bluuebunny/crossref_metadata_2025 huggingface dataset:
A FE-Based Macro-Element for the Assessment of Masonry Structures: Linear Static, Vibration, and Non-Linear Cyclic Analyses
Authors: da Silva Luis C. M. , Milani Gabriele
Published: January 2022

A Finite Element (FE) based macro–element is described for the mechanical response of masonry structures within different ranges of analysis. The macro–element is composed of discrete rigid quadrilateral FE plates whose adjoining interfaces are connected through FE trusses. It allows representing both elasticity and strength orthotropy, full material nonlinearity and damage through a scalar–based model. The possibility of coupling with a so–called FE2 (multi–scale) strategy is also addressed. Validation of the macro–element is conducted within linear static, vibration, and cyclic (nonlinear) problems, in which both static and dynamic ranges are explored. Results are compared with those retrieved from traditional FE continuous models. Advantages are highlighted, as well as its robustness to cope with convergence issues and suitability to be applied within more general and larger–scale scenarios, such as the analysis of anisotropic materials subjected to static and dynamic loading. Formal details are given for its reproducibility by academics and practitioners—eventually within other FE platforms—as the improved running times may be of utmost importance in dynamic problems or highly nonlinear (material) quasi–static analysis.
Mobility Management Scheme with Mobility Prediction in Wireless Communication Networks
Authors: Jang Hee-Seon , Baek Jang-Hyun
Published: January 2022

Many registration schemes have been proposed to reduce the signaling cost required for user’s mobility management in wireless communication networks. Various results on mobility management schemes to minimize the total signaling cost have been reported. The objective of this study was to analyze a registration scheme that could deal with mobility prediction and corresponding flexible tracking area list (TAL) forming. In this scheme, based on mobility prediction and corresponding TAL forms, a new TAL was constructed such that the registration cost could be minimized. In addition, a semi-Markov process model was newly presented for the registration scheme considering mobility prediction and corresponding flexible TAL forming for two different environments: urban and rural. Simulation studies were also performed to validate the accuracy of the semi-Markov process model. Numerical results showed that analytical and simulation results were very close (average relative error of 1.4%). The registration cost decreased as the moving probability (q) to the predicted direction increased. The performance of the proposed scheme was superior to distance-based registration (DBR) or TAL-based scheme especially when q was high. When call-to-mobility ratio was less than or equal to 1 corresponding to current small cell configurations, the proposed scheme outperformed the DBR or TAL-based scheme.
Horizon Targeted Loss-Based Diverse Realistic Marine Image Generation Method Using a Multimodal Style Transfer Network for Training Autonomous Vessels
Authors: Park Jisun , Choi Tae Hyeok , Cho Kyungeun
Published: January 2022

Studies on virtual-to-realistic image style transfer have been conducted to minimize the difference between virtual simulators and real-world environments and improve the training of artificial intelligence (AI)-based autonomous driving models using virtual simulators. However, when applying an image style transfer network architecture that achieves good performance using land-based data for autonomous vehicles to marine data for autonomous vessels, structures such as horizon lines and autonomous vessel shapes often lose their structural consistency. Marine data exhibit substantial environmental complexity, which depends on the size, position, and direction of the vessels because there are no lanes such as those for cars, and the colors of the sky and ocean are similar. To overcome these limitations, we propose a virtual-to-realistic marine image style transfer method using horizon-targeted loss for marine data. Horizon-targeted loss helps distinguish the structure of the horizon within the input and output images by comparing the segmented shape. Additionally, the design of the proposed network architecture involves a one-to-many style mapping technique, which is based on the multimodal style transfer method to generate marine images of diverse styles using a single network. Experiments demonstrate that the proposed method preserves the structural shapes on the horizon more accurately than existing algorithms. Moreover, the object detection accuracy using various augmented training data was higher than that observed in the case of training using only virtual data. The proposed method allows us to generate realistic data to train AI models of vision-based autonomous vessels by actualizing and augmenting virtual images acquired from virtual autonomous vessel simulators.
Emotion Estimation Method Based on Emoticon Image Features and Distributed Representations of Sentences
Authors: Fujisawa Akira , Matsumoto Kazuyuki , Yoshida Minoru , Kita Kenji
Published: January 2022

This paper proposes an emotion recognition method for tweets containing emoticons using their emoticon image and language features. Some of the existing methods register emoticons and their facial expression categories in a dictionary and use them, while other methods recognize emoticon facial expressions based on the various elements of the emoticons. However, highly accurate emotion recognition cannot be performed unless the recognition is based on a combination of the features of sentences and emoticons. Therefore, we propose a model that recognizes emotions by extracting the shape features of emoticons from their image data and applying the feature vector input that combines the image features with features extracted from the text of the tweets. Based on evaluation experiments, the proposed method is confirmed to achieve high accuracy and shown to be more effective than methods that use text features only.
An Integrated Analysis of Metabolomics and Transcriptomics Reveals Significant Differences in Floral Scents and Related Gene Expression between Two Varieties of Dendrobium loddigesii
Authors: Wang Sha , Du Zhihui , Yang Xiyu , Wang Lanlan , Xia Kuaifei , Chen Zhilin
Published: January 2022

