Search results for: electrical state prediction

Authors: Seyedmahdi Mousavihashemi

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One of the most important subjects of interest in researches is 'improving' which result in various algorithms. In so many geometrical problems we are faced with target functions which should be optimized. In group practices, all the functions’ cooperation lead to convergence. In the study, the optimization algorithm of dense particles is used. Usage of the algorithm improves the given performance norms. The results reveal that usage of swarm algorithm for reinforced particles in designing state feedback improves the given performance norm and in optimized designing of multi-target state feedback controlling, the network will maintain its bearing structure. The results also show that PSO is usable for optimization of state feedback controllers.

Keywords: multi-objective, enhanced, feedback, optimization, algorithm, particle, design

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Authors: R. Loukil, M. Chtourou, T. Damak

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This paper investigates the training of multilayer neural network using the classic approach. Then, for estimation purposes, we suggest the use of a specific neural observer that we study its training algorithm which is the back-propagation one in the case of the disponibility of the state and in the case of an unmeasurable state. A MATLAB simulation example will be studied to highlight the usefulness of this kind of observer.

Keywords: training, estimation purposes, neural observer, back-propagation, unmeasurable state

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Authors: Diogo Ferreira Lima Filho, José Roberto Amazonas

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Nanotechnology opens the door to new paradigms that introduces a variety of novel tools enabling a plethora of potential applications in the biomedical, industrial, environmental, and military fields. This work proposes an integrated node model by applying the same concepts of TCNet to networks of nanodevices where the nodes are cooperatively interconnected with a low-complexity Mealy Machine (MM) topology integrating in the same electronic system the modules necessary for independent operation in wireless sensor networks (WSNs), consisting of Rectennas (RF to DC power converters), Code Generators based on Finite State Machine (FSM) & Trellis Decoder and On-chip Transmit/Receive with autonomy in terms of energy sources applying the Energy Harvesting technique. This approach considers the use of a Graphene Composite Substrate (GCS) for the integrated electronic circuits meeting the following characteristics: mechanical flexibility, miniaturization, and optical transparency, besides being ecological. In addition, graphene consists of a layer of carbon atoms with the configuration of a honeycomb crystal lattice, which has attracted the attention of the scientific community due to its unique Electrical Characteristics.

Keywords: composite substrate, energy harvesting, finite state machine, graphene, nanotechnology, rectennas, wireless sensor networks

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Authors: Aboussalih Amira, Zarza Tahar, Fedaoui Kamel, Hammoudi Saleh

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This work focuses on the simulation of the prediction of the behaviour of austenitic stainless steel (SS) 304L under complex loading in stress and imposed strain. The Chaboche model is a cable to describe the response of the material by the combination of two isotropic and nonlinear kinematic work hardening, the model is implemented in the ZébuLon computer code. First, we represent the evolution of the axial stress as a function of the plastic strain through hysteresis loops revealing a hardening behaviour caused by the increase in stress by stress in the direction of tension/compression. In a second step, the study of the ratcheting phenomenon takes a key place in this work by the appearance of the average stress. In addition to the solicitation of the material in the biaxial direction in traction / torsion.

Keywords: damage, 304L, Ratcheting, plastic strain

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Authors: Adegbite Bashiru Adeniyi, Olaniyi Akeem Olawale, Ayobamidele Sinatu Juliet

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The study was carried out in the 6 South Western states of Nigeria to analyze socio-economic characteristic of garri processors and their anthropometric qualities with respect to modern technologies used in garri processing. About 20 respondents were randomly selected from each of the 6 workstations purposively considered for the study due to their daily processing activities already attracted high patronage of customers. These include Oguntolu village (Ogun State), Igoba-Akure (Ondo State), Imo-Ilesa (Osun State), Odo Oba-Ileri (Oyo State), Irasa village (Ekiti State) and Epe in Lagos state. Interview schedule was conducted for 120 respondents to elicit information. Data were analyzed using descriptive statistical tools. It was observed from the findings that respondents were in their most productive age range (36-45 years) except Ogun state where majority (45%) were relatively older than 45 years. A fewer processors were much younger than 26 years old. It furthers revealed that not less than 55% have body weight greater than 50.0 kilogram, also not less than 70% were taller than 1.5 meter. So also, the hand length and hand thickness of the majority were long and bulky which are considered suitable for operating some modern and improved technologies in garri-frying process. This information could be used by various technological developers to enhance production of modern equipment and tools for a greater efficiency.

