Search results for: building performance rating tool

Authors: Andrea Treviño Gavito, Diego Klabjan, Sanjiv J. Shah

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Echocardiograms serve as pivotal resources for clinicians in diagnosing cardiac conditions, offering non-invasive insights into a heart’s structure and function. When echocardiographic studies are conducted, no standardized labeling of the acquired views is performed. Employing machine learning algorithms for automated echocardiogram view detection has emerged as a promising solution to enhance efficiency in echocardiogram use for diagnosis. However, existing approaches predominantly rely on supervised learning, necessitating labor-intensive expert labeling. In this paper, we introduce a fully unsupervised echocardiographic view detection framework that leverages convolutional autoencoders to obtain lower dimensional representations and the K-means algorithm for clustering them into view-related groups. Our approach focuses on discriminative patches from echocardiographic frames. Additionally, we propose a trainable inverse average layer to optimize decoding of average operations. By integrating both public and proprietary datasets, we obtain a marked improvement in model performance when compared to utilizing a proprietary dataset alone. Our experiments show boosts of 15.5% in accuracy and 9.0% in the F-1 score for frame-based clustering, and 25.9% in accuracy and 19.8% in the F-1 score for view-based clustering. Our research highlights the potential of unsupervised learning methodologies and the utilization of open-sourced data in addressing the complexities of echocardiogram interpretation, paving the way for more accurate and efficient cardiac diagnoses.

Keywords: artificial intelligence, echocardiographic view detection, echocardiography, machine learning, self-supervised representation learning, unsupervised learning

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Authors: Michael Karnes, Alper Yilmaz

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This paper presents the application of the omni-modeler towards pedestrian redetection. The pedestrian redetection task creates several challenges when applying deep neural networks (DNN) due to the variety of pedestrian appearance with camera position, the variety of environmental conditions, and the specificity required to recognize one pedestrian from another. DNNs require significant training sets and are not easily adapted for changes in class appearances or changes in the set of classes held in its knowledge domain. Pedestrian redetection requires an algorithm that can actively manage its knowledge domain as individuals move in and out of the scene, as well as learn individual appearances from a few frames of a video. The Omni-Modeler is a dynamically learning few-shot visual recognition algorithm developed for tasks with limited training data availability. The Omni-Modeler adapts the knowledge domain of pre-trained deep neural networks to novel concepts with a calculated localized language encoder. The Omni-Modeler knowledge domain is generated by creating a dynamic dictionary of concept definitions, which are directly updatable as new information becomes available. Query images are identified through nearest neighbor comparison to the learned object definitions. The study presented in this paper evaluates its performance in re-identifying individuals as they move through a scene in both single-camera and multi-camera tracking applications. The results demonstrate that the Omni-Modeler shows potential for across-camera view pedestrian redetection and is highly effective for single-camera redetection with a 93% accuracy across 30 individuals using 64 example images for each individual.

Keywords: dynamic learning, few-shot learning, pedestrian redetection, visual recognition

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Authors: Thanakorn Thanawat, Nomjit Nualnetr

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Background and Purpose: In Thailand, rice farmers have the most prevalence of low back pain when compared with other manual workers. Exercises have been suggested to be a principal part of treatment programs for low back pain. However, the programs should be tailored to an individual’s readiness to change categorized by a behavioral approach. This study aimed to evaluate a difference between the responses of rice farmers with chronic low back pain who received an exercise program integrated with the transtheoretical model of behavior change (TTM) and those of the comparison group regarding severity of pain and trunk muscle endurance. Materials and Methods: An 8-week exercise program was conducted to rice farmers with chronic low back pain who were randomized to either the TTM (n=62, 52 woman and 10 men, mean age ± SD 45.0±5.4 years) or non-TTM (n=64, 53 woman and 11 men, mean age ± SD 44.7±5.4 years) groups. All participants were tested for their severity of pain and trunk (abdominal and back) muscle endurance at baseline (week 0) and immediately after termination of the program (week 8). Data were analysed by using descriptive statistics and student’s t-tests. The results revealed that both TTM and non-TTM groups could decrease their severity of pain and improve trunk muscle endurance after participating in the 8-week exercise program. When compared with the non-TTM group, however, the TTM showed a significantly greater increase in abdominal muscle endurance than did the non-TTM (P=0.004, 95% CI -12.4 to -2.3). Conclusions and Clinical Relevance: An exercise program integrated with the TTM could provide benefits to rice farmers with chronic low back pain. Future studies with a longitudinal design and more outcome measures such as physical performance and quality of life are suggested to reveal further benefits of the program.

Keywords: chronic low back pain, transtheoretical model, rice farmers, exercise program

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Authors: Min Jindong, Wang Mingxia

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With the widespread use of artificial intelligence in life, computer vision, especially deep convolutional neural network models, has developed rapidly. With the increase of the complexity of the real visual target detection task and the improvement of the recognition accuracy, the target detection network model is also very large. The huge deep neural network model is not conducive to deployment on edge devices with limited resources, and the timeliness of network model inference is poor. In this paper, knowledge distillation is used to compress the huge and complex deep neural network model, and the knowledge contained in the complex network model is comprehensively transferred to another lightweight network model. Different from traditional knowledge distillation methods, we propose a novel knowledge distillation that incorporates multi-faceted features, called M-KD. In this paper, when training and optimizing the deep neural network model for target detection, the knowledge of the soft target output of the teacher network in knowledge distillation, the relationship between the layers of the teacher network and the feature attention map of the hidden layer of the teacher network are transferred to the student network as all knowledge. in the model. At the same time, we also introduce an intermediate transition layer, that is, an intermediate guidance layer, between the teacher network and the student network to make up for the huge difference between the teacher network and the student network. Finally, this paper adds an exploration module to the traditional knowledge distillation teacher-student network model. The student network model not only inherits the knowledge of the teacher network but also explores some new knowledge and characteristics. Comprehensive experiments in this paper using different distillation parameter configurations across multiple datasets and convolutional neural network models demonstrate that our proposed new network model achieves substantial improvements in speed and accuracy performance.

