Minimal Sensor Research Articles

Prediction of dynamic and structural responses of submerged floating tunnel using artificial neural network and minimum sensors

This paper presents a machine learning method to predict the dynamic and structural behaviors of the submerged floating tunnel (SFT) based on an artificial neural network (ANN) and numerical sensors. The training and testing data are generated by the tunnel-mooring coupled time-domain hydro-elastic simulations under various random wave excitations. Then, ANN is constructed with an input layer, hidden layers, and an output layer. The input layer consists of the numerical angle and acceleration signals while the output is the tunnel's estimated displacements, bending moments, and mooring tensions. The number of hidden layers, neurons in each layer, and epochs for stable performance are selected based on the parametric study. For high prediction accuracy, Rectified Linear Unit (ReLU) and Root Mean Square Propagation (RMSProp) are adopted as activation functions and optimizers. In addition, a cost-effective sensor combination at an optimal location is investigated to achieve good performance while using a minimal number of sensors. The optimized sensor combination with one angle sensor and one accelerometer successfully predicts the dynamic and structural responses based on not only R-squared and root-mean-square errors but also representative time histories, spectra, and prediction accuracy plots.

A Novel Approach for Deploying Minimum Sensors in Smart Buildings

Buildings, viewed as cyber-physical systems, become smart by deploying Building Management Systems (BMS). They should be aware about the state and environment of the building. This is achieved by developing a sensing system that senses different interesting factors of the building, called as “facets of sensing.” Depending on the application, different facets need to be sensed at various locations. Existing approaches for sensing these facets consist of deploying sensors at all the places so they can be sensed directly. But installing numerous sensors often aggravate the issues of user inconvenience, cost of installation and maintenance, and generation of e-waste. This article proposes how intelligently using the existing information can help to estimate the facets in cyber-physical systems like buildings, thereby reducing the sensors to be deployed. In this article, an optimization framework has been developed, which optimally deploys sensors in a building such that it satisfies BMS requirements with minimum number of sensors. The proposed solution is applied to real-world scenarios with cyber-physical systems. The results indicate that the proposed optimization framework is able to reduce the number of sensors by 59% and 49% when compared to the baseline and heuristic approach, respectively.

Gait-based Human Identification through Minimum Gait-phases and Sensors.

The incredible pace at which the world's elderly population is growing will put severe burdens on current healthcare systems and resources. To alleviate this concern the health care systems must rely on the transformation of eldercare and old homes to use Ambient Assisted Living (AAL). Human identification is one of the most common and critical tasks for condition monitoring, human-machine interaction, and providing assistive services in such environments. Recently, human gait has gained new attention as a biometric for identification to achieve contactless identification from a distance robust to physical appearances. However, an important aspect of gait identification through wearables and image-based systems alike is accurate identification when limited information is available for example, when only a fraction of the whole gait cycle or only a part of the subject's body is visible. In this paper, we present a gait identification technique based on temporal and descriptive statistic parameters of different gait phases as the features and we investigate the performance of using only single gait phases for the identification task using a minimum number of sensors. Gait data were collected from 60 individuals through pelvis and foot sensors. Six different machine learning algorithms were used for identification. It was shown that it is possible to achieve high accuracy of over 95.5% by monitoring a single phase of the whole gait cycle through only a single sensor. It was also shown that the proposed methodology could be used to achieve 100% identification accuracy when the whole gait cycle was monitored through pelvis and foot sensors combined. The ANN was found to be more robust to less number of data features compared to SVM and was concluded as the best machine algorithm for the purpose.

