bluuebunny/crossref_metadata_2025 huggingface dataset:
Achieving Reliability in Cloud Computing by a Novel Hybrid Approach
Authors: Shahid Muhammad Asim , Alam Muhammad Mansoor , Su’ud Mazliham Mohd
Published: February 2023

Cloud computing (CC) benefits and opportunities are among the fastest growing technologies in the computer industry. Cloud computing’s challenges include resource allocation, security, quality of service, availability, privacy, data management, performance compatibility, and fault tolerance. Fault tolerance (FT) refers to a system’s ability to continue performing its intended task in the presence of defects. Fault-tolerance challenges include heterogeneity and a lack of standards, the need for automation, cloud downtime reliability, consideration for recovery point objects, recovery time objects, and cloud workload. The proposed research includes machine learning (ML) algorithms such as naïve Bayes (NB), library support vector machine (LibSVM), multinomial logistic regression (MLR), sequential minimal optimization (SMO), K-nearest neighbor (KNN), and random forest (RF) as well as a fault-tolerance method known as delta-checkpointing to achieve higher accuracy, lesser fault prediction error, and reliability. Furthermore, the secondary data were collected from the homonymous, experimental high-performance computing (HPC) system at the Swiss Federal Institute of Technology (ETH), Zurich, and the primary data were generated using virtual machines (VMs) to select the best machine learning classifier. In this article, the secondary and primary data were divided into two split ratios of 80/20 and 70/30, respectively, and cross-validation (5-fold) was used to identify more accuracy and less prediction of faults in terms of true, false, repair, and failure of virtual machines. Secondary data results show that naïve Bayes performed exceptionally well on CPU-Mem mono and multi blocks, and sequential minimal optimization performed very well on HDD mono and multi blocks in terms of accuracy and fault prediction. In the case of greater accuracy and less fault prediction, primary data results revealed that random forest performed very well in terms of accuracy and fault prediction but not with good time complexity. Sequential minimal optimization has good time complexity with minor differences in random forest accuracy and fault prediction. We decided to modify sequential minimal optimization. Finally, the modified sequential minimal optimization (MSMO) algorithm with the fault-tolerance delta-checkpointing (D-CP) method is proposed to improve accuracy, fault prediction error, and reliability in cloud computing.
Training of Classification Models via Federated Learning and Homomorphic Encryption
Authors: Angulo Eduardo , Márquez José , Villanueva-Polanco Ricardo
Published: February 2023

With the rise of social networks and the introduction of data protection laws, companies are training machine learning models using data generated locally by their users or customers in various types of devices. The data may include sensitive information such as family information, medical records, personal habits, or financial records that, if leaked, can generate problems. For this reason, this paper aims to introduce a protocol for training Multi-Layer Perceptron (MLP) neural networks via combining federated learning and homomorphic encryption, where the data are distributed in multiple clients, and the data privacy is preserved. This proposal was validated by running several simulations using a dataset for a multi-class classification problem, different MLP neural network architectures, and different numbers of participating clients. The results are shown for several metrics in the local and federated settings, and a comparative analysis is carried out. Additionally, the privacy guarantees of the proposal are formally analyzed under a set of defined assumptions, and the added value of the proposed protocol is identified compared with previous works in the same area of knowledge.
Smartphone LiDAR Data: A Case Study for Numerisation of Indoor Buildings in Railway Stations
Authors: Catharia Orphé , Richard Franck , Vignoles Henri , Véron Philippe , Aoussat Améziane , Segonds Frédéric
Published: February 2023

The combination of LiDAR with other technologies for numerisation is increasingly applied in the field of building, design, and geoscience, as it often brings time and cost advantages in 3D data survey processes. In this paper, the reconstruction of 3D point cloud datasets is studied, through an experimental protocol evaluation of new LiDAR sensors on smartphones. To evaluate and analyse the 3D point cloud datasets, different experimental conditions are considered depending on the acquisition mode and the type of object or surface being scanned. The conditions allowing us to obtain the most accurate data are identified and used to propose which acquisition protocol to use. This protocol seems to be the most adapted when using these LiDAR sensors to digitise complex interior buildings such as railway stations. This paper aims to propose: (i) a methodology to suggest the adaptation of an experimental protocol based on factors (distance, luminosity, surface, time, and incidence) to assess the precision and accuracy of the smartphone LiDAR sensor in a controlled environment; (ii) a comparison, both qualitative and quantitative, of smartphone LiDAR data with other traditional 3D scanner alternatives (Faro X130, VLX, and Vz400i) while considering three representative building interior environments; and (iii) a discussion of the results obtained in a controlled and a field environment, making it possible to propose recommendations for the use of the LiDAR smartphone at the end of the numerisation of the interior space of a building.
Development of a Core Body Thermometer Applicable for High-Temperature Environment Based on the Zero-Heat-Flux Method
Authors: Lu Hanzi , Aratake Shun , Naito Hisashi , Nogawa Masamichi , Nemoto Tetsu , Togawa Tatsuo , Tanaka Shinobu
Published: February 2023