Keywords: agro-business, anthromorphic, modernization, proficiency

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Authors: C. Boubechou, A. Bouchoucha, H. Zaidi

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The aim of this paper is to study the tribological behavior of a dynamic contact steel-brass couple with electric current. This study looks at a dry contact brass-steel couple where friction and wear are studied in terms of mechanical and electrical parameters. For this reason, a tribometer, pin-rotary disc is used in an atmospheric atmosphere. The test parameters are as follows: the normal load (5-30N), the sliding speed (0.1 to 0.5 m / s) and the electric current (3-10A). The duration of each test is 30 minutes. The experimental results show that these parameters have a significant effect on the tribological behavior of the couple studied. The discussion of results is based on observations, using an optical microscope, MEB and a profilometer, worn surfaces and interface phenomena resulting from the process of sliding contact.

Keywords: brass-steel couple, dry friction, electrical current, morphology, normal load, sliding speeds, wear

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Authors: Jon Cobb, Nasir

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Due to higher switching frequencies, the conducted Electromagnetic interference (EMI) noise is generated in a converter. It degrades the performance of a switching converter. Therefore, it is an essential requirement to mitigate EMI noise of high performance converter. Moreover, it includes two types of emission such as common mode (CM) and differential mode (DM) noise. CM noise is due to parasitic capacitance present in a converter and DM noise is caused by switching current. However, there is dire need to understand the main cause of EMI noise. Hence, we propose a novel method to predict conducted EMI noise of different converter topologies during early stage. This paper also presents the comparison of conducted electromagnetic interference (EMI) noise due to different SMPS topologies. We also make an attempt to develop an EMI noise model for a converter which allows detailed performance analysis. The proposed method is applied to different converter, as an example, and experimental results are verified the novel prediction technique.

Keywords: EMI, electromagnetic interference, SMPS, switch-mode power supply, common mode, CM, differential mode, DM, noise

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Authors: Hakan Aydogan, Fatih Bozkurt, Huseyin Coskun

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In this study, the signal of brain electrical activities of the sixteen students selected from the Department of Electrical and Energy at Usak University have been recorded during a lecturer performed happiness emotions for the first group and anger emotions for the second group in different time while the groups were in the classroom separately. The attention and meditation data extracted from the recorded signals have been analyzed and evaluated toward the teacher’s specific emotion states simultaneously. Attention levels of students who are under influence of happiness emotions of the lecturer have a positive trend and attention levels of students who are under influence of anger emotions of the lecturer have a negative trend. The meditation or mental relaxation levels of students who are under influence of happiness emotions of the lecturer are 34.3% higher comparing with the mental relaxation levels of students who are under influence of anger emotions of the lecturer.

Keywords: brainwave, attention, meditation, education

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Authors: P. Rutkowski, G. Górny, L. Stobierski, D. Zientara, W. Piekarczyk, K. Tran

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The polycrystalline dense alumina shows thermal conductivity about 30 W/mK and very high electrical resistivity. These last two properties can be modified by introducing commercial relatively cheap graphene nanoparticles which, as two-dimensional flakes show very high thermal and electrical properties. The aim of this work is to show that it is possible to manufacture the anisotropic alumina-graphene material with directed multilayer graphene particles. Such materials can show the anisotropic properties mentioned before.

