bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Gripper with Integrated Rotary Unit and Force Control for Pick and Place Applications
Authors: Romanov Alexey M. , Gyrichidi Ntmitrii , Romanov Mikhail P.
Published: December 2022

Modern electrical grippers have lower life-cycle costs compared to pneumatic ones. Furthermore, they provide force control, making it possible to grasp objects with different fragility using a single device. At the same time, electrical grippers have a higher end-effector weight, installed on the robot’s flange and lower closing speed, preventing them from replacing pneumatic solutions in high dynamic Pick and Place applications. This research faces both issues by synthesizing a novel gripper mechanism based on a Torque Distribution Gearbox, which makes it possible to relocate the electric motors to the static frame of a delta robot. The proposed gripper not only has a lower mass and a higher closing speed than competitive electric solutions, but it also provides unlimited rotation around the vertical axis. The performance of the gripper was tested in experimental studies, which showed that a created aluminum prototype provides a precise force control in the range from 3 N to 48 N with an accuracy not worse than 1.27 N. Moreover, its finger’s speed is 3.1–56 times higher than market available electrical grippers, which makes it comparable by this parameter with pneumatic solutions used in high dynamic Pick and Place applications.
The Atmospheric Correction of COCTS on the HY-1C and HY-1D Satellites
Authors: Mao Zhihua , Zhang Yiwei , Tao Bangyi , Chen Jianyu , Hao Zengzhou , Zhu Qiankun , Huang Haiqing
Published: December 2022

The data quality of the remote sensing reflectance (Rrs) from the two ocean color satellites HaiYang-1C (HY-1C) and HaiYang-1D (HY-1D) and the consistency with other satellites are critical for the products. The Layer Removal Scheme for Atmospheric Correction (LRSAC) has been applied to process the data of the Chinese Ocean Color and Temperature Scanner (COCTS) on HY-1C/1D. The accuracy of the Rrs products was evaluated by the in situ dataset from the Marine Optical BuoY (MOBY) with a mean relative error (MRE) of −1.56% and a mean absolute relative error (MAE) of 17.31% for HY-1C. The MRE and MAE of HY-1D are 1.05% and 15.68%, respectively. The comparisons of the global daily Rrs imagery with the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra show an MRE of 10.94% and an MAE of 21.38%. The comparisons between HY-1D and Aqua exhibit similar results, with an MRE of 13.31% and an MAE of 21.46%. The percentages of valid pixels of the global daily images of HY-1C and HY-1D are 32.3% and 32.6%, much higher than that of Terra (11.9%) and Aqua (11.9%). The gaps in the 8-day composite images have been significantly reduced, with 83.9% of valid pixels for HY-1C and 85.4% for HY-1D, which are also much higher than that of Terra (52.9%) and Aqua (50.9%). The gaps due to the contamination of sun glint have been almost removed from the 3-day composite imagery, with valid pixels of 63.5% for HY-1C and 65.6% for HY-1D, which are higher than that of the 8-day imagery of Terra and Aqua. The patterns of HY-1C imagery exhibit a similarity with those of HY-1D, but they are different on a pixel scale, mainly due to the changes in the ocean dynamic features within 3 h. The evaluations of the COCTS indicate that the imagery of HY-1C/1D can be used as a kind of standard product.
Three-Dimensional Mapping on Lightning Discharge Processes Using Two VHF Broadband Interferometers
Authors: Sun Zhuling , Qie Xiushu , Liu Mingyuan , Jiang Rubin , Zhang Hongbo
Published: December 2022

Lightning Very-high-frequency (VHF) broadband interferometer has become an effective approach to map lightning channels in two dimensions with high time resolution. This paper reports an approach to mapping lightning channels in three dimensions (3D) using two simultaneous interferometers separated by about 10 km. A 3D mapping algorithm was developed based on the triangular intersection method considering the location accuracy of both interferometers and the arrival time of lightning VHF radiation. Simulation results reveal that the horizontal and vertical location errors within 10 km of the center of the two stations are less than 500 m and 700 m, respectively. The 3D development of an intra-cloud (IC) lightning flash and a negative cloud-to-ground (-CG) lightning flash with two different ground terminations in the same thunderstorm are reconstructed, and the extension direction and speed of lightning channels are estimated consequently. Both IC and CG flash discharges showed a two-layer structure in the cloud with discharges occurring in the upper positive charge region and the lower negative charge region, and two horizontally separated positive charge regions were involved in the two flashes. The average distance of the CG ground terminations between the interferometer results and the CG location system was about 448 m. Although disadvantages may still exist in 3D real-time location compared with the lightning mapping array system working with the principle of the time of arrival, interferometry with two or more stations has the advantage of lower station number and is feasible in regions with poor installation conditions, such as heavy-radio-frequency-noise regions or regions that are difficult for the long-baseline location system.
A Robust Adaptive Filtering Algorithm for GNSS Single-Frequency RTK of Smartphone
Authors: Li Yuxing , Mi Jinzhong , Xu Yantian , Li Bo , Jiang Dingxuan , Liu Weifeng
Published: December 2022