Monitoring core body temperature (CBT) allows observation of heat stress and thermal comfort in various environments. By introducing a Peltier element, we improved the zero-heat-flux core body thermometer for hot environments. In this study, we performed a theoretical analysis, designed a prototype probe, and evaluated its performance through simulator experiments with human subjects. The finite element analysis shows that our design can reduce the influence of external temperature variations by as much as 1%. In the simulator experiment, the prototype probe could measure deep temperatures within an error of less than 0.1 °C, regardless of outside temperature change. In the ergometer experiment with four subjects, the average difference between the prototype probe and a commercial zero-heat-flux probe was +0.1 °C, with a 95% LOA of −0.23 °C to +0.21 °C. In the dome sauna test, the results measured in six of the seven subjects exhibited the same trend as the reference temperature. These results show that the newly developed probe with the Peltier module can measure CBT accurately, even when the ambient temperature is higher than CBT up to 42 °C.
Future Speech Interfaces with Sensors and Machine Intelligence
Authors: Denby Bruce , Csapó Tamás Gábor , Wand Michael
Published: February 2023

Speech is the most spontaneous and natural means of communication. Speech is also becoming the preferred modality for interacting with mobile or fixed electronic devices. However, speech interfaces have drawbacks, including a lack of user privacy; non-inclusivity for certain users; poor robustness in noisy conditions; and the difficulty of creating complex man–machine interfaces. To help address these problems, the Special Issue “Future Speech Interfaces with Sensors and Machine Intelligence” assembles eleven contributions covering multimodal and silent speech interfaces; lip reading applications; novel sensors for speech interfaces; and enhanced speech inclusivity tools for future speech interfaces. Short summaries of the articles are presented, followed by an overall evaluation. The success of this Special Issue has led to its being re-issued as “Future Speech Interfaces with Sensors and Machine Intelligence-II” with a deadline in March of 2023.
High-Efficiency Wireless-Power-Transfer System Using Fully Rollable Tx/Rx Coils and Metasurface Screen
Authors: Lee Woosol , Yoon Yong-Kyu
Published: February 2023

This work presents a high-efficiency reconfigurable wireless-power-transfer (WPT) system using fully rollable Tx/Rx coils and a metasurface (MS) screen working at 6.78 MHz, for the first time. The MS screens are placed between the Tx and Rx to magnify the power-transfer efficiency (PTE) of the WPT system. The proposed MS-based WPT can be rolled down or rolled up as required, which allows end-users to use the space more flexibly. In the measurement results, the PTE of the WPT is improved from 13.32% to 32.49% at a power-transfer distance (PTD) of 40 cm with one MS screen, 5.42% to 42.25% at a PTD of 50 cm with two MS screens, 1.78% to 49% at a PTD of 60 cm with three MS screens, 0.85% to 46.24% at a PTD of 70 cm with four MS screens. The measured PTE results indicate that the demonstrated MS screens are greatly effective for magnifying the PTE and the PTD of the WPT. In addition, the measured PTE results in the misaligned condition verify that the MS screens also help increase the PTE of the WPT even in the misalignment condition.
AST-GIN: Attribute-Augmented Spatiotemporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting
Authors: Luo Ruikang , Song Yaofeng , Huang Liping , Zhang Yicheng , Su Rong
Published: February 2023

Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With accurate EV station availability prediction, suitable charging behaviors can be scheduled in advance to relieve range anxiety. Many existing deep learning methods have been proposed to address this issue; however, due to the complex road network structure and complex external factors, such as points of interest (POIs) and weather effects, many commonly used algorithms can only extract the historical usage information and do not consider the comprehensive influence of external factors. To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatiotemporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network (GCN) layer and the Informer layer to extract both the external and internal spatiotemporal dependence of relevant transportation data. The external factors are modeled as dynamic attributes by the attributeaugmented encoder for training. The AST-GIN model was tested on the data collected in Dundee City, and the experimental results showed the effectiveness of our model considering external factors’ influence on various horizon settings compared with other baselines.
Development of a Soil Moisture Prediction Model Based on Recurrent Neural Network Long Short-Term Memory (RNN-LSTM) in Soybean Cultivation
Authors: Park Soo-Hwan , Lee Bo-Young , Kim Min-Jee , Sang Wangyu , Seo Myung Chul , Baek Jae-Kyeong , Yang Jae E , Mo Changyeun
Published: February 2023