Flower fragrance is one of the traits that holds important economical values in flowering plants. Extensive attention has converged on fragrance preservation in flower cultivation and breeding. Dendrobium loddigesii is an important species for cultivating aromatic Dendrobium orchid varieties for the long term due to its fragrance. Few studies focus on exploring related genes responsible for the aroma components in D. loddigesii. We analyzed flowers from two aromatic D. loddigesii varieties using high-throughput RNA sequencing and gas chromatography-mass spectrometry (GC-MS). The metabolomics results showed that the main volatile compounds responsible for the aroma formation of D. loddigesii were terpenes, especially monoterpenes. The de novo transcriptome assembly comprised 175,089 unigenes, and 24,570 unigenes of the genes were identified as differential expressed genes (DEGs) between the two varieties. Among these DEGs, 525 genes were mapped into seven pathways that related to the floral scent synthesis. Seventeen key genes were significantly correlated with volatile aroma metabolites, including geraniol, α-pinene, eugenol, and (Z)-3-hexenal. These results provide references for understanding the aroma biosynthesis and perfume formulations of D. loddigesii.
Comparing the Effectiveness of Speech and Physiological Features in Explaining Emotional Responses during Voice User Interface Interactions
Authors: Swoboda Danya , Boasen Jared , Léger Pierre-Majorique , Pourchon Romain , Sénécal Sylvain
Published: January 2022

The rapid rise of voice user interface technology has changed the way users traditionally interact with interfaces, as tasks requiring gestural or visual attention are swapped by vocal commands. This shift has equally affected designers, required to disregard common digital interface guidelines in order to adapt to non-visual user interaction (No-UI) methods. The guidelines regarding voice user interface evaluation are far from the maturity of those surrounding digital interface evaluation, resulting in a lack of consensus and clarity. Thus, we sought to contribute to the emerging literature regarding voice user interface evaluation and, consequently, assist user experience professionals in their quest to create optimal vocal experiences. To do so, we compared the effectiveness of physiological features (e.g., phasic electrodermal activity amplitude) and speech features (e.g., spectral slope amplitude) to predict the intensity of users’ emotional responses during voice user interface interactions. We performed a within-subjects experiment in which the speech, facial expression, and electrodermal activity responses of 16 participants were recorded during voice user interface interactions that were purposely designed to elicit frustration and shock, resulting in 188 analyzed interactions. Our results suggest that the physiological measure of facial expression and its extracted feature, automatic facial expression-based valence, is most informative of emotional events lived through voice user interface interactions. By comparing the unique effectiveness of each feature, theoretical and practical contributions may be noted, as the results contribute to voice user interface literature while providing key insights favoring efficient voice user interface evaluation.
Utilization of Unsupervised Machine Learning for Detection of Duct Voids inside PSC Box Girder Bridges
Authors: Lee Da-In , Choi Hyung , Kim Jong-Dae , Park Chan-Young , Kim Yu-Seop
Published: January 2022

The PSC box girder bridge is a pre-stressed box girder bridge that accounts for a considerable part of large-scale bridges. However, when concrete is poured, even small mistakes might result in voids that appear during long-term maintenance. In this paper, we present a technique for detecting the void in the duct inside the PSC box girder bridge. Data are acquired utilizing the non-destructive impact-echo (IE) approach to detect these voids. IE creates time-series data as signal data initially; however, we want to use a CNN auto-encoder (AE). A scalogram, which is a kind of wavelet transformation, is used to convert time series data into an image. An AE is a type of unsupervised learning that aims to minimize the difference between the input and output. By comparing histograms, the difference is calculated. To begin, we create scalogram images from all IE signal data, which were randomly sampled as 98% normal and 2% void. The CNN AE is then trained and evaluated utilizing all the data. Finally, we examine the input and output histogram similarity distributions. As a consequence, only 4% of the normal data had a similarity of less than two standard deviations from the mean, whereas 34.7% of the void data did. As a result, the existence of voids inside the PSC duct could be demonstrated to be predictive in the absence of annotated data.
Developing Community Disaster Resilience in the Lembang Fault Area, Indonesia: Lessons Learned from Japanese Experience
Authors: Abdulharis Rizqi , Handayani Alfita Puspa , Isouchi Chikako , Meilano Irwan
Published: January 2022