In this paper, a single-frequency real-time kinematic positioning (RTK) robust adaptive Kalman filtering algorithm is proposed in order to realize real-time dynamic high-precision positioning of smartphone global navigation satellite systems (GNSSs). A robust model is established by using the quartile method to dynamically determine the threshold value and eliminate the gross error of observation. The Institute of Geodesy and Geophysics Ⅲ (IGG Ⅲ) weight function is used to construct the position and speed classification adaptive factors to weaken the impact of state mutation errors. Based on the analysis of the measured data of Xiaomi 8 and Huawei P40 smartphones, simulated dynamic tests show that the overall accuracy of the Xiaomi 8 is improved by more than 85% with the proposed robust RTK algorithm, and the overall positioning error is less than 0.5 m in both open and sheltered environments. The overall accuracy of the Huawei P40 is improved by more than 25%. Furthermore, the overall positioning accuracy is better than 0.3 m in open environments, and about 0.8 m in blocked situations. Dynamic experiments show that the use of the robust adaptive RTK algorithm improves the full-time solution planar positioning accuracy of the Xiaomi 8 by more than 15%. In addition, the planar positioning accuracy under open and occluded conditions is 0.8 m and 1.5 m, respectively, and the overall positioning accuracy of key nodes whose movement state exhibits major changes improves by more than 20%.
Integration of Terrestrial Laser Scanner (TLS) and Ground Penetrating Radar (GPR) to Characterize the Three-Dimensional (3D) Geometry of the Maoyaba Segment of the Litang Fault, Southeastern Tibetan Plateau
Authors: Zhang Di , Wu Zhonghai , Shi Danni , Li Jiacun , Lu Yan
Published: December 2022

High-resolution topographic and stratigraphic datasets have been increasing applied in active fault investigation and seismic hazard assessment. There is a need for the comprehensive analysis of active faults on the basis of the correlating geomorphologic features and stratigraphic data. The integration of TLS and GPR was adopted to characterize the 3D geometry of the fault on the Maoyaba segment of Litang fault. The TLS was used to obtain the high-resolution topographic data for establishing the 3D surficial model of the fault. The 2D 250 MHz and 500 MHz GPR profiles were carried out to image the shallow geometry of the fault along four survey lines. In addition, the 3D GPR survey was performed by ten 2D 500 MHz GPR profiles with 1 m spacing. From the 2D and 3D GPR results, a wedge-shaped deformation zone of the electromagnetic wave was clearly found on the GPR profiles, and it was considered to be the main fault zone with a small graben structure. Three faults were identified on the main fault zone, and fault F1 and F3 were the boundary faults, while the fault F2 was the secondary fault. The subsurface geometry of the fault on the GPR interpreted results is consistent with the geomorphologic features of the TLS-derived data, and it indicates that the Maoyaba fault is a typical, normal fault. For reducing the environmental disruption and economic losses, GPR was the most optimal method for detecting the subsurface structures of active faults in the Litang fault with a non-destructive and cost-effective fashion. The 3D surface and subsurface geometry of the fault was interpreted from the integrated data of TLS and GPR. The fusion data also offers the chance for the subsurface structures of active faults on the GPR profiles to be better understood with its corresponding superficial features. The study results demonstrate that the integration of TLS and GPR has the capability to obtain the high-resolution micro geomorphology and shallow geometry of active faults on the Maoyaba segment of the Litang fault, and it also provides a future prospect for the integration of TLS and GPR, and is valuable for active fault investigation and seismic hazard assessment, especially in the Qinghai-Tibet Plateau area.
Comprehensive Evaluation of Data-Related Factors on BDS-3 B1I + B2b Real-Time PPP/INS Tightly Coupled Integration
Authors: Kan Junyao , Gao Zhouzheng , Xu Qiaozhuang , Lan Ruohua , Lv Jie , Yang Cheng
Published: December 2022

Owing to the developments of satellite-based and network-based real-time satellite precise products, the Precise Point Positioning (PPP) technique has been applied far and wide, especially since the PPP-B2b service was provided by the third-generation BeiDou Navigation Satellite System (BDS-3). However, satellite outages during dynamic application lead to significant degradation of the accuracy and continuity of PPP. A generally used method is integrating PPP with Inertial Measurement Units (IMUs) to enhance positioning performance. Previous works on this topic are usually based on IMU data at a high sampling rate and are mostly implemented in post-processing mode. This paper will carry out a compressive assessment of the impacts of different types of precise satellite products (real-time products from the CAS, DLR, GFZ, WHU, and the final one from GFZ), Doppler observations, and different sampling rates of IMU data on the performance of the tightly coupled integration of the BDS-3 B1I/B2b and the Inertial Navigation System (INS). Results based on a group of on-board experimental data illustrate that (1) the positioning accuracy with products supplied by the CAS and WHU are roughly consistent with those using the final products; (2) the Doppler observations can effectively improve the accuracies of velocity, attitude, and vertical position at the initial epochs and during the reconvergence periods, but have invisible influences on the overall positioning, velocity, and attitude determination; and (3) the impact of IMU data interval on the performance of PPP/INS tightly coupled integration is insignificant when there are enough available satellites. However, the divergent speed of position is visibly affected by the IMU sampling rate during satellite outage periods.
An Improved Exponential Model Considering a Spectrally Effective Moisture Threshold for Proximal Hyperspectral Reflectance Simulation and Soil Salinity Estimation
Authors: Huang Xi , Bai Tiecheng , Guan Huade , Wei Xiayong , Wang Yali , Mao Xiaomin
Published: December 2022

