Search results for: deep profile control

Authors: Bruno B. Oliveira, Arnaldo P. Mourão, Teógenes A. da Silva

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The Brazilian legislation has only established diagnostic reference levels (DRLs) in terms of Multiple Scan Average Dose (MSAD) as a quality control parameter for computed tomography (CT) scanners. Compliance with DRLs can be verified by measuring the Computed Tomography Kerma Index (Ca,100) with a pencil ionization chamber or by obtaining the kerma distribution in CT scans with radiochromic films or rod shape lithium fluoride termoluminescent dosimeters (TLD-100). TL dosimeters were used to record kerma profiles and to determine MSAD values of a Bright Speed model GE CT scanner. Measurements were done with radiochromic films and TL dosimeters distributed in cylinders positioned in the center and in four peripheral bores of a standard polymethylmethacrylate (PMMA) body CT dosimetry phantom. Irradiations were done using a protocol for adult chest. The maximum values were found at the midpoint of the longitudinal axis. The MSAD values obtained with three dosimetric techniques were compared.

Keywords: Kerma profile, CT, MSAD, patient dosimetry

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Authors: Romisaa Ali

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The Policy Gradient approach is a subset of the Deep Reinforcement Learning (DRL) combines Deep Neural Networks (DNN) with Reinforcement Learning (RL). This approach finds the optimal policy of robot movement, based on the experience it gains from interaction with its environment. Unlike previous policy gradient algorithms, which were unable to handle the two types of error variance and bias introduced by the DNN model due to over- or underestimation, this algorithm is capable of handling both types of error variance and bias. This article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: Deep neural networks, deep reinforcement learning, Proximal Policy Optimization, state-of-the-art, trust region policy optimization.

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Authors: Humaira Liaquat, Nadeem Iftikhar, Shaukat Ali, Zohaib Zafar Iqbal

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In the current age, retrieval of relevant information from massive amount of data is a challenging job. Over the years, precise and relevant retrieval of information has attained high significance. There is a growing need in the market to build systems, which can retrieve multimedia information that precisely meets the user's current needs. In this paper, we have introduced a framework for refining query results before showing it to the user, using ambient intelligence, user profile, group profile, user location, time, day, user device type and extracted features. A prototypic tool was also developed to demonstrate the efficiency of the proposed approach.

Keywords: Context aware retrieval, Information retrieval, Ambient Intelligence, Multimedia databases, User and group profile.

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Authors: Nazmona Mat Ali, Kecheng Liu

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Knowledge is a key asset for any organisation to sustain competitive advantages, but it is difficult to identify and represent knowledge which is needed to perform activities in business processes. The effective knowledge management and support for relevant business activities definitely gives a huge impact to the performance of the organisation as a whole. This is because that knowledge have the functions of directing, coordinating and controlling actions within business processes. The study has introduced organisational morphology, a norm-based approach by applying semiotic theories which emphasise on the representation of knowledge in norms. This approach is concerned with the identification of activities into three categories: substantive, communication and control activities. All activities are directed by norms; hence three types of norms exist; each is associated to a category of activities. The paper describes the approach briefly and illustrates the application of this approach through a case study of academic activities in higher education institutions. The result of the study shows that the approach provides an effective way to profile business knowledge and the profile enables the understanding and specification of business requirements of an organisation.

Keywords: Business knowledge, Business process, Norms, Semiotics, Organisational morphology

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Authors: Firas Gerges, Frank Y. Shih

Abstract:

Malignant Melanoma, known simply as Melanoma, is a type of skin cancer that appears as a mole on the skin. It is critical to detect this cancer at an early stage because it can spread across the body and may lead to the patient death. When detected early, Melanoma is curable. In this paper we propose a deep learning model (Convolutional Neural Networks) in order to automatically classify skin lesion images as Malignant or Benign. Images underwent certain pre-processing steps to diminish the effect of the normal skin region on the model. The result of the proposed model showed a significant improvement over previous work, achieving an accuracy of 97%.

Keywords: Deep learning, skin cancer, image processing, melanoma.

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Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

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As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling. The research proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling. The paper concludes the challenges and improvement directions for Deep Reinforcement Learning-based resource scheduling algorithms.

Keywords: Resource scheduling, deep reinforcement learning, distributed system, artificial intelligence.