Keywords: alumina, composite, hot-pressed, graphene, properties

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Authors: Pei-Chann Chang, Jhen-Fu Liao, Chin-Hung Teng, Meng-Hui Chen

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This research combines artificial immune system with user and item based collaborative filtering to create an efficient and accurate recommendation system. By applying the characteristic of antibodies and antigens in the artificial immune system and using Pearson correlation coefficient as the affinity threshold to cluster the data, our collaborative filtering can effectively find useful users and items for rating prediction. This research uses MovieLens dataset as our testing target to evaluate the effectiveness of the algorithm developed in this study. The experimental results show that the algorithm can effectively and accurately predict the movie ratings. Compared to some state of the art collaborative filtering systems, our system outperforms them in terms of the mean absolute error on the MovieLens dataset.

Keywords: artificial immune system, collaborative filtering, recommendation system, similarity

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Authors: Shayla He

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Background and Purpose: According to Chamie (2017), it’s estimated that no less than 150 million people, or about 2 percent of the world’s population, are homeless. The homeless population in the United States has grown rapidly in the past four decades. In New York City, the sheltered homeless population has increased from 12,830 in 1983 to 62,679 in 2020. Knowing the trend on the homeless population is crucial at helping the states and the cities make affordable housing plans, and other community service plans ahead of time to better prepare for the situation. This study utilized the data from New York City, examined the key factors associated with the homelessness, and developed systematic modeling to predict homeless populations of the future. Using the best model developed, named HP-RNN, an analysis on the homeless population change during the months of 2020 and 2021, which were impacted by the COVID-19 pandemic, was conducted. Moreover, HP-RNN was tested on the data from Seattle. Methods: The methodology involves four phases in developing robust prediction methods. Phase 1 gathered and analyzed raw data of homeless population and demographic conditions from five urban centers. Phase 2 identified the key factors that contribute to the rate of homelessness. In Phase 3, three models were built using Linear Regression, Random Forest, and Recurrent Neural Network (RNN), respectively, to predict the future trend of society's homeless population. Each model was trained and tuned based on the dataset from New York City for its accuracy measured by Mean Squared Error (MSE). In Phase 4, the final phase, the best model from Phase 3 was evaluated using the data from Seattle that was not part of the model training and tuning process in Phase 3. Results: Compared to the Linear Regression based model used by HUD et al (2019), HP-RNN significantly improved the prediction metrics of Coefficient of Determination (R2) from -11.73 to 0.88 and MSE by 99%. HP-RNN was then validated on the data from Seattle, WA, which showed a peak %error of 14.5% between the actual and the predicted count. Finally, the modeling results were collected to predict the trend during the COVID-19 pandemic. It shows a good correlation between the actual and the predicted homeless population, with the peak %error less than 8.6%. Conclusions and Implications: This work is the first work to apply RNN to model the time series of the homeless related data. The Model shows a close correlation between the actual and the predicted homeless population. There are two major implications of this result. First, the model can be used to predict the homeless population for the next several years, and the prediction can help the states and the cities plan ahead on affordable housing allocation and other community service to better prepare for the future. Moreover, this prediction can serve as a reference to policy makers and legislators as they seek to make changes that may impact the factors closely associated with the future homeless population trend.

Keywords: homeless, prediction, model, RNN

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Authors: M. Ben Slimene, M.-S. Ouali

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Asset management of urban infrastructures faces a multitude of challenges that need to be overcome to obtain a reliable measurement of performances. Predicting the performance of slowly aging systems is one of those challenges, which helps the asset manager to investigate specific failure modes and to undertake the appropriate maintenance and rehabilitation interventions to avoid catastrophic failures as well as to optimize the maintenance costs. This article presents a methodology for modeling the deterioration of slowly degrading assets based on an operating history. It consists of extracting degradation profiles by grouping together assets that exhibit similar degradation sequences using an unsupervised classification technique derived from artificial intelligence. The obtained clusters are used to build the performance prediction models. This methodology is applied to a sample of a stormwater drainage culvert dataset.

