Search results for: deep profile control

Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu

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Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.

Keywords: satellite imagery classification, deep convolution network, genetic algorithm, hyper-parameter optimization

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Authors: A. Boualouch, A. Frigui, T. Nasser, A. Essadki, A.Boukhriss

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This work deals with the vector control of the active and reactive powers of a Double-Fed Induction generator DFIG used as a wind generator by the polynomial RST controller. The control of the statoric power transfer between the machine and the grid is achieved by acting on the rotor parameters and control is provided by the polynomial controller RST. The performance and robustness of the controller are compared with PI controller and evaluated by simulation results in MATLAB/simulink.

Keywords: DFIG, RST, vector control, wind turbine

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Authors: Tokihiko Akita, Seiichi Mita

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A millimeter-wave radar is the most robust against adverse environments, making it an essential environment recognition sensor for automated driving. However, the reflection signal is sparse and unstable, so it is difficult to obtain the high recognition accuracy. Deep learning provides high accuracy even for them in recognition, but requires large scale datasets with ground truth. Specially, it takes a lot of cost to annotate for a millimeter-wave radar. For the solution, utilizing a simulator that can generate an annotated huge dataset is effective. Simulation of the radar is more difficult to match with real world data than camera image, and recognition by deep learning with higher-order features using the simulator causes further deviation. We have challenged to improve the accuracy of traffic participant classification by fusing simulator and real-world data with domain adaptation technique. Experimental results with the domain adaptation network created by us show that classification accuracy can be improved even with a few real-world data.

Keywords: millimeter-wave radar, object classification, deep learning, simulation, domain adaptation

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

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This paper presents an approach for the easy creation of an institutional risk profile for endangerment analysis of file formats. The main contribution of this work is the employment of data mining techniques to support risk factors set up with just the most important values that are important for a particular organisation. Subsequently, the risk profile employs fuzzy models and associated configurations for the file format metadata aggregator to support digital preservation experts with a semi-automatic estimation of endangerment level for file formats. Our goal is to make use of a domain expert knowledge base aggregated from a digital preservation survey in order to detect preservation risks for a particular institution. Another contribution is support for visualisation and analysis of risk factors for a requried dimension. The proposed methods improve the visibility of risk factor information and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and automatically aggregated file format metadata from linked open data sources. To facilitate decision-making, the aggregated information about the risk factors is presented as a multidimensional vector. The goal is to visualise particular dimensions of this vector for analysis by an expert. The sample risk profile calculation and the visualisation of some risk factor dimensions is presented in the evaluation section.

Keywords: digital information management, file format, endangerment analysis, fuzzy models

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Xabier Olaz Moratinos

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Drone navigation is critical, particularly during the initial phases, such as the initial ascension, where pilots may fail due to strong external interferences that could potentially lead to a crash. In this ongoing work, a drone has been successfully trained to perform an ascent of up to 6 meters at speeds with external disturbances pushing it up to 24 mph, with the DQN algorithm managing external forces affecting the system. It has been demonstrated that the system can control its height, position, and stability in all three axes (roll, pitch, and yaw) throughout the process. The learning process is carried out in the Gazebo simulator, which emulates interferences, while ROS is used to communicate with the agent.

Keywords: machine learning, DQN, Gazebo, navigation

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Authors: Dharminder Singh, Sanjeev Yadav, Pravakar Mohanty

