bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimating PM2.5 Concentrations Using Spatially Local Xgboost Based on Full-Covered SARA AOD at the Urban Scale
Authors: Fan Zhiyu , Zhan Qingming , Yang Chen , Liu Huimin , Bilal Muhammad
Published: October 2020

The adverse effects caused by PM2.5 have drawn extensive concern and it is of great significance to identify its spatial distribution. Satellite-derived aerosol optical depth (AOD) has been widely used for PM2.5 estimation. However, the coarse spatial resolution and the gaps caused by data deficiency impede its better application at the urban scale. Additionally, obtaining accurate results in unsampled spatial areas when PM2.5 ground sites are insufficient and distribute sparsely is also a challenging issue for PM2.5 spatial distribution estimation. This paper aimed to develop a model, i.e., spatially local extreme gradient boosting (SL-XGB), combining the powerful fitting ability of machine learning and optimal bandwidths of local models, to better estimate PM2.5 concentration at the urban scale by using Beijing as the study area. This paper adopted simplified high-resolution MODIS aerosol retrieval algorithm (SARA) AOD at 500 m resolution as the major independent variable, hence, ensuring the estimation can be operated at a fine scale. Moreover, the extreme gradient boosting (XGBoost) model was adopted to fill the gaps in SARA AOD, thus improving its availability. Then, based on full-covered SARA AOD and other multisource data, the SL-XGB model, integrating multiple local XGBoost models and particular optimal bandwidths, was trained to estimate PM2.5 concentration. For comparison, SL-XGB and two other models, XGBoost and geographically weighted regression (GWR), were evaluated by 10-fold cross validation (CV). The sample-based CV results reveal that the SL-XGB performed the best as assessed through R2 (0.88), root mean square error (RMSE = 24.08 μg/m3) and mean prediction error (MPE = 16.90 μg/m3). Additionally, SL-XGB also performed the best in the site-based CV with a R2 of 0.86, a RMSE of 26.15 μg/m3 and a MPE of 17.97 μg/m3, which shows its good spatial generalization ability. These results demonstrate that SL-XGB can better simultaneously handle non-linear and spatial heterogeneity issues despite spatially limited data at the urban scale. As far as the PM2.5 concentration distribution was concerned, it presented a gradient increase in PM2.5 concentrations from the northwest to the southeast in Beijing, with abundant spatial details. Overall, the proposed approach for PM2.5 estimation showed outstanding performance and can support preventive pollution control and mitigation at the urban scale.
Classification of Landscape Affected by Deforestation Using High-Resolution Remote Sensing Data and Deep-Learning Techniques
Authors: Lee Seong-Hyeok , Han Kuk-Jin , Lee Kwon , Lee Kwang-Jae , Oh Kwan-Young , Lee Moung-Jin
Published: October 2020

Human-induced deforestation has a major impact on forest ecosystems and therefore its detection and analysis methods should be improved. This study classified landscape affected by human-induced deforestation efficiently using high-resolution remote sensing and deep-learning. The SegNet and U-Net algorithms were selected for application with high-resolution remote sensing data obtained by the Kompsat-3 satellite. Land and forest cover maps were used as base data to construct accurate deep-learning datasets of deforested areas at high spatial resolution, and digital maps and a softwood database were used as reference data. Sites were classified into forest and non-forest areas, and a total of 13 areas (2 forest and 11 non-forest) were selected for analysis. Overall, U-Net was more accurate than SegNet (74.8% vs. 63.3%). The U-Net algorithm was about 11.5% more accurate than the SegNet algorithm, although SegNet performed better for the hardwood and bare land classes. The SegNet algorithm misclassified many forest areas, but no non-forest area. There was reduced accuracy of the U-Net algorithm due to misclassification among sub-items, but U-Net performed very well at the forest/non-forest area classification level, with 98.4% accuracy for forest areas and 88.5% for non-forest areas. Thus, deep-learning modeling has great potential for estimating human-induced deforestation in mountain areas. The findings of this study will contribute to more efficient monitoring of damaged mountain forests and the determination of policy priorities for mountain area restoration.
Design of an Interactive Mind Calligraphy System by Affective Computing and Visualization Techniques for Real-Time Reflections of the Writer’s Emotions
Authors: Wang Chao-Ming , Chen Yu-Chen
Published: October 2020