Keywords: artificial Intelligence, clustering, culvert, regression model, slow degradation

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Authors: Osama Moustafa Zayed

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Aim of the work: To evaluate the effectiveness of venous glucose, levels of serum lactate and base deficit in polytraumatized patients as simple parameters to predict the mortality in these patients. Compared to the predictive value of Trauma and injury severity (TRISS) and Acute Physiology And Chronic Health Evaluation IV (APACHE IV). Introduction: Trauma is a serious global health problem, accounting for approximately one in 10 deaths worldwide. Trauma accounts for 5 million deaths per year. Prediction of mortality in trauma patients is an important part of trauma care. Several trauma scores have been devised to predict injury severity and risk of mortality. The trauma and injury severity score (TRISS) was most common used. Regardless of the accuracy of trauma scores, is based on an anatomical description of every injury and cannot be assigned to the patients until a full diagnostic procedure has been performed. So we hypothesized that alterations in admission glucose, lactate levels and base deficit would be an early and easy rapid predictor of mortality. Patient and Method: a comparative cross-sectional study. 282 Polytraumatized patients attended to the Emergency Department(ED) of the Suez Canal university Hospital constituted. The period from 1/1/2012 to 1/4/2013 was included. Results: We found that the best cut off value of TRISS probability of survival score for prediction of mortality among poly-traumatized patients is = 90, with 77% sensitivity and 89% specificity using area under the ROC curve (0.89) at (95%CI). APACHE IV demonstrated 67% sensitivity and 95% specificity at 95% CI at cut off point 99. The best cutoff value of Random Blood Sugar (RBS) for prediction of mortality was>140 mg/dl, with 89%, sensitivity, 49% specificity. The best cut off value of base deficit for prediction of mortality was less than -5.6 with 64% sensitivity, 93% specificity. The best cutoff point of lactate for prediction of mortality was > 2.6 mmol/L with 92%, sensitivity, 42% specificity. Conclusion: According to our results from all evaluated predictors of mortality (laboratory and scores) and mortality based on the estimated cutoff values using ROC curves analysis, the highest risk of mortality was found using a cutoff value of 90 in TRISS score while with laboratory parameters the highest risk of mortality was with serum lactate > 2.6 . Although that all of the three parameter are accurate in predicting mortality in poly-traumatized patients and near with each other, as in serum lactate the area under the curve 0.82, in BD 0.79 and 0.77 in RBS.

Keywords: APACHE IV, emergency department, polytraumatized patients, serum lactate

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Authors: Olubusayo Asikhia

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The purpose of the study was to compare the incidence of indiscipline and guidance and counseling programmes among federal and state-owned tertiary institutions in Lagos State, Southwest, Nigeria. This study was borne out of persistent students’ revolt and unrest in Nigerian tertiary institutions with its concomitant breakdown of law and order, disruption of academic activities, closure of institutions and the disruption of lives and property among others. A sample of 300 students, 270 staff (including academic and non-academic, heads of disciplinary committees, deans of student affairs and counselors) from 6 federal and state-owned tertiary institutions were selected through stratified random sampling. Three structured questionnaires with reliability coefficients that range from 0.78 to 0.91 were used to collect data for the study. The data were tested at 0.05 level of significance and analyzed using descriptive, paired samples statistics and regression analysis. Findings from the four hypotheses generated for the study revealed that student indiscipline occurs more in state colleges of education and polytechnics than in their federal counterparts. It was also higher in federal universities than the state. The study also revealed a significant difference in the causes of indiscipline and assessment of guidance and counseling programmes in state and federal universities and no significant difference in state and federal colleges and polytechnics. It was recommended that the guidance and counseling department in tertiary institutions should create a well-structured program that will enable it to address issues of indiscipline among students and that counselors should use small-group counseling approach and student-focused interventions.

