Accurate Location Information Research Articles

Improved thermal network model for hot spot location in traction transformer windings

Obtaining accurate location information of the hot spot in advance is a prerequisite for locating the hot spot. To tackle this problem, we propose an improved thermal network model, which provides a quantitative method for placing fiber optic sensors at hot-spot locations. The initial step is to construct an enhanced thermal network model that takes into account the thermal resistance of the boundary layer using heat transfer theory. Then, we compare this model with Computational Fluid Dynamics (CFD) in example calculations to validate its effectiveness and precision. The results show that our proposed model can accurately and rapidly calculate temperature distribution within winding areas, enabling the identification of hot spots. This information is reliable for installing monitoring equipment to measure transformer temperatures.

LR-MPIBS: A LoRa-Based Maritime Position-Indicating Beacon System

Human marine activities are becoming increasingly frequent. The adverse marine environment has led to an increase in man overboard incidents, resulting in significant losses of life and property. After a drowning accident, the accurate location information of the drowning victim can help improve the success rate of rescue. In this paper, we explore a LoRa-based Maritime Position-Indicating Beacon System (LR-MPIBS). A low-power drowning detection circuit is designed in LR-MPIBS to detect drowning accidents in a timely manner after a person falls into the water. The instantaneous high current of the LoRa RF can lower the supply voltage and cause other modules to work abnormally. A fast current transient response circuit is proposed to solve the problem. LR-MPIBS includes a power ripple suppression circuit that can reduce the measurement errors and operational abnormalities caused by power ripple interference. We explore the impedance matching law of LoRa RF circuits through simulation experiments to improve the quality of LoRa communication. A data processing algorithm for personnel drift trajectory is proposed to alleviate the challenges caused by the raw positioning data with large deviations and high communication cost. The experimental results show that LR-MPIBS can automatically start and actively alarm within 3 s after a person falls into the water. The positioning cold start time is less than 50 s. The performance of communication distance is more than 5 km. The endurance of LR-MPIBS is 25 h (with a 30 s communication cycle).

Ultrasound-Powered Wireless Underwater Acoustic Identification Tags for Backscatter Communication.

Autonomous underwater vehicle (AUV) operations are limited by currently achievable underwater localization and navigation solutions; hence, the development of low-cost and passive (i.e., operable without an active power supply) acoustic underwater markers (or tags) can provide accurate localization information to AUVs improving their situational awareness, especially when operating in small scales or confined missions. This work presents an acoustic identification (AID) tag that can be powered wirelessly with ultrasonic power transfer from a remote acoustic source (e.g., mounted on an interrogating AUV) and provide localization information using backscatter communication. The AID tag harvests energy from the acoustic signal generated from the AUV and communicates by modulating the reflected signals from an embedded piezoelectric transducer. A scaled broadband AID tag prototype that achieves concurrent acoustic energy harvesting (tuned around 1.3 MHz) and backscatter communication (in wider frequency band 600 and 800 kHz) using frequency-domain multiplexing is implemented using a custom broadband impedance matching-based transducer design approach. During concurrent power and data operation, this prototype AID tag achieves data rates up to 200 kb/s using amplitude- and frequency-based modulation communication. The use of broadband schemes to achieve robust communications in low SNR (tested here down to -6 dB) is also demonstrated using linear frequency-modulated data carriers. Finally, the extension to full-scale devices of this AID tag concept and potential applications for short-range AUV routing and navigation such as homing and docking are discussed.

A deep learning approach to recognizing fine-grained expressway location reference from unstructured texts in Chinese

Complex nested and discontinuous location references are common in unstructured text. Extracting them is essential for accurate location information retrieval and spatial inference. However, traditional methods struggle with these references due to annotation system and model architecture limitations. In this study, we introduce a deep learning approach to uniformly recognize flat, nested, and discontinuous location references, motivated by recognizing fine-grained expressway location references. The approach uses a pre-trained language model to generate semantic sentence representations and a distance and direction-aware Transformer for contextual encoding. Then, it recognizes location references by modeling the adjacency and boundary relations between word pairs. We evaluated the approach on seven benchmark datasets and compared it with state-of-the-art methods. The results show that the approach achieves higher accuracy with faster inference, validating our modeling paradigm and architecture. The ablation study further confirms the effectiveness of submodules in architecture. These findings can provide valuable insights for developing advanced unified location reference recognition methods. Moreover, the detailed labeled dataset for location references can facilitate the evaluation and comparison of unified recognition methods and systems.