A novel interactive system for calligraphy called mind calligraphy that reflects the writer’s emotions in real time by affective computing and visualization techniques is proposed. Differently from traditional calligraphy, which emphasizes artistic expression, the system is designed to visualize the writer’s mental-state changes during writing using audio-visual tools. The writer’s mental state is measured with a brain wave machine to yield attention and meditation signals, which are classified next into the four types of emotion, namely, focusing, relaxation, calmness, and anxiety. These emotion types then are represented both by animations and color palettes for by-standing observers to appreciate. Based on conclusions drawn from data collected from on-site observations, surveys via Likert-scale questionnaires, and semi-structured interviews, the proposed system was improved gradually. The participating writers’ cognitive, emotional, and behavioral engagements in the system were recorded and analyzed to obtain the following findings: (1) the interactions with the system raise the writer’s interest in calligraphy; (2) the proposed system reveals the writer’s emotions during the writing process in real time via animations of mixtures of fish swimming and sounds of raindrops, insects, and thunder; (3) the dynamic visualization of the writer’s emotion through animations and color-palette displays makes the writer understand better the connection of calligraphy and personal emotions; (4) the real-time audio-visual feedback increases the writer’s willingness to continue in calligraphy; and (5) the engagement of the writer in the system with interactions of diversified forms provides the writer with a new experience of calligraphy.
Dosimetry and Calorimetry Performance of a Scientific CMOS Camera for Environmental Monitoring
Authors: Aguilar-Arevalo Alexis , Bertou Xavier , Canet Carles , Cruz-Pérez Miguel Angel , Deisting Alexander , Dias Adriana , D’Olivo Juan Carlos , Favela-Pérez Francisco , Garcés Estela A. , González Muñoz Adiv , Guerra-Pulido Jaime Octavio , Mancera-Alejandrez Javier , Marín-Lámbarri Daniel José , Martinez Montero Mauricio , Monroe Jocelyn , Paling Sean , Peeters Simon J. M. , Scovell Paul , Türkoğlu Cenk , Vázquez-Jáuregui Eric , Walding Joseph
Published: October 2020

This paper explores the prospect of CMOS devices to assay lead in drinking water, using calorimetry. Lead occurs together with traces of radioisotopes, e.g., 210Pb, producing γ-emissions with energies ranging from 10 keV to several 100 keV when they decay; this range is detectable in silicon sensors. In this paper we test a CMOS camera (Oxford Instruments Neo 5.5) for its general performance as a detector of X-rays and low energy γ-rays and assess its sensitivity relative to the World Health Organization upper limit on lead in drinking water. Energies from 6 keV to 60 keV are examined. The CMOS camera has a linear energy response over this range and its energy resolution is for the most part slightly better than 2%. The Neo sCMOS is not sensitive to X-rays with energies below ∼10 keV. The smallest detectable rate is 40±3mHz, corresponding to an incident activity on the chip of 7±4Bq. The estimation of the incident activity sensitivity from the detected activity relies on geometric acceptance and the measured efficiency vs. energy. We report the efficiency measurement, which is 0.08(2)% (0.0011(2)%) at 26.3keV (59.5keV). Taking calorimetric information into account we measure a minimal detectable rate of 4±1mHz (1.5±1mHz) for 26.3keV (59.5keV) γ-rays, which corresponds to an incident activity of 1.0±6Bq (57±33Bq). Toy Monte Carlo and Geant4 simulations agree with these results. These results show this CMOS sensor is well-suited as a γ- and X-ray detector with sensitivity at the few to 100 ppb level for 210Pb in a sample.
Fusing Accelerometry with Videography to Monitor the Effect of Fatigue on Punching Performance in Elite Boxers
Authors: Haralabidis Nicos , Saxby David John , Pizzolato Claudio , Needham Laurie , Cazzola Dario , Minahan Clare
Published: October 2020

Wearable sensors and motion capture technology are accepted instruments to measure spatiotemporal variables during punching performance and to study the externally observable effects of fatigue. This study aimed to develop a computational framework enabling three-dimensional inverse dynamics analysis through the tracking of punching kinematics obtained from inertial measurement units and uniplanar videography. The framework was applied to six elite male boxers performing a boxing-specific punch fatigue protocol. OpenPose was used to label left side upper-limb landmarks from which sagittal plane kinematics were computed. Custom-made inertial measurement units were embedded into the boxing gloves, and three-dimensional punch accelerations were analyzed using statistical parametric mapping to evaluate the effects of both fatigue and laterality. Tracking simulations of a sub-set of left-handed punches were formulated as optimal control problems and converted to nonlinear programming problems for solution with a trapezoid collocation method. The laterality analysis revealed the dominant side fatigued more than the non-dominant, while tracking simulations revealed shoulder abduction and elevation moments increased across the fatigue protocol. In future, such advanced simulation and analysis could be performed in ecologically valid contexts, whereby multiple inertial measurement units and video cameras might be used to model a more complete set of dynamics.
Wearable Hearing Device Spectral Enhancement Driven by Non-Negative Sparse Coding-Based Residual Noise Reduction
Authors: Kim Seon Man
Published: October 2020

