Search Results (6)

To ensure the operational safety of oil transportation stations, it is crucial to predict the impact of pressure and temperature before crude oil enters the pipeline network. Accurate predictions enable the assessment of the pipeline’s load-bearing capacity and the prevention of potential safety incidents. Most existing studies primarily focus on describing and modeling the mechanisms of the oil flow process. However, monitoring data can be skewed by factors such as instrument aging and pipeline friction, leading to inaccurate predictions when relying solely on mechanistic or data-driven approaches. To address these limitations, this paper proposes a Temporal-Spatial Three-stream Temporal Convolutional Network (TS-TTCN) model that integrates mechanistic knowledge with data-driven methods. Building upon Temporal Convolutional Networks (TCN), the TS-TTCN model synthesizes mechanistic insights into the oil transport process to establish a hybrid driving mechanism. In the temporal dimension, it incorporates real-time operating parameters and applies temporal convolution techniques to capture the time-series characteristics of the oil transportation pipeline network. In the spatial dimension, it constructs a directed topological map based on the pipeline network’s node structure to characterize spatial features. Data analysis and experimental results show that the Three-stream Temporal Convolutional Network (TTCN) model, which uses a Tanh activation function, achieves an error rate below 5%. By analyzing and validating real-time data from the Dongying oil transportation station, the proposed hybrid model proves to be more stable, reliable, and accurate under varying operating conditions. Full article

In recent years, the Internet of Things (IoT) has not only become ubiquitous in daily life but has also emerged as a pivotal technology across various sectors, including smart factories and smart cities. Consequently, there is a pressing need to ensure the consistent and uninterrupted delivery of IoT services. Conformance testing has thus become an integral aspect of IoT technologies. However, traditional methods of IoT conformance testing fall short of addressing the evolving requirements put forth by both industry and academia. Historically, IoT testing has necessitated a visit to a testing laboratory, implying that both the testing systems and testers must be co-located. Furthermore, there is a notable absence of a comprehensive method for testing an array of IoT standards, especially given their inherent heterogeneity. With a surge in the development of diverse IoT standards, crafting an appropriate testing environment poses challenges. To address these concerns, this article introduces a method for remote IoT conformance testing, underpinned by a novel conceptual architecture termed CLOCIS. This architecture encompasses an extensible approach tailored for a myriad of IoT standards. Moreover, we elucidate the methods and procedures integral to testing IoT devices. CLOCIS, predicated on this conceptual framework, is actualized, and to attest to its viability, we undertake IoT conformance testing and present the results. When leveraging CLOCIS, small and medium-sized enterprises (SMEs) and entities in the throes of IoT service development stand to benefit from a reduced time to market and cost-efficient testing procedures. Additionally, this innovation holds promise for IoT standardization communities, enabling them to champion their standards with renewed vigor. Full article

As shipping continues to play an increasingly important role in world trade, there are consequently a large number of ships at sea at any given time, posing a risk to maritime traffic safety. Therefore, the tracking and monitoring of ships at sea has gradually attracted the attention of scholars. Ship trajectory prediction comprises an important aspect of ship tracking and monitoring. Trajectory prediction describes the forecasting of a ship’s future trajectory over a period of time through use of historical trajectory information of the ship, so as to predict the sailing dynamics of the ship in advance. Accurate trajectory prediction can help maritime regulatory authorities improve supervision efficiency and reduce collisions between ships. Temporal Convolutional Network (TCN) offers good time memory ability and has shown better performance in time series prediction in recent years. Ship trajectory sequence belongs to the category of time series. Thus, in this paper, we introduce TCN into the field of ship trajectory prediction and improve on it, and propose Tiered-TCN (TTCN). The attention mechanism is a way to help neural networks learn data features by highlighting features that have a greater impact on predicted values. Gate Recurrent Unit (GRU) is an important variant of Recurrent Neural Networks (RNN), which bears a strong nonlinear fitting ability. In this paper, TTCN, attention mechanism and GRU network are integrated to construct a hybrid model for trajectory prediction, which is referred to as TTCN-Attention-GRU (TTAG). By optimizing the advantages of each module, the prediction effect is achieved with high precision. The experimental results show that the TTAG model is superior to all the baseline models presented in this paper. Full article