Keywords: object detection, knowledge distillation, convolutional network, model compression

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Authors: Ma Chu Jui

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Culture plays a pivotal role in shaping societal responses to change and fostering adaptability. In the realm of healthcare provision, hospitals serve as dynamic settings molded by the cultural consciousness of healthcare professionals. This intricate interplay extends to their expectations of leadership, communication styles, and attitudes towards patient care. Recognizing the cultural inclinations of healthcare professionals becomes imperative in navigating this complex landscape. This study will utilize Hofstede's Value Survey Module 2013 (VSM 2013) as a comprehensive analytical tool. The targeted participants for this research are in-service nursing professionals with a tenure of at least three months, specifically employed in the nursing department of an Eastern hospital. This quantitative approach seeks to quantify diverse cultural tendencies among the targeted nursing professionals, elucidating not only abstract cultural concepts but also revealing their cultural inclinations across different dimensions. The study anticipates gathering between 400 to 500 responses, ensuring a robust dataset for a comprehensive analysis. The focused approach on nursing professionals within the Eastern hospital setting enhances the relevance and specificity of the cultural insights obtained. The research aims to contribute valuable knowledge to the understanding of cultural tendencies among in-service nursing personnel in the nursing department of this specific Eastern hospital. The VSM 2013 will be initially distributed to this specific group to collect responses, aiming to calculate scores on each of Hofstede's six cultural dimensions—Power Distance Index (PDI), Individualism vs. Collectivism (IDV), Uncertainty Avoidance Index (UAI), Masculinity vs. Femininity (MAS), Long-Term Orientation vs. Short-Term Normative Orientation (LTO), and Indulgence vs. Restraint (IVR). the study unveils a significant correlation between different cultural dimensions and healthcare professionals' tendencies in understanding leadership expectations through PDI, grasping behavioral patterns via IDV, acknowledging risk acceptance through UAI, and understanding their long-term and short-term behaviors through LTO. These tendencies extend to communication styles and attitudes towards patient care. These findings provide valuable insights into the nuanced interconnections between cultural factors and healthcare practices. Through a detailed analysis of the varying levels of these cultural dimensions, we gain a comprehensive understanding of the predominant inclinations among the majority of healthcare professionals. This nuanced perspective adds depth to our comprehension of how cultural values shape their approach to leadership, communication, and patient care, contributing to a more holistic understanding of the healthcare landscape. A profound comprehension of the cultural paradigms embraced by healthcare professionals holds transformative potential. Beyond a mere understanding, it acts as a catalyst for elevating the caliber of healthcare services. This heightened awareness fosters cohesive collaboration among healthcare teams, paving the way for the establishment of a unified healthcare ethos. By cultivating shared values, our study envisions a healthcare environment characterized by enhanced quality, improved teamwork, and ultimately, a more favorable and patient-centric healthcare landscape. In essence, our research underscores the critical role of cultural awareness in shaping the future of healthcare delivery.

Keywords: hofstede's cultural, cultural dimensions, cultural values in healthcare, cultural awareness in nursing

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Authors: Ying Wang, Jianhong Xiang, Yu Zhong

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The offset Quadrature Amplitude Modulation-based Filter Bank Multi-Carrier (FBMC) system provides superior spectral properties over Orthogonal Frequency Division Multiplexing. However, seriously affected by imaginary interference, its performances are hampered in many areas. In this paper, we propose a Precoding-Assisted Frequency Division Multiple Access (PA-FDMA) modulation scheme. By spreading FBMC symbols into the frequency domain and transmitting them with a precoding matrix, the impact of imaginary interference can be eliminated. Specifically, we first generate the coding pre-solution matrix with a nonuniform Fast Fourier Transform and pick the best columns by introducing auxiliary factors. Secondly, according to the column indexes, we obtain the precoding matrix for one symbol and impose scaling factors to ensure that the power is approximately constant throughout the transmission time. Finally, we map the precoding matrix of one symbol to multiple symbols and transmit multiple data frames, thus achieving frequency-division multiple access. Additionally, observing the interference between adjacent frames, we mitigate them by adding frequency Cyclic Prefixes (CP) and evaluating them with a signal-to-interference ratio. Note that PA-FDMA can be considered a CP-available FBMC technique because the underlying strategy is FBMC. Simulation results show that the proposed scheme has better performance compared to Single Carrier Frequency Division Multiple Access (SC-FDMA), etc.

Keywords: PA-FDMA, SC-FDMA, FBMC, non-uniform fast fourier transform

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Authors: Jalal Eddine Benmansour, Khouane Boulanoir, Nacera Bekhadda, Elhassen Benfriha

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This paper introduces a novel composite control approach that addresses the challenge of stabilizing the three-axis attitude of a flexible satellite in the presence of vibrations caused by flexible appendages. The key contribution of this research lies in the development of a disturbance observer, which effectively observes and estimates the unwanted torques induced by the vibrations. By utilizing the estimated disturbance, the proposed approach enables efficient compensation for the detrimental effects of vibrations on the satellite system. To govern the attitude angles of the spacecraft, a proportional derivative controller (PD) is specifically designed and proposed. The PD controller ensures precise control over all attitude angles, facilitating stable and accurate spacecraft maneuvering. In order to demonstrate the global stability of the system, the Lyapunov method, a well-established technique in control theory, is employed. Through rigorous analysis, the Lyapunov method verifies the convergence of system dynamics, providing strong evidence of system stability. To evaluate the performance and efficacy of the proposed control algorithm, extensive simulations are conducted. The simulation results validate the effectiveness of the combined approach, showcasing significant improvements in the stabilization and control of the satellite's attitude, even in the presence of disruptive vibrations from flexible appendages. This novel composite control approach presented in this paper contributes to the advancement of satellite attitude control techniques, offering a promising solution for achieving enhanced stability and precision in challenging operational environments.