This paper proposes a novel technique to improve a spectral statistical filter for speech enhancement, to be applied in wearable hearing devices such as hearing aids. The proposed method is implemented considering a 32-channel uniform polyphase discrete Fourier transform filter bank, for which the overall algorithm processing delay is 8 ms in accordance with the hearing device requirements. The proposed speech enhancement technique, which exploits the concepts of both non-negative sparse coding (NNSC) and spectral statistical filtering, provides an online unified framework to overcome the problem of residual noise in spectral statistical filters under noisy environments. First, the spectral gain attenuator of the statistical Wiener filter is obtained using the a priori signal-to-noise ratio (SNR) estimated through a decision-directed approach. Next, the spectrum estimated using the Wiener spectral gain attenuator is decomposed by applying the NNSC technique to the target speech and residual noise components. These components are used to develop an NNSC-based Wiener spectral gain attenuator to achieve enhanced speech. The performance of the proposed NNSC–Wiener filter was evaluated through a perceptual evaluation of the speech quality scores under various noise conditions with SNRs ranging from -5 to 20 dB. The results indicated that the proposed NNSC–Wiener filter can outperform the conventional Wiener filter and NNSC-based speech enhancement methods at all SNRs.
I4.0 Pilot Project on a Semiconductor Industry: Implementation and Lessons Learned
Authors: Silveira Gabriel do N. , Viana Rafael F. , Lima Miromar J. , Kuhn Henrique C. , Crovato Cesar David P. , Ferreira Sandro B. , Pesenti Giovani , Storck Erick , Righi Rodrigo da Rosa
Published: October 2020

Industry 4.0 considers the combinations of Internet of Things, computing and communication infrastructure, sensors, and artificial intelligence (AI) to provide predictive maintenance and process optimization. These benefits are very relevant to the semiconductor industry, where high reliability and low operating costs are critical for a business’ success. Analyzing the state-of-the-art of the projects that present the implementation of the fourth industrial revolution in semiconductor companies, we noticed mainly two branches of initiatives: (i) articles that explain the final achievements, not detailing how they were assembled and structured; (ii) articles that detail a part of the industry ecosystem, for example, approaching only the communication system or AI algorithms. In this context, this article proposes an I4.0 Pilot as a compilation of lessons learned during an end-to-end development of a reference design applied to a semiconductor packaging and test company. We explore the requirements of clean rooms and information related to sensors and data acquisition boards, in addition to performance details and configurations pertaining to visualization tools and warning notifications. The main contribution appears in presenting the advantages of adopting flexible decisions in the pilot to enable the best characteristics for a final expandable solution. Our final idea is to emphasize the importance of having a pilot project without significant expenses, presenting the reader with the acquired knowledge, and how they can benefit from it.
Consistent Monocular Ackermann Visual–Inertial Odometry for Intelligent and Connected Vehicle Localization
Authors: Ma Fangwu , Shi Jinzhu , Wu Liang , Dai Kai , Zhong Shouren
Published: October 2020

The observability of the scale direction in visual–inertial odometry (VIO) under degenerate motions of intelligent and connected vehicles can be improved by fusing Ackermann error state measurements. However, the relative kinematic error measurement model assumes that the vehicle velocity is constant between two consecutive camera states, which degrades the positioning accuracy. To address this problem, a consistent monocular Ackermann VIO, termed MAVIO, is proposed to combine the vehicle velocity and yaw angular rate error measurements, taking into account the lever arm effect between the vehicle and inertial measurement unit (IMU) coordinates with a tightly coupled filter-based mechanism. The lever arm effect is firstly introduced to improve the reliability for information exchange between the vehicle and IMU coordinates. Then, the process model and monocular visual measurement model are presented. Subsequently, the vehicle velocity and yaw angular rate error measurements are directly used to refine the estimator after visual observation. To obtain a global position for the vehicle, the raw Global Navigation Satellite System (GNSS) error measurement model, termed MAVIO-GNSS, is introduced to further improve the performance of MAVIO. The observability, consistency and positioning accuracy were comprehensively compared using real-world datasets. The experimental results demonstrated that MAVIO not only improved the observability of the VIO scale direction under the degenerate motions of ground vehicles, but also resolved the inconsistency problem of the relative kinematic error measurement model of the vehicle to further improve the positioning accuracy. Moreover, MAVIO-GNSS further improved the vehicle positioning accuracy under a long-distance driving state. The source code is publicly available for the benefit of the robotics community.
Automatic System for Visual Detection of Dirt Buildup on Conveyor Belts Using Convolutional Neural Networks
Authors: Santos André A. , Rocha Filipe A. S. , Reis Agnaldo J. da R. , Guimarães Frederico G.
Published: October 2020

Conveyor belts are the most widespread means of transportation for large quantities of materials in the mining sector. Therefore, autonomous methods that can help human beings to perform the inspection of the belt conveyor system is a major concern for companies. In this context, we present in this work a novel and automatic visual detector that recognizes dirt buildup on the structures of conveyor belts, which is one of the tasks of the maintenance inspectors. This visual detector can be embedded as sensors in autonomous robots for the inspection activity. The proposed system involves training a convolutional neural network from RGB images. The use of the transfer learning technique, i.e., retraining consolidated networks for image classification with our collected images has shown very effective. Two different approaches for transfer learning have been analyzed. The best one presented an average accuracy of 0.8975 with an F-1 Score of 0.8773 for the dirt recognition. A field validation experiment served to evaluate the performance of the proposed system in a real time classification task.
Latent 3D Volume for Joint Depth Estimation and Semantic Segmentation from a Single Image
Authors: Ito Seiya , Kaneko Naoshi , Sumi Kazuhiko
Published: October 2020