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In this work, study of temperature profile in a pilot scale air bubbling fluidized bed (ABFB) gasifier for rice husk gasification was carried out. Effects of different factors such as multiple cyclones, gas cooling system, ventilate gas pipe length, and catalyst on temperature profile was examined. ABFB gasifier used in this study had two sections, one is bed section and the other is freeboard section. River sand was used as bed material with air as gasification agent, and conventional charcoal as start-up heating medium in this gasifier. Temperature of different point in both sections of ABFB gasifier was recorded at different ER value and ER value was changed by changing the feed rate of biomass (rice husk) and by keeping the air flow rate constant for long durational of gasifier operation. ABFB with double cyclone with gas coolant system and with short length ventilate gas pipe was found out to be optimal gasifier design to give temperature profile required for high gasification performance in long duration operation. This optimal design was tested with different ER values and it was found that ER of 0.33 was most favourable for long duration operation (8 hr continuous operation), giving highest carbon conversion efficiency. At optimal ER of 0.33, bed temperature was found to be stable at 700 °C, above bed temperature was found to be at 628.63 °C, bottom of freeboard temperature was found to be at 600 °C, top of freeboard temperature was found to be at 517.5 °C, gas temperature was found to be at 195 °C, and flame temperature was found to be 676 °C. Temperature at all the points showed fluctuations of 10 – 20 °C. Effect of catalyst i.e. dolomite (20% with sand bed) was also examined on temperature profile, and it was found that at optimal ER of 0.33, the bed temperature got increased to 795 °C, above bed temperature got decreased to 523 °C, bottom of freeboard temperature got decreased to 548 °C, top of freeboard got decreased to 475 °C, gas temperature got decreased to 220 °C, and flame temperature got increased to 703 °C. Increase in bed temperature leads to higher flame temperature due to presence of more hydrocarbons generated from more tar cracking at higher temperature. It was also found that the use of dolomite with sand bed eliminated the agglomeration in the reactor at such high bed temperature (795 °C).

Keywords: air bubbling fluidized bed gasifier, bed temperature, charcoal heating, dolomite, flame temperature, rice husk

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Authors: Varsha Chorsiya, Pooja Aneja, Dhananjay Kaushik, Abhinav Yadav

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Intoduction: Women’s Health Initiatives (WHI) focuses on defining the risks and benefits of strategies that could potentially reduce the incidence of obesity, heart disease, breast cancer and colorectal cancer, and fractures in menopause women. The utility of the present research work determines to find the role of Hormone Replacement Therapy (HRT) in changing the different component of body composition during perimenopause period. Methods: A comparative cross-sectional study included 30 subjects, aged between 40 and 50 years which were assigned into 2 groups i.e. 15 subjects in HRT (Group A) and 15 subjects in non-HRT (Group B). The subjects were taken from the hospitals and clinics of Faridabad undergoing HRT in supervision of the consultant gynecologist. The informed consents were signed before including the participants in the study. The body composition and lipid profile were evaluated for all the subjects. Result and Discussion: The BMI, body density, percent body fats and fat mass in both groups showed statistically significant differences i.e. p < 0.05. Our study did not reveal any statistically significant difference between non-HRT and HRT for lipid profile composition of HDL, LDL, VLDL, ratio, triglycerides and total cholesterol although these indicators (LDL, VLDL, ratio, triglycerides and total cholesterol) showed difference clinically with a higher mean values for non-HRT as compared to HRT group. The mean value for HDL was higher for HRT group in contrast to non-HRT group. The result clearly showed that HRT group has a good lipid profile composition. Conclusion: In conclusion, our data show that HRT has statistically significant role in determining BMI, fat percent mass and fat mass. The lipid profile including LDL, HDL, VLDL, ratio, triglycerides and total cholesterol found to be clinically better in HRT group as compared to the non-HRT group. The rationale for non-significant lipid profile probably lie in the fact that hormonal changes need a particular time period and might become significant in post-menopausal period.

Keywords: body composition, hormone replacement therapy, perimenopause, women health

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Authors: Kashika P. H.

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The development of computer vision systems has been greatly aided by the efficient and precise detection of images and videos. Although the ability to recognize and comprehend images is a strength of the human brain, employing technology to tackle this issue is exceedingly challenging. In the past few years, the use of Deep Learning algorithms to treat object detection has dramatically expanded. One of the key issues in the realm of image recognition is the recognition and detection of certain notable people from randomly acquired photographs. Face recognition uses a way to identify, assess, and compare faces for a variety of purposes, including user identification, user counting, and classification. With the aid of an accessible deep learning-based API, this article intends to recognize various faces of people and their facial descriptors more accurately. The purpose of this study is to locate suitable individuals and deliver accurate information about them by using the Amazon Rekognition system to identify a specific human from a vast image dataset. We have chosen the Amazon Rekognition system, which allows for more accurate face analysis, face comparison, and face search, to tackle this difficulty.

