Search Results (891)

Images acquired by different sensors exhibit different characteristics because of the varied imaging mechanisms of sensors. The fusion of visible and infrared images is valuable for specific image applications. While infrared images provide stronger object features under poor illumination and smoke interference, visible images have rich texture features and color information about the target. This study uses dual optical fusion as an example to explore fusion detection methods at different levels and proposes a multimodal decision-level fusion detection method based on category probability sets (CPROS). YOLOv8—a single-mode detector with good detection performance—was chosen as the benchmark. Next, we innovatively introduced the improved Yager formula and proposed a simple non-learning fusion strategy based on CPROS, which can combine the detection results of multiple modes and effectively improve target confidence. We validated the proposed algorithm using the VEDAI public dataset, which was captured from a drone perspective. The results showed that the mean average precision (mAP) of YOLOv8 using the CPROS method was 8.6% and 16.4% higher than that of the YOLOv8 detection single-mode dataset. The proposed method significantly reduces the missed detection rate (MR) and number of false detections per image (FPPI), and it can be generalized. Full article

This study focuses on the successful fabrication of a cost-effective strain sensor using exclusively additive manufacturing Fused Deposition Modeling (FDM) technology, enabling fast on-site production, which is particularly advantageous in maritime settings, reducing downtime, and supporting a circular economy approach by minimizing inventory needs and environmental footprint. The principle of operation of the developed device is based on the piezoresistive characteristics of a carbon nanotube (CNT)-enriched building material, from which the main sensing element consists. The prototype exhibited reliable piezoresistive properties, and a clear correlation was observed between the thermal treatment of the printed piezoresistor and the resulting gauge factor, linearity, and hysteresis. Its robustness, simple design, and single-step manufacturing process, together with its ability to be integrated into the readout circuitry through standard soldering, enhance its reliability and durability. The key advantages of the proposed device include its low cost, simple design, and rapid remote production. Full article

The NOAA-AVHRR (National Oceanographic and Atmospheric Administration–Advanced Very High-Resolution Radiometer) archive includes data from 1981 onwards, which allow for estimating land surface temperature (LST), a key parameter for the study of global warming as well as surface characterization. However, algorithms for LST retrieval were developed before the latest sensors and were based on more reduced atmospheric datasets. Here, we present 50 novel sets of coefficients for an LST retrieval algorithm from NOAA-AVHRR sensors, to which we added one historical methodology, which we validate against historical in situ as well as independent satellite data. This validation shows that the historical algorithm performs surprisingly well, with an in situ RMSE below 1.5 K and a quasi-null bias when compared with independent satellite data. A couple of the novel algorithms also perform within expectations (errors below 1.5 K), so any of these could be used for the complete processing of the AVHRR dataset. In our case, considering consistency with previous works, we opt for the use of the historical algorithm, now also tested for more recent periods. Full article

While cycling presents environmental benefits and promotes a healthy lifestyle, the risks associated with overtaking maneuvers by motorized vehicles represent a significant barrier for many potential cyclists. A large-scale analysis of overtaking maneuvers could inform traffic researchers and city planners how to reduce these risks by better understanding these maneuvers. Drawing from the fields of sensor-based cycling research and from LiDAR-based traffic data sets, this paper provides a step towards addressing these safety concerns by introducing the Salzburg Bicycle 3d (SaBi3d) data set, which consists of LiDAR point clouds capturing car-to-bicycle overtaking maneuvers. The data set, collected using a LiDAR-equipped bicycle, facilitates the detailed analysis of a large quantity of overtaking maneuvers without the need for manual annotation through enabling automatic labeling by a neural network. Additionally, a benchmark result for 3D object detection using a competitive neural network is provided as a baseline for future research. The SaBi3d data set is structured identically to the nuScenes data set, and therefore offers compatibility with numerous existing object detection systems. This work provides valuable resources for future researchers to better understand cycling infrastructure and mitigate risks, thus promoting cycling as a viable mode of transportation. Full article