This paper proposes a novel 3D representation, namely, a latent 3D volume, for joint depth estimation and semantic segmentation. Most previous studies encoded an input scene (typically given as a 2D image) into a set of feature vectors arranged over a 2D plane. However, considering the real world is three-dimensional, this 2D arrangement reduces one dimension and may limit the capacity of feature representation. In contrast, we examine the idea of arranging the feature vectors in 3D space rather than in a 2D plane. We refer to this 3D volumetric arrangement as a latent 3D volume. We will show that the latent 3D volume is beneficial to the tasks of depth estimation and semantic segmentation because these tasks require an understanding of the 3D structure of the scene. Our network first constructs an initial 3D volume using image features and then generates latent 3D volume by passing the initial 3D volume through several 3D convolutional layers. We apply depth regression and semantic segmentation by projecting the latent 3D volume onto a 2D plane. The evaluation results show that our method outperforms previous approaches on the NYU Depth v2 dataset.
A Framework for an Indoor Safety Management System Based on Digital Twin
Authors: Liu Zhansheng , Zhang Anshan , Wang Wensi
Published: October 2020

With the development of the next generation of information technology, an increasing amount of attention is being paid to smart residential spaces, including smart cities, smart buildings, and smart homes. Building indoor safety intelligence is an important research topic. However, current indoor safety management methods cannot comprehensively analyse safety data, owing to a poor combination of safety management and building information. Additionally, the judgement of danger depends significantly on the experience of the safety management staff. In this study, digital twins (DTs) are introduced to building indoor safety management. A framework for an indoor safety management system based on DT is proposed which exploits the Internet of Things (IoT), building information modelling (BIM), the Internet, and support vector machines (SVMs) to improve the level of intelligence for building indoor safety management. A DT model (DTM) is developed using BIM integrated with operation information collected by IoT sensors. The trained SVM model is used to automatically obtain the types and levels of danger by processing the data in the DTM. The Internet is a medium for interactions between people and systems. A building in the bobsleigh and sled stadium for the Beijing Winter Olympics is considered as an example; the proposed system realises the functions of the scene display of the operation status, danger warning and positioning, danger classification and level assessment, and danger handling suggestions.
Towards a Personalized Multi-Domain Digital Neurophenotyping Model for the Detection and Treatment of Mood Trajectories
Authors: Sela Yaron , Santamaria Lorena , Amichai-Hamburge Yair , Leong Victoria
Published: October 2020

The commercial availability of many real-life smart sensors, wearables, and mobile apps provides a valuable source of information about a wide range of human behavioral, physiological, and social markers that can be used to infer the user’s mental state and mood. However, there are currently no commercial digital products that integrate these psychosocial metrics with the real-time measurement of neural activity. In particular, electroencephalography (EEG) is a well-validated and highly sensitive neuroimaging method that yields robust markers of mood and affective processing, and has been widely used in mental health research for decades. The integration of wearable neuro-sensors into existing multimodal sensor arrays could hold great promise for deep digital neurophenotyping in the detection and personalized treatment of mood disorders. In this paper, we propose a multi-domain digital neurophenotyping model based on the socioecological model of health. The proposed model presents a holistic approach to digital mental health, leveraging recent neuroscientific advances, and could deliver highly personalized diagnoses and treatments. The technological and ethical challenges of this model are discussed.
Instrumented Cone Penetrometer for Dense Layer Characterization
Authors: Lee Jong-Sub , Byun Yong-Hoon
Published: October 2020

Subsurface characterization is essential for a successful infrastructure design and construction. This paper demonstrates the use of an instrumented cone penetrometer (ICP) for a dense layer characterization at two sites. The ICP consists of a cone tip and rods equipped with an accelerometer and four strain gauges, which allow dynamic driving, in addition to quasi-static pushing of the cone. The force and velocity of the cone are measured using the ICP instrumentation and compared with the N value, dynamic cone penetration index, and static cone resistance. A strong correlation has been observed between the total cone resistance estimated from the ICP and the dynamic cone penetration index and static cone resistance. After the correction of the dynamic cone resistance effect, the static component of the total cone resistance can be used as an alternative to a static cone resistance. This novel approach of soil resistance estimation using the ICP may be useful for dense layer characterization.
Measurement of Early Disease Blueberries Based on Vis/NIR Hyperspectral Imaging System
Authors: Huang Yuping , Wang Dezhen , Liu Ying , Zhou Haiyan , Sun Ye
Published: October 2020