Keywords: Amazon rekognition, API, deep learning, computer vision, face detection, text detection

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Authors: Ramji Kalidoss, Snekhalatha Umapathy, V. Dhinakaran, J. M. Mathana

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Chemi-resistive sensors used in breathalyzers have become a hotspot between the international breath research communities. These sensors exhibit a significant change in its resistance depending on the temperature it gets heated thus demanding high power leading to non-portable instrumentation. In this work, numerical simulation to identify the suitable combination of substrate and heater profile using COMSOL multiphysics was studied. Ni-Cr and Pt-100 joule resistive heater with various profiles were studied beneath the square and circular alumina substrates. The temperature distribution was uniform throughout the square substrate with the meander shaped pt100 heater with 48 mW power consumption for 200 oC. Moreover, this heater profile induced minimal stress on the substrate with 0.5 mm thick. A novel Graphene based ternary metal oxide nanocomposite (GO/SnO2/TiO2) was coated on the optimized substrate and heater to elucidate the response of diabetes biomarker (acetone). The sensor exhibited superior gas sensing performance towards acetone in the exhaled breath concentration range for diabetes (0.25 – 3 ppm). These results indicated the importance of substrate and heater properties along with sensing material for low power portable breathalyzers.

Keywords: Breath Analysis, Chemical Sensors, Diabetes Mellitus, Graphene Nanocomposites, Heater, Substrate

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Authors: Avalloy McCarthy-Curvin

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For over fifty years there has been widespread dissatisfaction with the teaching of Mathematics in Jamaica. Studies, done in the Jamaican context highlight that teachers at the end of training do not have a deep understanding of the mathematics content they teach. Little research has been done in the Jamaican context that targets the advancement of contextual knowledge on the problem to ultimately provide a solution. The aim of the study is to identify what influences this outcome of teacher education in Jamaica so as to remedy the problem. This study formatively evaluated the curriculum documents, assessments and the delivery of the curriculum that are being used in teacher training institutions in Jamaica to determine their rigor -the extent to which written document, instruction, and the assessments focused on enabling pre-service teachers to develop deep understanding of mathematics and relevance- the extent to which the curriculum document, instruction, and the assessments are focus on developing the requisite knowledge for teaching mathematics. The findings show that neither the curriculum document, instruction nor assessments ensure rigor and enable pre-service teachers to develop the knowledge and skills they need to teach mathematics effectively.

Keywords: relevance, rigor, deep understanding, formative evaluation

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Authors: Tomoaki Hashimoto

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Model predictive control is a kind of optimal feedback control in which control performance over a finite future is optimized with a performance index that has a moving initial time and a moving terminal time. This paper examines the stability of model predictive control for linear discrete-time systems with additive stochastic disturbances. A sufficient condition for the stability of the closed-loop system with model predictive control is derived by means of a linear matrix inequality. The objective of this paper is to show the results of computational simulations in order to verify the validity of the obtained stability condition.

Keywords: computational simulations, optimal control, predictive control, stochastic systems, discrete-time systems

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Authors: A. Djahbar, E. Bounadja, A. Zegaoui, H. Allouache

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In this paper, vector control of a series-connected two-machine drive system fed by a single inverter (CSI/VSI) is presented. The two stator windings of both machines are connected in series while the rotors may be connected to different loads, are called series-connected two-machine drive. Appropriate phase transposition is introduced while connecting the series stator winding to obtain decoupled control the two-machines. The dynamic decoupling of each machine from the group is obtained using the vector control algorithm. The independent control is demonstrated by analyzing the characteristics of torque and speed of each machine obtained via simulation under vector control scheme. The viability of the control techniques is proved using analytically and simulation approach.