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Authors: Dzierzbicka-Glowacka, A. Lemieszek, M. Figiela

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In 2010-2011, the research on zooplankton was conducted in the southern part of the Baltic Sea to determine seasonal variability in changes occurring throughout the zooplankton in 2010 and 2011, both in the region of Gdańsk Deep, and in the western part of Gdańsk Bay. The research in the sea showed that the taxonomic composition of holoplankton in the southern part of the Baltic Sea was similar to that recorded in this region for many years. The maximum values of abundance and biomass of zooplankton both in the Deep and the Bay of Gdańsk were observed in the summer season. Copepoda dominated in the composition of zooplankton for almost the entire study period, while rotifers occurred in larger numbers only in the summer 2010 in the Gdańsk Deep as well as in May and July 2010 in the western part of Gdańsk Bay, and meroplankton – in April 2011.

Keywords: Baltic Sea, composition, Gdańsk Bay, zooplankton.

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Authors: M. Sedighizadeh, A. Rezazadeh

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This paper presents a method for the optimal allocation of Distributed generation in distribution systems. In this paper, our aim would be optimal distributed generation allocation for voltage profile improvement and loss reduction in distribution network. Genetic Algorithm (GA) was used as the solving tool, which referring two determined aim; the problem is defined and objective function is introduced. Considering to fitness values sensitivity in genetic algorithm process, there is needed to apply load flow for decision-making. Load flow algorithm is combined appropriately with GA, till access to acceptable results of this operation. We used MATPOWER package for load flow algorithm and composed it with our Genetic Algorithm. The suggested method is programmed under MATLAB software and applied ETAP software for evaluating of results correctness. It was implemented on part of Tehran electricity distributing grid. The resulting operation of this method on some testing system is illuminated improvement of voltage profile and loss reduction indexes.

Keywords: Distributed Generation, Allocation, Voltage Profile, losses, Genetic Algorithm.

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Authors: I. Oloyede

Abstract:

The study investigates the accuracy and loss of deep learning algorithms with the set of coronavirus (COVID-19) images dataset by comparing Bayesian convolutional neural network and traditional convolutional neural network in low dimensional dataset. 50 sets of X-ray images out of which 25 were COVID-19 and the remaining 20 were normal, twenty images were set as training while five were set as validation that were used to ascertained the accuracy of the model. The study found out that Bayesian convolution neural network outperformed conventional neural network at low dimensional dataset that could have exhibited under fitting. The study therefore recommended Bayesian Convolutional neural network (BCNN) for android apps in computer vision for image detection.

Keywords: BCNN, CNN, Images, COVID-19, Deep Learning.

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Authors: Nurcihan Hacioglu, Cigdem Gul, Murat Tosunoglu

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In this article, the antibiogram and heavy metal resistance profile of the bacteria isolated from total 34 studied animals (Pelophylax ridibundus = 12; Mauremys rivulata = 14; Natrix natrix = 8) captured around the Biga Stream, are described. There was no database information on antibiogram and heavy metal resistance profile of bacteria from these area’s amphibians and reptiles. A total of 200 bacteria were successfully isolated from cloaca and oral samples of the aquatic amphibians and reptiles as well as from the water sample. According to Jaccard’s similarity index, the degree of similarity in the bacterial flora was quite high among the amphibian and reptile species under examination, whereas it was different from the bacterial diversity in the water sample. The most frequent isolates were A. hydrophila (31.5%), B. pseudomallei (8.5%), and C. freundii (7%). The total numbers of bacteria obtained were as follows: 45 in P. ridibundus, 45 in N. natrix 30 in M. rivulata, and 80 in the water sample. The result showed that cefmetazole was the most effective antibiotic to control the bacteria isolated in this study and that approximately 93.33% of the bacterial isolates were sensitive to this antibiotic. The multiple antibiotic resistances (MAR) index indicated that P. ridibundus (0.95) > N. natrix (0.89) > M. rivulata (0.39). Furthermore, all the tested heavy metals (Pb+2, Cu+2, Cr+3, and Mn+2) inhibit the growth of the bacterial isolates at different rates. Therefore, it indicated that the water source of the animals was contaminated with both antibiotic residues and heavy metals.

Keywords: Amphibian, Bacteriological Quality, Reptile, Antibiotic & Heavy Metal Resistance.

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Authors: Khaing Yadana Swe, Lillie Dewan

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At present, the cascade PID control is widely used to control the superheating temperature (main steam temperature). As Main Steam Temperature has the characteristics of large inertia, large time-delay and time varying, etc., conventional PID control strategy cannot achieve good control performance. In order to overcome the bad performance and deficiencies of main steam temperature control system, Model Free Adaptive Control (MFAC) - P cascade control system is proposed in this paper. By substituting MFAC in PID of the main control loop of the main steam temperature control, it can overcome time delays, non-linearity, disturbance and time variation.