Blueberries, which are rich in nutrition, are susceptible to fungal infection during postharvest or storage. However, early detection of diseases in blueberry is challenging because of their opaque appearance and the inconspicuousness of spots in the early stage of disease. The goal of this study was to investigate the potential of hyperspectral imaging over the spectral range of 400–1000 nm to discriminate early disease in blueberries. Scanning electron microscope observation verified that fungal damage to the cellular structure takes place during the early stages. A total of 400 hyperspectral images, 200 samples each of healthy and early disease groups, were collected to obtain mean spectra of each blueberry samples. Spectral correlation analysis was performed to select an effective spectral range. Partial least square discrimination analysis (PLSDA) models were developed using two types of spectral range (i.e., full wavelength range of 400–1000 nm and effective spectral range of 685–1000 nm). The results showed that the effective spectral range made it possible to provide better classification results due to the elimination of the influence of irrelevant variables. Moreover, the effective spectral range combined with an autoscale preprocessing method was able to obtain optimal classification accuracies, with recognition rates of 100% and 99% for healthy and early disease blueberries. This study demonstrated that it is feasible to use hyperspectral imaging to measure early disease blueberries.
Distributed Nodes-Based Collaborative Sustaining of Precision Clock Synchronization upon Master Clock Failure in IEEE 1588 System
Authors: Son Kyou Jung , Chang Tae Gyu
Published: October 2020

This paper proposes a distributed nodes-based clock synchronization method to sustain sub-microsecond precision synchronization of slave clocks upon master clock failure in IEEE 1588 PTP (precision time protocol) system. The sustaining is achieved by synchronizing the slave clocks to the estimated reference clock which is obtained from the analysis of distributed slave clocks. The proposed method consists of two clock correction functions (i.e., a self-correction and a collaborative correction, respectively). Upon master failure, the self-correction estimates a clock correction value based on the clock model which is constructed during normal PTP operation. The collaborative correction is performed in the preselected management node. The management node estimates a reference clock by collecting and analyzing clock information gathered from the other slave clocks. The performance of the proposed method is simulated by computer to show its usefulness. It is confirmed that the fifty (50) clock model-based collaborative correction maintains 10−6 second PTP accuracy for 10 min prolonged period after the master failure when tested with clock offset variations less than 50 ppm.
Effective Melanoma Recognition Using Deep Convolutional Neural Network with Covariance Discriminant Loss
Authors: Guo Lei , Xie Gang , Xu Xinying , Ren Jinchang
Published: October 2020

Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discriminant loss in dermoscopy images. Deep convolutional neural network is trained under the joint supervision of cross entropy loss and covariance discriminant loss, rectifying the model outputs and the extracted features simultaneously. Specifically, we design an embedding loss, namely covariance discriminant loss, which takes the first and second distance into account simultaneously for providing more constraints. By constraining the distance between hard samples and minority class center, the deep features of melanoma and non-melanoma can be separated effectively. To mine the hard samples, we also design the corresponding algorithm. Further, we analyze the relationship between the proposed loss and other losses. On the International Symposium on Biomedical Imaging (ISBI) 2018 Skin Lesion Analysis dataset, the two schemes in the proposed method can yield a sensitivity of 0.942 and 0.917, respectively. The comprehensive results have demonstrated the efficacy of the designed embedding loss and the proposed methodology.
On the Performance of a Capacitively Coupled Electrical Impedance Tomography Sensor with Different Configurations
Authors: Jiang Yandan , He Xuekai , Wang Baoliang , Huang Zhiyao , Soleimani Manuchehr
Published: October 2020

Capacitively coupled electrical impedance tomography (CCEIT) is a new kind of electrical resistance tomography (ERT) which realizes contactless measurement by capacitive coupling and extends traditional resistance measurement to total impedance measurement. This work investigates the performance of a CCEIT sensor with three different configurations, including the unshielded configuration, the shielded configuration A (the CCEIT sensor with the external shield) and the shielded configuration B (the CCEIT sensor with both the external shield and the radial screens). The equivalent circuit models of the measurement electrode pair of the CCEIT sensor with different configurations were developed. Additionally, three CCEIT prototypes corresponding to the three configurations were developed. Both the simulation work and experiments were carried out to compare various aspects of the three CCEIT prototypes, including the sensitivity distribution, the impedance measurement and the practical imaging performance. Simulation results show that shielded configurations improve the overall average sensitivity of the sensitivity distributions. Shielded configuration A contributes to improve the uniformity of the sensitivity distributions, while shielded configuration B reduces the uniformity in most cases. Experimental results show that the shielded configurations have no significant influence on the imaging quality of the real part of impedance measurement, but do make sense in improving the imaging performance of the imaginary part and the amplitude of impedance measurement. However, configuration B (with radial screens) has no significant advantage over configuration A (without radial screens). This work provides an insight into how shielding measures influence the performance of the CCEIT sensor, in addition to playing an important role in shielding unwanted noise and disturbances. The research results can provide a useful reference for further development of CCEIT sensors.
Distributed Optical Fiber Sensing Bonding Techniques Performance for Embedment inside Reinforced Concrete Structures
Authors: Bado Mattia Francesco , Casas Joan R. , Dey Alinda , Berrocal Carlos Gil
Published: October 2020