Floor level estimation using MEMS pressure sensors

Enhanced 911 systems aim to increase the efficiency of first responders by automatically locating the origin of a 911 call even if the caller is unable to provide it. However, in a high-rise building, the location of a cell phone call would appear the same to dispatchers regardless of elevation. If emergency services had more accurate location information, they could save countless more lives. In this study, we compared multiple methods for determining a caller’s floor level inside a building, including GPS, WiFi, a magnetic sensor, and a micro-electromechanical system (MEMS) pressure sensor. GPS provided a reasonable estimate of vertical height, but accuracy was strongly dependent on the reception, which was often poor inside the building. WiFi signals and magnetic fields were not strong predictors of vertical height. WiFi was limited by short signal range, need for network access, and knowledge of router locations. Magnetic sensor readings required a pre-existing magnetic field contour map of the building in order to interpret the data. The MEMS pressure sensor was the most accurate predictor of elevation and does not rely on communication with other equipment. A detection accuracy of ±1 floor can be achieved using the MEMS pressure sensor after correcting the output using publicly available data and assuming an average floor height. The proposed technique provides a self-contained solution, using a sensor available in most smartphones, to determine the floor of a caller. Adding floor level data to emergency call locations would provide valuable information to first responders in densely populated urban areas.

Exploring the Value, Risks and Its Path of Digital Transformation of Civics Teaching in Colleges and Universities

Abstract This paper uses deep learning algorithms to detect and extract student behavioral features in the Civics classroom. It first predicts and detects the target location and behavior of students in classroom images by FastR-CNN algorithm, extracts the detected student data features by MSResNet-50 algorithm, and then introduces the attention mechanism to screen the extracted behavioral features, and analyzes the accurate location information and channel information to get the student’s behavioral performance in the Civics classroom. The Civics element framework is used to construct a digital transformation path for Civics courses in this study. In order to reflect the value of the digital transformation of Civics teaching, the study conducted a teaching comparison practice. After a semester’s test, the Civics scores of the students in the five classes were above 80 (good or excellent). In contrast, the mean score of Civic Literacy in the post-test reached 3.863, which was significantly different from that before the practice (p=0.007<0.05). The value of “student-initiated speaking” behavior was higher than 45 in the practice class, and the student’s participation in the class was much higher than that in the control class. Analysis of the effect of teaching implementation found that the motivation score of students in class E increased from 3.511 to 4.957, with a significant difference (p=0.048<0.05). It was also found that the posttest score of “value concept” of students’ Civics literacy increased by 21.19%, and the score of “honesty and friendliness” also increased by 0.959 points. The massive amount of digital information generated by the implementation of the digital Civics teaching path in this paper may affect the student’s ability to think independently. However, the results achieved by the teaching path still provide reference ideas for the innovation of Civics teaching and reference data for the value and significance of the path’s implementation.

Research on CBF-YOLO detection model for common soybean pests in complex environment

The intensive cultivation of large soybean fields, combined with environmental factors such as light and shadow, challenges the accuracy of traditional manual and machine learning algorithms in identifying insect pests in these fields. In this study, a CBF-YOLO network was proposed for detecting common soybean pests in complex environments. The network was composed primarily of the CSE-ELAN, Bi-PAN, and FFE modules. The CSE-ELAN module enhanced feature extraction in both spatial and channel dimensions by incorporating the CSE feature enhancement structure into the ELAN structure of YOLOv7. The Bi-PAN module fused the features of three different scaled feature layers to provide more accurate pest detection features and localization information. The FFE module consisted of spatial and channel feature purification modules that refined the multi-scale fused features from Bi-PAN, further improving the expression ability of the fused features. Experimental results showed that the mAP of CBF-YOLO network reached 86.9% for detecting common soybean pests, with average precisions for detecting Caterpillar and Diabrotica speciosa pest-damaged leaves reaching 86.5% and 87.3%, respectively. Compared to the original model, the mAP of the CBF-YOLO network for detecting common soybean pests increased by 6.3%, significantly improving the model's detection performance. The CBF-YOLO network exhibited the highest mAP of 81.6% and performed well in detecting common soybean pests in actual complex environments, compared to deep learning networks like YOLOv5. This network provides a technical basis for detecting common soybean pests in challenging environments. The data and code used in this study can be accessed at https://github.com/2peacock/CBF-YOLO.