Due to climate change, soil moisture may increase, and outflows could become more frequent, which will have a considerable impact on crop growth. Crops are affected by soil moisture; thus, soil moisture prediction is necessary for irrigating at an appropriate time according to weather changes. Therefore, the aim of this study is to develop a future soil moisture (SM) prediction model to determine whether to conduct irrigation according to changes in soil moisture due to weather conditions. Sensors were used to measure soil moisture and soil temperature at a depth of 10 cm, 20 cm, and 30 cm from the topsoil. The combination of optimal variables was investigated using soil moisture and soil temperature at depths between 10 cm and 30 cm and weather data as input variables. The recurrent neural network long short-term memory (RNN-LSTM) models for predicting SM was developed using time series data. The loss and the coefficient of determination (R2) values were used as indicators for evaluating the model performance and two verification datasets were used to test various conditions. The best model performance for 10 cm depth was an R2 of 0.999, a loss of 0.022, and a validation loss of 0.105, and the best results for 20 cm and 30 cm depths were an R2 of 0.999, a loss of 0.016, and a validation loss of 0.098 and an R2 of 0.956, a loss of 0.057, and a validation loss of 2.883, respectively. The RNN-LSTM model was used to confirm the SM predictability in soybean arable land and could be applied to supply the appropriate moisture needed for crop growth. The results of this study show that a soil moisture prediction model based on time-series weather data can help determine the appropriate amount of irrigation required for crop cultivation.
Manipulation Tasks in Hazardous Environments Using a Teleoperated Robot: A Case Study at CERN
Authors: Gentile Cosimo , Lunghi Giacomo , Buonocore Luca Rosario , Cordella Francesca , Di Castro Mario , Masi Alessandro , Zollo Loredana
Published: February 2023

Remote robotic systems are employed in the CERN accelerator complex to perform different tasks, such as the safe handling of cables and their connectors. Without dedicated control, these kinds of actions are difficult and require the operators’ intervention, which is subjected to dangerous external agents. In this paper, two novel modules of the CERNTAURO framework are presented to provide a safe and usable solution for managing optical fibres and their connectors. The first module is used to detect touch and slippage, while the second one is used to regulate the grasping force and contrast slippage. The force reference was obtained with a combination of object recognition and a look-up table method. The proposed strategy was validated with tests in the CERN laboratory, and the preliminary experimental results demonstrated statistically significant increases in time-based efficiency and in the overall relative efficiency of the tasks.
Task-Specific Adaptive Differential Privacy Method for Structured Data
Authors: Utaliyeva Assem , Shin Jinmyeong , Choi Yoon-Ho
Published: February 2023

Data are needed to train machine learning (ML) algorithms, and in many cases often include private datasets that contain sensitive information. To preserve the privacy of data used while training ML algorithms, computer scientists have widely deployed anonymization techniques. These anonymization techniques have been widely used but are not foolproof. Many studies showed that ML models using anonymization techniques are vulnerable to various privacy attacks willing to expose sensitive information. As a privacy-preserving machine learning (PPML) technique that protects private data with sensitive information in ML, we propose a new task-specific adaptive differential privacy (DP) technique for structured data. The main idea of the proposed DP method is to adaptively calibrate the amount and distribution of random noise applied to each attribute according to the feature importance for the specific tasks of ML models and different types of data. From experimental results under various datasets, tasks of ML models, different DP mechanisms, and so on, we evaluate the effectiveness of the proposed task-specific adaptive DP method. Thus, we show that the proposed task-specific adaptive DP technique satisfies the model-agnostic property to be applied to a wide range of ML tasks and various types of data while resolving the privacy–utility trade-off problem.
Minimum-Time Trajectory Generation for Wheeled Mobile Systems Using Bézier Curves with Constraints on Velocity, Acceleration and Jerk
Authors: Benko Loknar Martina , Klančar Gregor , Blažič Sašo
Published: February 2023

This paper considers the problem of minimum-time smooth trajectory planning for wheeled mobile robots. The smooth path is defined by several Bézier curves and the calculated velocity profiles on individual segments are minimum-time with continuous velocity and acceleration in the joints. We describe a novel solution for the construction of a 5th order Bézier curve that enables a simple and intuitive parameterization. The proposed trajectory optimization considers environment space constraints and constraints on the velocity, acceleration, and jerk. The operation of the trajectory planning algorithm has been demonstrated in two simulations: on a racetrack and in a warehouse environment. Therefore, we have shown that the proposed path construction and trajectory generation algorithm can be applied to a constrained environment and can also be used in real-world driving scenarios.
Reproducibility and Repeatability Tests on (SnTiNb)O2 Sensors in Detecting ppm-Concentrations of CO and Up to 40% of Humidity: A Statistical Approach
Authors: Astolfi Michele , Rispoli Giorgio , Gherardi Sandro , Zonta Giulia , Malagù Cesare
Published: February 2023

Nowadays, most medical-diagnostic, environmental monitoring, etc. devices employ sensors whose fabrication reproducibility and response repeatability assessment are crucial. The former consists of large-scale sensor manufacture through a standardized process with almost identical morphology and behavior, while the latter consists of giving the same response upon repeating the same stimulus. The thermo-activated chemoresistive sensors, which change their conductance by interacting with the molecules composing the surrounding gas, are currently employed in many devices: in particular, thick-film (SnTiNb)O2 nanosensors were demonstrated to be particularly suitable in the medical and biological fields. Therefore, a set of thirteen of them, randomly selected from the same screen-printing deposition, were laboratory tested, and the outcomes were statistically analyzed in order to assess their consistency. At first, the working temperature that maximized both the sensor sensitivity and response repeatability was identified. Then, the sensors were subjected to different gas concentrations and humidities at this optimal working temperature. It resulted in the (SnTiNb)O2 nanosensors detecting and discriminating CO concentrations as low as 1 ppm and at high humidity degrees (up to 40%) with high repeatability since the response relative standard error ranged from 0.8 to 3.3% for CO and from 3.6 to 5.4% for water vapor.
Inception Convolution and Feature Fusion for Person Search
Authors: Ouyang Huan , Zeng Jiexian , Leng Lu
Published: February 2023