Keywords: colleges of education, guidance and counseling, indiscipline, polytechnics, students and universities

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Authors: Bilal Jabakhanji, Dimitris Kazazis, Adrien Michon, Christophe Consejo, Wilfried Desrat, Benoit Jouault

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In this paper, we investigate the electrical properties of graphene grown by Chemical Vapor Deposition (CVD) on the Si face of SiC substrates. Depending on the growth condition, hole or electron doping can be achieved, down to a few 1011cm−2. The high homogeneity of the graphene and the low intrinsic carrier concentration, allow the remarkable observation of the Half Integer Quantum Hall Effect, typical of graphene, at the centimeter scale.

Keywords: graphene, quantum hall effect, chemical vapor, deposition, silicon carbide

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Authors: Caroline E. Mendes, Alberto C. Badino

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Pneumatic reactors have been widely employed in various sectors of the chemical industry, especially where are required high heat and mass transfer rates. This study aimed to obtain correlations that allow the prediction of gas hold-up (Ԑ) and volumetric oxygen transfer coefficient (kLa), and compare these values, for three models of pneumatic reactors on two scales utilizing Newtonian fluids. Values of kLa were obtained using the dynamic pressure-step method, while  was used for a new proposed measure. Comparing the three models of reactors studied, it was observed that the mass transfer was superior to draft-tube airlift, reaching  of 0.173 and kLa of 0.00904s-1. All correlations showed good fit to the experimental data (R2≥94%), and comparisons with correlations from the literature demonstrate the need for further similar studies due to shortage of data available, mainly for airlift reactors and high viscosity fluids.

Keywords: bubble column, internal loop airlift, gas hold-up, kLa

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Authors: Marda Vidrianto, Tania Surya Utami

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Electrical Submersible Pump (ESP) is an artificial lift of choice to produce oil on Offshore Java Sea. It is selected based on the production rate capacity and running life expectation. ESP performance in a mature field is highly affected by oil well conditions. The presence of sand, scale, gas, and low influx will create unstable ESP operation hence lowering the run life expectation and system efficiency. This paper reviews the current energy usage and efficiency on every part of the ESP system. The hydraulic and electrical losses, as well as system efficiency for each well, are calculated to identify energy losses and the possibility for improvement. It is shown that high back pressure on the system and low-efficiency pump are the major contributors to energy losses. It was found that optimized production rate and the use of advanced technology on pump and motor unit could improve energy efficiency.

Keywords: advance technology, energy efficiency, ESP, mature field, production rate

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Authors: Aziza Tahar Djebbar

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Throughout American history, many American presidents have tried to create new reforms to enhance the living conditions of the American citizens and promote their welfare. Among these reforms were those dealing with health care, education, as well as social security, which would facilitate accordingly the evolution of the American welfare state. After the New Deal, from 1945 to 1969, American presidents sought to carry out and enlarge the scope of the welfare state that emerged during the Roosevelt Administration by introducing new social reforms. Yet, there were some American presidents who succeeded, and there were some presidents who failed.The task of this research work is to depict the postwar period from 1945 to 1969. Some light will be shed on the main causes that led to the delay of many programs from 1945 to 1960. Further, the focus will be on the main factors that contributed to the reappearance of many social reforms and the dramatic expansion of the welfare state all along the 1960s.

Keywords: new deal, great society, medicaid, medicare, war on poverty, social reforms, welfare reforms