A Positioning and Navigation Method Combining Multimotion Features Dead Reckoning with Acoustic Localization.

Accurate location information can offer huge commercial and social value and has become a key research topic. Acoustic-based positioning has high positioning accuracy, although some anomalies that affect the positioning performance arise. Inertia-assisted positioning has excellent autonomous characteristics, but its localization errors accumulate over time. To address these issues, we propose a novel positioning navigation system that integrates acoustic estimation and dead reckoning with a novel step-length model. First, the features that include acceleration peak-to-valley amplitude difference, walk frequency, variance of acceleration, mean acceleration, peak median, and valley median are extracted from the collected motion data. The previous three steps and the maximum and minimum values of the acceleration measurement at the current step are extracted to predict step length. Then, the LASSO regularization spatial constraint under the extracted features optimizes and solves for the accurate step length. The acoustic estimation is determined by a hybrid CHAN-Taylor algorithm. Finally, the location is determined using an extended Kalman filter (EKF) merged with the improved pedestrian dead reckoning (PDR) estimation and acoustic estimation. We conducted some comparative experiments in two different scenarios using two heterogeneous devices. The experimental results show that the proposed fusion positioning navigation method achieves 8~56.28 cm localization accuracy. The proposed method can significantly migrate the cumulative error of PDR and high-robustness localization under different experimental conditions.

DESIGN AND DEVELOPMENT OF AN INTEGRATED MOBILE APPLICATION COMBINING THE FEATURES OF 2GIS, ZENLY, AND GOOGLE MAPS

This article focuses on creating a universal mobile application that combines the power of 2GIS, Zenly and Google Maps to optimize user navigation and improve the location experience. This innovative application integrates the strengths of these platforms, simplifying navigation, route planning, real-time tracking of public transport and promoting social connectivity. The study covers the design, implementation and evaluation of this innovative solution, highlighting its potential to revolutionize modern location services. The main goal of the project is to create and evaluate a comprehensive mobile application that provides users with a single tool for location services, integrating the core functions of 2GIS, Zenly and Google Maps. The app's design pays attention to usability, using detailed Google Maps for accurate and up-todate location information. In summary, this study represents a significant contribution to the field of mobile navigation applications, demonstrating the potential to transform modern location services.

Assessing the performance of national sentinel food lists at subnational levels in six countries.

To assess how well national sentinel lists of the most frequently consumed foods in each food group capture data at subnational levels to measure minimum diet diversity (MDD). We analysed data from seven surveys with 24-h open dietary recalls to evaluate: (1) the percentage of reported foods that were included in each sentinel food list; (2) whether these lists captured consumption of some food groups better than others and (3) differences between estimates of dietary diversity calculated from all food items mentioned in the open 24-h recall v. only food items included in the sentinel lists. Seven subnational areas: Bangladesh (2), Benin, Colombia, Kenya, Malawi and Nepal. 8094 women 15-49 years; 4588 children 6-23 months. National sentinel food lists captured most foods reportedly consumed by women (84 %) and children (86 %). Food groups with the highest variability were 'other fruits' and 'other vegetables.' MDD calculated from the sentinel list was, on average, 6·5 (women) and 4·1 (children) percentage points lower than when calculated from open 24-h recalls, with a statistically significant difference in most subnational areas. National sentinel food lists can provide reliable data at subnational levels for most food groups, with some variability by country and sub-region. Assessing the accuracy of national sentinel food lists, especially for fruits and vegetables, before using them at the subnational level could avoid potentially underestimating dietary diversity and provide more accurate local information for programmes, policy and research.