Keywords: Model free Adaptive Control, Cascade Control, Adaptive Control, PID.

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: Deep learning, convolutional neural network, LSTM, housing prediction.

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Authors: K. Varesi

Abstract:

This paper proposes a Particle Swarm Optimization (PSO) based technique for the optimal allocation of Distributed Generation (DG) units in the power systems. In this paper our aim is to decide optimal number, type, size and location of DG units for voltage profile improvement and power loss reduction in distribution network. Two types of DGs are considered and the distribution load flow is used to calculate exact loss. Load flow algorithm is combined appropriately with PSO till access to acceptable results of this operation. The suggested method is programmed under MATLAB software. Test results indicate that PSO method can obtain better results than the simple heuristic search method on the 30-bus and 33- bus radial distribution systems. It can obtain maximum loss reduction for each of two types of optimally placed multi-DGs. Moreover, voltage profile improvement is achieved.

Keywords: Distributed Generation (DG), Optimal Allocation, Particle Swarm Optimization (PSO), Power Loss Minimization, Voltage Profile Improvement.

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Authors: Kaustubh Chakradeo, Michael Delves, Sofya Titarenko

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Malaria is a serious disease which affects hundreds of millions of people around the world, each year. If not treated in time, it can be fatal. Despite recent developments in malaria diagnostics, the microscopy method to detect malaria remains the most common. Unfortunately, the accuracy of microscopic diagnostics is dependent on the skill of the microscopist and limits the throughput of malaria diagnosis. With the development of Artificial Intelligence tools and Deep Learning techniques in particular, it is possible to lower the cost, while achieving an overall higher accuracy. In this paper, we present a VGG-based model and compare it with previously developed models for identifying infected cells. Our model surpasses most previously developed models in a range of the accuracy metrics. The model has an advantage of being constructed from a relatively small number of layers. This reduces the computer resources and computational time. Moreover, we test our model on two types of datasets and argue that the currently developed deep-learning-based methods cannot efficiently distinguish between infected and contaminated cells. A more precise study of suspicious regions is required.

Keywords: Malaria, deep learning, DL, convolution neural network, CNN, thin blood smears.

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Authors: N. Pekin Alakoç, A. Apaydın

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Quality control charts indicate out of control conditions if any nonrandom pattern of the points is observed or any point is plotted beyond the control limits. Nonrandom patterns of Shewhart control charts are tested with sensitizing rules. When the processes are defined with fuzzy set theory, traditional sensitizing rules are insufficient for defining all out of control conditions. This is due to the fact that fuzzy numbers increase the number of out of control conditions. The purpose of the study is to develop a set of fuzzy sensitizing rules, which increase the flexibility and sensitivity of fuzzy control charts. Fuzzy sensitizing rules simplify the identification of out of control situations that results in a decrease in the calculation time and number of evaluations in fuzzy control chart approach.

Keywords: Fuzzy set theory, Quality control charts, Run Rules, Unnatural patterns.

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Authors: A. Abbasi Moshaii, M. Soltan Rezaee, M. Mohammadi Moghaddam

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Control of some mechanisms is hard because of their complex dynamic equations. If part of the complexity is resulting from uncertainties, an efficient way for solving that is robust control. By this way, the control procedure could be simple and fast and finally, a simple controller can be designed. One kind of these mechanisms is 3-RRR which is a parallel mechanism and has three revolute joints. This paper aims to robust control a 3-RRR planner mechanism and it presents that this could be used for other mechanisms. So, a significant problem in mechanisms control could be solved. The relevant diagrams are drawn and they show the correctness of control process.

Keywords: 3-RRR, dynamic equations, mechanisms control, structural uncertainty.

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Authors: Taylan Kabbani, Ekrem Duman

Abstract:

Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining  the financial assets price ”prediction” step and the ”allocation” step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. This work represents a DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem as a Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. We then solved the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and achieved a 2.68 Sharpe ratio on the test dataset. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of DRL in financial markets over other types of machine learning and proves its credibility and advantages of strategic decision-making.

Keywords: Autonomous agent, deep reinforcement learning, MDP, sentiment analysis, stock market, technical indicators, twin delayed deep deterministic policy gradient.

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Authors: Djarot B. Darmadi, John Norrish, Anh Kiet Tieu

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Solutions for the temperature profile around a moving heat source are obtained using both analytic and finite element (FEM) methods. Analytic and FEM solutions are applied to study the temperature profile in welding. A moving heat source is represented using both point heat source and uniform distributed disc heat source models. Analytic solutions are obtained by solving the partial differential equation for energy conservation in a solid, and FEM results are provided by simulating welding using the ANSYS software. Comparison is made for quasi steady state conditions. The results provided by the analytic solutions are in good agreement with results obtained by FEM.