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Authors: Akbar Jamali

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According to Hegel’s philosophy, constitution of any given nation is the best expression of its national Self-Consciousness. Since constitution is the place in which freedom and Universal Rights is expressed, and since the essence of Self-consciousness is freedom, the development of self-consciousness and consequently freedom, is the direct cause of the development of constitution. Self-consciousness develops in the human history according to its own internal and external dialectic; therefore, it is essentially a dynamic phenomenon. However, constitution is supposed to be a stable foundation for the legal system of state and society. Therefore, the dilemma is: how the dynamic and contradictory nature of Self-Consciousness is the foundation of constitution that supposed to be the stable base of legal system of state and society. According to Hegel’s philosophy, the contradiction between the dynamic self- consciousness and the static constitution and state has an essential role in the formation of social movements within any given state. Self-consciousness is the phenomenology of Spirit in the human history. Subjective Spirit expresses itself in the different shapes of Self-consciousness in human spirit. These different shapes of self-consciousness must be identical with its contradiction; Objective Spirit. State is the highest form of the objective Spirit. Therefore, state and its foundation namely ‘constitution’ must be identical with Self-consciousness. "Spirit cannot remain forever alienated from its expression." Hegel states. Self-consciousness is the Subjective Spirit, it freely develops according to its internal and external contradictions, but since it must be always identical with its expression namely constitution, its development results to alienation. They way by which self-consciousness became again identical with the constitution determines the nature of legal and political development of any given society and state. In the democratic states, self-consciousness shows itself partially in the public opinion. In the process of election, this public opinion changes the ruling parties that construct the government. In democracies, self-consciousness or subjective spirit is in a dialectical relationship with state or the Objective Spirit. Therefore, it cannot remain alienated with its expression that is political system and its constitution. But, in the autocracies Self-consciousness cannot easily express itself in the government and its constitution. More Self-consciousness develops more it becomes alienated with its expression that is the state and its constitution. Rebel and revolution are the symptom of alienation of Spirit (self-consciousness) with its expression (state and its constitution).

Keywords: alienation, constitution, self-consciousness, spirit

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Authors: Temitope Francis Abiodun

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The practice of intelligence is not limited to the machinery of a nation state alone, yet not much research or analysis has been directed towards the spy-crafts and tradecrafts engaged in by violent non-state actors (VNSAs) in the international community. The rise of 'private sector intelligence' in more recent years has only just begun to be interrogated by practitioners and academics. However, the use of intelligence by insurgents and other groups assembled to achieve varied forms of politico-military outcomes has often been overlooked. This paper examined the factors and conditions that gave rise to an increase in violent non-state actors (VNSAs), strategies aiding their deployment of insurgent intelligence, and as well the implications of their activities on global security. The failed state theory was adopted, while a descriptive research design served as the framework for the study. Data were collected from primary and secondary sources. The paper, however, revealed there were massive deployments of insurgent intelligence by violent non-state actors in contrast to a faulty pre-conception that insurgents were not as highly trained in deployment of intelligence as state actors, having assumed that the VNSAs lacked the sophistication to produce intelligence. However, the strategic objectives of insurgents (VNSAs) were revealed to depend on well-organized information gathering operations that feed into the tactical executions of their insurgency. The paper recommends, therefore, there is a need for adequate training on the part of security personnel in the states to be alive to their responsibilities; and there is also a need to ensure adequate border control and management to checkmate the influx of the various violent or deadly movements across global frontiers.

Keywords: terrorism, non-violent state actors, private sector intelligence, security

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Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Tina Beyene

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In this paper examines the state of grassroots feminist activism in the backdrop of state feminism in Ethiopia. Specifically, I examine the impact of the Charities and Societies Proclamation (aka CSO law), a 2009 law that banned so-called foreign NGOs—i.e., those receiving more than 10% of its operating budget from non-local sources— from working in the areas of human rights, democracy, governance, and gender equality. Viewed as government retribution for the NGO opposition to the government in the 2005 elections, the law aimed to halt the work groups such as the Ethiopian Women Lawyers Association (EWLA), who were defined as a “foreign” NGO. Based on interviews with prominent Ethiopian women’s rights leaders in Addis Ababa, Ethiopia, I assess how grassroots feminist organizing adapts to state suppression on the one hand, and the aggressive entry of the state into women’s rights work on the other hand. While the 2009 law has slowed down the work of women’s rights activism, displaced feminists view feminist advocacy as cyclical and the state as neither fully adversarial nor an ally but rather as an instable entity that at times provides political openings to push ambitious feminist agendas. Grassroots activists are regrouping and developing new political responses strategies such as coding rights issues to fit state mandate; dissembling rights work in permissible social provision language; rechanneling political work into informal spaces and unregistered social clubs; innovating new funding partnerships, and reassembling as privately held research and advocacy companies. my study reveals how grassroots feminist politics operates in the shadow of a hostile state and within the confines of local politics.