Keywords: attitude control, flexible satellite, vibration control, disturbance observer

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Authors: Elizabeta Sivevska, Sunchica Petrovska, Vaska Antevska, Lidija Todorovska, Sanja Manchevska, Beti Dejanova, Ivanka Karagjozova, Jasmina Pluncevic Gligoroska

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Introduction: Body composition plays an important role in the selection of young soccer players and it is associated with their successful performance. The most commonly used model of body composition divides the body into two compartments: fat components and fat-free mass (muscular and bone components). The aims of the study were to determine the body composition parameters of young male soccer players and to show the differences in age groups. Material and methods: A sample of 52 young male soccer players, with an age span from 9 to 14 years were divided into two groups according to the age (group 1 aged 9 to 12 years and group 2 aged 12 to 14 years). Anthropometric measurements were taken according to the method of Mateigka. The following measurements were made: body weight, body height, circumferences (arm, forearm, thigh and calf), diameters (elbow, knee, wrist, ankle) and skinfold thickness (biceps, triceps, thigh, leg, chest, abdomen). The measurements were used in Mateigka’s equations. Results: Body mass components were analyzed as absolute values (in kilograms) and as percentage values: the muscular component (MC kg and MC%), the bone component (BCkg and BC%) and the body fat (BFkg and BF%). The group up to 12 years showed the following mean values of the analyzed parameters: MM=21.5kg; MM%=46.3%; BC=8.1kg; BC%=19.1%; BF= 6.3kg; BF%= 15.7%. The second group aged 12-14 year had mean values of body composition parameters as follows: MM=25.6 kg; MM%=48.2%; BC = 11.4 kg; BC%=21.6%; BF= 8.5 kg; BF%= 14. 7%. Conclusions: The young soccer players aged 12 up to 14 years who are in the pre-pubertal phase of growth and development had higher bone component (p<0.05) compared to younger players. There is no significant difference in muscular and fat body component between the two groups of young soccer players.

Keywords: body composition, young soccer players, body fat, fat-free mass

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Authors: Oluwafemi Akinlawon Atoyebi

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This paper examines alienation and fixation as critical issues of/around mental health -crisis, sickness, and healing- through ‘Bode Ojoniyi’s dramaturgy. Two of his dramatic memoirs, arguably written to address such a life-threatening crisis between him and his employer, where he externalizes perhaps his psychological crisis, are critically analysed. This is done through a reading of the three theatrical phenomena of the actor, the character, and the audience against how he plays around the concepts of alienation and fixation within the totality of his dramaturgy beyond what could be seen as a mere academic exercise. The paper situates his apt understanding of their representations as a reflective force of a consciousness that defies psychosomatic existential conflicts. It does so by adopting a qualitative method of analysis through a critical reading of the two dramatic memoirs. It also carries out a survey on the audience that experienced the performances of the memoirs and an interview with Ojoniyi. Using Jean-Paul Sartre’s Theory of Existential Consciousness, the study discovers that there is a way the three phenomena of the actor, the character, and the audience do find expression in Ojoniyi as an existential omniscient playwright-actor-character-audience who is able to transcend the parochialism of an alienated and a fixated self; that beyond the limiting artistic purview, the theatre as a stage is a phenomenon that is capable of capturing the totality of the experiences of a man in his world and that, often time, the depressed are victims of the myopic syndrome as they probably could not see or reflect on/about their realities beyond the self and the play of a casual order. The study concludes that the therapeutic effect of Ojoniyi’s dramatic memoirs, in their reading or performance, is needed by all and should be explored in proffering cures for psychosomatic patients, for it promises to be essentially useful beyond its confine –the Arts.

Keywords: alienation, fixation, the healing theatre, theatrical personalities

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Authors: Hazrat Salman Sidique, Muhammad Tahir Khan, Haq Aman Ullah, Muhammad Mobashar, Muhammad Ishtiaq Sohail Mehmood

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The poor quality conventional feed for the livestock production in Pakistan are wheat straw, tops of sugar cane and tree leaves. To enhance the nutritive value of feed, this study focused on investigating the effects of fibrolytic enzyme (Fibrozyme®, Alltech Inc. Company, USA) at different levels i.e. 0, 5, 10, and 15g/kg of total mix ration on feed intake, digestibility, milk yield and composition, and economics of the ration in Holstein Friesians cows. Twelve Holstein Friesians cows of almost the same age, and lactation stage were randomly allocated into 4 equal groups i.e. A, B, C, and D. Four experimental rations supplemented with Fibrozyme® 0g, 5g, 10g, and 15g/Kg of total mix ration were assigned to these sets correspondingly. The dry matter intake was linearly and significantly (P<0.05) improved. A significant effect of Fibrozyme® was observed for organic matter digestibility, ether extract digestibility, crude fiber digestibility, nitrogen free extract digestibility, and acid detergent fiber digestibility while the results were statistically non-significant for crude protein digestibility, neutral detergent fiber digestibility, and ash digestibility. Milk yield and composition except fat were significantly (P<0.05) increased in all Fibrozyme® treated groups. This study concludes that supplementation of Fibrozyme® at the rate of 15g/Kg total mix ration improved the dry matter intake, nutrients digestibility, and milk production and constituents like protein, lactose, and solid not fat. Therefore, treatment of total mix ration with Fibrozyme® was desirably reasonable and profitable.

Keywords: digestibility, fibrozyme, TMR, digestibility, lactating cow

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Authors: Mihai Caramihai, Dan Vasilescu

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The main research objective of the experimental bioprocess analyzed in this paper was to obtain large biomass quantities. The bioprocess is performed in 100 L Bioengineering bioreactor with 42 L cultivation medium made of peptone, meat extract and sodium chloride. The reactor was equipped with pH, temperature, dissolved oxygen, and agitation controllers. The operating parameters were 37 oC, 1.2 atm, 250 rpm and air flow rate of 15 L/min. The main objective of this paper is to present a case study to demonstrate that intelligent control, describing the complexity of the biological process in a qualitative and subjective manner as perceived by human operator, is an efficient control strategy for this kind of bioprocesses. In order to simulate the bioprocess evolution, an intelligent control structure, based on fuzzy logic has been designed. The specific objective is to present a fuzzy control approach, based on human expert’ rules vs. a modeling approach of the cells growth based on bioprocess experimental data. The kinetic modeling may represent only a small number of bioprocesses for overall biosystem behavior while fuzzy control system (FCS) can manipulate incomplete and uncertain information about the process assuring high control performance and provides an alternative solution to non-linear control as it is closer to the real world. Due to the high degree of non-linearity and time variance of bioprocesses, the need of control mechanism arises. BIOSIM, an original developed software package, implements such a control structure. The simulation study has showed that the fuzzy technique is quite appropriate for this non-linear, time-varying system vs. the classical control method based on a priori model.

