classifier and the fault diagnosis processes are executed to test. The fault diagnosis results were analyzed and the comparisons between the PNN and the BP research results show that BP neural network is an effective method for fault detection of electronic throttle pedal, which is obviously superior to PNN neural network based on the experiment data.

Analysis and Optimization of Sb2Te3 and Bi2Te3 Materials for Enhancing the Performance of Thermoelectric Energy Harvester for WSN Applications

Abstract: Thermoelectric (TE) materials are used to fabricate the thermoelectric generator (TEG). Thermoelectric Generator (TEG) is used to convert thermal energy into electrical energy and vice-versa. Bismuth-Telluride and Antimony Telluride (Bi/Sb)2Te3 alloys are popular in the research community due to its capability of electrical energy generation in the range of room temperature. The Phase Change Material (PCM) is a good source of thermal energy storage in thermal energy harvesting. We have reviewed patents having the information of thermal energy storage and tried to provide a better cost-effective solution in thermal energy harvesting using Phase Change Material (PCM) and material used in thermoelectric generator. Finding the most appropriate TE alloy for a particular application is a challenge in the research community. The objective of this paper is to conduct a study and analysis of performance parameter of (Bi/Sb)-Te based TE alloy along with the effect of Phase Change Material (PCM) on energy generation. An investigation over a wide range of temperature is performed. A Bi2Te3 based Commercial- of-the-shelf (COTS) Thermoelectric Generator (TEG) has been experimentally tested in a controlled temperature environment for the analysis of its efficiency. This is found that maximum efficiency of 2.12% is achieved at a temperature difference of 60°C. This investigation will be useful for the selection of material for thermal energy harvesting techniques and helps to provide an optimized framework for the research community to decide the (Bi1-xSbx)2Te3 mixed crystal alloy for their applications.

Technological Aids for Deaf and Mute in The Modern World

Abstract: People suffering from hearing and speaking disabilities have a few ways of communicating with other people. One of these is to communicate through the use of sign language. Developing a system for sign language recognition becomes essential for deaf as well as a mute person. The recognition system acts as a translator between a disabled and an able person. This eliminates the hindrances in exchange of ideas. Most of the existing systems are very poorly designed with limited support for the needs of their day to day facilities. The proposed system embedded with gesture recognition capability has been introduced here which extracts signs from a video sequence and displays them on screen. On the other hand, a speech to text as well as text to speech system is also introduced to further facilitate the grieved people. To get the best out of human computer relationship, the proposed solution consists of various cutting-edge technologies and Machine Learning based sign recognition models which have been trained by using Tensor Flow and Keras library. The proposed architecture works better than several gesture recognition techniques like background elimination and conversion to HSV because of sharply defined image provided to the model for classification. The results of testing indicate reliable recognition systems with high accuracy that includes most of the essential and necessary features for any deaf and dumb person in his/her day to day tasks. It’s the need of current technological advances to develop reliable solutions which can be deployed to assist deaf and dumb people to adjust to normal life. Instead of focusing on a standalone technology, a plethora of them have been introduced in this proposed work. Proposed Sign Recognition System is based on feature extraction and classification. The trained model helps in identification of different gestures.

Route Aggregation Approach - An Efficacious Technique for Energy Enrichment

Abstract: Recent years have accentuated a remarkable rise in the popularity in the field of mobile computing owing to the size reduction of the computing devices and it is also contributed by the exceptional increase in the processing power availability of mobile laptops rendering various computer applications to reach the ever-rising population segment. This increase in the number of users poses more challenges in the field of networking. The various most specific constraints that exist in the wireless mobile infrastructure-less networking environment having compact batteries as the storehouses of power are power consumption and energy enrichment. The application of Route Aggregation in MANET thrives to improve the energy efficiency in MANET by aggregation of routes and therefore achieving a significant saving in the scarce resources. The methodology takes into account a Hybrid protocol, Zone Routing Protocol with aggregated routes and selection of a Head in the aggregation Zone. The Head now takes the responsibility of the routes and the data delivery along the routes within the aggregation Zone. The formation of such Heads and Aggregation Zones reduces the energy dissipated at each node while route discovery phase as the routes are readily available at the Head. The procedure thus proves effective in saving energy as well as bandwidth. Aggregation has so far has been able to curb the problems caused in order to improve the scalability of the network. This paper studies and analyses various aggregation techniques applied in Mobile Ad-hoc Network (MANET) points out the improvements in various QOS parameters resulted due to the application of aggregation on routes, data and address in the field of MANETs. The results of graphs validate the proposed approach and specify improvement of energy consumption which is the need of hour. The results also justify the novel approach by varying various parameters like number of nodes, pause time and simulation time showing improvement in average energy consumption for the novel RA-ZRP approach. The random cluster head selection based routing approach also referred as route aggregation approach is used for energy enhancement which is depicted in section above. NS2 simulated results presented in this paper validate the proposed approach. The ZRP-RA based random cluster selection energy enrichment approach helps in reducing the various control and routing overheads involved in the functioning of ZRP. It also reduces the number of route requests to be sent and thus superfluous route requests can be avoided and thus saving in bandwidth is achieved also the reduction in the routing and control overhead reduces the probability of collisions and thus the data is effectively forwarded to the destination. This also helps improve latency as the queuing up of data packets is lessened owing to the less overhead.