Keywords: drives, inverter, multi-phase induction machine, vector control

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Authors: Shariful Islam, Sharun Akter Khushbu, S. M. Shaqib, Shahriar Sultan Ramit

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In the construction sector, ensuring worker safety is of the utmost significance. In this study, a deep learning-based technique is presented for identifying safety gear worn by construction workers, such as helmets, goggles, jackets, gloves, and footwear. The suggested method precisely locates these safety items by using the YOLO v7 (You Only Look Once) object detection algorithm. The dataset utilized in this work consists of labeled images split into training, testing and validation sets. Each image has bounding box labels that indicate where the safety equipment is located within the image. The model is trained to identify and categorize the safety equipment based on the labeled dataset through an iterative training approach. We used custom dataset to train this model. Our trained model performed admirably well, with good precision, recall, and F1-score for safety equipment recognition. Also, the model's evaluation produced encouraging results, with a [email protected] score of 87.7%. The model performs effectively, making it possible to quickly identify safety equipment violations on building sites. A thorough evaluation of the outcomes reveals the model's advantages and points up potential areas for development. By offering an automatic and trustworthy method for safety equipment detection, this research contributes to the fields of computer vision and workplace safety. The proposed deep learning-based approach will increase safety compliance and reduce the risk of accidents in the construction industry.

Keywords: deep learning, safety equipment detection, YOLOv7, computer vision, workplace safety

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Authors: Sadaoui Djaouida

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In this paper, the predictive control method is proposed to control the synchronization of two perturbed satellites attitude motion. Based on delayed feedback control of continuous-time systems combines with the prediction-based method of discrete-time systems, this approach only needs a single controller to realize synchronization, which has considerable significance in reducing the cost and complexity for controller implementation.

Keywords: predictive control, synchronization, satellite attitude, control engineering

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Authors: Jiabei Zhang, Ying Qi

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The purpose of this study was to develop a descriptive profile of the adapted physical activity research using single subject experimental designs. All research articles using single subject experimental designs published in the journal of Adapted Physical Activity Quarterly from 1984 to 2013 were employed as the data source. Each of the articles was coded in a subcategory of seven categories: (a) the size of sample; (b) the age of participants; (c) the type of disabilities; (d) the type of data analysis; (e) the type of designs, (f) the independent variable, and (g) the dependent variable. Frequencies, percentages, and trend inspection were used to analyze the data and develop a profile. The profile developed characterizes a small portion of research articles used single subject designs, in which most researchers used a small sample size, recruited children as subjects, emphasized learning and behavior impairments, selected visual inspection with descriptive statistics, preferred a multiple baseline design, focused on effects of therapy, inclusion, and strategy, and measured desired behaviors more often, with a decreasing trend over years.

Keywords: adapted physical activity research, single subject experimental designs, physical education, sport science

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Authors: Aly Kassem, Eman Sapet, Eman Abdelbaset, Hosam Mahmoud