Soil salinization has become one of the main factors restricting sustainable development of agriculture. Field spectrometry provides a quick way to predict the soil salinization. However, soil moisture content (SMC) seriously interferes with the spectral information of saline soil in arid areas. It is vital to establish a model that is insensitive to SMC for potential in situ field applications. The soil spectral reflectance exponential model (SSREM) has been widely employed for reflectance simulation and SSC inversion. However, its reliability for saline soils with high SMC has not been verified yet. Based on hyperspectral remote sensing data (400~1000 nm) on 459 saline soil samples in Shiyang River Basin of Northwest China, we investigated the role of SMC and SSC in soil spectral reflectance from 29 October 2020 to 22 January 2021. Targeted at saline soils, soil spectral moisture threshold (MT) was introduced to improve the SSREM toward a modified spectral reflectance exponential model (MT-SSREM). The bands that are sensitive to SSC but not sensitive to SMC were obtained based on a method of correlation analysis between original spectra, four kinds of spectral data, and SSC. SSREM and MT-SSREM were finally applied to inversely estimate SSC. Results show that wavelengths at 658~660, 671~685, 938 nm were suitable for SSC estimation. Furthermore, although SSREM was able to simulate the spectral reflectance of most saline soils, its simulation accuracy was low for saline soil samples with high SMC (SMC > MT(i), 400 nm≤i≤1000 nm), while MT-SSREM performed well over the whole range of SMC. The simulated spectral reflectance from MT-SSREM agreed well with the measured reflectance, with the R2 being generally larger than 0.9 and RMSE being less than 0.1. More importantly, MT-SSREM performed substantially better than SSREM for SSC estimation; in the statistical performance of the former case, R2 was in range of 0.60~0.66, RMSE was in range of 0.29~0.33 dS m−1; in the latter case, R2 was in range of 0.10~0.16, RMSE was in the range of 0.26~0.29 dS m−1. MT-SSREM proposed in this study thus provides a new direction for estimating hyperspectral reflectance and SSC under various soil moisture conditions at wavelengths from 400 to 1000 nm. It also provides an approach for SSC and SMC mapping in salinization regions by incorporating remote sensing data, such as GF-5.
Spatial Downscaling of NPP-VIIRS Nighttime Light Data Using Multiscale Geographically Weighted Regression and Multi-Source Variables
Authors: Liu Shangqin , Zhao Xizhi , Zhang Fuhao , Qiu Agen , Chen Liujia , Huang Jing , Chen Song , Zhang Shu
Published: December 2022

Remote sensing images of nighttime lights (NTL) were successfully used at global and regional scales for various applications, including studies on population, politics, economics, and environmental protection. The Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data has the advantages of high temporal resolution, long coverage time series, and wide spatial range. The spatial resolution of the monthly and annual composite data of NPP-VIIRS NTL is only 500 m, which hinders studies requiring higher resolution. We propose a multi-source spatial variable and Multiscale Geographically Weighted Regression (MGWR)-based method to achieve the downscaling of NPP-VIIRS NTL data. An MGWR downscaling framework was implemented to obtain NTL data at 120 m resolution based on auxiliary data representing socioeconomic or physical geographic attributes. The downscaled NTL data were validated against LuoJia1-01 imagery based on the coefficient of determination (R2) and the root-mean-square error (RMSE). The results suggested that the spatial resolution of the data was enhanced after downscaling, and the MGWR-based downscaling results demonstrated higher R2 (R2 = 0.9141) and lower RMSE than those of Geographically Weighted Regression and Random Forest-based algorithms. Additionally, MGWR can reveal the different relationships between multiple auxiliary and NTL data. Therefore, this study demonstrates that the spatial resolution of NPP-VIIRS NTL data is improved from 500 m to 120 m upon downscaling, thereby facilitating NTL-based applications.
Table-Based Adaptive Digital Phase-Locked Loop for GNSS Receivers Operating in Moon Exploration Missions
Authors: Song Young-Jin , Won Jong-Hoon
Published: December 2022

An adaptive digital phase-locked loop (DPLL) continually adjusts the noise bandwidth of the loop filter in global navigation satellite system (GNSS) receivers to track signals by measuring the signal-to-noise ratio and/or dynamic stress. Such DPLLs have a relatively large amount of computational complexity compared with the conventional DPLL. A table-based adaptive DPLL is proposed that adjusts the noise bandwidth value by extracting it from the pre-generated table without additional calculations. The values of the noise bandwidth table are computed in an optimal manner in consideration of the thermal noise, oscillator phase noise, and dynamic stress error. The calculation method of the proper integration time to maintain the stability of the loop filter is presented. Additionally, the simulation is configured using the trajectory analysis results from the Moon exploration mission and shows that the proposed algorithm operates stably in harsh environments, while a conventional fixed bandwidth loop cannot. The proposed algorithm has a similar phase jitter performance to the existing adaptive DPLL algorithms and has an execution time that is approximately 2.4–5.4 times faster. It is verified that the proposed algorithm is computationally efficient while maintaining jitter performance.
Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems
Authors: Li Yongbo , Li Bing , Ji Jinchen , Kalhori Hamed
Published: December 2022