Keywords: intelligent, control, fuzzy model, bioprocess optimization

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Authors: Mariam Alsharifi, Hussein Znad, Ming Ang

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Considering the environmental issues and the shortage in the conventional fossil fuel sources, biodiesel has gained a promising solution to shift away from fossil based fuel as one of the sustainable and renewable energy. It is synthesized by transesterification of vegetable oils or animal fats with alcohol (methanol or ethanol) in the presence of a catalyst. This study focuses on synthesizing a high efficient Li/TiO2 heterogeneous catalyst for biodiesel production from canola oil. In this work, lithium immobilized onto TiO2 by the simple impregnation method. The catalyst was evaluated by transesterification reaction in a batch reactor under moderate reaction conditions. To study the effect of Li concentrations, a series of LiNO3 concentrations (20, 30, 40 wt. %) at different calcination temperatures (450, 600, 750 ºC) were evaluated. The Li/TiO2 catalysts are characterized by several spectroscopic and analytical techniques such as XRD, FT-IR, BET, TG-DSC and FESEM. The optimum values of impregnated Lithium nitrate on TiO2 and calcination temperature are 30 wt. % and 600 ºC, respectively, along with a high conversion to be 98 %. The XRD study revealed that the insertion of Li improved the catalyst efficiency without any alteration in structure of TiO2 The best performance of the catalyst was achieved when using a methanol to oil ratio of 24:1, 5 wt. % of catalyst loading, at 65◦C reaction temperature for 3 hours of reaction time. Moreover, the experimental kinetic data were compatible with the pseudo-first order model and the activation energy was (39.366) kJ/mol. The synthesized catalyst Li/TiO2 was applied to trans- esterify used cooking oil and exhibited a 91.73% conversion. The prepared catalyst has shown a high catalytic activity to produce biodiesel from fresh and used oil within mild reaction conditions.

Keywords: biodiesel, canola oil, environment, heterogeneous catalyst, impregnation method, renewable energy, transesterification

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Authors: Shweta Singh, Sudaman Katti

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The most well-known sequence models make use of complex recurrent neural networks in an encoder-decoder configuration. The model used in this research makes use of a transformer, which is based purely on a self-attention mechanism, without relying on recurrence at all. More specifically, encoders and decoders which make use of self-attention and operate based on a memory, are used. In this research work, results for various 3D visual and non-visual reinforcement learning tasks designed in Unity software were obtained. Convolutional neural networks, more specifically, nature CNN architecture, are used for input processing in visual tasks, and comparison with standard long short-term memory (LSTM) architecture is performed for both visual tasks based on CNNs and non-visual tasks based on coordinate inputs. This research work combines the transformer architecture with the proximal policy optimization technique used popularly in reinforcement learning for stability and better policy updates while training, especially for continuous action spaces, which are used in this research work. Certain tasks in this paper are long horizon tasks that carry on for a longer duration and require extensive use of memory-based functionalities like storage of experiences and choosing appropriate actions based on recall. The transformer, which makes use of memory and self-attention mechanism in an encoder-decoder configuration proved to have better performance when compared to LSTM in terms of exploration and rewards achieved. Such memory based architectures can be used extensively in the field of cognitive robotics and reinforcement learning.

Keywords: convolutional neural networks, reinforcement learning, self-attention, transformers, unity

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Authors: Farzam Chakherlouy, Amirmehdi Dokhanchi

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Nowadays organizations have to identify, evaluate and manage intangible assets which enable them to provide maximum requirements to achieve their goals and strategies. Organizations also have to try to promote and improve these kinds of assets continuously. It seems necessary to implement developed strategies in today’s competitive world where all the organizations and companies spend great amounts of expenses for developing their own strategies. In fact, after determining strategies to be implemented, the management process is not completed and it will not have any effect on the success and existence of the organization until these strategies are implemented. The objective of this article is to define the intellectual capital and it components and studying the impact of intellectual capital on the implementation of strategy based upon the Bozbura model. Three dimensions of human capital, relational capital, and the structural capital. According to the test’s results, the correlation between the intellectual capital and three components of strategic implementation (leadership, human resource management, and culture) has not been approved yet. According to results of Friedman’s test in relation with the intellectual capital, the maximum inadequacy of this company is in the field of human capital (with an average of 3.59) and the minimum inadequacy is in the field of the relational capital (customer) with an average of 2.83. Besides, according to Friedman test in relation with implementation of the strategy, the maximum inadequacy relates to the culture of the organization and the corporate control with averages of 2.60 and 3.45 respectively. In addition, they demonstrate a good performance in scopes of human resources management and financial resources management strategies.

Keywords: Bozbura model, intellectual capital, strategic management, implementation of strategy, Modiran Ideh Pardaz company

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Authors: Maria Luisa Boninelli

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This study wants to explore the effects of a mediated intervention program in a primary school. The participants where 120 students aged 8-9, half of them Italian and half immigrants of first or second generation. The activities consisted on the cognitive enhancement of the participants through Feuerstein’s Instrumental Enrichment, (IE) and on an activity centred on body awareness and mediated learning experience. Given that there are limited studied on learners in remedial schools, the current study intented to hypothesized that participants exposed to mediation would yiel a significant improvement in cognitive functioning. Hypothesis One proposed that, following the intervention, improved Q1vata scores of the participants would occur in each of the groups. Hypothesis two postulated that participants within the Mediated Learning Experience would perform significantly better than those group of control. For the intervention a group of 60 participants constituted a group of Mediation sample and were exposed to Mediated Learning Experience through Enrichment Programm. Similiary the other 60 were control group. Both the groups have students with special needs and were exposed to the same learning goals. A pre-experimental research design, in particular a one-group pretest-posttest approach was adopted. All the participants in this study underwent pretest and post test phases whereby they completed measures according to the standard instructions. During the pretest phase, all the participants were simultaneously exposed to Q1vata test for logical and linguistic evaluation skill. During the mediation intervention, significant improvement was demonstrated with the group of mediation. This supports Feuerstein's Theory that initial poor performance was a result of a lack of mediated learning experience rather than inherent difference or deficiencies. Furthermore the use of an appropriate mediated learning enabled the participants to function adequately.