As life expectancy increases, the prevalence of osteoporosis is increasing. In addition to vitamin D which is well established to have an association with osteoporosis, B vitamins, such as thiamine, folate (vitamin B9), and cobalamin (vitamin B12), could affect bone metabolism, bone quality, and fracture risk in humans by influencing homocysteine/folate metabolism. Despite the crucial role of B vitamins in bone metabolism, there are few studies regarding associations between B vitamin-related genes and osteoporosis. In this study, we investigated the genetic association of four single nucleotide polymorphisms (SNPs) within the 3’-untranslated regions of vitamin B-related genes, including TCN2 (encodes transcobalamin II), CD320 (encodes transcobalamin II receptor), SLC19A1 (encodes reduced folate carrier protein 1), and SLC19A2 (encodes thiamine carrier 1), with osteoporosis and osteoporotic vertebral compression fracture (OVCF). We recruited 301 postmenopausal women and performed genotyping of CD320 rs9426 C>T, TCN2 rs10418 C>T, SLC19A1 rs1051296 G>T, and SLC19A2 rs16862199 C>T using a polymerization chain reaction-restriction fragment length polymorphism assay. There was a significantly higher incidence of both osteoporosis (AOR 5.019; 95% CI, 1.533–16.430, p < 0.05) and OVCF (AOR, 5.760; 95% CI, 1.480–22.417, p < 0.05) in individuals with genotype CD320 CT+TT and high homocysteine concentrations. Allele combination analysis revealed that two combinations, namely CD320 C-TCN2 T-SLC19A1 T-SLC19A2 C (OR, 3.244; 95% CI, 1.478–7.120, p < 0.05) and CD320 T-TCN2 C-SLC19A1 G-SLC19A2 C (OR, 2.287; 95% CI, 1.094–4.782, p < 0.05), were significantly more frequent among the osteoporosis group. Our findings suggest that SNPs within the CD320 gene in 3´-UTR may contribute to osteoporosis and OVCF occurrences in some individuals. Furthermore, specific allele combinations of CD320, TCN2, SLC19A1, and SLC19A2 may contribute to increased susceptibility to osteoporosis and OVCF. Full article

The electric vehicle market is rapidly growing due to its environmental friendliness and governmental support. As electric vehicles are powered by electricity, the interoperability between the vehicles and the chargers made by multiple vendors is crucial for the success of the technology. Relevant standards are being published, but the methods for conformance testing need to be developed. In this paper, we present our conformance test system for the electric vehicle charger in accordance with the standards ISO/IEC 15118, IEC 61851 and IEC 61850-90-8. Our test system leverages the TTCN-3 framework for its flexibility and productivity. We evaluate the test system by lab tests with two reference chargers that we built. We also present the test results in two international testival events for the ISO/IEC 15118 interoperability. We confirmed that our test system is robust, efficient and practical. Full article

This paper reports on the use of nanoporous Pt-TiO₂ thin films coated onto magnetoelastic sensors for the detection of ethylene, an important plant growth hormone. Five different metal oxide coatings, TiO₂, TiO₂+ZrO₂, TiO₂+TTCN(1,4,7-Trithiacyclononane)+Ag, SiO₂+Fe, and TiO₂+Pt, each having demonstrated photocatalytic activity in response to ethylene, were investigated for their ability to change mass or elasticity in response to changing ethylene concentration. Pt-TiO₂ films were found to possess the highest sensitivities, and coupled with the magnetoelastic sensor platform capable of sensing ethylene levels of < 1 ppm. Full article