Fault diagnosis and health condition monitoring have always been critical issues in the engineering research community [...]
Common Frame Dynamics for Conically-Constrained Spacecraft Attitude Control
Authors: Christopher Cruz Arnold , Bani Younes Ahmad
Published: December 2022

Attitude control subjected to pointing constraints is a requirement for most spacecraft missions carrying sensitive on-board equipment. Pointing constraints can be divided into two categories: exclusion zones that are defined for sensitive equipment such as telescopes or cameras that can be damaged from celestial objects, and inclusion zones that are defined for communication hardware and solar arrays. This work derives common frame dynamics that are fully derived for Modified Rodrigues Parameters and introduced to an existing novel technique for constrained spacecraft attitude control, which uses a kinematic steering law and servo sub-system. Lyapunov methods are used to redevelop the steering law and servo sub-system in the common frame for the tracking problem for both static and dynamic conic constraints. A numerical example and comparison between the original frame and the common frame for the static constrained tracking problem are presented under both unbounded and limited torque capabilities. Monte Carlo simulations are performed to validate the convergence of the constrained tracking problem for static conic constraints under small perturbations of the initial conditions. The performance of dynamic conic constraints in the tracking problem is addressed and a numerical example is presented. The result of using common frame dynamics in the constrained problem shows decreased control effort required to rotate the spacecraft.
Research on Smart Tourism Oriented Sensor Network Construction and Information Service Mode
Authors: Tang Ruomei , Huang Chenyue , Zhao Xinyu , Tang Yunbing
Published: December 2022

Smart tourism is the latest achievement of tourism development at home and abroad. It is also an essential part of the smart city. Promoting the application of computer and sensor technology in smart tourism is conducive to improving the efficiency of public tourism services and guiding the innovation of the tourism public service mode. In this paper, we have proposed a new method of using data collected by sensor networks. We have developed and deployed sensors to collect data, which are transmitted to the modular cloud platform, and combined with cluster technology and an Uncertain Support Vector Classifier (A-USVC) location prediction method to assist in emergency events. Considering the attraction of tourists, the system also incorporated human trajectory analysis and intensity of interaction as consideration factors to validate the spatial dynamics of different interests and enhance the tourists’ experience. The system explored the innovative road of computer technology to boost the development of smart tourism, which helps to promote the high-quality development of tourism.
Nanocomposite Based on HA/PVTMS/Cl2FeH8O4 as a Gas and Temperature Sensor
Authors: Nasiri Sohrab , Rabiei Marzieh , Markuniene Ieva , Hosseinnezhad Mozhgan , Ebrahimi-Kahrizsangi Reza , Palevicius Arvydas , Vilkauskas Andrius , Janusas Giedrius
Published: December 2022

In this paper, a novel nanocrystalline composite material of hydroxyapatite (HA)/polyvinyltrimethoxysilane (PVTMS)/iron(II)chloride tetrahydrate (Cl2FeH8-O4) with hexagonal structure is proposed for the fabrication of a gas/temperature sensor. Taking into account the sensitivity of HA to high temperatures, to prevent the collapse and breakdown of bonds and the leakage of volatiles without damaging the composite structure, a freeze-drying machine is designed and fabricated. X-ray diffraction, FTIR, SEM, EDAX, TEM, absorption and photoluminescence analyses of composite are studied. XRD is used to confirm the material structure and the crystallite size of the composite is calculated by the Monshi–Scherrer method, and a value of 81.60 ± 0.06 nm is obtained. The influence of the oxygen environment on the absorption and photoluminescence measurements of the composite and the influence of vaporized ethanol, N2 and CO on the SiO2/composite/Ag sensor device are investigated. The sensor with a 30 nm-thick layer of composite shows the highest response to vaporized ethanol, N2 and ambient CO. Overall, the composite and sensor exhibit a good selectivity to oxygen, vaporized ethanol, N2 and CO environments.
Defect Severity Identification for a Catenary System Based on Deep Semantic Learning
Authors: Wang Jian , Gao Shibin , Yu Long , Zhang Dongkai , Kou Lei
Published: December 2022

A variety of Chinese textual operational text data has been recorded during the operation and maintenance of the high-speed railway catenary system. Such defect text records can facilitate defect detection and defect severity analysis if mined efficiently and accurately. Therefore, in this context, this paper focuses on a specific problem in defect text mining, which is to efficiently extract defect-relevant information from catenary defect text records and automatically identify catenary defect severity. The specific task is transformed into a machine learning problem for defect text classification. First, we summarize the characteristics of catenary defect texts and construct a text dataset. Second, we use BERT to learn defect texts and generate word embedding vectors with contextual features, fed into the classification model. Third, we developed a deep text categorization network (DTCN) to distinguish the catenary defect level, considering the contextualized semantic features. Finally, the effectiveness of our proposed method (BERT-DTCN) is validated using a catenary defect textual dataset collected from 2016 to 2018 in the China Railway Administration in Chengdu, Lanzhou, and Hengshui. Moreover, BERT-DTCN outperforms several competitive methods in terms of accuracy, precision, recall, and F1-score value.
Using Wearable Sensors to Measure Goal Achievement in Older Veterans with Dementia
Authors: Freytag Jennifer , Mishra Ram Kinker , Street Richard L. , Catic Angela , Dindo Lilian , Kiefer Lea , Najafi Bijan , Naik Aanand D.
Published: December 2022