Keywords: cognitive structural modifiability, learning to learn, mediated learning experience, Reuven Feuerstein, special needs

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Authors: Dachuan Shi, M. Hecht, Y. Ye

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With the trend of digitalization in railway freight transport, a large number of freight wagons in Germany have been equipped with telematics devices, commonly placed on the wagon body. A telematics device contains a GPS module for tracking and a 3-axis accelerometer for shock detection. Besides these basic functions, it is desired to use the integrated accelerometer for condition monitoring without any additional sensors. Wheel flats as a common type of failure on wheel tread cause large impacts on wagons and infrastructure as well as impulsive noise. A large wheel flat may even cause safety issues such as derailments. In this sense, this paper proposes a machine learning approach for wheel flat detection by using car body accelerations. Due to suspension systems, impulsive signals caused by wheel flats are damped significantly and thus could be buried in signal noise and disturbances. Therefore, it is very challenging to detect wheel flats using car body accelerations. The proposed algorithm considers the envelope spectrum of car body accelerations to eliminate the effect of noise and disturbances. Subsequently, a 1-D convolutional neural network (CNN), which is well known as a deep learning method, is constructed to automatically extract features in the envelope-frequency domain and conduct classification. The constructed CNN is trained and tested on field test data, which are measured on the underframe of a tank wagon with a wheel flat of 20 mm length in the operational condition. The test results demonstrate the good performance of the proposed algorithm for real-time fault detection.

Keywords: fault detection, wheel flat, convolutional neural network, machine learning

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Authors: Ngoc-Hieu Vu

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A study was conducted to estimate the record keeping, genetic selection, educational experience, and farm management effect on monthly milk yield per farm, average milk yield per cow, monthly milk revenue per farm, and monthly milk revenue per cow of dairy farms in the Southern region of Vietnam. The dataset contained 5448 monthly record collected from January 2013 to May 2015. Results showed that longer experience increased (P < 0.001) monthly milk yields and revenues. Better educated farmers produced more monthly milk per farm and monthly milk per cow and revenues (P < 0.001) than lower educated farmers. Farm that kept records on individual animals had higher (P < 0.001) for monthly milk yields and revenues than farms that did not. Farms that used hired people produced the highest (p < 0.05) monthly milk yield per farm, milk yield per cow and revenues, followed by farms that used both hire and family members, and lowest values were for farms that used family members only. Farms that used crosses Holstein in herd were higher performance (p < 0.001) for all traits than farms that used purebred Holstein and other breeds. Farms that used genetic information and phenotypes when selecting sires were higher (p < 0.05) for all traits than farms that used only phenotypes and personal option. Farms that received help from Vet, organization staff, or government officials had higher monthly milk yield and revenues than those that decided by owner. These findings suggest that dairy farmers should be training in systematic, must be considered and continuous support to improve farm milk production and revenues, to increase the likelihood of adoption on a sustainable way.

Keywords: dairy farming, education, milk yield, Southern Vietnam

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Authors: Ju-Hong Lee, Yi-Lin Shieh

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Two-dimensional (2-D) quadrature mirror filter (QMF) banks have been widely considered for high-quality coding of image and video data at low bit rates. Without implementing subband coding, a 2-D QMF bank is required to have an exactly linear-phase response without magnitude distortion, i.e., the perfect reconstruction (PR) characteristics. The design problem of 2-D QMF banks with the PR characteristics has been considered in the literature for many years. This paper presents a transformation approach for designing 2-D two-channel QMF banks. Under a suitable one-dimensional (1-D) to two-dimensional (2-D) transformation with a specified decimation/interpolation matrix, the analysis and synthesis filters of the QMF bank are composed of 1-D causal and stable digital allpass filters (DAFs) and possess the 2-D doubly complementary half-band (DC-HB) property. This facilitates the design problem of the two-channel QMF banks by finding the real coefficients of the 1-D recursive DAFs. The design problem is formulated based on the minimax phase approximation for the 1-D DAFs. A novel objective function is then derived to obtain an optimization for 1-D minimax phase approximation. As a result, the problem of minimizing the objective function can be simply solved by using the well-known weighted least-squares (WLS) algorithm in the minimax (L∞) optimal sense. The novelty of the proposed design method is that the design procedure is very simple and the designed 2-D QMF bank achieves perfect magnitude response and possesses satisfactory phase response. Simulation results show that the proposed design method provides much better design performance and much less design complexity as compared with the existing techniques.

Keywords: Quincunx QMF bank, doubly complementary filter, digital allpass filter, WLS algorithm

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Authors: Catherine Thompson, Maryam Jalali, Peter Hills

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The allocation of visual attention is critical when driving and anything that limits attention will have a detrimental impact on safety. Engaging in a secondary task reduces the amount of attention directed to the road because drivers allocate resources towards this task, leaving fewer resources to process driving-relevant information. Yet the dangers associated with a secondary task do not end when the driver returns their attention to the road. Instead, the attentional settings adopted to complete a secondary task may persist to the road, affecting attention, and therefore affecting driver performance. This 'attentional inertia' effect was investigated in the current work. Forty drivers searched for hazards in driving video clips while their eye-movements were recorded. At varying intervals they were instructed to attend to a secondary task displayed on a tablet situated to their left-hand side. The secondary task consisted of three separate computer games that induced horizontal, vertical, and random eye movements. Visual search and hazard detection in the driving clips were compared across the three conditions of the secondary task. Results showed that the layout of information in the secondary task, and therefore the allocation of attention in this task, had an impact on subsequent search in the driving clips. Vertically presented information reduced the wide horizontal spread of search usually associated with accurate driving and had a negative influence on the detection of hazards. The findings show the additional dangers of engaging in a secondary task while driving. The attentional inertia effect has significant implications for semi-autonomous and autonomous vehicles in which drivers have greater opportunity to direct their attention away from the driving task.