Keywords: grassroots feminism, ethiopian feminism, civil society and gender, state feminism

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Authors: Gonzalez Carlos, Martinez Fransisco

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The creation of a seismic prediction model that considers all the regional variations and perfectly adjusts its results to the response spectra is very complicated. To achieve statistically acceptable results, it is necessary to process a sufficiently robust data set, and even if high efficiencies are achieved, this model will only work properly in this region. However, when using it in other regions, differences are found due to different parameters that have not been calibrated to other regions, such as the site effect. The fact that impedance contrasts, as well as other factors belonging to the site, have a great influence on the local response is well known, which is why this work, using the residual method, is intended to establish a regional calibration of the corresponding parameters site effect for the Spain region in the global GMPM BSSA 14.

Keywords: GMPM, seismic prediction equations, residual method, response spectra, impedance contrast

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Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

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Authors: R. Simutis, V. Galvanauskas, D. Levisauskas, J. Repsyte, V. Grincas

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This paper deals with advanced state estimation algorithms for estimation of biomass concentration and specific growth rate in a typical fed-batch biotechnological process. This biotechnological process was represented by a nonlinear mass-balance based process model. Extended Kalman Filter (EKF) and Particle Filter (PF) was used to estimate the unmeasured state variables from oxygen uptake rate (OUR) and base consumption (BC) measurements. To obtain more general results, a simplified process model was involved in EKF and PF estimation algorithms. This model doesn’t require any special growth kinetic equations and could be applied for state estimation in various bioprocesses. The focus of this investigation was concentrated on the comparison of the estimation quality of the EKF and PF estimators by applying different measurement noises. The simulation results show that Particle Filter algorithm requires significantly more computation time for state estimation but gives lower estimation errors both for biomass concentration and specific growth rate. Also the tuning procedure for Particle Filter is simpler than for EKF. Consequently, Particle Filter should be preferred in real applications, especially for monitoring of industrial bioprocesses where the simplified implementation procedures are always desirable.

Keywords: biomass concentration, extended Kalman filter, particle filter, state estimation, specific growth rate

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Authors: Mouna Abarkan, Abdelillah Byou, Nacer M'Sirdi, El Hossain Abarkan

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This paper presents the estimation of the state of charge of the battery using two types of observers. The battery model used is the combination of a voltage source, which is the open circuit battery voltage of a strength corresponding to the connection of resistors and electrolyte and a series of parallel RC circuits representing charge transfer phenomena and diffusion. An adaptive observer applied to this model is proposed, this observer to estimate the battery state of charge of the battery is based on EFK and sliding mode that is known for their robustness and simplicity implementation. The results are validated by simulation under MATLAB/Simulink and implemented in Arduino-LabView.

Keywords: model of the battery, adaptive sliding mode observer, the EFK observer, estimation of state of charge, SOC, implementation in Arduino/LabView

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Authors: Mukesh Khadka, Alexander Paz, Hanns de la Fuente-Mella

Abstract:

Typically, pavement performance models are developed in two steps: (i) pavement segments with similar characteristics are grouped together to form a cluster, and (ii) the corresponding performance models are developed using statistical techniques. A challenge is to select the characteristics that define clusters and the segments associated with them. If inappropriate characteristics are used, clusters may include homogeneous segments with different performance behavior or heterogeneous segments with similar performance behavior. Prediction accuracy of performance models can be improved by grouping the pavement segments into more uniform clusters by including both characteristics and a performance measure. This grouping is not always possible due to limited information. It is impractical to include all the potential significant factors because some of them are potentially unobserved or difficult to measure. Historical performance of pavement segments could be used as a proxy to incorporate the effect of the missing potential significant factors in clustering process. The current state-of-the-art proposes Clusterwise Linear Regression (CLR) to determine the pavement clusters and the associated performance models simultaneously. CLR incorporates the effect of significant factors as well as a performance measure. In this study, a mathematical program was formulated for CLR models including multiple explanatory variables. Pavement data collected recently over the entire state of Nevada were used. International Roughness Index (IRI) was used as a pavement performance measure because it serves as a unified standard that is widely accepted for evaluating pavement performance, especially in terms of riding quality. Results illustrate the advantage of the using CLR. Previous studies have used CLR along with experimental data. This study uses actual field data collected across a variety of environmental, traffic, design, and construction and maintenance conditions.