Aligning treatment with patients’ self-determined goals and health priorities is challenging in dementia care. Wearable-based remote health monitoring may facilitate determining the active participation of individuals with dementia towards achieving the determined goals. The present study aimed to demonstrate the feasibility of using wearables to assess healthcare goals set by older adults with cognitive impairment. We present four specific cases that assess (1) the feasibility of using wearables to monitor healthcare goals, (2) differences in function after goal-setting visits, and (3) goal achievement. Older veterans (n = 17) with cognitive impairment completed self-report assessments of mobility, then had an audio-recorded encounter with a geriatrician and wore a pendant sensor for 48 h. Follow-up was conducted at 4–6 months. Data obtained by wearables augments self-reported data and assessed function over time. Four patient cases illustrate the utility of combining sensors, self-report, notes from electronic health records, and visit transcripts at baseline and follow-up to assess goal achievement. Using data from multiple sources, we showed that the use of wearable devices could support clinical communication, mainly when patients, clinicians, and caregivers work to align care with the patient’s priorities.
Daily Changes in Channel Occupancy in the 868 MHz ISM Band
Authors: Kozłowski Sebastian , Kurek Krzysztof
Published: December 2022

Due to the rapid development of Internet of Things (IoT) systems operating in industrial, scientific and medical (ISM) frequency bands, many researchers have attempted to determine the amount of interference that can be expected in such systems. The basic information required for this purpose is the current occupancy of frequency channels in various geographical locations. It is known that the occupancy measurement must last long enough to allow for the detection of low duty cycle transmissions. In this paper, it is shown that fulfilling only this criterion may lead to unreliable results being obtained. In two measurement campaigns performed in two different locations, the occupancy of a selected sub-band in the 868 MHz ISM band was determined on the basis of two hour-long observations repeated several times a day. During a typical day, the ratio of the maximum and the minimum result depended on the location and reached a value of eight; however, on one day, a period of abnormally high channel usage reaching 65% was observed in the location in which typical values did not exceed 1%.
Differences in Driver Behavior between Manual and Automatic Turning of an Inverted Pendulum Vehicle
Authors: Nakagawa Chihiro , Yamada Seiya , Hirata Daichi , Shintani Atsuhiko
Published: December 2022

Personal mobility vehicles (PMVs) are compact and lightweight compared to automobiles; hence, human dynamic behavior affects a vehicle’s postural stability. In this study, the dynamic behaviors of drivers of inverted pendulum vehicles (IPV) under manual and automatic driving were investigated. One particular feature of applying automatic driving to IPV is constant posture stabilization control. In this study, the drivers’ center of gravity (COG)/center of foot pressure position (COP) and joint moments during turning were investigated experimentally. It was found that the drivers’ COG shifted backward during turning and deceleration. For COP, it was found that drivers maintained balance by moving their inner foot more inward and their outer foot more outward during turning. These results are significant for understanding the steps taken to withstand centrifugal forces during turning. The joint moments of the foot were more significant in automatic turning than in manual turning to prevent falling owing to centrifugal force. These findings can facilitate the development of an automatic control method that shifts the COG of a driver, as in manual turning.
Linear Spatial Misregistration Detection and Correction Based on Spectral Unmixing for FAHI Hyperspectral Imagery
Authors: Zhang Xiangyue , Cheng Xiaoyu , Xue Tianru , Wang Yueming
Published: December 2022

In push-broom hyperspectral imaging systems, the sensor rotation to the optical plane leads to linear spatial misregistration (LSM) in hyperspectral images (HSIs). To compensate for hardware defects through software, this paper develops four methods to detect LSM in HSIs. Different from traditional methods for grayscale images, the method of fitting the sum of abundance (FSAM) and the method of searching for equal abundance (SEAM) are achieved by hyperspectral unmixing for a selected rectangular transition areas containing an edge, which makes good use of spatial and spectral information. The method based on line detection for band-interleaved-by-line (BIL) images (LDBM) and the method based on the Fourier transform of BIL images (FTBM) aim to characterize the slope of line structure in BIL images and get rid of the dependence on scene and wavelength. A full strategy is detailed from aspects of data selection, LSM detection, and image correction. The full spectrum airborne hyperspectral imager (FAHI) is China’s new generation push-broom scanner. The HSIs obtained by FAHI are tested and analyzed. Experiments on simulation data compare the four proposed methods with traditional methods and prove that FSAM outperforms other methods in terms of accuracy and stability. In experiments on real data, the application of the full strategy on FAHI verifies its effectiveness. This work not only provides reference for other push-broom imagers with similar problems, but also helps to reduce the requirement for hardware calibration.
Can Gait Features Help in Differentiating Parkinson’s Disease Medication States and Severity Levels? A Machine Learning Approach
Authors: Chatzaki Chariklia , Skaramagkas Vasileios , Kefalopoulou Zinovia , Tachos Nikolaos , Kostikis Nicholas , Kanellos Foivos , Triantafyllou Eleftherios , Chroni Elisabeth , Fotiadis Dimitrios I. , Tsiknakis Manolis
Published: December 2022