With the rapid advancement of deep learning theory and hardware device computing capacity, computer vision tasks, such as object detection and instance segmentation, have entered a revolutionary phase in recent years. As a result, extremely challenging integrated tasks, such as person search, might develop quickly. The majority of efficient network frameworks, such as Seq-Net, are based on Faster R-CNN. However, because of the parallel structure of Faster R-CNN, the performance of re-ID can be significantly impacted by the single-layer, low resolution, and occasionally overlooked check feature diagrams retrieved during pedestrian detection. To address these issues, this paper proposed a person search methodology based on an inception convolution and feature fusion module (IC-FFM) using Seq-Net (Sequential End-to-end Network) as the benchmark. First, we replaced the general convolution in ResNet-50 with the new inception convolution module (ICM), allowing the convolution operation to effectively and dynamically distribute various channels. Then, to improve the accuracy of information extraction, the feature fusion module (FFM) was created to combine multi-level information using various levels of convolution. Finally, Bounding Box regression was created using convolution and the double-head module (DHM), which considerably enhanced the accuracy of pedestrian retrieval by combining global and fine-grained information. Experiments on CHUK-SYSU and PRW datasets showed that our method has higher accuracy than Seq-Net. In addition, our method is simpler and can be easily integrated into existing two-stage frameworks.
Identification of a Gait Pattern for Detecting Mild Cognitive Impairment in Parkinson’s Disease
Authors: Russo Michela , Amboni Marianna , Barone Paolo , Pellecchia Maria Teresa , Romano Maria , Ricciardi Carlo , Amato Francesco
Published: February 2023

The aim of this study was to determine a gait pattern, i.e., a subset of spatial and temporal parameters, through a supervised machine learning (ML) approach, which could be used to reliably distinguish Parkinson’s Disease (PD) patients with and without mild cognitive impairment (MCI). Thus, 80 PD patients underwent gait analysis and spatial–temporal parameters were acquired in three different conditions (normal gait, motor dual task and cognitive dual task). Statistical analysis was performed to investigate the data and, then, five ML algorithms and the wrapper method were implemented: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN). First, the algorithms for classifying PD patients with MCI were trained and validated on an internal dataset (sixty patients) and, then, the performance was tested by using an external dataset (twenty patients). Specificity, sensitivity, precision, accuracy and area under the receiver operating characteristic curve were calculated. SVM and RF showed the best performance and detected MCI with an accuracy of over 80.0%. The key features emerging from this study are stance phase, mean velocity, step length and cycle length; moreover, the major number of features selected by the wrapper belonged to the cognitive dual task, thus, supporting the close relationship between gait dysfunction and MCI in PD.
Modal Decomposition of Acoustic Emissions from Pencil-Lead Breaks in an Isotropic Thin Plate
Authors: Yao Xinyue , Vien Benjamin Steven , Rajic Nik , Rosalie Cedric , Rose L. R. Francis , Davies Chris , Chiu Wing Kong
Published: February 2023

Acoustic emission (AE) testing and Lamb wave inspection techniques have been widely used in non-destructive testing and structural health monitoring. For thin plates, the AEs arising from structural defect development (e.g., fatigue crack propagation) propagate as Lamb waves, and Lamb wave modes can be used to provide important information about the growth and localisation of defects. However, few sensors can be used to achieve the in situ wavenumber–frequency modal decomposition of AEs. This study explores the ability of a new multi-element piezoelectric sensor array to decompose AEs excited by pencil lead breaks (PLBs) on a thin isotropic plate. In this study, AEs were generated by out-of-plane (transverse) and in-plane (longitudinal) PLBs applied at the edge of the plate, and waveforms were recorded by both the new sensor array and a commercial AE sensor. Finite element analysis (FEA) simulations of PLBs were also conducted and the results were compared with the experimental results. To identify the wave modes present, the longitudinal and transverse PLB test results recorded by the new sensor array at five different plate locations were compared with FEA simulations using the same arrangement. Two-dimensional fast Fourier Transforms were then applied to the AE wavefields. It was found that the AE modal composition was dependent on the orientation of the PLB direction. The results suggest that this new sensor array can be used to identify the AE wave modes excited by PLBs in both in-plane and out-of-plane directions.
DEHA-Net: A Dual-Encoder-Based Hard Attention Network with an Adaptive ROI Mechanism for Lung Nodule Segmentation
Authors: Usman Muhammad , Shin Yeong-Gil
Published: February 2023