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Objective: Hypertension is an important public health challenge being one of the most common worldwide disease-affecting human. Our aim is to study the clinical characteristics, therapeutic regimens, treatment compliance, and risk factors in a sector of of hypertensive patients at Sohag City. Subject and Methods: A cross sectional study; conducted in Sohag city; it involved 520 patients; males (45.7 %) and females (54.3 %). Their ages ranged between 35-85 years. BP measurements, BMI, blood glucose, Serum creatinine, urine analysis, serum Lipids, blood picture and ECG were done all the studied patients. Results: Hypertension presented more between non-smokers (72.55%), females (54.3%), educated patients (50.99%) and patients with low SES (54.9%). CAD presented in (51.63%) of patients, while laboratory investigations showed hyperglycaemia in (28.7%), anemia in (18.3%), high serum creatinine level in (8.49%) and proteinuria in (10.45%) of patient. Adequate BP control was achieved in (49.67%); older patients had lower adequacy of BP control in spite of the extensive use of multiple-drug therapy. Most hypertensive patients had more than one coexistent CV risk factor. Aging, being a female (54.3%), DM (32.3%), family history of hypertension (28.7%), family history of CAD (25.4%), and obesity (10%) were the common contributing risk factors. ACE-inhibitors were prescribed in (58.16%), Beta-blockers in (34.64%) of the patients. Monotherapy was prescribed for (41.17%) of the patients. (75.81%) of patients had regular use of their drug regimens. (49.67%) only of patients had their condition under control, the number of drugs was inversely related to BP control. Conclusion: Hypertensive patients in Sohag city had a profile of high CV risks, and poor blood pressure control particularly in the elderly. A multidisciplinary approach for routine clinical check-up, follow-up, physicians and patients training, prescribing simple once-daily regimens and encouraging life style modifications are recommended. Anti hypertensives, hypertension, elderly patients, risk factors, treatment compliance.

Keywords: anti hypertensives, hypertension, elderly patients, risk factors, treatment compliance

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Authors: Qingqing Feng, S. Thomas Ng, Frank Xu

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Energy consumption in domestic sector has been increasing rapidly in China all along these years. Confronted with environmental challenges, the international society has made a concerted effort by setting the Paris Agreement, the Sustainable Development Goals, and the New Urban Agenda. Thus it’s very important for China to put forward reasonable countermeasures to boost building energy conservation which necessitates looking into the actuality of residential energy end-use profile and its influence factors. In this study, questionnaire surveys have been conducted in Zhuhai city in China, a typical city in hot summer warm winter climate zone. The data solicited mainly include the occupancy schedule, building’s information, residents’ information, household energy uses, the type, quantity and use patterns of appliances and occupants’ satisfaction. Over 200 valid samples have been collected through face-to-face interviews. Descriptive analysis, clustering analysis, correlation analysis and sensitivity analysis were then conducted on the dataset to understand the energy end-use profile. The findings identify: 1) several typical clusters of occupancy patterns and appliances utilization patterns; 2) the top three sensitive factors influencing energy consumption; 3) the correlations between satisfaction and energy consumption. For China with many different climates zones, it’s difficult to find a silver bullet on energy conservation. The aim of this paper is to provide a theoretical basis for multi-stakeholders including policy makers, residents, and academic communities to formulate reasonable energy saving blueprints for hot and humid urban residential buildings in China.

Keywords: residential building, energy end-use profile, questionnaire survey, sustainability

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Authors: Winfred Okoe Addy, Jean Jacques Dominique Beraud

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Access control systems (ACs) are some of the most important components in safety areas. Inaccuracies of regulatory frameworks make personal policies and remedies more appropriate than standard models or protocols. This problem is exacerbated by the increasing complexity of software, such as integrated Big Data (BD) software for controlling large volumes of encrypted data and resources embedded in a dedicated BD production system. This paper proposes a general access control strategy system for the diffusion of Big Data domains since it is crucial to secure the data provided to data consumers (DC). We presented a general access control circulation strategy for the Big Data domain by describing the benefit of using designated access control for BD units and performance and taking into consideration the need for BD and AC system. We then presented a generic of Big Data access control system to improve the dissemination of Big Data.

Keywords: access control, security, Big Data, domain

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Authors: Jing Zhang, Daniel Nikovski

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We propose an approximation algorithm called LINKUMP to compute the Pan Matrix Profile (PMP) under the unnormalized l∞ distance (useful for value-based similarity search) using double-ended queue and linear interpolation. The algorithm has comparable time/space complexities as the state-of-the-art algorithm for typical PMP computation under the normalized l₂ distance (useful for shape-based similarity search). We validate its efficiency and effectiveness through extensive numerical experiments and a real-world anomaly detection application.