Rapid Calibration Method for 3D Laser Scanner

Abstract: The 3D laser scanner is a non-contact active-sensing system, which has a number of applications. Many patents have been filed on the technologies for calibrating 3D laser scanner. A precise calibration method is important for measuring the accuracy of the 3D laser scanner. The system model contains three categories of parameters to be calibrated which include the camera intrinsic parameters, distortion coefficients and the light plane parameters. Typically, the calibration process is completed in two steps. Based on Zhang’s method, the calibration of the camera intrinsic parameters and distortion coefficients can be performed. Then, 3D feature points on the light plane should precisely be formed and extracted. Finally, the points are used to calculate the light plane parameters. In this paper, a rapid calibration method is presented. Without any high precision auxiliary device, only one coplanar reference target is used. By using a group of captured images of the coplanar reference target placed in the field of view arbitrarily, calibration can be performed in one step. Based on the constraint from the planes formed by the target in different directions and the camera imaging model, a large amount of 3D points on the light plane can easily be obtained. The light plane equation in the camera coordinates system can be gathered by executing plane fitting to the 3D points. During the experimental process, the developed 3D laser scanner was calibrated by the proposed method. Then, the measuring accuracy of the system was verified with known distance in vertical direction of 1mm with sequential shifting motion generated by precision translation stage. The average value of the measured distances was found to be 1.010mm. The standard deviation was 0.008mm. Experimental results prove that the proposed calibration method is simple and reliable.

Design Patent: A Unique Tool for Design Protection

Abstract: Design patent is deemed as a superb competitive tool in the design industry. The author of this study delineates a co-opetition type of relationship of design patent with patent, trademark, and copyright to assist designers to construct a correct core concept of design patent quickly. The investigator proposed five key judgment factors based on infringement identification, and they are (1) literal infringement, (2) doctrine of equivalence, (3) points of novelty test, (4) prosecution history estoppel, and (5) prior art limitation. They can help designers clearly identify the legal right and scope of their design. Lastly, the author proposed three new competitive tools for a design patent, and they are (1) partial design patent, (2) computer-generated icons and graphic user interface, and (3) derivative design patent. Using these three new design patent tools will enable designers to effectively expand their design patent rights and maximize design benefits for their companies.

A Multi-parameter Joint Warning Mechanism for Physical Condition Monitoring System in Physical Education

Abstract: In recent years, sudden deaths of primary and secondary school students caused by sports activities have drawn great attention in education and medical circles. It is necessary for schools to monitor the physical condition of the students in order to reasonably set the duration of their physical activity. At present, the physical condition monitoring instruments used in various hospitals are expensive, bulky, and difficult to operate, and the detection process is complicated. Therefore, existing approaches cannot meet the needs of physical education teachers on campus for detecting the physical condition of students. This study designs a portable human-physiological-state monitoring and analysis system. Real-time communication between a wearable measurement device and a monitoring device can be ensured by real-time detection of the environment and power control of the transmitted signal. From a theoretical point of view, the larger the number of segments M, the more significantly the reduction of false alarm probability. The simulation results also show this fact. Compared with the conventional early warning mechanism, the probability of a false alarm for the proposed system is lower, and the greater the number of segments, the faster its reaction speed. The portable monitoring system of student physical condition for use in physical education of primary and middle school students proposed in this paper ensures real-time monitoring of the members within the system in an open environment, and further proposes an early warning mechanism for combining multiple vital sign parameters. In addition, the proposed system functions faster; the average early warning time required is only one-quarter of that of the conventional system.