A review of UWB indoor positioning

With the development of science and technology, more accurate and stable location information can better serve people’s lives. As people spend more time indoors, traditional positioning technologies such as Wi-Fi can no longer meet their needs due to a lack of positioning accuracy and interference resistance. UWB is an emerging positioning technology with high accuracy, robustness, and stability. This paper compares the advantages and disadvantages of UWB technology with those of Bluetooth and Wi-Fi. Common UWB geometric positioning methods such as TOA, TDOA, etc. are introduced. By introducing the latest research progress in UWB indoor positioning, an outlook on the future development of UWB indoor positioning is made. Compared with Bluetooth and other indoor positioning technologies, UWB positioning technology has the advantages of high positioning accuracy and good anti-interference. The UWB positioning system usually uses TOA, TDOA, AOA, and other positioning algorithms. In the future, UWB positioning technology will serve more and more complex positioning environments.

A New Method of Fuzzy Multicriteria Routing in Vehicle Ad Hoc Network

The internet of vehicles (IoVs) provides delay-sensitive services, but high-latency communication with roadside units causes service failures and high costs. Mobile edge computing (EC) is migrating cloud computing platforms from the core network to the edge of mobile networks, giving vehicles local access to numerous computing resources. The classic greedy boundary stateless routing (GPSR) method is widely used to meet the communication requirements of vehicle self-organizing networks. In order to solve the problems of GPSR&#x2019;s neighbor node position acquisition lag and single routing criterion, a new routing method based on the fuzzy logic system Geographic Routing method based on Velocity, Angle, and Density (GRVAD) is proposed. This method gets the relative velocity between the nodes and the angle between the current node, the neighbor node and the target node, and the node density of the neighbor node as the input of fuzzy logic, and the unscented Kalman filter is used to predict the location of the neighbor node to obtain more accurate location information of neighbor nodes. Simulation results show that the routing method compensates for some of the shortcomings of the GPSR method and reasonably considers the delivery rate and end-to-end delay of data packets and is more in line with the communication requirements of the vehicle <i>ad hoc</i> network.

Two-Timescale Transmission Design and RIS Optimization for Integrated Localization and Communications

Reconfigurable intelligent surfaces (RISs) have tremendous potential to boost communication performance, especially when the line-of-sight (LOS) path between the user equipment (UE) and base station (BS) is blocked. To control the RIS, channel state information (CSI) is needed, which entails significant pilot overhead. To reduce this overhead and the need for frequent RIS reconfiguration, we propose a novel framework for integrated localization and communications, where RIS configurations are fixed during location coherence intervals, while BS precoders are optimized every channel coherence interval. This framework leverages accurate location information obtained with the aid of several RISs as well as novel RIS optimization and channel estimation methods. Performance in terms of localization accuracy, channel estimation error, and achievable rate demonstrates the effectiveness of the proposed approach.

Application of 3D visualization technology based on hematoma edge key points setting for emergency hypertensive cerebral hemorrhage surgery in primary hospitals

ObjectiveThis study aims to explore the application potential of 3D visualization technology based in emergency hypertensive cerebral hemorrhage surgery in primary hospitals. The specific goal is to use 3DSlicer software to perform 3D reconstruction and body surface projection on patients with hypertensive cerebral hemorrhage, provide accurate hematoma location information, help surgeons determine the specific location of hematoma on the body surface, and reduce the expansion of surgical incisions. Methods3D reconstruction technology based on 3DSlicer software was employed to process CT images of patients with cerebral hemorrhage. By segmenting and reconstructing the images, a 3D model of the hematoma was generated and projected onto the patient's body surface. Utilizing the functionalities of 3DSlicer software in conjunction with the surgeon's anatomical knowledge, accurate hematoma positioning on the body surface was achieved. Results23 patients were enrolled in this study, and underwent successful surgical evacuation. The implementation of 3D visualization technology using 3DSlicer software is expected to provide precise hematoma localization information for emergency hypertensive intracerebral hemorrhage surgery in primary hospitals. This approach will enable surgeons to accurately determine the appropriate surgical incision, thereby minimizing unnecessary trauma and improving the overall success rate of surgery. ConclusionThis study demonstrates the potential application of 3D visualization technology based on 3DSlicer software in emergency hypertensive cerebral hemorrhage surgery within primary hospitals. By utilizing 3DSlicer software for hematoma localization, accurate information support can be provided to assist surgeons in managing patients with hypertensive cerebral hemorrhage.