Dual scaled approach SPR-based PCF RI sensor with ultra-low loss

We demonstrate an ultra-low loss photonic crystal fiber (PCF) sensor based on surface plasmon resonance (SPR)in this paper. In this refractive index (RI) sensor, we explored hexagonal-arrangement of airholes and employed only two different sizes of it. The formation of airholes makes the confinement loss (CL) surprisingly low. The maximum CL is as low as 10.71 and 28.58 dB/cm for x and y-pol modes, respectively within a range of refractive indices 1.33-1.40. The maximum gained amplitude sensitivity is -1212 RIU−1 and -2430 RIU−1, and the maximum figure of merit is as high as 583 and 467 respectively for x and y-polarization (pol) modes respectively. In addition to that, we got a maximum wavelength sensitivity, Sw of 14,000nm/RIU for both x and y-pol modes with a minimum sensor resolution of 7.143x10−6. Gold is preferred over other materials as the plasmonic material for its inert behaviour and higher chemical stability. The analysis was carried out using the finite element method (FEM). This sensor, with its elegant configuration, fabrication feasibility, ultra-low loss, stands out to be an effective and eminent prospect in the current burgeoning SPR sensor realm and also prompts further creative exploration in its hexagonal lattice arrangements.

Maximizing spectral sensitivity of plasmonic photonic crystal fiber sensor

We propose a novel design for a plasmonic photonic crystal fiber (PCF) sensor. Our design includes a metal film incorporated within the PCF structure. Compared to previously discussed sensing configurations, the metal surface does not come in direct contact with the sample being tested in our design. The metal film serves to provide absorption due to the excitation of surface plasmon-polaritons (SPP). This absorption is enhanced when a PCF-guided mode resonantly couples to the SPP, or in other words when phase matching between the mode and the SPP is satisfied. We consider a configuration where a hollow PCF core is filled with a liquid sample, while the metal surfaces are kept tens of microns away within the PCF cladding. Our sensor detects small changes in the sample's refractive index, over a broad wavelength range, by taking advantage of the refractive index sensitivity of the resonance/phase-matching condition. We model the sensor and characterize its performance by using the COMSOL environment based on the finite element method. We evaluate and compare three elements: gold, silver, and copper, in order to increase the absorption at particular wavelengths. We also vary the thickness of the metal layer and optimize it to enhance the sensitivity. The results show a performance that exceeds a minimal sensor resolution over an ultra-broad spectral range while maintaining a reasonable amplitude sensitivity.

Rollover Index for Rollover Mitigation Function of Intelligent Commercial Vehicle’s Electronic Stability Control

This paper describes a rollover index for detection or prediction of impending rollover in different driving situations using minimum sensor signals which can be easily obtained from an electronic stability control (ESC) system. The estimated lateral load transfer ratio (LTR) was used as a rollover index with only limited information such as the roll state of the vehicle and some constant parameters. A commercial vehicle has parameter uncertainties because of its load variation. This is likely to affect the driving performance and the estimation of the dynamic state of the vehicle. The main purpose of this paper is to determine the rollover index based on reliable measurements and the parameters of the vehicle. For this purpose, a simplified lateral and vertical vehicle dynamic model was used with some assumptions. The index is appropriate for various situations although the vehicle parameters may change. As part of the index, the road bank angle was investigated in this study, using limited information. Since the vehicle roll dynamics are affected by the road bank angle, the road bank angle should be incorporated, although previous studies ignore this factor in order to simplify the problem. Because it increases or reduces the chances of rollover, consideration of the road bank angle is indispensable in the rollover detection and mitigation function of the ESC system. The performance of the proposed algorithm was investigated via computer simulation studies. The simulation studies showed that the proposed estimation method of the LTR and road bank angle with limited sensor information followed the actual LTR value, reducing the parameter uncertainties. The simulation model was constructed based on a heavy bus (12 tons).

Development of an Automatic Lawnmower with Real-Time Computer Vision for Obstacle Avoidance