Active Tip Fins for Box Wing Aircraft

Abstract: Induced drag accounts for significant percentage of cruise and total aircraft drag. In agreement with Prandtl’s theorem, the ideal arrangement for minimum induced drag is a closed biplane design. Past studies have implemented fixed tip fins for closed biplane design, with the reduction of induced drag associated with fixed tip fin found to be less than optimal. A review of patents related to the box-wing aircraft was carried out. In an attempt to further reduce the induced drag for box wing aircraft, this study proposed the implementation of Active Tip Fins (ATF) for aircraft design. Athena Vortex Lattice (AVL) software was used to simulate the induced drag associated with AFT in comparison with that of a fixed tip fin. From the result, the ATF design shows a superior induced drag reduction. ATF design is a novel concept that has the potential of improving box wing aircraft performance.

Design of Insertion Mechanism with Friction Wheels for Cannula Flexible Needle Based on TRIZ

Abstract: Flexible needle insertion is one of the minimally invasive surgeries, which takes advantage of the lateral force acted on the bevel tip to make the needle shaft bend when inserted into the tissue. The bending makes the needle avoid the obstacles (like bones, veins, nerves, etc.) in order to reach the target. However, the traditional flexible needle neither can change its curvature of the path, nor can realize a precise control because of the torsional friction between the needle and the tissue. Hence, a cannula flexible needle was proposed to overcome the drawbacks, which consists of a cannula and a stylet. Also, there is a need of an insertion mechanism for the cannula flexible needle in robot-assisted surgery. The aim of this paper is to innovatively design an insertion mechanism with friction wheels for the cannula flexible needle, which is used as an end-effector in robot-assisted surgery system. The mechanism is supposed to realize the coordinated driving of the cannula and the stylet in order to achieve variable curvatures of paths. Making references of the patents and research papers on needle insertion mechanisms, and based on the requirement of degree of freedom for the cannula flexible needle insertion, the insertion mechanism for the cannula flexible needle is designed by using the TRIZ theory. The conflicts matrix analysis, the invention principles and the substance-field analysis are used to innovatively design the driving scheme and the synchronous motion mechanism. In this paper, the concrete structure design of the insertion mechanism for the cannula flexible needle is achieved, which is compact and simple. The friction coefficient between the needle and the wheels, and the pretightening force between both wheels are calculated through the data from experiments. The insertion accuracy of friction wheel mechanism is tested and analyzed through experimentation. The results show that the maximum placement error of the needle executed by the insertion mechanism is less than 0.5 mm, which meets the demands of surgical operations.

Advanced Planning and Scheduling Based on the Constraint Theory and Improved Tabu Search Algorithm

Abstract: Planning and scheduling of manufacturing enterprises are required to be rapid and accurate, which make is easy for the traditional enterprise resource planning system to meet the needs of enterprises. This paper introduces the theory of constraints and explains the control methods for production planning and scheduling. The main characteristics of the advanced planning and scheduling system are analyzed. Then, the modeling method based on the theory of constraints is used to study the scheduling path. An example is given to validate the scheduling model and realize the optimization of production tasks on the bottleneck resources. The feasibility of the advanced planning and scheduling methods based on the theory of constraints is proved.

A Human Memory Process Modeling

Abstract: The cognitive models based agents proposed in the existing patents are not able to create knowledge by themselves. They also did not have the inference mechanism to take decisions and perform planning in novel situations. This patent proposes a method to mimic the human memory process for decision making. The proposed model simulates the functionality of episodic, semantic and procedural memory along with their interaction system. The sensory information activates the activity nodes which is a binding of concept and the sensory values. These activated activity nodes are captured by the episodic memory in the form of an event node. Each activity node has some participation strength in each event depending upon its involvement among other events. Recalling of events and frequent usage of some coactive activity nodes constitute the semantic knowledge in the form of associations between the activity nodes. The model also learns the actions in context to the activity nodes by using reinforcement learning. The proposed model uses an energy-based inference mechanism for planning and decision making. The proposed model is validated by deploying it in a virtual war game agent and analysing the results. The obtained results show that the proposed model is significantly associated with all the biological findings and theories related to memories. The implementation of this model allows humanoid and game agents to take decisions and perform planning in novel situations.