Measuring pulmonary nodules accurately can help the early diagnosis of lung cancer, which can increase the survival rate among patients. Numerous techniques for lung nodule segmentation have been developed; however, most of them either rely on the 3D volumetric region of interest (VOI) input by radiologists or use the 2D fixed region of interest (ROI) for all the slices of computed tomography (CT) scan. These methods only consider the presence of nodules within the given VOI, which limits the networks’ ability to detect nodules outside the VOI and can also encompass unnecessary structures in the VOI, leading to potentially inaccurate segmentation. In this work, we propose a novel approach for 3D lung nodule segmentation that utilizes the 2D region of interest (ROI) inputted from a radiologist or computer-aided detection (CADe) system. Concretely, we developed a two-stage lung nodule segmentation technique. Firstly, we designed a dual-encoder-based hard attention network (DEHA-Net) in which the full axial slice of thoracic computed tomography (CT) scan, along with an ROI mask, were considered as input to segment the lung nodule in the given slice. The output of DEHA-Net, the segmentation mask of the lung nodule, was inputted to the adaptive region of interest (A-ROI) algorithm to automatically generate the ROI masks for the surrounding slices, which eliminated the need for any further inputs from radiologists. After extracting the segmentation along the axial axis, at the second stage, we further investigated the lung nodule along sagittal and coronal views by employing DEHA-Net. All the estimated masks were inputted into the consensus module to obtain the final volumetric segmentation of the nodule. The proposed scheme was rigorously evaluated on the lung image database consortium and image database resource initiative (LIDC/IDRI) dataset, and an extensive analysis of the results was performed. The quantitative analysis showed that the proposed method not only improved the existing state-of-the-art methods in terms of dice score but also showed significant robustness against different types, shapes, and dimensions of the lung nodules. The proposed framework achieved the average dice score, sensitivity, and positive predictive value of 87.91%, 90.84%, and 89.56%, respectively.
Research Challenges, Recent Advances, and Popular Datasets in Deep Learning-Based Underwater Marine Object Detection: A Review
Authors: Er Meng Joo , Chen Jie , Zhang Yani , Gao Wenxiao
Published: February 2023

Underwater marine object detection, as one of the most fundamental techniques in the community of marine science and engineering, has been shown to exhibit tremendous potential for exploring the oceans in recent years. It has been widely applied in practical applications, such as monitoring of underwater ecosystems, exploration of natural resources, management of commercial fisheries, etc. However, due to complexity of the underwater environment, characteristics of marine objects, and limitations imposed by exploration equipment, detection performance in terms of speed, accuracy, and robustness can be dramatically degraded when conventional approaches are used. Deep learning has been found to have significant impact on a variety of applications, including marine engineering. In this context, we offer a review of deep learning-based underwater marine object detection techniques. Underwater object detection can be performed by different sensors, such as acoustic sonar or optical cameras. In this paper, we focus on vision-based object detection due to several significant advantages. To facilitate a thorough understanding of this subject, we organize research challenges of vision-based underwater object detection into four categories: image quality degradation, small object detection, poor generalization, and real-time detection. We review recent advances in underwater marine object detection and highlight advantages and disadvantages of existing solutions for each challenge. In addition, we provide a detailed critical examination of the most extensively used datasets. In addition, we present comparative studies with previous reviews, notably those approaches that leverage artificial intelligence, as well as future trends related to this hot topic.
DMU-Net: A Dual-Stream Multi-Scale U-Net Network Using Multi-Dimensional Spatial Information for Urban Building Extraction
Authors: Li Peihang , Sun Zhenhui , Duan Guangyao , Wang Dongchuan , Meng Qingyan , Sun Yunxiao
Published: February 2023

Automatically extracting urban buildings from remote sensing images has essential application value, such as urban planning and management. Gaofen-7 (GF-7) provides multi-perspective and multispectral satellite images, which can obtain three-dimensional spatial information. Previous studies on building extraction often ignored information outside the red–green–blue (RGB) bands. To utilize the multi-dimensional spatial information of GF-7, we propose a dual-stream multi-scale network (DMU-Net) for urban building extraction. DMU-Net is based on U-Net, and the encoder is designed as the dual-stream CNN structure, which inputs RGB images, near-infrared (NIR), and normalized digital surface model (nDSM) fusion images, respectively. In addition, the improved FPN (IFPN) structure is integrated into the decoder. It enables DMU-Net to fuse different band features and multi-scale features of images effectively. This new method is tested with the study area within the Fourth Ring Road in Beijing, and the conclusions are as follows: (1) Our network achieves an overall accuracy (OA) of 96.16% and an intersection-over-union (IoU) of 84.49% for the GF-7 self-annotated building dataset, outperforms other state-of-the-art (SOTA) models. (2) Three-dimensional information significantly improved the accuracy of building extraction. Compared with RGB and RGB + NIR, the IoU increased by 7.61% and 3.19% after using nDSM data, respectively. (3) DMU-Net is superior to SMU-Net, DU-Net, and IEU-Net. The IoU is improved by 0.74%, 0.55%, and 1.65%, respectively, indicating the superiority of the dual-stream CNN structure and the IFPN structure.
Deep Adaptive Ensemble Filter for Non-Intrusive Residential Load Monitoring
Authors: Kianpoor Nasrin , Hoff Bjarte , Østrem Trond
Published: February 2023

