Effects of Wet-Pressing and Cross-Linking on the Tensile Properties of Carbon Nanotube Fibers

To increase the strength of carbon nanotube (CNT) fibers (CNTFs), the mean size of voids between bundles of CNTs was reduced by wet-pressing, and the CNTs were cross-linked. Separate and simultaneous physical (roller pressing) and chemical methods (cross-linking) were tested to confirm each method's effects on the CNTF strength. By reducing the fraction of pores, roller pressing decreased the cross-sectional area from 160 mu m(2) to 66 mu m(2) and increased the average load-at-break from 2.83 +/- 0.25 cN to 4.41 +/- 0.16 cN. Simultaneous injection of crosslinker and roller pressing augmented the cross-linking effect by increasing the infiltration of the crosslinker solution into the CNTF, so the specific strength increased from 0.40 +/- 0.05 N/tex to 0.67 +/- 0.04 N/tex. To increase the strength by cross-linking, it was necessary that the size of the pores inside the CNTF were reduced, and the infiltration of the solution was increased. These results suggest that combined physical and chemical treatment is effective to increase the strength of CNTFs.11Ysciescopu

Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications

This paper proposes a novel centralized training and distributed execution (CTDE)-based multi-agent deep reinforcement learning (MADRL) method for multiple unmanned aerial vehicles (UAVs) control in autonomous mobile access applications. For the purpose, a single neural network is utilized in centralized training for cooperation among multiple agents while maximizing the total quality of service (QoS) in mobile access applications.Comment: 2 pages, 4 figure

Gypsum-Dependent Effect of NaCl on Strength Enhancement of CaO-Activated Slag Binders

This study explores the combined effect of NaCl and gypsum on the strength of the CaO-activated ground-granulated blast furnace slag (GGBFS) binder system. In the CaO-activated GGBFS system, the incorporation of NaCl without gypsum did not improve the strength of the system. However, with the presence of gypsum, the use of NaCl yielded significantly greater strength than the use of either gypsum or NaCl alone. The presence of NaCl largely increases the solubility of gypsum in a solution, leading to a higher concentration of sulfate ions, which is essential for generating more and faster formations of ettringite in a fresh mixture of paste. The significant strength enhancement of gypsum was likely due to the accelerated and increased formation of ettringite, accompanied by more efficient filling of pores in the system

SlAction: Non-intrusive, Lightweight Obstructive Sleep Apnea Detection using Infrared Video

Obstructive sleep apnea (OSA) is a prevalent sleep disorder affecting approximately one billion people world-wide. The current gold standard for diagnosing OSA, Polysomnography (PSG), involves an overnight hospital stay with multiple attached sensors, leading to potential inaccuracies due to the first-night effect. To address this, we present SlAction, a non-intrusive OSA detection system for daily sleep environments using infrared videos. Recognizing that sleep videos exhibit minimal motion, this work investigates the fundamental question: "Are respiratory events adequately reflected in human motions during sleep?" Analyzing the largest sleep video dataset of 5,098 hours, we establish correlations between OSA events and human motions during sleep. Our approach uses a low frame rate (2.5 FPS), a large size (60 seconds) and step (30 seconds) for sliding window analysis to capture slow and long-term motions related to OSA. Furthermore, we utilize a lightweight deep neural network for resource-constrained devices, ensuring all video streams are processed locally without compromising privacy. Evaluations show that SlAction achieves an average F1 score of 87.6% in detecting OSA across various environments. Implementing SlAction on NVIDIA Jetson Nano enables real-time inference (~3 seconds for a 60-second video clip), highlighting its potential for early detection and personalized treatment of OSA.Comment: Accepted to ICCV CVAMD 2023, poste

An Integrated Raw Data Simulator for Airborne Spotlight ECCM SAR

Airborne synthetic aperture radar (SAR) systems often encounter the threats of interceptors or electronic countermeasures (ECM) and suffer from motion measurement errors. In order to design and analyze SAR systems while considering such threats and errors, an integrated raw data simulator is proposed for airborne spotlight electronic counter-countermeasure (ECCM) SAR. The raw data for reflected echo signals and jamming signals are generated in arbitrary waveform to achieve pulse diversity. The echo signals are simulated based on the scene model computed through the inverse polar reformatting of the reflectivity map. The reflectivity map is generated by applying a noise-like speckle to an arbitrary grayscale optical image. The received jamming signals are generated by the jamming model, and their powers are determined by the jamming equivalent sigma zero (JESZ), a newly proposed quantitative measure for designing ECCM SAR systems. The phase errors due to the inaccuracy of the navigation system are also considered in the design of the proposed simulator, as navigation sensor errors were added in the motion measurement process, with the results used for the motion compensation. The validity and usefulness of the proposed simulator is verified through the simulation of autofocus algorithms, SAR jamming, and SAR ECCM with pulse diversity. Various types of autofocus algorithms were performed through the proposed simulator and, as a result, the performance trends were identified to be similar to those of the real data from actual flight tests. The simulation results of the SAR jamming and SAR ECCM indicate that the proposed JESZ is well-defined measure for quantifying the power requirements of ECCM SAR and SAR jammers