Parkinson’s disease (PD) is one of the most prevalent neurological diseases, described by complex clinical phenotypes. The manifestations of PD include both motor and non-motor symptoms. We constituted an experimental protocol for the assessment of PD motor signs of lower extremities. Using a pair of sensor insoles, data were recorded from PD patients, Elderly and Adult groups. Assessment of PD patients has been performed by neurologists specialized in movement disorders using the Movement Disorder Society—Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)-Part III: Motor Examination, on both ON and OFF medication states. Using as a reference point the quantified metrics of MDS-UPDRS-Part III, severity levels were explored by classifying normal, mild, moderate, and severe levels of PD. Elaborating the recorded gait data, 18 temporal and spatial characteristics have been extracted. Subsequently, feature selection techniques were applied to reveal the dominant features to be used for four classification tasks. Specifically, for identifying relations between the spatial and temporal gait features on: PD and non-PD groups; PD, Elderly and Adults groups; PD and ON/OFF medication states; MDS-UPDRS: Part III and PD severity levels. AdaBoost, Extra Trees, and Random Forest classifiers, were trained and tested. Results showed a recognition accuracy of 88%, 73% and 81% for, the PD and non-PD groups, PD-related medication states, and PD severity levels relevant to MDS-UPDRS: Part III ratings, respectively.
Nanoporous Graphene Oxide-Based Quartz Crystal Microbalance Gas Sensor with Dual-Signal Responses for Trimethylamine Detection
Authors: Qi Guangyu , Qu Fangfang , Zhang Lu , Chen Shihao , Bai Mengyuan , Hu Mengjiao , Lv Xinyan , Zhang Jinglei , Wang Zhenhe , Chen Wei
Published: December 2022

This paper presents a straightforward method to develop a nanoporous graphene oxide (NGO)-functionalized quartz crystal microbalance (QCM) gas sensor for the detection of trimethylamine (TMA), aiming to form a reliable monitoring mechanism strategy for low-concentration TMA that can still cause serious odor nuisance. The synthesized NGO material was characterized by transmission electron microscopy, X-ray photoelectron spectroscopy, and Fourier transform infrared spectroscopy to verify its structure and morphology. Compared with the bare and GO-based QCM sensors, the NGO-based QCM sensor exhibited ultra-high sensitivity (65.23 Hz/μL), excellent linearity (R2 = 0.98), high response/recovery capability (3 s/20 s) and excellent repeatability (RSD = 0.02, n = 3) toward TMA with frequency shift and resistance. Furthermore, the selectivity of the proposed NGO-based sensor to TMA was verified by analysis of the dual-signal responses. It is also proved that increasing the conductivity did not improve the resistance signal. This work confirms that the proposed NGO-based sensor with dual signals provides a new avenue for TMA sensing, and the sensor is expected to become a potential candidate for gas detection.
Zinc Phthalocyanine Sensing Mechanism Quantification for Potential Application in Chemical Warfare Agent Detectors
Authors: Powroźnik Paulina , Solecka Barbara , Pander Piotr , Jakubik Wiesław , Dias Fernando B. , Krzywiecki Maciej
Published: December 2022

Rapid and accurate detection of lethal volatile compounds is an emerging requirement to ensure the security of the current and future society. Since the threats are becoming more complex, the assurance of future sensing devices’ performance can be obtained solely based on a thorough fundamental approach, by utilizing physics and chemistry together. In this work, we have applied thermal desorption spectroscopy (TDS) to study dimethyl methylophosphate (DMMP, sarin analogue) adsorption on zinc phthalocyanine (ZnPc), aiming to achieve the quantification of the sensing mechanism. Furthermore, we utilize a novel approach to TDS that involves quantum chemistry calculations for the determination of desorption activation energies. As a result, we have provided a comprehensive description of DMMP desorption processes from ZnPc, which is the basis for successful future applications of sarin ZnPc-based sensors. Finally, we have verified the sensing capability of the studied material at room temperature using impedance spectroscopy and took the final steps towards demonstrating ZnPc as a promising sarin sensor candidate.
Multiscale Cascaded Attention Network for Saliency Detection Based on ResNet
Authors: Jian Muwei , Jin Haodong , Liu Xiangyu , Zhang Linsong
Published: December 2022