Identifying flexible loads, such as a heat pump, has an essential role in a home energy management system. In this study, an adaptive ensemble filtering framework integrated with long short-term memory (LSTM) is proposed for identifying flexible loads. The proposed framework, called AEFLSTM, takes advantage of filtering techniques and the representational power of LSTM for load disaggregation by filtering noise from the total power and learning the long-term dependencies of flexible loads. Furthermore, the proposed framework is adaptive and searches ensemble filtering techniques, including discrete wavelet transform, low-pass filter, and seasonality decomposition, to find the best filtering method for disaggregating different flexible loads (e.g., heat pumps). Experimental results are presented for estimating the electricity consumption of a heat pump, a refrigerator, and a dishwasher from the total power of a residential house in British Columbia (a publicly available use case). The results show that AEFLSTM can reduce the loss error (mean absolute error) by 57.4%, 44%, and 55.5% for estimating the power consumption of the heat pump, refrigerator, and dishwasher, respectively, compared to the stand-alone LSTM model. The proposed approach is used for another dataset containing measurements of an electric vehicle to further support the validity of the method. AEFLSTM is able to improve the result for disaggregating an electric vehicle by 22.5%.
Instrumental and Non-Instrumental Measurements in Patients with Peripheral Vestibular Dysfunctions
Authors: Gawronska Anna , Rosiak Oskar , Pajor Anna , Janc Magdalena , Kotas Rafal , Kaminski Marek , Zamyslowska-Szmytke Ewa , Jozefowicz-Korczynska Magdalena
Published: February 2023

Vestibular dysfunction is a disturbance of the body’s balance system. The control of balance and gait has a particular influence on the quality of life. Currently, assessing patients with these problems is mainly subjective. New assessment options using wearables may provide complementary and more objective information. Posturography makes it possible to determine the extent and type of posture dysfunction, which makes it possible to plan and monitor the effectiveness of physical rehabilitation therapy. This study evaluates the effectiveness of non-instrumental clinical tests and the instrumental mobile posturography MediPost device for patients with unilateral vestibular disorders. The study group included 40 patients. A subjective description of the symptoms was evaluated using a questionnaire about the intensity of dizziness using the Dizziness Handicap Inventory (DHI) and Vertigo Syndrome Scale—short form (VSS-sf). The clinical protocol contained clinical tests and MediPost measurements using a Modified Clinical Test of Sensory Interaction on Balance. All patients underwent vestibular rehabilitation therapy (VRT) for four weeks. The non-instrumental measurement results were statistically significant, and the best was in the Timed Up and Go test (TUG). In MediPost, condition 4 was the most valuable. This research demonstrated the possibilities of using an instrumental test (MediPost) as an alternative method to assess balance.
Dynamic Range Compression of Thermograms for Assessment of Welded Joint Face Quality
Authors: Jamrozik Wojciech , Górka Jacek , Batalha Gilmar Ferreira
Published: February 2023

Temperature is one of the essential parameters in fusion welding. Typically, an uncooled infrared detector acquires 14-bit data, while a human observer can only distinguish about 128 levels of grey. For IR HDR (high dynamic range) images, one of the main goals of dynamic range compression is to enhance the visibility of low-contrast details. It is an important issue because the temperature span in the cross-section of a welded joint and its length are large. In the paper, global approaches for range compression are investigated, such as algorithms that include pixel transformations, histogram equalization (‘he’) and some of its variants. Additionally, multiscale decomposition methods were investigated. All results are obtained for the sequences of thermograms acquired during the TIG welding of plates made of Inconel 625 superalloy. The process was observed with an uncooled IR camera. The application of compression methods led to the generation of low-dynamic-range (LDR) IR images. The algorithms allowed the preservation of global contrast and enhancement of the visibility of hot details in dark and low-contrast areas. All IR representations of the welded samples were evaluated, and relationships between apparent temperature counted in the pixel-level value and weld-face geometry were revealed. Methods based on wavelet transforms were found to be the most suitable for this type of image; nevertheless, a relatively large local noise was generated.
Trusted Operation of Cyber-Physical Processes Based on Assessment of the System’s State and Operating Mode
Authors: Basan Elena , Basan Alexandr , Nekrasov Alexey , Fidge Colin , Ishchukova Evgeniya , Basyuk Anatoly , Lesnikov Alexandr
Published: February 2023