Having experienced large-scale disasters between 2004 and 2006, the fatalities due to large-scale disasters in 2018 in Indonesia were still high. In contrast, community disaster resilience (CDR) and disaster risk management (DRM) in Japan have been continuously improved. Thus, there is a need to develop CDR for supporting DRM in Indonesia by learning from the Japanese experience, particularly in a disaster-prone area without large-scale disaster experience. This research was a pilot project on the development of CDR in Indonesia. The case study was Lembang Fault area, which is a geologic hazard-prone area. People’s perception was collected using structured interviews, while demographic and local economic data were acquired from official statistical publications. Satellite images were utilized to acquire the imageries of natural and built environment, as well as land use/land cover and its changes, between 2019 and 2021. Based on CDR assessment in the Lembang Fault area, the levels of people’s participation and capacity on DRM were low. This may be caused by the low level of training and education, linking of social capital and past disaster experience, as well as the inability of the people to interpret the symbols in indigenous knowledge. Moreover, government interventions on DRM and land administration are required to develop CDR in the Lembang Fault area. Organized community development is expected rather than to solely involve universities and NGOs. Furthermore, strategies to develop economic resilience are needed to allow the community to bounce back from future disaster. Finally, baseline data should be collected and managed to develop DRM strategy and CDR.
The Hamstring and ACL Injury Incidence during a Season Is Not Directly Related to Preseason Knee Strength Ratios in Elite Male Soccer Players
Authors: Izovska Jana , Hank Mikulas , Cabell Lee , Kalata Maros , Bujnovsky David , Zahalka Frantisek , Maly Tomas
Published: January 2022

Background and Objectives: The aim of this study was to identify preseason isokinetic strength differences in the knee flexors and extensors and their ipsilateral/bilateral ratios and, furthermore, to compare the results among players who subsequently overcame a hamstring strain injury (HSI) or anterior cruciate ligament (ACL) rupture during the season and those who did not. Materials and Methods: A total of 134 professional soccer players underwent isokinetic strength assessment at a velocity of 60°·s−1 (knee flexors and extensors) for the dominant and non-dominant lower limb to determine preseason peak torque values and bilateral and ipsilateral strength ratios. Subsequently, the incidence of injuries during the season was recorded, and players were divided into groups according to ACL rupture injuries (n = 10), hamstring strains (n = 10), and a control group of non-injured players who were selected on a random basis (n = 20). A retrospective approach was used to analyze and compare the preseason strength characteristics and whether some variance among groups was relevant among the injured leg and non-injured leg groups. Results: The results of our study show that low-angular velocity preseason testing did not result in a player’s HSI or ACL injury during the season. The difference between the monitored groups ranged from 1.5% to 3%. The comparison showed low evidence for significant differences. Conclusions: An angular velocity of 60°·s−1 within concentric muscle contraction alone was not linked to subsequent injury of the hamstring or anterior cruciate ligament and acted as an insufficient factor of injury risk in adult professional soccer players.
Numerical Analysis of Shear Keys for Offshore Wind Turbine Monopile Grouted Connection with Elastomeric Bearings
Authors: You Young-Suk , Sun Min-Young
Published: January 2022

Wind power is one of the best-known renewable energy sources, and it is mainly generated by wind turbines. With the recent development of large-scale offshore wind turbine technology and the improvements in capacity factor, the demand for offshore wind power is rapidly increasing for energy system applications worldwide. Such offshore wind turbine structures require structural capacity to withstand loads from offshore environments for a predetermined period of time. Generally, the load of the upper turbine system is transmitted through the grouted connection to the substructure. However, there are many cases of grout failure of the grouted connection between the tubular steels. This paper deals with the analysis of monopile grouted connections to which elastomeric bearings are applied. The grouted connection for the ultimate load of a 3.6 MW offshore wind turbine was analyzed using the three-dimensional finite element method. Furthermore, the changes in the contact pressure and shear stress were analyzed due to the installation of elastomeric bearings around the shear keys. As a result, when the elastomeric bearing was installed, the contact pressure for all grout contact areas increased about 2.5-fold. Specifically, the contact pressure with the shear key was 1.9-fold lower when natural rubber was used as the rubber plate material instead of chloroprene rubber. In addition, the maximum shear stress values of grout filler when installing the elastomeric bearings were 5.78 MPa for chloroprene rubber material and 4.90 MPa for natural rubber material, which were reduced by about 77–81% compared to the value of 25.95 MPa when only shear key was used.
Investigation of UHF Signal Strength Propagation at Warehouse Management Applications Based on Drones and RFID Technology Utilization
Authors: Benes Filip , Stasa Pavel , Svub Jiri , Alfian Ganjar , Kang Yong-Shin , Rhee Jong-Tae
Published: January 2022