Keywords: clusterwise regression, pavement management system, performance model, optimization

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Authors: Daniel S. Brox

Abstract:

Starting from earthquake fault dynamic equations, a correspondence between earthquake occurrence statistics in a seismic region before a major earthquake and eigenvalue statistics of a differential operator whose bound state eigenfunctions characterize the distribution of stress in the seismic region is derived. Modeling these eigenvalue statistics with a 2D Coulomb gas statistical physics model, previously reported deviation of seismic activation earthquake occurrence statistics from Gutenberg-Richter statistics in time intervals preceding the major earthquake is derived. It also explains how statistical physics modeling predicts a finite-dimensional nonlinear dynamic system that describes real-time velocity model evolution in the region undergoing seismic activation and how this prediction can be tested experimentally.

Keywords: seismic activation, statistical physics, geodynamics, signal processing

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Authors: Ravneil Nand

Abstract:

Cooperative coevolution uses problem decomposition methods to solve a larger problem. The problem decomposition deals with breaking down the larger problem into a number of smaller sub-problems depending on their method. Different problem decomposition methods have their own strengths and limitations depending on the neural network used and application problem. In this paper we are introducing a new problem decomposition method known as Hidden-Neuron Level Decomposition (HNL). The HNL method is competing with established problem decomposition method in time series prediction. The results show that the proposed approach has improved the results in some benchmark data sets when compared to the standalone method and has competitive results when compared to methods from literature.

Keywords: cooperative coevaluation, feed forward network, problem decomposition, neuron, synapse

Procedia PDF Downloads 336

Authors: Wee Akina Sia Seng Lee, Shabeshan Rengasamy, Lim Boon Hooi, Chandrakalavaratharajoo, Mohd Ibrahim K. Azeez

Abstract:

This study was conducted to examine the effectiveness of Teaching Games For Understanding (TGFU) in improving the hockey tactical skills and state self-confidence among 16-year-old students. Two hundred fifty nine (259) school students were selected for the study based on the intact sampling method. One class was used as the control group (Boys=60, Girls=70), while another as the treatment group (Boys=60, Girls=69) underwent intervention with TGFU in physical education class conducted twice a week for four weeks. The Games Performance Assessment Instrument was used to observe the hockey tactical skills and The State Self-Confidence Inventory was used to determine the state of self-confidence among the students. After four weeks, ANCOVA analysis indicated the treatment groups had significant improvement in hockey tactical skills with F (1, 118) =313.37, p < .05 for school boys, and F (1, 136) =92.62, p < .05 for school girls. The Mann Whitney U test also showed the treatment groups had significant improvement in state self-confidence with U=428.50, z= -7.22, p < .05, r=.06 for school boys. ANCOVA analysis also showed the treatment group had significant improvement in state self-confidence with F (1, 136) =74.40, p < .05 for school girls. This indicates that TGFU in a 40 minute physical education class conducted twice a week for four weeks can significantly improve the hockey tactical skills and state self-confidence among 16-year-old students. The findings give new knowledge to PE teachers to implement the TGFU method as it enhances the hockey tactical skills and state self-confidence among 16-year-old students. Some recommendation was suggested for future research.

Keywords: Teaching Games For Understanding (TGFU), traditional teaching, hockey tactical skills, state self-confidence

Procedia PDF Downloads 354