Saliency detection is a key research topic in the field of computer vision. Humans can be accurately and quickly mesmerized by an area of interest in complex and changing scenes through the visual perception area of the brain. Although existing saliency-detection methods can achieve competent performance, they have deficiencies such as unclear margins of salient objects and the interference of background information on the saliency map. In this study, to improve the defects during saliency detection, a multiscale cascaded attention network was designed based on ResNet34. Different from the typical U-shaped encoding–decoding architecture, we devised a contextual feature extraction module to enhance the advanced semantic feature extraction. Specifically, a multiscale cascade block (MCB) and a lightweight channel attention (CA) module were added between the encoding and decoding networks for optimization. To address the blur edge issue, which is neglected by many previous approaches, we adopted the edge thinning module to carry out a deeper edge-thinning process on the output layer image. The experimental results illustrate that this method can achieve competitive saliency-detection performance, and the accuracy and recall rate are improved compared with those of other representative methods.
Method for Recognizing Pressing Position and Shear Force Using Active Acoustic Sensing on Gel Plates
Authors: Watanabe Hiroki , Sasaki Kaito , Terada Tsutomu , Tsukamoto Masahiko
Published: December 2022

A touch interface is an important technology used in many devices, including touch panels in smartphones. Many touch panels only detect the contact position. If devices can detect shear force in addition to the contact position, various touch interactions are possible. We propose a two-step recognition method for recognizing the pressing position and shear force using active acoustic sensing, which transmits acoustic signals to an object and recognizes the state of the object by analyzing its response. Specifically, we attach a contact speaker transmitting an ultrasonic sweep signal and a contact microphone receiving ultrasonic waves to a plate of gel. The propagation characteristics of ultrasonic waves differ due to changes in the shape of the gel caused by the user’s actions on the gel. This system recognizes the pressing position and shear force on the basis of the difference in acoustic characteristics. An evaluation of our method involving a user-independent model confirmed that four pressing positions were recognized with an F1 score of 85.4%, and four shear-force directions were recognized with an F1 score of 69.4%.
Morphological Configuration of Sensory Biomedical Receptors Based on Structures Integrated by Electric Circuits and Utilizing Magnetic-Responsive Hybrid Fluid (HF)
Authors: Shimada Kunio
Published: December 2022

Biomedical receptors such as cutaneous receptors or intelligent cells with tactile, auditory, gustatory, and olfactory sensations function in the five senses of the human body. Investigations focusing on the configuration of such receptors are useful in the fields of robotics and sensors in the food industry, among others, which involve artificial organs or sensory machines. In the present study, we aimed to produce the receptors for four senses (excepting vision) by morphologically mimicking virtual human ones. The mimicked receptors were categorized into eight types of configured structure. Our proposed magnetic-responsive hybrid fluid (HF) in elastic and soft rubber and proposed electrolytic polymerization technique gave the solidified HF rubber electric characteristics of piezoelectricity and piezo-capacity, among others. On the basis of these electric characteristics, the mimicked receptors were configured in various types of electric circuits. Through experimental estimation of mechanical force, vibration, thermal, auditory, gustatory, and olfactory responses of each receptor, the optimum function of each was specified by comparison with the actual sensations of the receptors. The effect of hairs fabricated in the receptors was also clarified to viably reproduce the distinctive functions of these sensations.
Intelligent Rolling Bearing Fault Diagnosis Method Using Symmetrized Dot Pattern Images and CBAM-DRN
Authors: Cui Wei , Meng Guoying , Gou Tingxi , Wang Aiming , Xiao Rui , Zhang Xinge
Published: December 2022

Rolling bearings are a vital component of mechanical equipment. It is crucial to implement rolling bearing fault diagnosis research to guarantee the stability of the long-term action of mechanical equipment. Conversion of rolling bearing vibration signals into images for fault diagnosis research has been a practical diagnostic approach. The current paper presents a rolling bearing fault diagnosis method using symmetrized dot pattern (SDP) images and a deep residual network with convolutional block attention module (CBAM-DRN). The rolling bearing vibration signal is first visualized and transformed into an SDP image with distinct fault characteristics. Then, CBAM-DRN is utilized to derive characteristics directly and detect faults from the input SDP images. In order to prevent conventional time-frequency images from being limited by their inherent flaws and avoid missing the fault features, the SDP technique is employed to convert vibration signals into images for visualization. DRN enables adequate extraction of rolling bearing fault characteristics and prevents training difficulties and gradient vanishing in deep level networks. CBAM assists the diagnostic model in concentrating on the image’s more distinctive parts and preventing the interference of non-featured parts. Finally, the method’s validity was tested with a composite fault dataset of motor bearings containing multiple loads and fault diameters. The experimental results reflect that the presented approach can attain a diagnostic precision of over 99% and good stability and generalization.
Research of Machine Learning Algorithms for the Development of Intrusion Detection Systems in 5G Mobile Networks and Beyond
Authors: Imanbayev Azamat , Tynymbayev Sakhybay , Odarchenko Roman , Gnatyuk Sergiy , Berdibayev Rat , Baikenov Alimzhan , Kaniyeva Nargiz
Published: December 2022