We consider the trusted operation of cyber-physical processes based on an assessment of the system’s state and operating mode and present a method for detecting anomalies in the behavior of a cyber-physical system (CPS) based on the analysis of the data transmitted by its sensory subsystem. Probability theory and mathematical statistics are used to process and normalize the data in order to determine whether or not the system is in the correct operating mode and control process state. To describe the mode-specific control processes of a CPS, the paradigm of using cyber-physical parameters is taken as a basis, as it is the feature that most clearly reflects the system’s interaction with physical processes. In this study, two metrics were taken as a sign of an anomaly: the probability of falling into the sensor values’ confidence interval and parameter change monitoring. These two metrics, as well as the current mode evaluation, produce a final probability function for our trust in the CPS’s currently executing control process, which is, in turn, determined by the operating mode of the system. Based on the results of this trust assessment, it is possible to draw a conclusion about the processing state in which the system is operating. If the score is higher than 0.6, it means the system is in a trusted state. If the score is equal to 0.6, it means the system is in an uncertain state. If the trust score tends towards zero, then the system can be interpreted as unstable or under stress due to a system failure or deliberate attack. Through a case study using cyber-attack data for an unmanned aerial vehicle (UAV), it was found that the method works well. When we were evaluating the normal flight mode, there were no false positive anomaly estimates. When we were evaluating the UAV’s state during an attack, a deviation and an untrusted state were detected. This method can be used to implement software solutions aimed at detecting system faults and cyber-attacks, and thus make decisions about the presence of malfunctions in the operation of a CPS, thereby minimizing the amount of knowledge and initial data about the system.
A Miniaturized Full-Ground Dual-Band MIMO Spiral Button Wearable Antenna for 5G and Sub-6 GHz Communications
Authors: Saeidi Tale , Al-Gburi Ahmed Jamal Abdullah , Karamzadeh Saeid
Published: February 2023

A detachable miniaturized three-element spirals radiator button antenna integrated with a compact leaky-wave wearable antenna forming a dual-band three-port antenna is proposed. The leaky-wave antenna is fabricated on a denim (εr = 1.6, tan δ = 0.006) textile substrate with dimensions of 0.37 λ0 × 0.25 λ0 × 0.01 λ0 mm3 and a detachable rigid button of 20 mm diameter (on a PTFE substrate εr = 2.01, tan δ = 0.001). It augments users’ comfort, making it one of the smallest to date in the literature. The designed antenna, with 3.25 to 3.65 GHz and 5.4 to 5.85 GHz operational bands, covers the wireless local area network (WLAN) frequency (5.1–5.5 GHz), the fifth-generation (5G) communication band. Low mutual coupling between the ports and the button antenna elements ensures high diversity performance. The performance of the specific absorption rate (SAR) and the envelope correlation coefficient (ECC) are also examined. The simulation and measurement findings agree well. Low SAR, <−0.05 of LCC, more than 9.5 dBi diversity gain, dual polarization, and strong isolation between every two ports all point to the proposed antenna being an ideal option for use as a MIMO antenna for communications.
ESVIO: Event-Based Stereo Visual-Inertial Odometry
Authors: Liu Zhe , Shi Dianxi , Li Ruihao , Yang Shaowu
Published: February 2023

The emerging event cameras are bio-inspired sensors that can output pixel-level brightness changes at extremely high rates, and event-based visual-inertial odometry (VIO) is widely studied and used in autonomous robots. In this paper, we propose an event-based stereo VIO system, namely ESVIO. Firstly, we present a novel direct event-based VIO method, which fuses events’ depth, Time-Surface images, and pre-integrated inertial measurement to estimate the camera motion and inertial measurement unit (IMU) biases in a sliding window non-linear optimization framework, effectively improving the state estimation accuracy and robustness. Secondly, we design an event-inertia semi-joint initialization method, through two steps of event-only initialization and event-inertia initial optimization, to rapidly and accurately solve the initialization parameters of the VIO system, thereby further improving the state estimation accuracy. Based on these two methods, we implement the ESVIO system and evaluate the effectiveness and robustness of ESVIO on various public datasets. The experimental results show that ESVIO achieves good performance in both accuracy and robustness when compared with other state-of-the-art event-based VIO and stereo visual odometry (VO) systems, and, at the same time, with no compromise to real-time performance.
Comparison of Individual Sensors in the Electronic Nose for Stress Detection in Forest Stands
Authors: Hüttnerová Tereza , Paczkowski Sebastian , Neubert Tarek , Jirošová Anna , Surový Peter
Published: February 2023