Quantitative Measurement of Carbon Nanotube Liquid Crystalline Transition

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Truthful electric vehicle charging via neural-architectural Myerson auction

The electric vehicle (EV) market increases due to the benefits of reducing greenhouse gas emissions using renewable energy resources. In this context, the charging scheme of electric vehicles in charging stations (CSs) is also important. Electronic devices’ charging between EV and multiple CS should consider EV’s short battery capacity, long charging time, residual energy in each CS, and time of use (ToU) for charging. In this paper, multiple CSs compete to offer electricity charging to a single EV. Based on this need, this paper proposes a deep learning-based auction which increases the charging amounts using Myerson auction while preserving truthfulness

Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge Graphs

Recommender systems play a crucial role in personalizing online user experiences by creating user profiles based on user–item interactions and preferences. Knowledge graphs (KGs) are intricate data structures that encapsulate semantic information, expressing users and items in a meaningful way. Although recent deep learning-based recommendation algorithms that embed KGs have demonstrated impressive performance, the richness of semantics and explainability embedded in the KGs are often lost due to the opaque nature of vector representations in deep neural networks. To address this issue, we propose a novel user profiling method for recommender systems that can encapsulate user preferences while preserving the original semantics of the KGs, using frequent subgraph mining. Our approach involves creating user profile vectors from a set of frequent subgraphs that contain information about user preferences and the strength of those preferences, measured by frequency. Subsequently, we trained a deep neural network model to learn the relationship between users and items, thereby facilitating effective recommendations using the neural network’s approximation ability. We evaluated our user profiling methodology on movie data and found that it demonstrated competitive performance, indicating that our approach can accurately represent user preferences while maintaining the semantics of the KGs. This work, therefore, presents a significant step towards creating more transparent and effective recommender systems that can be beneficial for a wide range of applications and readers interested in this field

Investigation of shear-induced rearrangement of carbon nanotube bundles using Taylor-Couette flow

Macroscopic assemblies of carbon nanotubes (CNTs) usually have a poor alignment and a low packing density due to their hierarchical structure. To realize the inherent properties of CNTs at the macroscopic scale, the CNT assemblies should have a highly aligned and densified structure. Shear-aligning processes are commonly employed for this purpose. This work investigates how shear flows affect the rearrangement of CNT bundles in macroscopic assemblies. We propose that buckling behavior of CNT bundles in a shear flow causes the poor alignment of CNT bundles and a low packing density of CNT assemblies; the flow pattern and the magnitude of shear stress induced by the flow are key factors to regulate this buckling behavior. To obtain CNT assemblies with a high packing density, the CNTs should undergo a laminar flow that has a sufficiently low shear stress. Understanding the effect of shear flow on the structure of CNT bundles may guide improvement of fabrication strategies.11Ysciescopu

Learning Knowledge Using Frequent Subgraph Mining from Ontology Graph Data

In many areas, vast amounts of information are rapidly accumulating in the form of ontology-based knowledge graphs, and the use of information in these forms of knowledge graphs is becoming increasingly important. This study proposes a novel method for efficiently learning frequent subgraphs (i.e., knowledge) from ontology-based graph data. An ontology-based large-scale graph is decomposed into small unit subgraphs, which are used as the unit to calculate the frequency of the subgraph. The frequent subgraphs are extracted through candidate generation and chunking processes. To verify the usefulness of the extracted frequent subgraphs, the methodology was applied to movie rating prediction. Using the frequent subgraphs as user profiles, the graph similarity between the rating graph and new item graph was calculated to predict the rating. The MovieLens dataset was used for the experiment, and a comparison showed that the proposed method outperformed other widely used recommendation methods. This study is meaningful in that it proposed an efficient method for extracting frequent subgraphs while maintaining semantic information and considering scalability in large-scale graphs. Furthermore, the proposed method can provide results that include semantic information to serve as a logical basis for rating prediction or recommendation, which existing methods are unable to provide