Distributed optical fiber sensors (DOFS) are modern-day cutting-edge monitoring tools that are quickly acquiring relevance in structural health monitoring engineering. Their most ambitious use is embedded inside plain or reinforced concrete (RC) structures with the scope of comprehending their inner-workings and the functioning of the concrete-reinforcement interaction. Yet, multiple studies have shown that the bonding technique with which the DOFS are bonded to the reinforcement bars has a significant role on the quality of the extracted strain data. Whilst this influence has been studied for externally bonded DOFS, it has not been done for embedded ones. The present article is set on performing such study by monitoring the strain measurement quality as sampled by DOFS bonded to multiple rebars with different techniques and adhesives. These instrumented rebars are used to produce differently sized RC ties later tested in tension. The discussion of the test outputs highlights the quasi-optimal performance of a DOFS/rebar bonding technique consisting of incising a groove in the rebar, positioning the DOFS inside it, bonding it with cyanoacrylate and later adding a protective layer of silicone. The resulting data is mostly noise-free and anomalies-free, yet still presents a newly diagnosed hitch that needs addressing in future research.
Brightness Invariant Deep Spectral Super-Resolution
Authors: Stiebel Tarek , Merhof Dorit
Published: October 2020

Spectral reconstruction from RGB or spectral super-resolution (SSR) offers a cheap alternative to otherwise costly and more complex spectral imaging devices. In recent years, deep learning based methods consistently achieved the best reconstruction quality in terms of spectral error metrics. However, there are important properties that are not maintained by deep neural networks. This work is primarily dedicated to scale invariance, also known as brightness invariance or exposure invariance. When RGB signals only differ in their absolute scale, they should lead to identical spectral reconstructions apart from the scaling factor. Scale invariance is an essential property that signal processing must guarantee for a wide range of practical applications. At the moment, scale invariance can only be achieved by relying on a diverse database during network training that covers all possibly occurring signal intensities. In contrast, we propose and evaluate a fundamental approach for deep learning based SSR that holds the property of scale invariance by design and is independent of the training data. The approach is independent of concrete network architectures and instead focuses on reevaluating what neural networks should actually predict. The key insight is that signal magnitudes are irrelevant for acquiring spectral reconstructions from camera signals and are only useful for a potential signal denoising.
Interterminal Truck Routing Optimization Using Deep Reinforcement Learning
Authors: Adi Taufik Nur , Iskandar Yelita Anggiane , Bae Hyerim
Published: October 2020

The continued growth of the volume of global containerized transport necessitates that most of the major ports in the world improve port productivity by investing in more interconnected terminals. The development of the multiterminal system escalates the complexity of the container transport process and increases the demand for container exchange between different terminals within a port, known as interterminal transport (ITT). Trucks are still the primary modes of freight transportation to transport containers among most terminals. A trucking company needs to consider proper truck routing planning because, based on several studies, it played an essential role in coordinating ITT flows. Furthermore, optimal truck routing in the context of ITT significantly affects port productivity and efficiency. The study of deep reinforcement learning in truck routing optimization is still limited. In this study, we propose deep reinforcement learning to provide truck routes of a given container transport order by considering several significant factors such as order origin, destination, time window, and due date. To assess its performance, we compared between the proposed method and two approaches that are used to solve truck routing problems. The experiment results showed that the proposed method obtains considerably better results compared to the other algorithms.
Tool Wear Prediction Based on Artificial Neural Network during Aluminum Matrix Composite Milling
Authors: Wiciak-Pikuła Martyna , Felusiak-Czyryca Agata , Twardowski Paweł
Published: October 2020

This article deals with the phenomenon of tool wear prediction in face milling of aluminum matrix composite materials (AMC), class as hard-to-cut materials. Artificial neural networks (ANN) are one of the tools used to predict tool wear or surface roughness in machining. Model development is applicable when regression models do not give satisfactory results. Because of their mechanical properties based on SiC or Al2O3 reinforcement, AMCs are applied in the automotive and aerospace industry. Due to these materials’ abrasive nature, a three-edged end mill with diamond coating was selected to carry out milling tests. In this work, multilayer perceptron (MLP) models were used to predict the tool flank wear VBB and tool corner wear VBC during milling of AMC with 10% SiC content. The signals of vibration acceleration and cutting forces were selected as input to the network, and the tests were carried out with three cutting speeds. Based on the analysis of the developed models, the models with the best efficiency were selected, and the quality of wear prediction was assessed. The main criterion for evaluating the quality of the developed models was the mean square error (MSE) in order to compare measured and predicted value of tool wear.
A Two-Step Guided Waves Based Damage Localization Technique Using Optical Fiber Sensors
Authors: Soman Rohan , Balasubramaniam Kaleeswaran , Golestani Ali , Karpiński Michał , Malinowski Pawel
Published: October 2020