This work develops an automatic lawnmower (Auto-Lawnmower) using computer vision technology for obstacle avoidance. Several critical issues have been overcome in this development work, including the development of a simplified convolutional neural network (CNN) for decision making, and sufficiently large datasets needed to train the Auto-Lawnmower. The following key strategies are adapted to ensure necessary functionality and efficacy with minimum cost, considering possible mass production of Auto-Lawnmowers. First, we use at-time avoidance strategy: meaning the Auto-Lawnmower makes turns when it encounters an obstacle. This is possible because of the fact that a lawn mower usually can move at a very low speed (walking speed of a man). Second, we decided to use minimum necessary sensors, so as to make the system of our Auto-Lawnmower as simple as possible and hence can be very practical. A single monocular video camera is, therefore, used as the single sensor for both: (1) to generate real-time movie images of a large number of situations that the Auto-Lawnmower may encounter, which is labelled and builds up the training datasets; (2) to capture the real-time image when the trained Auto-Lawnmower is in action, to decide on either to avoid the obstacle or move forward while cutting the grass. A Law dataset with labels, called LawNet, has been established for training of CNNs. It collects a total of 168,542 labelled images taken from the perspective of our Auto-Lawnmower for lawns at the university campus. A concise CNN is created for high efficiency and trained with our LawNet to drive the Auto-Lawnmower. A prototype of Auto-Lawnmower is finally designed, built and tested for lawn mowing in real situations. It is found that it works well as designed, with minimum sensors (a single camera), and hence it has a good potential for mass production.

Tempat sampah pintar berbasis sensor HC-SR04 menggunakan Aduino Uno R3

Humans can produce waste every day, both household waste and industrial waste which has various forms and types. Garbage can be a problem because it interferes with human health, causes unpleasant odors and air pollution. Awareness of disposing of waste in its place is currently considered very lacking. This is because trash cans in general still use a simple way, namely by opening and closing manually. In the design of this smart trash can it will facilitate the work of the cleaning agency. In this study, a smart trash can with HC-SR04 sensor was designed using Arduino Uno R3 With the HC-SR04 sensor, it can detect the distance of humans who want to throw away and detect garbage loads. The maximum and minimum distance from the sensor is 10-60 cm to open the lid of the trash can. Data processing uses Arduino Uno R3 with Atmega328p chip which functions as data processing and sending data. The testing process that is carried out periodically produces data in the form of differences in the original distance and the distance read by the sensor with a difference of 0-1 cm, while the minimum and maximum sensor distances are 10-60 cm. This system can also detect the garbage load if it is full and will send a notification in the form of SMS. Another benefit is to make people aware of the importance of health by disposing of waste in its place. The method used is the waterfall method to detect this object is the design method which consists of several stages, namely Requirements Analysis, Design, Circuit Implementation, Test Tools.

Harmonics Compensation by Using a Multi-Modular H-Bridge-Based Multilevel Converter

This paper presents an active power filter based on a seven-level cascade H-bridge where the main contribution is a control strategy that combines model-based predictive control, the voltage vectors of the converter output levels, the phase shift PWM technique, and suboptimal DC-link voltage control. The proposed scheme greatly simplified the overall control system, making it well suited to compensate the current harmonics distortion at the grid side, generated by nonlinear loads connected to the point of common coupling. In addition, the proposed method achieved a balancing of the capacitor voltages of the seven-level cascade H-bridge converter by using the minimum DC-link voltage sensors. This feature significantly reduced the control system complexity and provided a low computational burden. Experimental results confirmed the feasibility and effectiveness of the proposed controller.

State estimation of tire-road friction and suspension system coupling dynamic in braking process and change detection of road adhesive ability

During vehicle braking, when vehicles move on the road with unknown road roughness elevation and unknown tire/road friction coefficient, fewer sensors shall be used for vehicle braking closed-loop control and braking distance prediction to obtain the dynamic states of the vehicle suspension and tire systems. In this paper, a vehicle dynamic model is established in Carsim software. Modify lump LuGre friction model and road roughness elevation model of four tires are proposed based on matlab. When vehicles brake on the road with time-varying split-μ, a braking control algorithm established in this paper. The road roughness elevation and the braking force of each tire are supplied to the vehicle dynamic model in Carsim. A state estimate algorithm of suspension system is proposed. The scheme for minimum sensor of this estimator is determined. A state estimate algorithm of the tire/road friction using only tire angular velocity information is proposed. When vehicles brake on the road with different levels of roughness, the influence of the number of installation groups of the sensors, the tire vertical stiffness deviations, and the measurement noise on the estimation error of the estimator is analyzed. When the vehicle is driving on the road with unknown adhesive ability, based on the estimator of tire/road friction using only tire angular velocity information, the tire/road friction internal state, the changes of road adhesive ability, and the vehicle velocity are estimated well.