Short-Term (7 Day) Beaufort Sea Ice Extent Forecasting with Deep Learning

Abstract Ships inside the Arctic basin require high-resolution (1–5 km), near-term (days to semimonthly) forecasts for guidance on scales of interest to their operations where forecast model predictions are insufficient due to their coarse spatial and temporal resolutions. Deep learning techniques offer the capability of rapid assimilation and analysis of multiple sources of information for improved forecasting. Data from the National Oceanographic and Atmospheric Administration’s Global Forecast System, Multi-scale Ultra-high Resolution Sea Surface Temperature (MEaSUREs), and the National Snow and Ice Data Center’s Multisensor Analyzed Sea ice Extent (MASIE) were used to develop the sea ice extent deep learning forecast model, over the freeze-up periods of 2016, 2018, 2019, and 2020 in the Beaufort Sea. Sea ice extent forecasts were produced for 1–7 days in the future. The approach was novel for sea ice extent forecasting in using forecast data as model input to aid in the prediction of sea ice extent. Model accuracy was assessed against a persistence model. While the average accuracy of the persistence model dropped from 97% to 90% for forecast days 1–7, the deep learning model accuracy dropped only to 93%. A k-fold (fourfold) cross-validation study found that on all except the first day, the deep learning model, which includes a U-Net architecture with an 18-layer residual neural network (Resnet-18) backbone, does better than the persistence model. Skill scores improve the farther out in time to 0.27. The model demonstrated success in predicting changes in ice extent of significance for navigation in the Amundsen Gulf. Extensions to other Arctic seas, seasons, and sea ice parameters are under development. Significance Statement Ships traversing the Arctic require timely, accurate sea ice location information to successfully complete their transits. After testing several potential candidates, we have developed a short-term (7 day) forecast process using existing observations of ice extent and sea surface temperature, with operational forecasts of weather and oceanographic variables, and appropriate machine learning models. The process included using forecasts of atmospheric and oceanographic conditions, as a human forecaster/analyst would. The models were trained for the Beaufort Sea north of Alaska using data and forecasts from 2016 combined with 2018–20. The results showed improvement in short-term forecasts of ice locations over current methods and also demonstrated correctly predicted changes in the sea ice that are important for navigation.

Geographic Information System for Customer Distribution in PT. Dinamika Lubsindo Utama Using the Haversine Algorithm

This research was conducted because the distribution of customers who do not have accurate location information results in couriers who will deliver products often traveling long distances to get to the location of customers who make orders because they repeat routes that have been taken previously. The research problem formulation revolves around implementing the Haversine algorithm for calculating the distance between the courier's location and the customer's order location. Additionally, it focuses on designing and constructing a geographic information system for customer distribution, utilizing the Android-based Haversine algorithm. The research method used is Research &amp; Development where researchers will collect information first and develop the system to be built. For system development, the Waterfall method is used. To calculate the distance from the courier's location to the customer's ordering location, the Haversine algorithm is used. From this research, the results obtained are a geographic information system that will provide information on customers who ordered on that day as well as the distance from the courier's location to the location of the customer who made the order using the Android-based Haversine algorithm. The conclusion of this research is that the geographic information system that is built will be able to assist couriers in delivering products to customers who make orders that day. Because the system will sort the customer's location from closest to furthest so that the courier will not repeat the route he has previously taken, making it more effective.

Acoustic Characterization around the CalWave Wave Energy Converter

Sound generated by marine energy (ME) installations in the ocean environment remains a particular concern for environmental permitting despite the limited evidence showing low levels of ME sounds relative to other anthropogenic sounds. In an effort to increase understanding of potential environmental effects of marine energy projects and help reduce barriers to marine energy deployments, a new acoustic monitoring technology, the NoiseSpotter®, was developed and recently demonstrated around CalWave’s operational scaled xWave™ wave energy converter (WEC). The NoiseSpotter® improves upon traditional acoustic sensing techniques by use of a cost-effective, compact array of acoustic particle velocity sensors that characterizes, classifies, and provides accurate location information for anthropogenic and natural sounds in real time. Results are presented from co-deployments of NoiseSpotter® with the operational CalWave WEC that were conducted over a 9-day period in fall 2021 offshore of Scripps Research Pier in San Diego, California, USA. The multi-day deployment of the NoiseSpotter® at 100 m from the CalWave WEC revealed a rich library of sounds that include: Low-level (~95 dB re 1 µPa relative to an ambient noise floor between 80-90 dB re 1 µPa) sounds from the WEC associated with the deliberate actuation of mechanical components, Sounds from a hovering helicopter, Marine mammal vocalizations, and, Small boat engines. Sound levels from the WEC were placed within the context of ambient sounds, and reveal little deviation from the ambient soundscape. The azimuthal anisotropy of WEC sound was investigated via deployments along four cardinal directions around the WEC. While a noticeable increase was observed along the north-south orientation, the sound levels along all directions still showed little deviation relative to the ambient noise floor. Analysis of low-level WEC sounds in conjunction with exploratory machine learning techniques demonstrate the utility of directional acoustic sensing in distinguishing marine energy sounds from the myriad other sounds in the surrounding ocean environment.