Data from the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments onboard the Landsat 8 and Landsat 9 satellite platforms are subject to contamination by cloud cover, with cirrus contributions being the most difficult to detect and mask. To help address this issue, a cirrus detection channel (Band 9) centered within the 1.375-μm water vapor absorption region was implemented on OLI, with a spatial resolution of 30 m. However, this band has not yet been fully utilized in the Collection 2 Landsat 8/9 Level 2 surface temperature data products that are publicly released by U.S. Geological Survey (USGS). The temperature products are generated with a single-channel algorithm. During the surface temperature retrievals, the effects of absorption of infrared radiation originating from the warmer earth’s surfaces by ice clouds, typically located in the upper portion of the troposphere and re-emitting at much lower temperatures (approximately 220 K), are not taken into consideration. Through an analysis of sample Level 1 TOA and Level 2 surface data products, we have found that thin cirrus cloud features present in the Level 1 1.375-μm band images are directly propagated down to the Level 2 surface data products. The surface temperature errors resulting from thin cirrus contamination can be 10 K or larger. Previously, we reported an empirical and effective technique for removing thin cirrus scattering effects in OLI images, making use of the correlations between the 1.375-μm band image and images of any other OLI bands located in the 0.4–2.5 μm solar spectral region. In this article, we describe a variation of this technique that can be applied to the thermal bands, using the correlations between the Level 1 1.375-μm band image and the 11-μm BT image for the effective removal of thin cirrus absorption effects. Our results from three data sets acquired over spatially uniform water surfaces and over non-uniform land/water boundary areas suggest that if the cirrus-removed TOA 11-μm band BT images are used for the retrieval of the Level 2 surface temperature (ST) data products, the errors resulting from thin cirrus contaminations in the products can be reduced to about 1 K for spatially diffused cirrus scenes. Full article

Due to the complexity of the missile air data system (ADS) and the harshness of the environment in which its sensors operate, the effectiveness of traditional fault diagnosis methods is significantly reduced. To this end, this paper proposes a method fusing the model and neural network based on unscented Kalman filter (UKF) and Inception V3 to enhance fault diagnosis performance. Initially, the unscented Kalman filter model is established based on an atmospheric system model to accurately estimate normal states. Subsequently, in order to solve the difficulties such as threshold setting in existing fault diagnosis methods based on residual observers, the UKF model is combined with a neural network, where innovation and residual sequences of the UKF model are extracted as inputs for the neural network model to amplify fault characteristics. Then, multi-scale features are extracted by the Inception V3 network, combined with the efficient channel attention (ECA) mechanism to improve diagnostic results. Finally, the proposed algorithm is validated on a missile simulation platform. The results show that, compared to traditional methods, the proposed method achieves higher accuracy and maintains its lightweight nature simultaneously, which demonstrates its efficiency and potential of fault diagnosis in missile air data systems. Full article

Issues of size and power consumption in IoT devices can be addressed through triboelectricity-driven energy harvesting technology, which generates electrical signals without external power sources or batteries. This technology significantly reduces the complexity of devices, enhances installation flexibility, and minimizes power consumption. By utilizing shear thickening fluid (STF), which exhibits variable viscosity upon external impact, the sensitivity of triboelectric nanogenerator (TENG)-based sensors can be adjusted. For this study, the highest electrical outputs of STF and sponge-hybrid TENG (SSH-TENG) devices under various input forces and frequencies were generated with an open-circuit voltage (V_OC) of 98 V and a short-circuit current (I_SC) of 4.5 µA. The maximum power density was confirmed to be 0.853 mW/m² at a load resistance of 30 MΩ. Additionally, a lying state detection system for use in medical settings was implemented using SSH-TENG as a hybrid triboelectric motion sensor (HTMS). Each unit of a 3 × 2 HTMS array, connected to a half-wave rectifier and 1 MΩ parallel resistor, was interfaced with an MCU. Real-time detection of the patient’s condition through the HTMS array could enable the early identification of hazardous situations and alerts. The proposed HTMS continuously monitors the patient’s movements, promptly identifying areas prone to pressure ulcers, thus effectively contributing to pressure ulcer prevention. Full article