As a part of the supply chain, inventory management includes, among other things, maintaining the storage of stock, controlling the amount of product for sale and order fulfilment. In business terms, inventory management means the right stock, at the right levels, in the right place, at the right time. In the case of large outdoor warehouses, common identification methods are lengthy and inappropriate. One way to determine inventory easily and quickly is to deploy UAV’s (unmanned aerial vehicle) for product identification purposes. In this case, however, there is a problem in determining where the goods are located. A drone moves at higher altitudes, which can lead to a situation where we will not be able to determine the exact location of the goods. This article deals with a method of determining the correct flight level suitable to distinguish the identified items located at least 2 m apart. The evaluation is performed based on an RSSI (received signal strength indicator) value. The experiment proved that even at maximum reading distance of selected passive UHF RFID tags the two objects can be distinguished.
Intact, Commercial Lithium-Polymer Batteries: Spatially Resolved Grating-Based Interferometry Imaging, Bragg Edge Imaging, and Neutron Diffraction
Authors: Brooks Adam J. , Hussey Daniel S. , Ham Kyungmin , Jacobson David L. , Manke Ingo , Kardjilov Nikolay , Butler Leslie G.
Published: January 2022

We survey several neutron imaging and diffraction methods for non-destructive testing and evaluation of intact, commercial lithium-ion batteries. Specifically, far-field interferometry was explored as an option to probe a wide range of autocorrelation lengths within the batteries via neutron imaging. The dark-field interferometry images change remarkably from fresh to worn batteries, and from charged to discharged batteries. When attempting to search for visual evidence of battery degradation, neutron Talbot-Lau grating interferometry exposed battery layering and particle scattering through dark-field imaging. Bragg edge imaging also reveals battery wear and state of charge. Neutron diffraction observed chemical changes between fresh and worn, charged and discharged batteries. However, the utility of these methods, for commercial batteries, is dependent upon battery size and shape, with 19 to 43 mAh prismatic batteries proving most convenient for these experimental methods. This study reports some of the first spatially resolved, small angle scattering (dark-field) images showing battery degradation.
A GIS-Based Analysis of the Light Rail Transit Systems in Spain
Authors: Pantiga-Facal Estela , Plasencia-Lozano Pedro
Published: January 2022

Throughout the last decades, light rail transit systems (LRT) have been built around the world due to their strengths: regularity, savings in CO2 emissions, flexibility, image and optimization of urban space. This research proposes the analyses of a group of LRTs by using a method supported by GIS. Some data and parameters have been stated in order to define the systems. The range of values obtained allow us to characterize the LRT systems operating in Spain: for instance, mean distance range between stations is 405 m to 685 m, and potential users range (people who live no more than 500 m from a station) is 18,000 to 30,000, and population density in the capture areas is never below 1514 pop/km2 (in cities with no metro system). The benefit of the present study is to establish common data values that could be useful in future studies and preliminary projects carried out in other Spanish cities interested in introducing an LRT in their urban grid, and also for comparison with LRT in other countries. The method could be also applied to other public transportation systems, and to other countries.
Design Study of a Large-Angle Optical Scanning System for MEMS LIDAR
Authors: Pang Yajun , Zhang Kai , Bai Zhenxu , Sun Youchun , Yao Meiling
Published: January 2022

MEMS-based LIDAR has the advantages of fast-scanning, simple structure, small volume, and light weight, which make it a hot topic for 3D imaging. In order to overcome the limited scanning angle of the current MEMS mirrors, a large angle MEMS-based optical scanning system was designed using telephoto structure. In this design an f-θ lens group is used for flat field scanning and a symmetrical cemented lens group is used for angle expansion. The principle of this system is discussed in detail and an optical design is implemented by ZEMAX software. The designed system realized a scanning angle as large as 57° and is in excellent linear relation with the tilt angle of the MEMS mirror. At the target distance of 100 m, the maximum RMS radius of the light spot is only 7 cm. This optical scanning system shows a significant application potentiality for 3D imaging LIDAR.
Characteristic Aroma Compound in Cinnamon Bark Extract Using Soybean Oil and/or Water
Authors: Zhang Wenwen , Jia Chuankang , Yan Huimin , Peng Yalin , Hu Enmin , Qi Jun , Lin Qing
Published: January 2022

The effects of soybean oil (20%, v/w) and extraction time (30, 60, or 90 min) on volatile compounds in cinnamon bark extract were investigated. The relative content and odor activity values (OAVs) of volatile compounds were measured by Gas Chromatography-Mass Spectrometer (GC-MS). The results showed that a total of 26 and 27 volatile compounds were detected in the water extract and the aqueous phase of the water/oil extraction, respectively. Hexanal, nonanal, cinnamaldehyde, D-limonene, 1-octen-3-ol, linalool, and anethole were the major aroma-active compounds, accounting for 85% of the total substance content. Cinnamaldehyde had the highest contribution rate to the aroma of the water extract (26%), whereas anethole has the highest contribution rate to the aroma of the oil/water extract (30%). Whether or not the extraction medium contained soybean oil, the relative content of aroma-active compounds in the aqueous phase decreased with increased extraction time, and the relative content of these compounds in the aqueous phase further decreased when soybean oil was present. This should be due to the high hydrophobicity of these compounds, which were prone to dissolving in the oil layer during the extraction process, resulting in a decrease in the relative content of aroma-active compounds in the aqueous phase.
Automatic Defect Identification Method for Magnetic Particle Inspection of Bearing Rings Based on Visual Characteristics and High-Level Features
Authors: Yang Yun , Yang Yao , Li Long , Chen Cuili , Min Zhou
Published: January 2022