Keywords: pan matrix profile, unnormalized euclidean distance, double-ended queue, discord discovery, anomaly detection

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Yinggang Guo, Zongchun Li

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In order to decrease the accumulated survey error of tunnel installation control network of particle accelerator, a sectional control method is proposed. Firstly, the accumulation rule of positional error with the length of the control network is obtained by simulation calculation according to the shape of the tunnel installation-control-network. Then, the RMS of horizontal positional precision of tunnel backbone control network is taken as the threshold. When the accumulated error is bigger than the threshold, the tunnel installation control network should be divided into subsections reasonably. On each segment, the middle survey station is taken as the datum for independent adjustment calculation. Finally, by taking the backbone control points as faint datums, the weighted partial parameters adjustment is performed with the adjustment results of each segment and the coordinates of backbone control points. The subsections are jointed and unified into the global coordinate system in the adjustment process. An installation control network of the linac with a length of 1.6 km is simulated. The RMS of positional deviation of the proposed method is 2.583 mm, and the RMS of the difference of positional deviation between adjacent points reaches 0.035 mm. Experimental results show that the proposed sectional control method can not only effectively decrease the accumulated survey error but also guarantee the relative positional precision of the installation control network. So it can be applied in the data processing of tunnel installation control networks, especially for large particle accelerators.

Keywords: alignment, tunnel installation control network, accumulated survey error, sectional control method, datum

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Authors: Pia Berner Hansson, Robert Murison Anders Lund, Hammar Åsa

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Major Depressive Disorder (MDD) is often associated with high levels of stress and disturbances in the Hypothalamic Pituitary Adrenal (HPA) system, yielding high levels of cortisol, in addition to cognitive dysfunction. Previous studies in this patient group have shown a relationship between cortisol profile and cognitive functioning in the acute phase of MDD and that the patients had significantly less suppression after dexamethasone administration. However, few studies have investigated this relationship over time and in phases of symptom reduction. The aim of the present study was to examine the relationships between cortisol levels after the Dexamethasone Suppression Test (DST) and cognitive function in a long term perspective in MDD patients. Patients meeting the DSM-IV criteria for a MDD were included in the study and tested in symptom reduction. A control group was included. Cortisol was measured in saliva collected with Salivette sampling devices. Saliva samples were collected 4 times during a 24 hours period over two consecutive days: at awakening, after 45 minutes, after 7 hours and at 11 pm. Dexamethasone (1.0 mg) was given on Day 1 at 11 pm. The neuropsychological test battery consisted of standardized tests measuring memory and Executive Functioning (EF). Cortisol levels did not differ significantly between patients and controls on Day 1 or Day 2. Both groups showed significant suppression after Dexamethasone. There were no correlations between cortisol levels or suppression after Dexamethasone and cognitive measures. The results indicate that the HPA-axis functioning normalizes in phases of symptom reduction in MDD patients and that there no relation between cortisol profile and cognitive functioning in memory or EF.

Keywords: depression, MDD, cortisol, suppression, cognitive functioning

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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: Yupaporn Areepong

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The main goal of this paper is to study Statistical Process Control (SPC) with Exponentially Weighted Moving Average (EWMA) control chart when observations are serially-correlated. The characteristic of control chart is Average Run Length (ARL) which is the average number of samples taken before an action signal is given. Ideally, an acceptable ARL of in-control process should be enough large, so-called (ARL0). Otherwise it should be small when the process is out-of-control, so-called Average of Delay Time (ARL1) or a mean of true alarm. We find explicit formulas of ARL for EWMA control chart for Seasonal Autoregressive and Moving Average processes (SARMA) with Exponential white noise. The results of ARL obtained from explicit formula and Integral equation are in good agreement. In particular, this formulas for evaluating (ARL0) and (ARL1) be able to get a set of optimal parameters which depend on smoothing parameter (λ) and width of control limit (H) for designing EWMA chart with minimum of (ARL1).