PARK7, also known as DJ-1, plays a critical role in protecting cells by functioning as a sensitive oxidation sensor and modulator of antioxidants. DJ-1 acts to maintain mitochondrial function and regulate transcription in response to different stressors. In this study, we showed that cell lines vary based on their antioxidation potential under basal conditions. The transcriptome of HEK293 cells was tested following knockdown (KD) of DJ-1 using siRNAs, which reduced the DJ-1 transcripts to only 12% of the original level. We compared the expression levels of 14k protein-coding transcripts and 4.2k non-coding RNAs relative to cells treated with non-specific siRNAs. Among the coding genes, approximately 200 upregulated differentially expressed genes (DEGs) signified a coordinated antiviral innate immune response. Most genes were associated with the regulation of type 1 interferons (IFN) and the induction of inflammatory cytokines. About a quarter of these genes were also induced in cells treated with non-specific siRNAs that were used as a negative control. Beyond the antiviral-like response, 114 genes were specific to the KD of DJ-1 with enrichment in RNA metabolism and mitochondrial functions. A smaller set of downregulated genes (58 genes) was associated with dysregulation in membrane structure, cell viability, and mitophagy. We propose that the KD DJ-1 perturbation diminishes the protective potency against oxidative stress. Thus, it renders the cells labile and responsive to the dsRNA signal by activating a large number of genes, many of which drive apoptosis, cell death, and inflammatory signatures. The KD of DJ-1 highlights its potency in regulating genes associated with antiviral responses, RNA metabolism, and mitochondrial functions, apparently through alteration in STAT activity and downstream signaling. Given that DJ-1 also acts as an oncogene in metastatic cancers, targeting DJ-1 could be a promising therapeutic strategy where manipulation of the DJ-1 level may reduce cancer cell viability and enhance the efficacy of cancer treatments. Full article

This study demonstrates the effectiveness of a multi-method approach for the restoration of a historic building (train station) in Poland. The project employed field investigations, laboratory analyses, and close-range photogrammetry to create a Historic Building Information Model (HBIM). This comprehensive data set informed the development of targeted conservation strategies that addressed the station’s specific needs while respecting its historical significance. Interventions prioritized the use of locally sourced and sustainable materials, minimized the visual impact on the exterior, and achieved net-zero emissions through improvements to the building envelope and a switch to a heat pump heating system. Additionally, an intelligent monitoring system was implemented to continuously collect data on environmental conditions and structural displacement. These data will be used to develop a predictive model for future maintenance needs, allowing for a preventative approach to conservation and minimizing resource consumption. Overall, this project serves as a model for integrating advanced technologies in historical building conservation, promoting sustainable practices, and ensuring the longevity of irreplaceable cultural landmarks. The key findings derived from this approach encompass a comprehensive assessment of the station’s condition, optimized conservation strategies, insights from HBIM modeling, and the ongoing benefits of the intelligent monitoring system. Field investigations revealed several areas of concern, such as structural cracks, material deterioration, moisture infiltration, and significant heat loss through the building envelope. This information was crucial for developing targeted conservation strategies. The use of internal thermal insulation systems, particularly capillary active mineral blocks, significantly improved thermal performance. Moisture management interventions, including the restoration of the rainwater drainage system and the application of moisture-proof insulation, reduced reliance on the municipal water supply. The intelligent monitoring system, with sensors measuring temperature, humidity, and structural displacement, plays a crucial role in ongoing conservation efforts. This system allows for continuous monitoring and the development of predictive models, ensuring targeted and preventative maintenance, reducing resource consumption, and extending the lifespan of the building. Full article