Fluorescent magnetic particle inspection (MPI) is a conventional non-destructive testing process for railway bearing rings that still needs to be completed manually. Due to the complexity of bearing ring surfaces in inspection, automatic detection for bearing rings based on image processing is difficult to apply. Therefore, we proposed a bearing ring defect identification method based on visual characteristics and high-level features. Inspired by the mechanism of human visual perception, defects can be identified from the complex background conveniently by human eyes. According to the linear structure characteristics and greyscale distribution characteristics of cracks in the acquired images, we introduce the centerline extraction and Gaussian similarity measure to reduce background noise and obtain the crack candidate regions. Then, an improved MobileNetV3 is used to extract high-level features of the candidate regions and determine whether they are defective, which uses a new attention module, Coordinate Attention (CA), to substitute the Squeeze-and-Excitation (SE) attention to improve the performance. The experimental results show that the detection accuracy rate of the proposed method is 96.5%. Compared with traditional methods, the proposed method can efficiently extract crack defects in a complex textured background and shows high-quality performance in recall and precision.
A Preliminary Analysis on the Insecticidal Effect of Cyantraniliprole against Stored-Product Pests
Authors: Mantzoukas Spiridon , Kosmidou Gestimani , Gekas Anastasios , Kitsiou Foteini , Eliopoulos Panagiotis A. , Patakioutas George
Published: January 2022

While existing insecticides are becoming obsolete, the need for research and application of alternative substances is growing. Cyantraniliprole is a second-generation ryanodine receptor with the ability to bind and activate the ryanodine receptors in insect striated muscle cells, causing continuous muscular contraction, paralysis, and death. Many studies indicate its effectiveness on a variety of insects, but its action on storage pests has not yet been reported. We successfully conducted experiments for the first time on adult Tenebrio molitor, Tribolium confusum, Alphitobius diaperinus, Rhyzopertha dominica, and Trogoderma granarium with application of cyantraniliprole, causing dose-dependent mortality. Bioassays were carried out in the laboratory, where experimental adults were sprayed with six concentrations of cyantraniliprole. Mortality was recorded at 7, 14, 21, and 28 days after exposure. Mean mortality, survival concentration, and survival time were estimated for each species. The concentrations with both the highest mortality and the lowest survival rate were 2500 and 3000 ppm. Our results indicate that the tested insecticide was effective against T. confusum adults and is a promising pesticide for use in storage facilities.
Goal Shot Analysis in Elite Water Polo—World Cup Final 2018 in Berlin
Authors: Hochstein Stefan , Hohenstein Dirk , Hohmann Andreas
Published: January 2022

The subject of this game analysis was the throwing behavior of the world class players under competition match conditions during the final “World Cup Water Polo” tournament 2018 in Berlin. Specifically, we analyzed (a) the situational dependence of concrete environmental conditions (“constraints”) of successful throwing actions as well as (b) the goal throw biomechanics concerning throwing speed. Therefore, player’s and ball positions were recorded by video cameras as well as high-speed cameras. Based on the positions and trajectories parameters such as goal throw speed, Voronoi-cells as well as team centroids were calculated. The area of the Voronoi-cells differs concerning defending and attacking teams as well as between successful and non-successful teams and can be used as an indicator for goal or no goal. Under semi-collective tactical aspects, the comparison of the offensively and defensively controlled field areas (collective space control) between successful and unsuccessful goal throws shows that in the case of successful attacking completions, the attacking team (in the sum of its six players) each controlled significantly more field area in front of the opponent’s goal than the six defensive players together with their goalkeeper. In the case of unsuccessful attacking attempts, this area ratio was exactly reversed, i.e., the defensive team dominated the space.
Multi-Objective Optimization Design of Key Parameters of a Stepless Flow Control System with Multi-System Coupling Characteristics
Authors: Sun Xu , Wang Yao , Zhang Jinjie , Lei Fuchang , Zhao Degeng , Hong Huaibin
Published: January 2022