The introduction of fifth generation mobile networks is underway all over the world which makes many people think about the security of the network from any hacking. Over the past few years, researchers from around the world have raised this issue intensively as new technologies seek to integrate into many areas of business and human infrastructure. This paper proposes to implement an IDS (Intrusion Detection System) machine learning approach into the 5G core architecture to serve as part of the security architecture. This paper gives a brief overview of intrusion detection datasets and compares machine learning and deep learning algorithms for intrusion detection. The models are built on the basis of two network data CICIDS2017 and CSE-CIC-IDS-2018. After testing, the ML and DL models are compared to find the best fit with a high level of accuracy. Gradient Boost emerged as the top method when we compared the best results based on metrics, displaying 99.3% for a secure dataset and 96.4% for attacks on the test set.
Unconscious Other’s Impression Changer: A Method to Manipulate Cognitive Biases That Subtly Change Others’ Impressions Positively/Negatively by Making AI Bias in Emotion Estimation AI
Authors: Futami Kyosuke , Yanase Sadahiro , Murao Kazuya , Terada Tsutomu
Published: December 2022

Artificial Intelligence (AI) for human emotion estimation, such as facial emotion estimation, has been actively studied. On the other hand, there has been little research on unconscious phenomena in cognition and psychology (i.e., cognitive biases) caused by viewing AI emotion estimation information. Therefore, this study verifies RQ “Do people have a cognitive bias in which impressions of others (i.e., how to see and feel about others) are changed by viewing biased AI’s emotion estimation information? If it exists, can impression manipulation methods that intentionally use this cognitive bias be realized?” The proposed method for verification makes the emotion estimation system biased so as to estimate emotion more positively/negatively than AI without bias. A prototype system was implemented. Evaluation using video showed that the presentation of biased emotion estimation information causes a phenomenon that quickly and unconsciously changes the way people see and feel others’ impressions, which supported the RQ. Specifically, viewing information that estimated others’ emotions more positively/negatively caused the phenomenon in which the user’s self-judgment was overridden and others’ impressions of emotions, words, and actions were perceived more positively/negatively. The existence of this phenomenon and method indicates that biased emotion estimation AI has the potential to both cause adverse effects on people and support people for good purposes through the manipulation of their impressions. This study provides helpful insights for the design and use of emotion estimation AI considering cognitive biases.
Multi-Sensor Fault Diagnosis Based on Time Series in an Intelligent Mechanical System
Authors: Xu Zhuoran , Li Qianmu , Qian Linfang , Wang Manyi
Published: December 2022

Intelligent mechanical systems are a focused area nowadays. One of the requirements of intelligent mechanical systems is to achieve intelligent fault diagnosis through the real-time acquisition and analysis of data from various sensors installed on mechanical components. In this paper, a new fault diagnosis method is proposed to solve the problems of difficulty in integrating the fault diagnosis algorithm and locating fault parts due to the complexity of modern mechanical systems. The complexity of modern industrial intelligent systems is due to the fact that the systems are composed of multiple components and there are various connections between them. Common fault diagnosis is to design specialized fault identification algorithms for the physical characteristics of each component, and the integration of different algorithms is a major challenge for system performance. Therefore, this paper investigates a general algorithm for the fault diagnosis of complex systems using the timing characteristics of sensors and transfer entropy. The fault diagnosis algorithm is based on the prediction of multi-dimensional long time series using Autoformer, and fault identification is performed based on the deviation of the predicted value from the actual value. After fault identification, a root cause analysis method of faults based on transfer entropy is proposed. The method can locate the component where the fault occurs more accurately based on the analysis of the cause–effect relationship of each component and help maintenance personnel to troubleshoot the fault.
A Robust Method: Arbitrary Shape Text Detection Combining Semantic and Position Information
Authors: Wang Zhenchao , Silamu Wushour , Li Yuze , Xu Miaomiao
Published: December 2022

There is a growing interest in scene text detection for arbitrary shapes. The effectiveness of text detection has also evolved from horizontal text detection to the ability to perform text detection in multiple directions and arbitrary shapes. However, scene text detection is still a challenging task due to significant differences in size and aspect ratio and diversity in shape, as well as orientation, coarse annotations, and other factors. Regression-based methods are inspired by object detection and have limitations in fitting the edges of arbitrarily shaped text due to the characteristics of their methods. Segmentation-based methods, on the other hand, perform prediction at the pixel level and thus can fit arbitrarily shaped text better. However, the inaccuracy of image text annotations and the distribution characteristics of text pixels, which contain a large number of background pixels and misclassified pixels, degrades the performance of segmentation-based text detection methods to some extent. Usually, considering whether a pixel belongs to a text region is highly dependent on the strength of the semantic information it has and the position of the pixel in the text area. Based on the above two points, we propose an innovative and robust method for scene text detection combining position and semantic information. First, we add position information to the images using a position encoding module (PosEM) to help the model learn the implicit feature relationships associated with the position. Second, we use the semantic enhancement module (SEM) to enhance the model’s focus on the semantic information in the image during feature extraction. Then, to minimize the effect of noise due to inaccurate image text annotations and the distribution characteristics of text pixels, we convert the detection results into a probability map that can more reasonably represent the text distribution. Finally, we reconstruct and filter the text instances using a post-processing algorithm to reduce false positives. The experimental results show that our model improves significantly on the Total-Text, MSRA-TD500, and CTW1500 datasets, outperforming most previous advanced algorithms.