Keywords: attention, eye-movements, hazard perception, visual search

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Authors: Han Xiao, Zhang Lei, Huang Hai, Lv Shizeng

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Space environment simulator is a device for spacecraft test. KM8 large space environment simulator built in Tianjing Space City is the largest as well as the most advanced space environment simulator in China. Large deviation of spacecraft level will lead to abnormally work of the thermal control device in spacecraft during the thermal vacuum test. In order to avoid thermal vacuum test failure, level adjusting mechanism system is developed in the KM8 large space environment simulator as one of the most important subsystems. According to the level adjusting requirements of spacecraft’s thermal vacuum tests, the four fulcrums adjusting model is established. By means of collecting level instruments and displacement sensors data, stepping motors controlled by PLC drive four supporting legs simultaneous movement. In addition, a PID algorithm is used to control the temperature of supporting legs and level instruments which long time work under the vacuum cold and black environment in KM8 large space environment simulator during thermal vacuum tests. Based on the above methods, the data acquisition and processing, the analysis and calculation, real time adjustment and fault alarming of the level adjusting mechanism system are implemented. The level adjusting accuracy reaches 1mm/m, and carrying capacity is 20 tons. Debugging showed that the level adjusting mechanism system of KM8 large space environment simulator can meet the thermal vacuum test requirement of the new generation spacecraft. The performance and technical indicators of the level adjusting mechanism system which provides important support for the development of spacecraft in China have been ahead of similar equipment in the world.

Keywords: space environment simulator, thermal vacuum test, level adjusting, spacecraft, parallel mechanism

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Authors: K. Dhanalakshmi, N. Deepak, E. Manikandan, S. Kanagaraj, M. Sulthan Ariff Rahman, P. Chilambarasan C. Abhimanyu, C. A. Akaash Emmanuel Raj, V. R. Sanal Kumar

Abstract:

In this paper using a validated double precision, density-based implicit standard k-ε model, the detailed 2D and 3D numerical studies have been carried out to examine the external flow choking at wing-in-ground (WIG) effect craft. The CFD code is calibrated using the exact solution based on the Sanal flow choking condition for adiabatic flows. We observed that at the identical WIG effect conditions the numerically predicted 2D boundary layer blockage is significantly higher than the 3D case and as a result, the airfoil exhibited an early external flow choking than the corresponding wing, which is corroborated with the exact solution. We concluded that, in lieu of the conventional 2D numerical simulation, it is invariably beneficial to go for a realistic 3D simulation of the wing in ground effect, which is analogous and would have the aspects of a real-time parametric flow. We inferred that under the identical flying conditions the chances of external flow choking at WIG effect is higher for conventional aircraft than an aircraft facilitating a divergent channel effect at the bottom surface of the fuselage as proposed herein. We concluded that the fuselage and wings integrated geometry optimization can improve the overall aerodynamic performance of WIG craft. This study is a pointer to the designers and/or pilots for perceiving the zone of danger a priori due to the anticipated external flow choking at WIG effect craft for safe flying at the close proximity of the terrain and the dynamic surface of the marine.

Keywords: boundary layer blockage, chord dominated ground effect, external flow choking, WIG effect

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Authors: Ali Moradiamani, Samaneh Sadat Sajjadi, Mahdi Jalili

Abstract:

Power system analysis has been a major research topic in the generation and distribution sections, in both industry and academia, for a long time. Several load flow and fault analysis scenarios have been normally performed to study the performance of different parts of the grid in the context of, for example, voltage and frequency control. Software tools, such as PSCAD, PSSE, and PowerFactory DIgSILENT, have been developed to perform these analyses accurately. Distribution grid had been the passive part of the grid and had been known as the grid of consumers. However, a significant paradigm shift has happened with the emergence of Distributed Energy Resources (DERs) in the distribution level. It means that the concept of power system analysis needs to be extended to the distribution grid, especially considering self sufficient technologies such as microgrids. Compared to the generation and transmission levels, the distribution level includes significantly more generation/consumption nodes thanks to PV rooftop solar generation and battery energy storage systems. In addition, different consumption profile is expected from household residents resulting in a diverse set of scenarios. Emergence of electric vehicles will absolutely make the environment more complicated considering their charging (and possibly discharging) requirements. These complexities, as well as the large size of distribution grids, create challenges for the available power system analysis software. In this paper, we study the requirements of simulation tools in the distribution grid and how data-driven algorithms are required to increase the accuracy of the simulation results.

Keywords: smart grids, distributed energy resources, electric vehicles, battery storage systsms, simulation tools

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Authors: Long Wang, Yongjin Feng, Xiaofang Luo

Abstract:

Tritium permeation through structural materials is a significant issue for fusion demonstration (DEMO) reactor blankets in terms of fuel cycle efficiency and radiological safety. Reduced activation ferritic (RAFM) steel CLF-1 is a prime candidate for the China’s CFETR blanket structural material, facing high permeability of hydrogen isotopes at reactor operational temperature. To confine tritium as much as possible in the reactor, surface modification of the steels including fabrication of tritium permeation barrier (TPB) attracts much attention. As a new alloy system, high entropy alloy (HEA) contains at least five principal elements, each of which ranges from 5 at% to 35 at%. This high mixing effect entitles HEA extraordinary comprehensive performance. So it is attractive to lead HEA into surface alloying for protective use. At present, studies on the hydrogen isotope permeability of HEA coatings is still insufficient and corresponding mechanism isn’t clear. In our study, we prepared three kinds of HEA coatings, including AlCrTaTiZr, (AlCrTaTiZr)N and (AlCrTaTiZr)O. After comprehensive characterization of SEM, XPS, AFM, XRD and TEM, the structure and composition of the HEA coatings were obtained. Deuterium permeation tests were conducted to evaluate the hydrogen isotope permeability of AlCrTaTiZr, (AlCrTaTiZr)N and (AlCrTaTiZr)O HEA coatings. Results proved that the (AlCrTaTiZr)N and (AlCrTaTiZr)O HEA coatings had better hydrogen isotope permeation resistance. Through analyzing and characterizing the hydrogen isotope permeation results of the corroded samples, an internal link between hydrogen isotope permeation behavior and structure of HEA coatings was established. The results provide valuable reference in engineering design of structural and TPB materials for future fusion device.