The stepless flow control system offers important energy-saving technology for reciprocating compressors in the petrochemical and oil refining industries. Optimizing the movement characteristics of the unloader and suction valve is vital to improving the energy-saving level of the system and reducing the overall operating cost. However, the system has many of the characteristics of multi-system coupling and multi-parameter crossover; thus, it is difficult to optimize the key control parameters. In this study, to optimize the system inlet pressure, return pressure, and return spring stiffness parameters, a working model of the flow control system based on multi-system coupling was established. Using the ejection and withdrawal speeds of the unloader, the flow displacement deviation, and the gas work deviation of the control system as the optimization parameters, we used the response surface method to establish an optimization proxy model between the objective function and key parameters. Additionally, verification of the model’s accuracy and sensitivity analyses were completed. Finally, a double optimization scheme based on a non-dominated genetic algorithm (NSGA-II) was proposed. Simulation and experimental results show that with optimization of the return spring, oil inlet pressure, and oil return pressure, the unloader’s kinematic characteristics were also optimized at full load. The impact energy of the ejector and withdrawal speed of the unloader were reduced, and the compressor flow control error was less than 5%, which effectively improved the comprehensive working performance of the stepless flow control system.
A Review on Applications of Fuzzy Logic Control for Refrigeration Systems
Authors: Belman-Flores Juan Manuel , Rodríguez-Valderrama David Alejandro , Ledesma Sergio , García-Pabón Juan José , Hernández Donato , Pardo-Cely Diana Marcela
Published: January 2022

The use of fuzzy logic controllers in refrigeration and air conditioning systems, RACs, has as main objective to maintain certain thermal and comfort conditions. In this sense, fuzzy controllers have proven to be a viable option for use in RACs due to their ease of implementation and their ability to integrate with other control systems and control improvements, as well as their ability to achieve potential energy savings. In this document, we present a review of the application of fuzzy controls in RACs based on vapor compression technology. Application information is discussed for each type of controller, according to its application in chillers, air conditioning systems, refrigerators, and heat pumps. In addition, this review provides detailed information on controller design, focusing on the potential to achieve energy savings; this design discusses input and output variables, number and type of membership functions, and inference rules. The future perspectives on the use of fuzzy control systems applied to RACs are shown as well. In other words, the information in this document is intended to serve as a guide for the creation of controller designs to be applied to RACs.
BIM-Based Maintenance Data Processing Mechanism through COBie Standard Development for Port Facility
Authors: Shin Sangyun , Moon Hyounseok , Shin Jaeyoung
Published: January 2022

In the AECO field, there have been many efforts to transform the design, construction, and maintenance methods that have been carried out in the traditional way based on document-based data into digital ones. BIM makes it possible for the information necessary for the entire construction process to be transformed into a language that computers can understand. In the field of architecture, BIM has been actively used throughout the entire construction life cycle through steady research since the 2000s, while COBie was developed in order to deliver information generated in the design and construction phase to the maintenance phase based on the BIM environment. In the field of infrastructure, however, digital data-based maintenance information management has not been actively studied. In particular, in the case of a port, once a facility is built, it has to be maintained for a longer period than other facilities, so facility maintenance is of utmost importance. Therefore, this study applied COBie, which is being used in the architecture field (building), to the port field. To this end, the COBie standard format in the port field was developed based on the port facility object breakdown structure and property breakdown structure. The port COBie developed through this study is included when the BIM model is converted to the IFC model so that facility management can be performed through the IFC-based viewer in the future. The port COBie schema developed in this study can be expanded to other fields such as roads and bridges in the future.
Evaluation and Selection of the Railroad Route between Rijeka and Zagreb
Authors: Vilke Siniša , Petrović Ines , Tadić Frane
Published: January 2022

One of the biggest issues in railroad planning and design is determining the optimal railroad route. After the railroad route variants are determined, the major challenge is to identify and select the criteria which will be used for the analysis and evaluation of the variants. This paper is primarily concerned with the evaluation and selection of an optimal railroad route between Rijeka and Zagreb as part of the Mediterranean Corridor. The large number of criteria used to analyze solutions makes this decision-making complex. The objectives are usually in conflict with each other, and there are usually several groups of decision makers involved in the process. The aim of this paper was to analyze alternative railroad route variants using the optimization method of multi-criteria analysis. To achieve the research aim, a model comprising the defined criteria and sub-criteria including their weighting coefficients was set. To perform the analysis, the authors applied the defined model for evaluation and selection of a railway route between Rijeka and Zagreb using the PROMETHEE II method for multi-criteria ranking of alternatives and the computer software “Visual PROMETHEE”. The value of the defined model is expressed by the presented methodology of multi-criteria optimization, which is used in railroad planning and designing.
A Shallow Domain Knowledge Injection (SDK-Injection) Method for Improving CNN-Based ECG Pattern Classification
Authors: Oh Soyeon , Lee Minsoo
Published: January 2022

ECG pattern classification for identifying the progress status of various heart diseases is a typical nonlinear problem. Therefore, deep learning-based automatic ECG diagnosis is being widely studied, and for this purpose, the CNN is mainly used to classify ECG patterns. In this case, it is hard to expect any further improvement in accuracy after optimizing the parameters. We propose a shallow domain knowledge injection method that can improve the accuracy of the existing parameter-optimized CNN. The proposed method can improve the accuracy by effectively injecting shallow domain knowledge, that can be acquired by non-medical experts, into the existing parameter-optimized CNN. The experiments show that the proposed method can be applied to both heart disease diagnoses and general ECG classification tasks, while improving the existing accuracy for both types of tasks.
Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
Authors: Caruso Giovanni , Palai Giacomo , Gucci Riccardo , Priori Simone
Published: January 2022