Potential-Scanning Sensing for Refractive Index Using an Indium Tin Oxide (ITO)-Coated Long-Period Fiber Grating (LPFG)

Here is presented a potential-scanning sensing technique for refractive index detection using a long-period fiber grating (LPFG) coated with indium tin oxide (ITO). The LPFG was produced by an electric arc-induced technique. The sensing is achieved by controlling the electron density of the ITO electrode by potential scanning. The attenuation peak of the LPFG in the transmittance spectra is shifted by the application of a potential. Therefore, a transmittance–potential spectrum with an attenuation peak is obtained during potential scanning, and the refractive index near the sensor can be determined from the peak potential at attenuation. The ITO-LPFG was successfully employed to experimentally perform potential-scanning for refractive index detection in sucrose solutions with concentrations between 0% and 33%. A sensor resolution of 0.0063 ΔRI and a sensitivity of 9.9 V/RIU were obtained using this approach. These values were improved using wavelengths that were experimentally shown to provide high sensitivity and refractive index linearity. A minimum sensor resolution of 0.0017 ΔRI and a maximum sensitivity of 26.7 V/RIU were obtained at 1136 nm. The measurements determined from the peak potential in this work were unaffected by variations in the initial light intensity for individual samples. In addition, this sensing approach can be employed in the near-infrared region and is capable of real-time and remote monitoring.

An enhanced sequential sensor optimization scheme and its application in the system identification of a rail-sleeper-ballast system

The problem of optimal sensor placement for system identification and damage detection is addressed by the development of a robust method based on Bayesian theory. Information entropy is used as the optimality measure to select the optimal configuration from the candidate configurations for a given number of sensors. The enhanced sequential sensor placement (ESSP) algorithm was developed to efficiently address the computational bottlenecks that may arise when a large number of measurable degrees of freedom (DOFs) are considered for candidate configurations. In this paper, the sensor redundancy problem in finely meshed models was addressed by considering (1) the spatial correction of prediction errors at measurable DOFs and (2) a minimum sensor interval. The proposed ESSP algorithm and the two strategies for handling sensor redundancy were studied by comparing the optimal configurations from the conventional methods and that from the ESSP algorithm for a rail-sleeper-ballast system. Finally, the optimal sensor configurations thus obtained were verified via model updating of an in-situ ballasted track system using measured data from an impact hammer test. The analysis results clearly show improvements in the optimality of the sensor configuration with the proposed method relative to the conventional methods.

Yeast Synthetic Minimal Biosensors for Evaluating Protein Production.

The unfolded protein response (UPR) is a highly conserved cellular response in eukaryotic cells to counteract endoplasmic reticulum (ER) stress, typically triggered by unfolded protein accumulation. In addition to its relevance to human diseases like cancer, the induction of the UPR has a significant impact on the recombinant protein production in eukaryotic cell factories, including the industrial workhorseSaccharomyces cerevisiae. Being able to accurately detect and measure this ER stress response in single cells, enables the rapid optimization of protein production conditions and high-throughput strain selection strategies. Current methodologies to monitor the UPR in S. cerevisiae are often temporally and spatially removed from the cultivation stage or lack updated systematic evaluation. To this end, we constructed and systematically evaluated a series of high-throughput UPR sensors by different designs, incorporating either yeast native UPR promoters or novel synthetic minimal UPR promoters. The native promoters of DER1 and ERO1 were identified to have suitable UPR biosensor properties and served as an expression level guide for orthogonal sensor benchmarking. Our best synthetic minimal sensor is only 98 bp in length, has minimal homology to other native yeast sequences and displayed superior sensor characteristics. The synthetic minimal UPR sensor was able to accurately distinguish between cells expressing different heterologous proteins and between the different secretion levels of the same protein. This work demonstrated the potential of synthetic UPR biosensors as high-throughput tools to predict the protein production capacity of strains, interrogate protein properties hampering their secretion, and guide rational engineering strategies for optimal heterologous protein production.