Keywords: high entropy alloy, hydrogen isotope permeability, tritium permeation barrier, fusion demonstration reactor

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Authors: Farzan Farhangian, Behnam Shakibafar, Bobda Cedric, Rene Jr. Landry

Abstract:

Among the air navigation technologies, many of them are capable of increasing aviation sustainability as well as accuracy improvement in Alternative Positioning, Navigation, and Timing (APNT), especially avionics Distance Measuring Equipment (DME), Very high-frequency Omni-directional Range (VOR), etc. The integration of these air navigation solutions could make a robust and efficient accuracy in air mobility, air traffic management and autonomous operations. Designing a proper RF front-end, power amplifier and software-defined transponder could pave the way for reaching an optimized avionics navigation solution. In this article, the possibility of reaching an optimum front-end to be used with single low-cost Software-Defined Radio (SDR) has been investigated in order to reach a software-defined DME architecture. Our software-defined approach uses the firmware possibilities to design a real-time software architecture compatible with a Multi Input Multi Output (MIMO) BladeRF to estimate an accurate time delay between a Transmission (Tx) and the reception (Rx) channels using the synchronous scheduled communication. We could design a novel power amplifier for the transmission channel of the DME to pass the minimum transmission power. This article also investigates designing proper pair pulses based on the DO-178B avionics standard. Various guidelines have been tested, and the possibility of passing the certification process for each standard term has been analyzed. Finally, the performance of the DME was tested in the laboratory environment using an IFR6000, which showed that the proposed architecture reached an accuracy of less than 0.23 Nautical mile (Nmi) with 98% probability.

Keywords: avionics, DME, software defined radio, navigation

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Authors: Chin-Chih Chang

Abstract:

Many manufacturing systems suffer failures due to complex degradation processes and various environment conditions such as random shocks. Consider an operating system is subject to random shocks and works at random times for successive jobs. When successive jobs often result in production losses and performance deterioration, it would be better to do maintenance or replacement at a planned time. A preventive replacement (PR) policy is presented to replace the system before a failure occurs at a continuous time T. In such a policy, the failure characteristics of the system are designed as follows. Each job would cause a random amount of additive damage to the system, and the system fails when the cumulative damage has exceeded a failure threshold. Suppose that the deteriorating system suffers one of the two types of shocks with age-dependent probabilities: type-I (minor) shock is rectified by a minimal repair, or type-II (catastrophic) shock causes the system to fail. A corrective replacement (CR) is performed immediately when the system fails. In summary, a generalized maintenance model to scheduling replacement plan for an operating system is presented below. PR is carried out at time T, whereas CR is carried out when any type-II shock occurs and the total damage exceeded a failure level. The main objective is to determine the optimal continuous schedule time of preventive replacement through minimizing the mean cost rate function. The existence and uniqueness of optimal replacement policy are derived analytically. It can be seen that the present model is a generalization of the previous models, and the policy with preventive replacement outperforms the one without preventive replacement.

Keywords: preventive replacement, working time, cumulative damage model, minimal repair, imperfect maintenance, optimization

Procedia PDF Downloads 363

Authors: Zainal A. Muchlisin, Muhammad Iqbal, Abdullah A. Muhammadar

Abstract:

The objective of the present study was to determine the optimum dose of papain enzyme in the diet for growing, survival rate and feed efficacy of climbing perch (Anabas testudineus). The study was conducted at the Laboratory of Aquatic of Faculty of Veterinary, Syiah Kuala University from January to March 2016. The completely randomized design was used in this study. Six dosages level of papain enzyme were tested with 4 replications i.e. 0 g kg-1 of feed, 20.0 g kg-1 feed, 22.5 g kg-1 of feed, 25.0 g kg-1 of feed, 27.5 g kg-1 of feed, and 30.0 g kg-1 of feed. The experimental fish fed twice a day at feeding level of 5% for 60 days. The results showed that weight gain ranged from 2.41g to 7.37g, total length gain ranged from 0.67cm to 3.17cm, specific growth rate ranged from 1.46 % day to 3.41% day, daily growth rate ranged from 0.04 g day to 0.13 g day, feed conversion ratio ranged from 1.94 to 3.59, feed efficiency ranged from 27.99% to 51.37%, protein retention ranged from 3.38% to 28.28%, protein digestibility ranged from 50.63% to 90.38%, and survival rate ranged from 88.89% to 100%. The highest rate for all parameters was found in the dosage of 3.00% papain enzyme kg feed. The ANOVA test showed that enzyme papain gave a significant effect on the weight gain, total length gain, daily growth rate, specific growth rate, feed conversion ratio, feed efficiency, protein retention, protein digestibility, and survival rate of the climbing perch (Anabas testudieus). The best enzyme papain dosage was 3.0%.

Keywords: betok, feed conversion ratio, freshwater fish, nutrition, feeding

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Authors: Kenan Morani, Esra Kaya Ayana

Abstract:

This study aimed to develop a novel pipeline for COVID-19 detection using a large and rigorously annotated database of computed tomography (CT) images. The pipeline consists of UNet-based segmentation, lung extraction, and a classification part, with the addition of optional slice removal techniques following the segmentation part. In this work, a batch normalization was added to the original UNet model to produce lighter and better localization, which is then utilized to build a full pipeline for COVID-19 diagnosis. To evaluate the effectiveness of the proposed pipeline, various segmentation methods were compared in terms of their performance and complexity. The proposed segmentation method with batch normalization outperformed traditional methods and other alternatives, resulting in a higher dice score on a publicly available dataset. Moreover, at the slice level, the proposed pipeline demonstrated high validation accuracy, indicating the efficiency of predicting 2D slices. At the patient level, the full approach exhibited higher validation accuracy and macro F1 score compared to other alternatives, surpassing the baseline. The classification component of the proposed pipeline utilizes a convolutional neural network (CNN) to make final diagnosis decisions. The COV19-CT-DB dataset, which contains a large number of CT scans with various types of slices and rigorously annotated for COVID-19 detection, was utilized for classification. The proposed pipeline outperformed many other alternatives on the dataset.