Preliminary performance analysis of BeiDou-2/GPS navigation systems over the low latitude region

Abstract On a regional or worldwide scale, satellite-based navigation systems can offer three-dimensional Position, Velocity, and Timing (PVT) services to an indefinite number of users. BeiDou-2 is China’s regional navigation satellite system that encompasses the Asia-Pacific region. BeiDou-2’s space section comprises GEO, MEO, and IGSO satellites, making it unique among the navigation systems. This research focuses on key aspects, including satellite visibility and signal strength, as a function of the elevation angle across the low latitude region (Indian region). In addition, the results were compared with those obtained using the GPS. The data is acquired from a GNSS receiver located at the Hyderabad station (latitude:17°24′28″, longitude:78°31′04″). The results show that BeiDou-2 satellites have better visibility than GPS satellites at all elevation angles. However, visibility is low at high elevations; therefore, multiple systems are required to obtain user information. As the elevation angle increases, the carrier-to-noise density ratio (C/No) also increases. Additionally, the standard deviation (STD) was calculated and compared to that of the GPS. Despite the average signal strength of GPS satellites remaining high throughout the elevation range, the STD of BeiDou-2 satellites was found to be low. These results indicate that further work is needed to improve the interoperability of multiple navigation systems and to provide more accurate location information to Indian users.

Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning

Accurate location information has significant commercial and economic value as they are widely used in intelligent manufacturing, material localization and smart homes. Magnetic sequence-based approaches show great promise mainly due to their pervasiveness and stability. However, existing geomagnetic indoor localization methods are facing the problems of location ambiguity and feature extraction deficiency, which will lead to large localization errors. To address these issues, we propose a coarse-to-fine geomagnetic indoor localization method based on deep learning. First, a multidimensional geomagnetic feature extraction method is presented which can extract magnetic features from spatial and temporal aspects. Then, a hierarchical deep neural network model is devised to extract more accurate geomagnetic information and corresponding location clues for more accurate localization. Finally, localization is achieved through a particle filter combined with IMU localization. To evaluate the performance of the proposed methods, we carried out several experiments at three trial paths with two heterogeneous devices, Vivo X30 and Huawei Mate30. Experimental results demonstrate that the proposed algorithm can achieve more accurate localization performance than the state-of-the-art methods. Meanwhile, the proposed algorithm has low cost and good pervasiveness for different devices.

Automotive Radar Optimization Design in a Spectrally Crowded V2I Communication Environment

A main challenge for radar-aided millimeter wave (mmWave) vehicle-to-infrastructure (V2I) communication application, is that it requires mitigating the mutual interference between vehicular radar and communication operating at same frequency bands. This paper considers the joint design of the multiple-input multiple-output (MIMO) transmit waveform and receive filter bank for road side unit (RSU)-mounted radar in a spectrally crowded V2I communication environment. With the criterion of maximizing the average signal-to-interference-plus-noise ratio (SINR), a non-convex problem, which involves the weighted-sum waveform energy over the overlayed space-frequency bands, energy and similarity constraints, is formulated. An iterative algorithm is proposed to solve the joint optimization problem. At each iteration, the transmit waveform is optimized based on the alternating direction method of multipliers (ADMM) method with a significantly lower computational complexity. As a consequence, accurate location information derived from the optimized radar is used to reduce the beam training overhead of V2I communication links. Finally, simulation results display the effectiveness of the devised method in finding feasible and enhanced solutions, importantly outperforming several counterparts.