Wing Thickness Optimization for Box Wing Aircraft

Abstract: In the interest of improving aircraft performance, studies have highlighted the benefits of Box wing configurations over conventional cantilever aircraft configuration. Generally, the greater an aircraft's average thickness to chord ratio (τ), the lower the structural weight as well as volumetric capacity for fuel. On the other hand, the lower the , the greater the drag reduction. A review of patents related to the Box-wing aircraft was carried out. While methodologies for optimizing wing thickness of conventional aircrafts have been studied extensively, limited research work exist on the methodology for optimizing the wing thickness to chord ratio of the Box wing aircraft configurations. To address this gap, in this work, a two stage optimization methodology based on gradient search algorithm and regression analysis was implemented for the optimization of Box wing aircrafts wing thickness to chord ratio. The first stage involved optimizing the All Up Mass (AUM), Direct Operating Cost (DOC) and Zero Lift Drag Coefficients (CDO), with respect to the aft and fore sweep angle for some selected τ values. At the second stage, a suitability function (γ) was optimized with respect to the aft and fore sweep angle for some selected τ values. A comparative study was further carried out using the proposed methodology on similar size cantilever wing aircraft. From the result, an optimal τ value was reached. Also the τ value for the cantilever aircraft found based on the proposed methodology was similar to the true τ value of the adopted aircraft, thereby validating the methodology. Based on the optimal τ value reached from this work, the Box wing aircraft are suitable for thin airfoils.

Recent Patents on Nanoporous Filtration Membranes with Complex Pores

Abstract: The challenges to nanoporous filtration membranes are small fluxes and low membrane mechanical strengths. To introduce newly invented nanoporous filtration membranes with complex pores, improved fluxes and mechanical strengths as registered in patents. The analytical results are presented for the addressed membranes. The geometrical parameter values of the addressed membranes can be optimized for the highest fluxes. The overall performances of nanoporous filtration membranes with complex cylindrical or/and conical pores can be significantly better than that of the conventional nanoporous filtration membranes with single cylindrical or conical pores.

Design & Real-time Implementation of MIDO Converter Based PV Water Pumping System with BES

Abstract: Despite so many developments, most of the farmers in the rural areas are still dependent on rainwater, rivers or water wells, for irrigation, drinking water etc. The main reason behind such dependency is non-connectivity with the National grid and thus unavailability of electricity. To extract the maximum power from solar photovoltaic (SPV) based system, implementation of Maximum Power Point Tracking (MPPT) is mandatory. PV power is intermittent in nature. Variation in the irradiation level due to partial shading or mismatching phenomena leads to the development of modular DC-DC converters. A stand-alone Multi-Input Dual-Output (MIDO) DC-DC converter based SPV system, is installed at a farm; surrounded with plants for water pumping with stable flow (not pulsating) along with battery energy storage (BES) for lighting. The proposed work has two main objectives; first to maximize the available PV power under shadowing and mismatching condition in case of series/ parallel connected PV modules and second is to improve the utilization of available PV energy with dual loads connected to it. Implementation of proposed MIDO converter along with BES addresses these objectives. First, MIDO controller ensures the MPPT operation of the SPV system to extract maximum power even under partial shading condition and second, controls the power supplied to the motor-pump system and BES. The proposed system is simulated in MATLAB/ SIMULINK environment. Real-time experimental readings under natural sun irradiance through hardware set-up are also taken under dynamic field conditions to validate the performance. The inherent advantage of individual MPPT of each PV source in MIDO configuration, under varying shadow patterns due to surrounding plants and trees is added to common DC bus and therefore provides a better impact on PV power extraction as compared to conventional PV based water pumping system. Multi-outputs at different supply voltages is another flag of MIDO system. Both these aspects are implemented and working successfully at 92.75% efficiency.

A Hybrid Data Reduction and Knowledge Extraction Algorithm for Quality Prediction

Abstract: With the explosive growth of the manufacturing data, the manufacturing enterprises paid more and more attention to dealing with the manufacturing big data. The manufacturing big data also can be summarized as \"5Vs”, volume, variety, velocity, veracity and value. Recently, the researchers are focused on proposing better knowledge discovery algorithms to handling the manufacturing big data. The high dimensional data can be reduced from two directions. The one was the dimension reduction. It makes the data set simple and overcome the problem of curse dimensionality. This method reduced the data set form the data width. We proposed a hybrid data reduction and knowledge extraction algorithm (HDRKE) for quality prediction. There are 5 steps in the algorithm: Step 1: Data preprocessing; Step 2: Dimension reduction; Step 3: Extract SVs by SVM; Step 4: Extract rules from the subset; Step 5: Prediction by the rules extracted in step 3. The presented HDRKE method reduced the data scales from the data dimensions and the data attributions. Then, the prediction method was used on the subset of reduced data. At last, the HDRKE method was applied to a enterprise sample, the validation of the method can be validated on the enterprise sample. Quality prediction and control was an important procedure in manufacturing. The HDRKE algorithm was a novel method based on the attribution reduction and dimensionality reduce. The data set simplified from double direction made the data set easily to calculate. The HDRKE method also proposed a new thought of decision rules extracting on the low-embeddings. The HDRKE method also applied to a manufacturing instance and proved its validity.