Keywords: classification, computed tomography, lung extraction, macro F1 score, UNet segmentation

Procedia PDF Downloads 131

Authors: Jérémy Philippe, Lucien Baldas, Batoul Attar, Jean-Charles Mare

Abstract:

The proposed communication deals with the research and development of a rotary direct-drive servo valve for aerospace applications. A key challenge of the project is to downsize the electromagnetic torque motor by reducing the torque required to drive the rotary spool. It is intended to optimize the spool and the sleeve geometries by combining a Computational Fluid Dynamics (CFD) approach with commercial optimization software. The present communication addresses an important phase of the project, which consists firstly of gaining confidence in the simulation results. It is well known that the force needed to pilot a sliding spool valve comes from several physical effects: hydraulic forces, friction and inertia/mass of the moving assembly. Among them, the flow force is usually a major contributor to the steady-state (or Root Mean Square) driving torque. In recent decades, CFD has gradually become a standard simulation tool for studying fluid-structure interactions. However, in the particular case of high-pressure valve design, the authors have experienced that the calculated overall hydraulic force depends on the parameterization and options used to build and run the CFD model. To solve this issue, the authors have selected the standard case of the linear spool valve, which is addressed in detail in numerous scientific references (analytical models, experiments, CFD simulations). The first CFD simulations run by the authors have shown that the evolution of the equivalent discharge coefficient vs. Reynolds number at the metering orifice corresponds well to the values that can be predicted by the classical analytical models. Oppositely, the simulated flow force was found to be quite different from the value calculated analytically. This drove the authors to investigate minutely the influence of the studied domain and the setting of the CFD simulation. It was firstly shown that the flow recirculates in the inlet and outlet channels if their length is not sufficient regarding their hydraulic diameter. The dead volume on the uncontrolled orifice side also plays a significant role. These examples highlight the influence of the geometry of the fluid domain considered. The second action was to investigate the influence of the type of mesh, the turbulence models and near-wall approaches, and the numerical solver and discretization scheme order. Two approaches were used to determine the overall hydraulic force acting on the moving spool. First, the force was deduced from the momentum balance on a control domain delimited by the valve inlet and outlet and the spool walls. Second, the overall hydraulic force was calculated from the integral of pressure and shear forces acting at the boundaries of the fluid domain. This underlined the significant contribution of the viscous forces acting on the spool between the inlet and outlet orifices, which are generally not considered in the literature. This also emphasized the influence of the choices made for the implementation of CFD calculation and results analysis. With the step-by-step process adopted to increase confidence in the CFD simulations, the authors propose a set of best practices and recommendations for the efficient use of CFD to design high-pressure spool valves.

Keywords: computational fluid dynamics, hydraulic forces, servovalve, rotary servovalve

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Authors: Camilo Cerda Sarabia, Fernanda Bravo Cornejo, Diego Santibanez Oyarce, Hugo Osses Prado, Esteban Gómez Terán, Belén Diaz Diaz, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

Abstract:

Antimicrobial resistance (AMR) represents a significant and rapidly escalating global health threat. Projections estimate that by 2050, AMR infections could claim up to 10 million lives annually. Respiratory infections, in particular, pose a severe risk not only to individual patients but also to the broader public health system. Despite the alarming rise in resistant respiratory infections, AMR within the lung microbiome (microbial community) remains underexplored and poorly characterized. The lungs, as a complex and dynamic microbial environment, host diverse communities of microorganisms whose interactions and resistance mechanisms are not fully understood. Unlike studies that focus on individual genomes, analyzing the entire microbiome provides a comprehensive perspective on microbial interactions, resistance gene transfer, and community dynamics, which are crucial for understanding AMR. However, this holistic approach introduces significant computational challenges and exposes the limitations of traditional analytical methods such as the difficulty of identifying the AMR. Machine learning has emerged as a powerful tool to overcome these challenges, offering the ability to analyze complex genomic data and uncover novel insights into AMR that might be overlooked by conventional approaches. This study investigates microbial resistance within the lung microbiome using unsupervised machine learning approaches to uncover resistance patterns and potential clinical associations. it downloaded and selected lung microbiome data from HumanMetagenomeDB based on metadata characteristics such as relevant clinical information, patient demographics, environmental factors, and sample collection methods. The metadata was further complemented by details on antibiotic usage, disease status, and other relevant descriptions. The sequencing data underwent stringent quality control, followed by a functional profiling focus on identifying resistance genes through specialized databases like Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. Subsequent analyses employed unsupervised machine learning techniques to unravel the structure and diversity of resistomes in the microbial community. Some of the methods employed were clustering methods such as K-Means and Hierarchical Clustering enabled the identification of sample groups based on their resistance gene profiles. The work was implemented in python, leveraging a range of libraries such as biopython for biological sequence manipulation, NumPy for numerical operations, Scikit-learn for machine learning, Matplotlib for data visualization and Pandas for data manipulation. The findings from this study provide insights into the distribution and dynamics of antimicrobial resistance within the lung microbiome. By leveraging unsupervised machine learning, we identified novel resistance patterns and potential drivers within the microbial community.

Keywords: antibiotic resistance, microbial community, unsupervised machine learning., sequences of AMR gene

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Authors: Rihab Hamdi, Amel Hadri Hamida, Fatiha Khelili, Sakina Zerouali, Ouafae Bennis

Abstract:

This paper features a comparative study performance of sliding mode controller (SMC) for closed-loop voltage control of direct current to direct current (DC-DC) three-cells buck converter connected in parallel, operating in continuous conduction mode (CCM), based on pulse-width modulation (PWM) with SMC based on hysteresis modulation (HM) where an adaptive feedforward technique is adopted. On one hand, for the PWM-based SM, the approach is to incorporate a fixed-frequency PWM scheme which is effectively a variant of SM control. On the other hand, for the HM-based SM, oncoming an adaptive feedforward control that makes the hysteresis band variable in the hysteresis modulator of the SM controller in the aim to restrict the switching frequency variation in the case of any change of the line input voltage or output load variation are introduced. The results obtained under load change, input change and reference change clearly demonstrates a similar dynamic response of both proposed techniques, their effectiveness is fast and smooth tracking of the desired output voltage. The PWM-based SM technique has greatly improved the dynamic behavior with a bit advantageous compared to the HM-based SM technique, as well as provide stability in any operating conditions. Simulation studies in MATLAB/Simulink environment have been performed to verify the concept.

Keywords: DC-DC converter, hysteresis modulation, parallel multi-cells converter, pulse-width modulation, robustness, sliding mode control

Procedia PDF Downloads 167