Keywords: Analytic solution, FEM, Temperature profile, HeatSource Model

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Authors: Priyanka Sen, Kaibalya Prasad Panda, Soumyakanta Samantaray, Sreyasee Rout, Bishnupriya Biswal

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Deregulation in the power system industry and the invention of new technologies for producing electrical energy has led to innovations in power system planning. Distributed generation (DG) is one of the most attractive technologies that bring different kinds of advantages to a lot of entities, engaged in power systems. In this paper, a model for considering DGs in the power system planning problem is presented. Dynamic power system planning for reduction of maintenance and operational cost is presented in this paper. In addition to that, a modified particle swarm optimization (PSO) is used to find the optimal topology solution. Voltage Profile Improvement Index (VPII) and Line Loss Reduction Index (LLRI) are taken as benefit index of employing DG. The effectiveness of this method is demonstrated through examination of IEEE 30 bus test system.

Keywords: Distributed generation, line loss reduction index, particle swarm optimization, power system, voltage profile improvement index.

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Authors: A. K. Kamath, N. M. Singh, R. Pasumarthy

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This paper investigates the control of a bouncing ball using Model Predictive Control. Bouncing ball is a benchmark problem for various rhythmic tasks such as juggling, walking, hopping and running. Humans develop intentions which may be perceived as our reference trajectory and tries to track it. The human brain optimizes the control effort needed to track its reference; this forms the central theme for control of bouncing ball in our investigations.

Keywords: Bouncing Ball, impact dynamics, intermittent control, model predictive control.

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Authors: H.Mohammadi Majd, M.Jalali Azizpour, A.V. Hoseini

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In this paper back-propagation artificial neural network (BPANN) is employed to predict the limiting drawing ratio (LDR) of the deep drawing process. To prepare a training set for BPANN, some finite element simulations were carried out. die and punch radius, die arc radius, friction coefficient, thickness, yield strength of sheet and strain hardening exponent were used as the input data and the LDR as the specified output used in the training of neural network. As a result of the specified parameters, the program will be able to estimate the LDR for any new given condition. Comparing FEM and BPANN results, an acceptable correlation was found.

Keywords: Back-propagation artificial neural network(BPANN), deep drawing, prediction, limiting drawing ratio (LDR).

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Authors: Yong-can Cao, Wei-dong Zhang

Abstract:

To deal with random delays in Networked Control System (NCS), Modified Fuzzy PID Controller is introduced in this paper to implement real-time control adaptively. Via adjusting the control signal dynamically, the system performance is improved. In this paper, the design process and the ultimate simulation results are represented. Finally, examples and corresponding comparisons prove the significance of this method.

Keywords: Fuzzy Control, Networked Control System, PID, Random Delays

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Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao

Abstract:

Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.

Keywords: Personal information, deep learning, auto fill, NLP, document analysis.

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Authors: V. Ghadamyari, F. Samadi, F. Kowsary

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An inverse geometry problem is solved to predict an unknown irregular boundary profile. The aim is to minimize the objective function, which is the difference between real and computed temperatures, using three different versions of Conjugate Gradient Method. The gradient of the objective function, considered necessary in this method, obtained as a result of solving the adjoint equation. The abilities of three versions of Conjugate Gradient Method in predicting the boundary profile are compared using a numerical algorithm based on the method. The predicted shapes show that due to its convergence rate and accuracy of predicted values, the Powell-Beale version of the method is more effective than the Fletcher-Reeves and Polak –Ribiere versions.

Keywords: Boundary elements, Conjugate Gradient Method, Inverse Geometry Problem, Sensitivity equation.

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Authors: H. B. Tibar, Z. Y. Jiang

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In this study, the influence of rolling process parameters such as the work roll cross angle and work roll shifting value on the strip shape and profile of aluminum have been investigated under dry conditions at a speed ratio of 1.3 using Hille 100 experimental mill. The strip profile was found to improve significantly with increase in work roll cross angle from 0^o to 1^o, with an associated decrease in rolling force. The effect of roll shifting (from 0 to 8mm) was not as significant as the roll cross angle. However, an increase in work roll shifting value achieved a similar decrease in rolling force as that of work roll cross angle. The effect of work roll shifting was also found to be maximum at an optimum roll speed of 0.0986 m/s for the desired thickness. Of all these parameters, the most significant effect of the strip shape profile was observed with variation of work roll cross angle. However, the rolling force can be a significantly reduced by either increasing the the work roll cross angle or work roll shifting.