Structural health monitoring (SHM) systems help in reducing maintenance cost and avoiding catastrophic failure of the structure. As a result, they have been a focus of research for the past few decades. Ideally, the methods employed should be low cost and able to detect and localize small levels of damage reliably and accurately. This paper describes a guided waves (GW) based two-step technique for damage detection and localization using fiber Bragg grating (FBG) sensors. The FBG sensors offer benefits such as the ability to be embedded and multiplexed as well as being lightweight and insensitive to electric and magnetic fields, and they have long been seen as a promising solution for the GW measurements in structures. Unfortunately, in the conventional wavelength-based interrogation they have very low signal to noise ratio and as a result low sensitivity. Therefore, the FBG sensor is incorporated in the edge filtering configuration. The major challenges in the use of FBG sensors for GW-based detection are their directional sensitivity and passive nature. The passive nature leads to the reduction in the available actuator–sensor (AS) pairs while the directionality makes the signal processing a challenge. The proposed two-step methodology overcomes these shortcomings of FBG sensors. In the first step the amplitude weighted elliptical approach is used to identify the hotspots due to the inadequate number of AS pairs, the elliptical approach is not sufficient for damage localization. Therefore, in order to further localize the damage the edge reflection based ray-tracing approach is implemented in the second step. Through the two step method, the damage is accurately located. The paper provides the proof of concept of the proposed methodology on an aluminum plate with simulated damage. The results indicate, that indeed the two-step methodology allows accurate damage localization and overcomes the possibility of false detections.
Modeling of a Novel Coaxial Ducted Fan Aerial Robot Combined with Corner Environment by Using Artificial Neural Network
Authors: Ai Tianfu , Xu Bin , Xiang Changle , Fan Wei , Zhang Yibo
Published: October 2020

A novel coaxial ducted fan aerial robot with a manipulator is proposed which can achieve some hover operation tasks in a corner environment, such as switching on and off a wall-attached button on the corner. In order to study the aerodynamic interference between the prototype and the environment when the aerial robot is hovering in the corner environment, a method for the comprehensive modeling of the prototype and corner environment based on the artificial neural network is presented. By using the CFD simulation software, the flow field of the prototype at different positions with the corner effect is analyzed. After determining the input, output and structure of the neural network model, the Adam and gradient descent algorithms are selected as the neural network training algorithms, respectively. In addition, to optimize the initial weights and biases of the neural network model, the genetic algorithm is precisely used. The three-dimensional prediction surfaces generated by the three methods of the neural network, kriging surface and the polynomial fitting are compared. The results show that the neural network has high prediction accuracy, and can be applied to the comprehensive modeling of the prototype and the corner environment.
Industrial Wireless Sensor Networks: Protocols and Applications
Authors: Yoo Seong-eun , Kim Taehong
Published: October 2020

Wireless sensor networks are penetrating our daily lives, and they are starting to be deployed even in an industrial environment. The research on such industrial wireless sensor networks (IWSNs) considers more stringent requirements of robustness, reliability, and timeliness in each network layer. This Special Issue presents the recent research result on industrial wireless sensor networks. Each paper in the special issue has unique contributions in the advancements of industrial wireless sensor network research and we expect each paper to promote the relevant research and the deployment of IWSNs.
Automatic and Real-Time Computation of the 30-Seconds Chair-Stand Test without Professional Supervision for Community-Dwelling Older Adults
Authors: Cobo Antonio , Villalba-Mora Elena , Pérez-Rodríguez Rodrigo , Ferre Xavier , Escalante Walter , Moral Cristian , Rodriguez-Mañas Leocadio
Published: October 2020

The present paper describes a system for older people to self-administer the 30-s chair stand test (CST) at home without supervision. The system comprises a low-cost sensor to count sit-to-stand (SiSt) transitions, and an Android application to guide older people through the procedure. Two observational studies were conducted to test (i) the sensor in a supervised environment (n = 7; m = 83.29 years old, sd = 4.19; 5 female), and (ii) the complete system in an unsupervised one (n = 7; age 64–74 years old; 3 female). The participants in the supervised test were asked to perform a 30-s CST with the sensor, while a member of the research team manually counted valid transitions. Automatic and manual counts were perfectly correlated (Pearson’s r = 1, p = 0.00). Even though the sample was small, none of the signals around the critical score were affected by harmful noise; p (harmless noise) = 1, 95% CI = (0.98, 1). The participants in the unsupervised test used the system in their homes for a month. None of them dropped out, and they reported it to be easy to use, comfortable, and easy to understand. Thus, the system is suitable to be used by older adults in their homes without professional supervision.
Angle-Awareness Based Joint Cooperative Positioning and Warning for Intelligent Transportation Systems
Authors: Dong Zhi , Yao Bobin
Published: October 2020