This evaluation of deep learning and traditional machine learning methods for tool state recognition in milling processes aims to automate furniture manufacturing. It compares the performance of long short-term memory (LSTM) networks, support vector machines (SVMs), and boosting ensemble decision trees, utilizing sensor data from a CNC machining center. These methods focus on the challenges and importance of feature selection, data preprocessing, and the application of tailored machine learning models to specific industrial tasks. Results show that SVM, with an accuracy of 96%, excels in handling high-dimensional data and robust feature extraction. In contrast, LSTM, which is appropriate for sequential data, is constrained by limited training data and the absence of pre-trained networks. Boosting ensemble decision trees also demonstrate efficacy in reducing model bias and variance. Conclusively, selecting an optimal machine learning strategy is crucial, depending on task complexity and data characteristics, highlighting the need for further research into domain-specific models to improve performance in industrial settings. Full article

Bluetooth sensors in intelligent transportation systems possess extensive coverage and access to a large number of identity (ID) data, but they cannot distinguish between vehicles and persons. This study aims to classify and differentiate raw data collected from Bluetooth sensors positioned between various origin–destination (i–j) points into vehicles and persons and to determine their distribution ratios. To reduce data noise, two different filtering algorithms are proposed. The first algorithm employs time series simplification based on Simple Moving Average (SMA) and threshold models, which are tools of statistical analysis. The second algorithm is rule-based, using speed data of Bluetooth devices derived from sensor data to provide a simplification algorithm. The study area was the Historic Peninsula Traffic Cord Region of Istanbul, utilizing data from 39 sensors in the region. As a result of time-based filtering, the ratio of person ID addresses for Bluetooth devices participating in circulation in the region was found to be 65.57% (397,799 person IDs), while the ratio of vehicle ID addresses was 34.43% (208,941 vehicle IDs). In contrast, the rule-based algorithm based on speed data found that the ratio of vehicle ID addresses was 35.82% (389,392 vehicle IDs), while the ratio of person ID addresses was 64.17% (217,348 person IDs). The Jaccard similarity coefficient was utilized to identify similarities in the data obtained from the applied filtering approaches, yielding a coefficient (J) of 0.628. The identity addresses of the vehicles common throughout the two date sets which are obtained represent the sampling size for traffic measurements. Full article

Industry 4.0 integrates the intelligent networking of machines and processes through advanced information and communication technologies (ICTs). Despite advancements, small mechanical manufacturing enterprises face significant challenges transitioning to ICT-supported Industry 4.0 models due to a lack of technical expertise and infrastructure. These enterprises commonly encounter variable production volumes, differing priorities in customer orders, and diverse production capacities across low-, medium-, and high-level outputs. Frequent issues with machine health, glitches, and major breakdowns further complicate optimizing production scheduling. This paper presents a novel production management approach that harnesses bio-inspired methods alongside Internet of Things (IoT) technology to address these challenges. This comprehensive approach integrates the real-time monitoring and intelligent production order distribution, leveraging advanced LoRa wireless communication technology. The system ensures efficient and concurrent data acquisition from multiple sensors, facilitating accurate and prompt capture, transmission, and storage of machine status data. The experimental results demonstrate significant improvements in data collection time and system responsiveness, enabling the timely detection and resolution of machine failures. Additionally, an enhanced genetic algorithm dynamically allocates tasks based on machine status, effectively reducing production completion time and machine idle time. Case studies in a screw manufacturing facility validate the practical applicability and effectiveness of the proposed system. The seamless integration of the scheduling algorithm with the real-time monitoring subsystem ensures a coordinated and efficient production process, ultimately enhancing productivity and resource utilization. The proposed system’s robustness and efficiency highlight its potential to revolutionize production management in small-scale manufacturing settings. Full article