Forests are increasingly exposed to natural disturbances, including drought, wildfires, pest outbreaks, and windthrow events. Due to prolonged droughts in the last years in Europe, European forest stands significantly lost vitality, and their health condition deteriorated, leading to high mortality rates, especially, but not limited to, Norway spruce. This phenomenon is growing, and new regions are being affected; thus, it is necessary to identify stress in the early stages when actions can be taken to protect the forest and living trees. Current detection methods are based on field walks by forest workers or deploying remote sensing methods for coverage of the larger territory. These methods are based on changes in spectral reflectance that can detect attacks only at an advanced stage after the significant changes in the canopy. An innovative approach appears to be a method based on odor mapping, specifically detecting chemical substances which are present in the forest stands and indicate triggering of constitutive defense of stressed trees. The bark beetle attacking a tree, for example, produces a several times higher amount of defense-related volatile organic compounds. At the same time, the bark beetle has an aggregation pheromone to attract conspecifics to overcome the tree defense by mass attack. These substances can be detected using conventional chemical methods (solid-phase microextraction fibers and cartridges), and it is proven that they are detectable by dogs. The disadvantage of classic chemical analysis methods is the long sampling time in the forest, and at the same time, the results must be analyzed in the laboratory using a gas chromatograph. A potential alternative novel device appears to be an electronic nose, which is designed to detect chemical substances online (for example, dangerous gas leaks or measure concentrations above landfills, volcanic activity, etc.). We tested the possibility of early-stage stress detection in the forest stands using an electronic nose Sniffer4D and compared the individual sensors in it for detecting the presence of attacked and dead trees. Our results indicate the promising applicability of the electronic nose for stress mapping in the forest ecosystem, and more data collection could prove this approach.
Damped Cantilever Microprobes for High-Speed Contact Metrology with 3D Surface Topography
Authors: Fahrbach Michael , Xu Min , Nyang’au Wilson Ombati , Domanov Oleg , Schwalb Christian H. , Li Zhi , Kuhlmann Christian , Brand Uwe , Peiner Erwin
Published: February 2023

We addressed the coating 5 mm-long cantilever microprobes with a viscoelastic material, which was intended to considerably extend the range of the traverse speed during the measurements of the 3D surface topography by damping contact-induced oscillations. The damping material was composed of epoxy glue, isopropyl alcohol, and glycerol, and its deposition onto the cantilever is described, as well as the tests of the completed cantilevers under free-oscillating conditions and in contact during scanning on a rough surface. The amplitude and phase of the cantilever’s fundamental out-of-plane oscillation mode was investigated vs. the damping layer thickness, which was set via repeated coating steps. The resonance frequency and quality factor decreased with the increasing thickness of the damping layer for both the free-oscillating and in-contact scanning operation mode, as expected from viscoelastic theory. A very low storage modulus of E′≈100kPa, a loss modulus of E″≈434kPa, and a density of ρ≈1.2gcm−3 were yielded for the damping composite. Almost critical damping was observed with an approximately 130 µm-thick damping layer in the free-oscillating case, which was effective at suppressing the ringing behavior during the high-speed in-contact probing of the rough surface topography.
Weakly Supervised 2D Pose Adaptation and Body Part Segmentation for Concealed Object Detection
Authors: Amadi Lawrence , Agam Gady
Published: February 2023

Weakly supervised pose estimation can be used to assist unsupervised body part segmentation and concealed item detection. The accuracy of pose estimation is essential for precise body part segmentation and accurate concealed item detection. In this paper, we show how poses obtained from an RGB pretrained 2D pose detector can be modified for the backscatter image domain. The 2D poses are refined using RANSAC bundle adjustment to minimize the projection loss in 3D. Furthermore, we show how 2D poses can be optimized using a newly proposed 3D-to-2D pose correction network weakly supervised with pose prior regularizers and multi-view pose and posture consistency losses. The optimized 2D poses are used to segment human body parts. We then train a body-part-aware anomaly detection network to detect foreign (concealed threat) objects on segmented body parts. Our work is applied to the TSA passenger screening dataset containing millimeter wave scan images of airport travelers annotated with only binary labels that indicate whether a foreign object is concealed on a body part. Our proposed approach significantly improves the detection accuracy of TSA 2D backscatter images in existing works with a state-of-the-art performance of 97% F1-score, 0.0559 log-loss on the TSA-PSD test-set, and a 74% reduction in 2D pose error.
Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm
Authors: Srivastava Vikas , Singh Parulpreet , Malik Praveen Kumar , Singh Rajesh , Tanwar Sudeep , Alqahtani Fayez , Tolba Amr , Marina Verdes , Raboaca Maria Simona
Published: February 2023

A cognitive radio network (CRN) is an intelligent network that can detect unoccupied spectrum space without interfering with the primary user (PU). Spectrum scarcity arises due to the stable channel allocation, which the CRN handles. Spectrum handoff management is a critical problem that must be addressed in the CRN to ensure indefinite connection and profitable use of unallocated spectrum space for secondary users (SUs). Spectrum handoff (SHO) has some disadvantages, i.e., communication delay and power consumption. To overcome these drawbacks, a reduction in handoff should be a priority. This study proposes the use of dynamic spectrum access (DSA) to check for available channels for SU during handoff using a metaheuristic algorithm depending on machine learning. The simulation results show that the proposed “support vector machine-based red deer algorithm” (SVM-RDA) is resilient and has low complexity. The suggested algorithm’s experimental setup offers several handoffs, unsuccessful handoffs, handoff delay, throughput, signal-to-noise ratio (SNR), SU bandwidth, and total spectrum bandwidth. This study provides an improved system performance during SHO. The inferred technique anticipates handoff delay and minimizes the handoff numbers. The results show that the recommended method is better at making predictions with fewer handoffs compared to the other three.
Make a Stand(ard) for Science
Authors: Abdin Ahmad Yaman , Jacob Claus
Published: February 2023

During the global Corona pandemic, the validity of science has been challenged by sections of the public, often for political gains [...]