The aim of this study was to evaluate the potential use of remote and proximal sensing techniques to identify homogeneous zones in a high density irrigated olive (Olea europaea L.) orchard subjected to three irrigation regimes (full irrigation, deficit irrigation and rainfed conditions). An unmanned aerial vehicle equipped with a multispectral camera was used to measure the canopy NDVI and two different proximal soil sensors to map soil spatial variability at high resolution. We identified two clusters of trees showing differences in fruit yield (17.259 and 14.003 kg per tree in Cluster 1 and 2, respectively) and annual TCSA increment (0.26 and 0.24 dm2, respectively). The higher tree productivity measured in Cluster 1 also resulted in a higher water use efficiency for fruit (WUEf of 0.90 g dry weight L−1 H2O) and oil (WUEo of 0.32 g oil L−1 H2O) compared to Cluster 2 (0.67 and 0.27 for WUEf and WUEo, respectively). Remote and proximal sensing technologies allowed to determine that: (i) the effect of different irrigation regimes on tree performance and WUE depended on the location within the orchard; (ii) tree vigour played a major role in determining the final fruit yield under optimal soil water availability, whereas soil features prevailed under rainfed conditions.
Impact of Sentence Representation Matching in Neural Machine Translation
Authors: Jung Heeseung , Kim Kangil , Shin Jong-Hun , Na Seung-Hoon , Jung Sangkeun , Woo Sangmin
Published: January 2022

Most neural machine translation models are implemented as a conditional language model framework composed of encoder and decoder models. This framework learns complex and long-distant dependencies, but its deep structure causes inefficiency in training. Matching vector representations of source and target sentences improves the inefficiency by shortening the depth from parameters to costs and generalizes NMTs with a different perspective to cross-entropy loss. In this paper, we propose matching methods to derive the cost based on constant word-embedding vectors of source and target sentences. To find the best method, we analyze the impact of the methods with varying structures, distance metrics, and model capacity in a French to English translation task. An optimally configured method is applied to English translation tasks from and to French, Spanish, and German. In the tasks, the method showed performance improvement by 3.23 BLEU at maximum, with an improvement of 0.71 on average. We evaluated the robustness of this method to various embedding distributions and models, such as conventional gated structures and transformer networks, and empirical results showed that it has a higher chance to improve performance in those models.
Sort and Deep-SORT Based Multi-Object Tracking for Mobile Robotics: Evaluation with New Data Association Metrics
Authors: Pereira Ricardo , Carvalho Guilherme , Garrote Luís , Nunes Urbano J.
Published: January 2022

Multi-Object Tracking (MOT) techniques have been under continuous research and increasingly applied in a diverse range of tasks. One area in particular concerns its application in navigation tasks of assistive mobile robots, with the aim to increase the mobility and autonomy of people suffering from mobility decay, or severe motor impairments, due to muscular, neurological, or osteoarticular decay. Therefore, in this work, having in view navigation tasks for assistive mobile robots, an evaluation study of two MOTs by detection algorithms, SORT and Deep-SORT, is presented. To improve the data association of both methods, which are solved as a linear assignment problem with a generated cost matrix, a set of new object tracking data association cost matrices based on intersection over union, Euclidean distances, and bounding box metrics is proposed. For the evaluation of the MOT by detection in a real-time pipeline, the YOLOv3 is used to detect and classify the objects available on images. In addition, to perform the proposed evaluation aiming at assistive platforms, the ISR Tracking dataset, which represents the object conditions under which real robotic platforms may navigate, is presented. Experimental evaluations were also carried out on the MOT17 dataset. Promising results were achieved by the proposed object tracking data association cost matrices, showing an improvement in the majority of the MOT evaluation metrics compared to the default data association cost matrix. In addition, promising frame rate values were attained by the pipeline composed of the detector and the tracking module.
Computational Model Development for Hybrid Tilting Pad Journal Bearings Lubricated with Supercritical Carbon Dioxide
Authors: Mehdi Syed Muntazir , Kim Tae Ho
Published: January 2022