Keywords: Rolling speed ratio, strip shape, work roll cross angle, work roll shifting.

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Authors: M. Reza Bagerzadeh Karimi, M. Mahdi Bagerzadeh Karimi

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In the present paper, active control system is used in different heights of the building and the most effective part was studied where the active control system is applied. The mathematical model of the building is established in MATLAB and in order to active control the system FLC method was used. Three different locations of the building are chosen to apply active control system, namely at the lowest story, the middle height of the building, and at the highest point of the building with TMD system. The equation of motion was written for high rise building and it was solved by statespace method. Also passive control was used with Tuned Mass Damper (TMD) at the top floor of the building to show the robustness of FLC method when compared with passive control system.

Keywords: Fuzzy Logic Controller (FLC), Tuned Mass Damper(TMD), Active control, passive control

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Authors: H.Mohammadi Majd, M.Jalali Azizpour, M. Goodarzi

Abstract:

In this paper back-propagation artificial neural network (BPANN) with Levenberg–Marquardt algorithm is employed to predict the limiting drawing ratio (LDR) of the deep drawing process. To prepare a training set for BPANN, some finite element simulations were carried out. die and punch radius, die arc radius, friction coefficient, thickness, yield strength of sheet and strain hardening exponent were used as the input data and the LDR as the specified output used in the training of neural network. As a result of the specified parameters, the program will be able to estimate the LDR for any new given condition. Comparing FEM and BPANN results, an acceptable correlation was found.

Keywords: BPANN, deep drawing, prediction, limiting drawingratio (LDR), Levenberg–Marquardt algorithm

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Authors: Jonghyun Park, Toan Nguyen Dinh, Gueesang Lee

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In this paper, we present a novel approach to accurately detect text regions including shop name in signboard images with complex background for mobile system applications. The proposed method is based on the combination of text detection using edge profile and region segmentation using fuzzy c-means method. In the first step, we perform an elaborate canny edge operator to extract all possible object edges. Then, edge profile analysis with vertical and horizontal direction is performed on these edge pixels to detect potential text region existing shop name in a signboard. The edge profile and geometrical characteristics of each object contour are carefully examined to construct candidate text regions and classify the main text region from background. Finally, the fuzzy c-means algorithm is performed to segment and detected binarize text region. Experimental results show that our proposed method is robust in text detection with respect to different character size and color and can provide reliable text binarization result.

Keywords: Text detection, edge profile, signboard image, fuzzy clustering.

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Authors: Julius Onyancha, Valentina Plekhanova

Abstract:

One of the significant issues facing web users is the amount of noise in web data which hinders the process of finding useful information in relation to their dynamic interests. Current research works consider noise as any data that does not form part of the main web page and propose noise web data reduction tools which mainly focus on eliminating noise in relation to the content and layout of web data. This paper argues that not all data that form part of the main web page is of a user interest and not all noise data is actually noise to a given user. Therefore, learning of noise web data allocated to the user requests ensures not only reduction of noisiness level in a web user profile, but also a decrease in the loss of useful information hence improves the quality of a web user profile. Noise Web Data Learning (NWDL) tool/algorithm capable of learning noise web data in web user profile is proposed. The proposed work considers elimination of noise data in relation to dynamic user interest. In order to validate the performance of the proposed work, an experimental design setup is presented. The results obtained are compared with the current algorithms applied in noise web data reduction process. The experimental results show that the proposed work considers the dynamic change of user interest prior to elimination of noise data. The proposed work contributes towards improving the quality of a web user profile by reducing the amount of useful information eliminated as noise.

Keywords: Web log data, web user profile, user interest, noise web data learning, machine learning.

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Authors: Vasilescu M., Rusu L, Balseanu TA., Cosma G., Dragomir M.

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The aim of our research was to evaluate the effects of physical exercise on lipid profile and anthropometric characteristics in young subjects, diagnosed with metabolic syndrome (MS). The study has been developed during 28 weeks on 20 young obese patients which have undertaken an intermittent submaximal exercise program. After 28 weeks of physical activity, the results show significant effects on anthropometric characteristics and serum lipid profile of research subjects. Additionally, the results of this study confirms the major correlation between the variations of intraabdominal adiposity, determined ultrasonographycally, and the changes of serum lipid concentrations, a better correlation than it is used abdominal circumference or body weight index.

Keywords: lipid metabolism, adiposity, physical exercise

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