Aerodynamic Errors Mathematical Modeling in Air Data Systems Estimation Technology in Flight Tests Using Satellite Navigation Systems

Problems of mathematical modeling of onboard air data systems errors are of paramount importance in pitot-static sources errors determination, air data systems evaluation in flight tests. The problems of development, identification and assessment of the mathematical models of errors adequacy acquire main importance in the modern technology of the air parameters true values determination, air data systems evaluation using satellite navigation systems, developed and applied in the practice of flight tests at JSC "FRI n.a. M. M. Gromov". This paper gives a general description of an air data systems estimation technology using satellite navigation systems. The principles of solving problems of aircraft data systems aerodynamic errors mathematical modeling are stated. The structure of mathematical models, factors of the aerodynamic errors, relationship of the solving problems of errors modeling within the framework of technology with a flight experiment plan are shown. Mathematical models parameters identification are based on a complex solving of the problems of a true air data parameters values and aerodynamic errors determination in flight tests. New results of mathematical modeling of errors in tests at high angles of attack in 2018 year of medium-range and short-range aircraft are presented. The results illustrate the technology effectiveness in solving the problems of flight tests at high angles of attack information support, aerodynamic errors modeling, air data systems estimation. The applied modeling methods make it possible to allocate in the mathematical models of pitot-static sources aerodynamic errors even the factors of very weak aerodynamic influence, comparable with the minimum pressure sensors instrumental errors.

A Classification Approach Based on Directed Acyclic Graph to Predict Locomotion Activities With One Inertial Sensor on the Thigh

Current state-of-the-art locomotion mode classifiers for controlling robotic lower-limb prostheses rely on multiple sensors to achieve high accuracy, prediction performance, and robustness to both speed changes and subject-specific gait patterns. However, multiple sensors placed on different body parts usually entail discomfort and poor usability for the user. This paper presents an intention detection method that relies on the features extracted from an inertial measurement unit worn on the thigh and an online phase estimator. The algorithm classifies the locomotion mode of the upcoming stride among the three modes of ground-level walking, stair ascent, and stair descent. A two-stage classification process first distinguishes between transient and steady-state strides and then classifies the locomotion mode of the impending stride based on directed acyclic graphs of binary classifiers. The classification is performed at 75% or 85% of the previous stride phase, respectively for steady-state and transient strides. Data were gathered from 10 healthy subjects and processed offline. Feature design and selection were based on the data of all subjects, while the classification performance was assessed by leave-one-subject-out cross-validation. Results presented a median recognition accuracy of 98.7% for steady-state strides and 95.6% for transitions, suggesting that the method was inherently robust to variations in gait cadence, since all of the features were phase-based and not dependent on fixed time intervals. These results inform the design of control strategies for active transfemoral prostheses able to predict the user’s locomotion intention during the next stride, using minimum sensors.

The impact of thermal-noise on bifurcation MEMS sensors

In this work, we investigate and quantify intrinsic noise sources in electrostatic MEMS and study their impact on the mass sensitivity of bifurcation-based inertial sensors. Experiments are carried out to measure the power spectral densities (PSD) of the two leading intrinsic noise sources, mechanical-thermal noise and electric-thermal noise. We also present a stochastic model of the sensor that encompasses those noise sources. The model is deployed to obtain the probability distribution of the cyclic-fold bifurcation point in the presence of noise. It shows that thermal noise leads to an uncertainty of 2Hz in the bifurcation frequency. Within that range, stochastic switching occurs between the two co-existing stable orbits. Therefore, the closest operating frequency of the sensor should exclude this region to protect against false positives. This represents the lower bound on the sensor's minimum detectable mass. The predictions of this model were found to be in close agreement with previous experimental results.