Fluid film bearings lubricated with supercritical carbon dioxide (sCO2) eliminate the infrastructural requirement for oil lubricant supply and sealing in turbomachinery for sCO2 power systems. However, sCO2’s thermohydrodynamic properties, which depend on pressure and temperature, pose a challenge, particularly with computational model development for such bearings. This study develops a computational model for analyzing sCO2-lubricated tilting pad journal bearings (TPJBs) with external pressurization. Treating sCO2 as a real gas, the Reynolds equation for compressible turbulent flows solves the pressure distribution using the finite element method, and the Newton−Raphson method determines the static equilibrium position by simultaneously calculating forces, moments, flow rates of externally pressurized sCO2, and pressure drop due to flow inertia. The finite difference method solves the energy equation for temperature distribution. The density and viscosity of sCO2 are converged using the successive substitution method. The obtained predictions agree with the previous and authors’ computational fluid dynamics predictions, thus validating the developed model. Hybrid lubrication increases the minimum film thickness and stiffness up to 80% and 65%, respectively, and decreases the eccentricity ratio by up to 65% compared to those of pure hydrodynamic TPJB, indicating significant improvement in the load capacity. The bearing performance is further improved with increasing sCO2 supply pressure.
A Hierarchical Generative Embedding Model for Influence Maximization in Attributed Social Networks
Authors: Xie Luodi , Huang Huimin , Du Qing
Published: January 2022

Nowadays, we use social networks such as Twitter, Facebook, WeChat and Weibo as means to communicate with each other. Social networks have become so indispensable in our everyday life that we cannot imagine what daily life would be like without social networks. Through social networks, we can access friends’ opinions and behaviors easily and are influenced by them in turn. Thus, an effective algorithm to find the top-K influential nodes (the problem of influence maximization) in the social network is critical for various downstream tasks such as viral marketing, anticipating natural hazards, reducing gang violence, public opinion supervision, etc. Solving the problem of influence maximization in real-world propagation scenarios often involves estimating influence strength (influence probability between two nodes), which cannot be observed directly. To estimate influence strength, conventional approaches propose various humanly devised rules to extract features of user interactions, the effectiveness of which heavily depends on domain expert knowledge. Besides, they are often applicable for special scenarios or specific diffusion models. Consequently, they are difficult to generalize into different scenarios and diffusion models. Inspired by the powerful ability of neural networks in the field of representation learning, we designed a hierarchical generative embedding model (HGE) to map nodes into latent space automatically. Then, with the learned latent representation of each node, we proposed a HGE-GA algorithm to predict influence strength and compute the top-K influential nodes. Extensive experiments on real-world attributed networks demonstrate the outstanding superiority of the proposed HGE model and HGE-GA algorithm compared with the state-of-the-art methods, verifying the effectiveness of the proposed model and algorithm.
Advanced Analytical Tools for the Estimation of Gut Permeability of Compounds of Pharmaceutical Interest
Authors: Biancolillo Alessandra , Mennitti Luca , Foschi Martina , Marini Federico
Published: January 2022

The present study aims at developing a quantitative structure–activity relationship (QSAR) model for the determination of gut permeability of 228 pharmacological drugs at different pH conditions (3, 5, 7.4, 9, intrinsic). As a consequence, five different datasets (according to the diverse permeability shown by the compounds at the different pH values) were handled, with the aim of discriminating compounds as low-permeable or high-permeable. In order to achieve this goal, molecular descriptors for all the investigated compounds were computed and then classification models calculated by means of partial least squares discriminant analysis (PLS-DA). A high predictive capability was achieved for all models, providing correct classification rates in external validation between 80% and 96%. In order to test whether a reduction in the molecular descriptors would improve predictions and provide information about the most relevant variables, a feature selection approach, covariance selection, was used to select the most relevant subsets of predictors. This led to a slight improvement in the predictive accuracies, and it has indicated that the most relevant descriptors for the discrimination of the investigated compounds into low- and high-permeable were associated with the 2D and 3D structures.
Optimization of Valorization of Chicken MDCM to Produce Soluble Protein and Collagen Peptides
Authors: Pap Nora , Mäkinen Sari , Moilanen Ulla , Vahvaselkä Marjatta , Maunuksela Jyri , Kymäläinen Maritta , Pihlanto Anne
Published: January 2022

This study aimed to utilize enzymatic treatment and pressurized hot water extraction (PHWE) to recover soluble food-grade protein and collagen peptides from mechanically deboned chicken meat (MDCM), a side-stream from the meat industry. Food-grade enzyme blends Ermitase 1 and Ermitase 2 were used to fractionate the mechanically deboned meat into fat, soluble protein, and solids. Response surface methodology was utilized to optimize treatments to maximize the protein yield. At the optimum conditions (hydrolysis time 240 min, E:S 0.27%, and a hydromodule 1 L/kg), the enzymatic treatment produced high protein yield, approximately 90%. The protein hydrolysates showed a good solubility index, but weak gelling properties. The PHWE of the bones resulted in a high nitrogen yield, approximately 87%, at the optimum conditions of 190 °C and 83 min. Peptides in the bone extract were in the range of 0.5–13.7 kDa. Overall, our study highlights the importance of response surface methodology to optimize parameters for mechanically deboned chicken meat enzymatic and PHWE treatments to achieve high yields of protein for food applications and low-molecular-weight collagen peptides for cosmetic applications. The crucial role of protein and peptide prices was observed in preliminary profitability analysis.