This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical grid or solar panels. These sensors were strategically placed throughout the densely populated areas of North Texas to collect data on PM levels, weather conditions, and other gases from September 2021 to June 2023. The collected data were then used to create models that predict PM concentrations in different size categories, demonstrating high accuracy with correlation coefficients greater than 0.9. This highlights the importance of collecting hyperlocal data with precise geographic and temporal alignment for PM analysis. Furthermore, we expanded our analysis to a national scale by developing machine learning models that estimate hourly PM${ }_{2.5}$ levels throughout the continental United States. These models used high-resolution data from the Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) dataset, along with meteorological data from the European Center for Medium-Range Weather Forecasting (ECMWF), AOD reanalysis, and air pollutant information from the MERRA-2 database, covering the period from January 2020 to June 2023. Our models were refined using ground truth data from our IoT sensor network, the OpenAQ network, and the National Environmental Protection Agency (EPA) network, enhancing the accuracy of our remote sensing PM estimates. The findings demonstrate that the combination of AOD data with meteorological analyses and additional datasets can effectively model PM${ }_{2.5}$ concentrations, achieving a significant correlation coefficient of 0.849. The reconstructed PM${ }_{2.5}$ surfaces created in this study are invaluable for monitoring pollution events and performing detailed PM${ }_{2.5}$ analyses. These results were further validated through real-world observations from two in situ MINTS sensors located in Joppa (South Dallas) and Austin, confirming the effectiveness of our comprehensive approach to PM analysis. The US Environmental Protection Agency (EPA) recently updated the national standard for PM${ }_{2.5}$ to 9 $\mathsf{\mu }$g/m${ }^3$, a move aimed at significantly reducing air pollution and protecting public health by lowering the allowable concentration of harmful fine particles in the air. Using our analysis approach to reconstruct the fine-time resolution PM${ }_{2.5}$ distribution across the entire United States for our study period, we found that the entire nation encountered PM${ }_{2.5}$ levels that exceeded 9 $\mathsf{\mu }$g/m${ }^3$ for more than 20% of the time of our analysis period, with the eastern United States and California experiencing concentrations exceeding 9 $\mathsf{\mu }$g/m${ }^3$ for over 50% of the time, highlighting the importance of regulatory efforts to maintain annual PM${ }_{2.5}$ concentrations below 9 $\mathsf{\mu }$g/m${ }^3$. Full article

In the realm of electrochemical nitrite detection, the potent oxidizing nature of nitrite typically necessitates operation at high detection potentials. However, this study introduces a novel approach to address this challenge by developing a highly sensitive electrochemical sensor with a low reduction detection potential. Specifically, a copper metal nanosheet/carbon paper sensitive electrode (Cu/CP) was fabricated using a one-step electrodeposition method, leveraging the catalytic reduction properties of copper’s high occupancy d-orbital. The Cu/CP sensor exhibited remarkable performance in nitrite detection, featuring a low detection potential of −0.05 V vs. Hg/HgO, a wide linear range of 10~1000 μM, an impressive detection limit of 0.079 μM (S/N = 3), and a high sensitivity of 2140 μA mM⁻¹cm⁻². These findings underscore the efficacy of electrochemical nitrite detection through catalytic reduction as a means to reduce the operational voltage of the sensor. By showcasing the successful implementation of this strategy, this work sets a valuable precedent for the advancement of electrochemical low-potential nitrite detection methodologies. Full article

The article’s main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy and reliability of readings. A neural network has been developed that integrates closed loops for the helicopter turboshaft engine parameters, which are regulated based on the filtering method. This made achieving almost 100% (0.995 or 99.5%) accuracy possible and reduced the loss function to 0.005 (0.5%) after 280 training epochs. An algorithm has been developed for neural network training based on the errors in backpropagation for closed loops, integrating the helicopter turboshaft engine parameters regulated based on the filtering method. It combines increasing the validation set accuracy and controlling overfitting, considering error dynamics, which preserves the model generalization ability. The adaptive training rate improves adaptation to the data changes and training conditions, improving performance. It has been mathematically proven that the helicopter turboshaft engine parameters regulating neural network closed-loop integration using the filtering method, in comparison with traditional filters (median-recursive, recursive and median), significantly improve efficiency. Moreover, that enables reduction of the errors of the 1st and 2nd types: 2.11 times compared to the median-recursive filter, 2.89 times compared to the recursive filter, and 4.18 times compared to the median filter. The achieved results significantly increase the helicopter turboshaft engine sensor readings accuracy (up to 99.5%) and reliability, ensuring aircraft efficient and safe operations thanks to improved filtering methods and neural network data integration. These advances open up new prospects for the aviation industry, improving operational efficiency and overall helicopter flight safety through advanced data processing technologies. Full article