Automated eco-driving in urban scenarios using deep reinforcement learning

Urban settings are challenging environments to implement eco-driving strategies for automated vehicles. It is often assumed that sufficient information on the preceding vehicle pulk is available to accurately predict the traffic situation. Because vehicle-to-vehicle communication was introduced only recently, this assumption will not be valid until a sufficiently high penetration of the vehicle fleet has been reached. Thus, in the present study, we employed Reinforcement Learning (RL) to develop eco-driving strategies for cases where little data on the traffic situation are available.An A-segment electric vehicle was simulated using detailed efficiency models to accurately determine its energy-saving potential. A probabilistic traffic environment featuring signalized urban roads and multiple preceding vehicles was integrated into the simulation model. Only information on the traffic light timing and minimal sensor data were provided to the control algorithm. A twin-delayed deep deterministic policy gradient (TD3) agent was implemented and trained to control the vehicle efficiently and safely in this environment.Energy savings of up to 19% compared with a simulated human driver and up to 11% compared with a fine-tuned Green Light Optimal Speed Advice (GLOSA) algorithm were determined in a probabilistic traffic scenario reflecting real-world conditions. Overall, the RL agents showed a better travel time and energy consumption trade-off than the GLOSA reference.

Movement Control Method of Magnetic Levitation System Using Eccentricity of Non-Contact Position Sensor

This paper presents a method of utilizing a non-contact position sensor for the tilting and movement control of a rotor in a rotary magnetic levitation motor system. This system has been studied with the aim of having a relatively simple and highly clean alternative application compared to the spin coater used in the photoresist coating process in the semiconductor wafer process. To eliminate system wear and dust problems, a shaft-and-bearing-free magnetic levitation motor system was designed and a minimal non-contact position sensor was placed. An algorithm capable of preventing derailment and precise movement control by applying only control without additional mechanical devices to this magnetic levitation system was proposed. The proposed algorithm was verified through simulations and experiments, and the validity of the algorithm was verified by deriving a precision control result suitable for the movement control command in units of 0.1 mm at 50 rpm rotation drive.

Connected k-coverage in two-dimensional wireless sensor networks using hexagonal slicing and area stretching

The problem of coverage in two-dimensional (2D) wireless sensor networks is challenging and is still open. Precisely, determining the minimum sensor density (i.e, minimum number of sensors per unit area) that is required to cover a 2D field of interest (FoI), where every point in the field is covered by at least one sensor, is still under investigation. The problem of 2D k-coverage, which requires that every point in a 2D FoI be covered by at least k sensors, where k≥1, is more challenging. In this paper, we attempt to address the 2D connected k-coverage problem, where a 2D FoI is k-covered, while the underlying set of sensors k-covering the field forms a connected network. We propose to solve this problem using an approach based on slicing 2D FoI into convex regular hexagons. Our goal is to achieve k-coverage of a 2D FoI with a minimum number of sensors in order to maximize the network lifetime. First, we compute the minimum sensor density for 2D k-coverage using the regular convex hexagon, which is a 2D paver (i.e., covers a 2D field without gaps or overlaps). Indeed, we found that the regular convex hexagon best assimilates the sensors’ sensing disk with respect to our proposed metric, sensing range usage rate. Second, we derive the ratio of the communication range to the sensing range of the sensors to ensure connected k-coverage. Third, we propose an energy-efficient connected k-coverage protocol based on hexagonal slicing and area stretching. To this end, we formulate a multi-objective optimization problem, which computes an optimum solution to the 2D k-coverage problem that meets two requirements: Maximizing the size of the k-covered area, Ck, so as to minimize the sensor density to k-cover a 2D FoI (Requirement 1) and maximizing the area of the sensor locality, Lk, i.e., the region where at least k sensors are located to k-cover Ck, so as to minimize the interference between sensors (Requirement 2). Fourth, we show various simulation results to substantiate our theoretical analysis. We found a close-to-perfect match between our theoretical and simulation results.

Minimum battery capacity planning in wireless rechargeable sensor networks

This paper studies the problem of the minimum battery capacity required for the normal operation of each sensor when the charging path of the mobile charger is determined. When the parameters of the wireless rechargeable sensor network are determined, the aim is to minimize the battery capacity required by each sensor, and to ensure the permanent operation of the wireless rechargeable sensor network with the minimum sensor energy consumption. By analyzing the actual working conditions of mobile charger, a linear programming model with the goal of minimizing the battery capacity of each sensor is established, Lingo is used to solve the model, and the results are verified by computer simulation. The results show that the computer simulation results are basically consistent with the model solution results, which proved the reliability of the model.