In future intelligent vehicle-infrastructure cooperation frameworks, accurate self-positioning is an important prerequisite for better driving environment evaluation (e.g., traffic safety and traffic efficiency). We herein describe a joint cooperative positioning and warning (JCPW) system based on angle information. In this system, we first design the sequential task allocation of cooperative positioning (CP) warning and the related frame format of the positioning packet. With the cooperation of RSUs, multiple groups of the two-dimensional angle-of-departure (AOD) are estimated and then transformed into the vehicle’s positions. Considering the system computational efficiency, a novel AOD estimation algorithm based on a truncated signal subspace is proposed, which can avoid the eigen decomposition and exhaustive spectrum searching; and a distance based weighting strategy is also utilized to fuse multiple independent estimations. Numerical simulations prove that the proposed method can be a better alternative to achieve sub-lane level positioning if considering the accuracy and computational complexity.
Continuous In-Bed Monitoring of Vital Signs Using a Multi Radar Setup for Freely Moving Patients
Authors: Schellenberger Sven , Shi Kilin , Michler Fabian , Lurz Fabian , Weigel Robert , Koelpin Alexander
Published: October 2020

In hospitals, continuous monitoring of vital parameters can provide valuable information about the course of a patient’s illness and allows early warning of emergencies. To enable such monitoring without restricting the patient’s freedom of movement and comfort, a radar system is attached under the mattress which consists of four individual radar modules to cover the entire width of the bed. Using radar, heartbeat and respiration can be measured without contact and through clothing. By processing the raw radar data, the presence of a patient can be determined and movements are categorized into the classes “bed exit”, “bed entry”, and “on bed movement”. Using this information, the vital parameters can be assessed in sections where the patient lies calmly in bed. In the first step, the presence and movement classification is demonstrated using recorded training and test data. Next, the radar was modified to perform vital sign measurements synchronized to a gold standard device. The evaluation of the individual radar modules shows that, regardless of the lying position of the test person, at least one of the radar modules delivers accurate results for continuous monitoring.
Ethylene Sensor-Enabled Dynamic Monitoring and Multi-Strategies Control for Quality Management of Fruit Cold Chain Logistics
Authors: Wang Xuepei , Li Xinwu , Fu Daqi , Vidrih Rajko , Zhang Xiaoshuan
Published: October 2020

Due to the presence of bioactive compounds, fruits are an essential part of people’s healthy diet. However, endogenous ethylene produced by climacteric fruits and exogenous ethylene in the microenvironment could play a pivotal role in the physiological and metabolic activities, leading to quality losses during storage or shelf life. Moreover, due to the variety of fruits and complex scenarios, different ethylene control strategies need to be adapted to improve the marketability of fruits and maintain their high quality. Therefore, this study proposed an ethylene dynamic monitoring based on multi-strategies control to reduce the post-harvest quality loss of fruits, which was evaluated here for blueberries, sweet cherries, and apples. The results showed that the ethylene dynamic monitoring had rapid static/dynamic response speed (2 ppm/s) and accurately monitoring of ethylene content (99% accuracy). In addition, the quality parameters evolution (firmness, soluble solids contents, weight loss rate, and chromatic aberration) showed that the ethylene multi-strategies control could effectively reduce the quality loss of fruits studied, which showed great potential in improving the quality management of fruits in the supply chain.
Changes in Fabric Surface Pilling under Laser Ablation
Authors: Korzeniewska Ewa , Gocławski Jarosław , Sekulska-Nalewajko Joanna , Walczak Maria , Wilbik-Hałgas Bożena
Published: October 2020

Textiles require finishing to improve their usability and functionality but in the first place, to reduce of pilling tendency, which affects all kinds of synthetic and natural fabrics. Several laser ablation tests have been applied to the selected fabrics with different chemical composition to reveal the impact of this process on the pilling behavior. To reflect the pilling changes, two textural descriptors have been proposed to textile images obtained with optical coherence tomography (OCT). They showed the trend to reduce values with increasing laser power applied to the tested fabrics. It has been demonstrated, that in the case of textiles based on polyester threads, laser modification of the product surface led to a significant reduction in their tendency for pilling.
A Sensor Fault-Tolerant Accident Diagnosis System
Authors: Choi Jeonghun , Lee Seung Jun
Published: October 2020

Emergency situations in nuclear power plants are accompanied by an automatic reactor shutdown, which gives a big task burden to the plant operators under highly stressful conditions. Diagnosis of the occurred accident is an essential sequence for optimum mitigations; however, it is also a critical source of error because the results of accident identification determine the task flow connected to all subsequent tasks. To support accident identification in nuclear power plants, recurrent neural network (RNN)-based approaches have recently shown outstanding performances. Despite the achievements though, the robustness of RNN models is not promising because wrong inputs have been shown to degrade the performance of RNNs to a greater extent than other methods in some applications. In this research, an accident diagnosis system that is tolerant to sensor faults is developed based on an existing RNN model and tested with anticipated sensor errors. To find the optimum strategy to mitigate sensor error, Missforest, selected from among various imputation methods, and gated recurrent unit with decay (GRUD), developed for multivariate time series imputation based on the RNN model, are compared to examine the extent that they recover the diagnosis accuracies within a given threshold.