Keywords: average run length, optimal parameters, exponentially weighted moving average (EWMA), control chart

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Authors: S. E. Mohmad-Saberi, W. Song, N. Oliver, M. Adrian, T.C. Hsu, A. Darbyshire

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Biodegradable polyurethane (PU) has emerged as a potential material to promote repair and regeneration of damaged/diseased tissues in heart valve regeneration due to its excellent biomechanical profile. Understanding the effects of sterilization on their properties is vital since they are more sensitive and more critical of porous structures compared to bulk ones. In this study, the effects of low concentration of hydrogen peroxide (H₂O₂) solution sterilization has been investigated to determine whether the procedure would be efficient and non-destructive to porous three-dimensional (3D) elastomeric nanocomposite, polyhedral oligomeric silsesquioxane-terminated poly (ethylene-diethylene glycol succinate-sebacate) urea-urethane (POSS-EDSS-PU) scaffold. All the samples were tested for sterility following sterilization using phosphate buffer saline (PBS) as control and 5 % v/v H₂O₂ solution. The samples were incubated in tryptic soy broth for the cultivation of microorganisms under agitation at 37˚C for 72 hours. The effects of the 5 % v/v H₂O₂ solution sterilization were evaluated in terms of morphology, chemical and mechanical properties using scanning electron microscopy (SEM), Fourier transform infrared (FTIR) and tensile tester apparatus. Toxicity effects of the 5 % v/v H₂O₂ solution decontamination were studied by in vitro cytotoxicity test, where the cellular responses of human dermal fibroblast (HDF) were examined. A clear, uncontaminated broth using 5 % v/v H₂O₂ solution method indicated efficient sterilization after 3 days, while the non-sterilized control shows clouding broth indicated contamination. The morphology of 3D POSS-EDSS-PU scaffold appeared to have similar morphology after sterilization with 5 % v/v H₂O₂ solution regarding of pore size and surface. FTIR results show that the sterilized samples and non-sterilized control share the same spectra pattern, confirming no significant alterations over the surface chemistry. For the mechanical properties of the H₂O₂ solution-treated scaffolds, the tensile strain was not significantly decreased, however, become significantly stiffer after the sterilization. No cytotoxic effects were observed after the 5 % v/v H₂O₂ solution sterilization as confirmed by cell viability assessed by Alamar Blue assay. The results suggest that low concentration of 5 % v/v hydrogen peroxide solution can be used as an alternative method for sterilizing biodegradable 3D porous scaffold with micro/nano-architecture without structural deformation. This study provides the understanding of the sterilization effects on biomechanical profile and cell proliferation of 3D POSS-EDSS-PU scaffolds.

Keywords: biodegradable, hydrogen peroxide solution, POSS-EDSS-PU, sterilization

Procedia PDF Downloads 143

Authors: Debasish Chowdhury, Sunil Mekale, Sarvanamurthy Sakthivel, Sukhvinder Kaur, Rambabu Khambampati, Ashok Agarwal

Abstract:

Introduction: The migrant intervention in India was initiated during the National AIDS Control Program (NACP) Phase-2 (2002-2007). HIV Sentinel surveillance Studies (HSS) conducted in 2012-13 indicated higher HIV prevalence among migrants (0.99%) compared to general populations (0.35%). Migrants continue to bear a heightened risk of HIV infection which results from the condition and structure of the migration process. USAID PHFI-PIPPSE project in collaboration with the National AIDS Control Organization (NACO) developed a unique system called Migrant Service Delivery System (MSDS) to capture migrants profile with respect to their risk profile and to provide tailor made services to them. Description: MSDS is a web-based system, designed and implemented to increase service uptake among migrants through evidence based planning. 110 destination migrants Targeted Intervention (TI) from 11 states were selected for study with varied target populations in terms of occupations; to understand occupation related risk behaviors among the migrants. Occupation wise registration data of high risk vulnerable migrants were analyzed through MSDS for the period April 2014–June 2016. Analysis was made on specific indicators among these occupational groups to understand the risk behavior and their vulnerability to HIV and STIs. Findings: Out of total HIV positive migrant’s workers (N= 847) enrolled in MSDS HIV rate is found to be highest among Auto-Rickshaw (18.66%) followed by Daily wage laborers (14.46%), Loom workers (10.73%), Industrial workers (10.04%) and Construction worker 7.93%. With 45.14% positivity, industrial workers are found to be most vulnerable to Sexually Transmitted Infections (STIs) (N=10057) among all occupational categories followed by loom workers (16.28%), Skilled worker (Furniture, Jeweler)-7.14%, daily wage laborers (5.45%). Conclusion: MSDS is an effective tool to assess migrants’ risk and their vulnerability to HIV for designing evidence informed program. This system calls for a replication across all destination TIs by NACO for differential strategies for different occupation groups to ensure better yield through scientific planning of intervention among high risk and high vulnerable migrants.

Keywords: migrants, migrant service delivery system, risk, vulnerability

Procedia PDF Downloads 255

Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

Abstract:

A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

Procedia PDF Downloads 109

Authors: M. W. Sun, Y. Zhang, L. M. Zhang, Z. H. Wang, Z. Q. Chen

Abstract:

In the realm of flight control, the Proportional- Derivative (PD) control is still widely used for the attitude control in practice, particularly for the pitch control, and the attitude dynamics using PD controller should be investigated deeply. According to the empirical knowledge about the unstable flight dynamics, the control parameter combination conditions to generate sole or finite number of closed-loop oscillations, which is a quite smooth response and is more preferred by practitioners, are presented in analytical or numerical manners. To analyze the effects of the combination conditions of the control parameters, the roots of several polynomials are sought to obtain feasible solutions. These conditions can also be plotted in a 2-D plane which makes the conditions be more explicit by using multiple interval operations. Finally, numerical examples are used to validate the proposed methods and some comparisons are also performed.

Keywords: attitude control, dynamic performance, numerical solving method, interval, unstable flight dynamics

Procedia PDF Downloads 558

Authors: Monireh Ahmadi Bani, Adel Khorramrouz, Lalenoor Morvarid, Bagheri Mahtab

Abstract:

Background:- Foot disorders are common in musculoskeletal problems. Plantar pressure distribution measurement is one the most important part of foot disorders diagnosis for quantitative analysis. However, the association of plantar pressure and foot disorders is not clear. With the growth of dataset and machine learning methods, the relationship between foot disorders and plantar pressures can be detected. Significance of the study:- The purpose of this study was to predict the probability of common foot disorders based on peak plantar pressure distribution and center of pressure during walking. Methodologies:- 2323 participants were assessed in a foot therapy clinic between 2015 and 2021. Foot disorders were diagnosed by an experienced physician and then they were asked to walk on a force plate scanner. After the data preprocessing, due to the difference in walking time and foot size, we normalized the samples based on time and foot size. Some of force plate variables were selected as input to a deep neural network (DNN), and the probability of any each foot disorder was measured. In next step, we used support vector machine (SVM) and run dataset for each foot disorder (classification of yes or no). We compared DNN and SVM for foot disorders prediction based on plantar pressure distributions and center of pressure. Findings:- The results demonstrated that the accuracy of deep learning architecture is sufficient for most clinical and research applications in the study population. In addition, the SVM approach has more accuracy for predictions, enabling applications for foot disorders diagnosis. The detection accuracy was 71% by the deep learning algorithm and 78% by the SVM algorithm. Moreover, when we worked with peak plantar pressure distribution, it was more accurate than center of pressure dataset. Conclusion:- Both algorithms- deep learning and SVM will help therapist and patients to improve the data pool and enhance foot disorders prediction with less expense and error after removing some restrictions properly.

Keywords: deep neural network, foot disorder, plantar pressure, support vector machine

Procedia PDF Downloads 326