Proposed IMS infrastructure improvement project, Seward, Alaska. Final environmental impact statement

Energy Technology Data Exchange (ETDEWEB)

1994-09-01

This Environmental Impact Statement (EIS) examines a proposal for improvements at the existing University of Alaska, Fairbanks, Institute of Marine Science (IMS), Seward Marine Center. The Exxon Valdez Oil Spill (EVOS) Trustee Council is proposing to improve the existing research infrastructure to enhance the EVOS Trustee Council`s capabilities to study and rehabilitate marine mammals, marine birds, and the ecosystem injured by the Exxon Valdez oil spill. The analysis in this document focuses on the effects associated with construction and operation of the proposed project and its proposed alternatives. The EIS gives a detailed description of all major elements of the proposed project and its alternatives; identifies resources of major concern that were raised during the scoping process; describes the environmental background conditions of those resources; defines and analyzes the potential effects of the proposed project and its alternatives on these conditions; and identifies mitigating measures that are part of the project design as well as those proposed to minimize or reduce the adverse effects. Included in the EIS are written and oral comments received during the public comment period.

Proposed IMS infrastructure improvement project, Seward, Alaska. Final environmental impact statement

International Nuclear Information System (INIS)

1994-09-01

This Environmental Impact Statement (EIS) examines a proposal for improvements at the existing University of Alaska, Fairbanks, Institute of Marine Science (IMS), Seward Marine Center. The Exxon Valdez Oil Spill (EVOS) Trustee Council is proposing to improve the existing research infrastructure to enhance the EVOS Trustee Council's capabilities to study and rehabilitate marine mammals, marine birds, and the ecosystem injured by the Exxon Valdez oil spill. The analysis in this document focuses on the effects associated with construction and operation of the proposed project and its proposed alternatives. The EIS gives a detailed description of all major elements of the proposed project and its alternatives; identifies resources of major concern that were raised during the scoping process; describes the environmental background conditions of those resources; defines and analyzes the potential effects of the proposed project and its alternatives on these conditions; and identifies mitigating measures that are part of the project design as well as those proposed to minimize or reduce the adverse effects. Included in the EIS are written and oral comments received during the public comment period

DIAGNOSTIC AND PROPOSAL OF IMPROVEMENT FOR THE INNOVATION MANAGEMENT IN A TECHNOLOGICAL COMPANY

Directory of Open Access Journals (Sweden)

Rossetti, Germán

2017-12-01

Full Text Available The Innovation Management is defined as the process oriented to organize and lead available resources, both technical and economic, with the objective of increasing the creation of new products, processes, knowledge and their application in the structure of the company. Nowadays companies are immersed in a globalized world, where competition is higher, which implies their growing interest in innovating, developing and improving their products or services to take a leading position in the market. Therefore, it is essential to be at the vanguard of current needs and to use certain tools that help to offer better products or services, and to obtain higher benefits, economic and social, technological, prestige, among others. In this paper a diagnostic and proposal of improvement for the Innovation Management in a technology-based company, located in the province of Santa Fe, Argentina, is made. For this, a methodology that allows to evaluate the capacity to innovate of the company is applied. As a main conclusion, it can be said that the diagnostic and proposal of improvement provided to the company is the starting point to ensure a successful and continuous innovation management.

Improved delayed signal cancellation-based SRF-PLL for unbalanced grid

DEFF Research Database (Denmark)

Messo, Tuomas; Sihvo, Jussi; Yang, Dongsheng

2017-01-01

Problems with power quality in the grid have gained a lot of attention recently due to rapid increase in the amount of grid-connected power converters. The converter should produce sinusoidal currents also during abnormal conditions, such as unbalanced grid voltages. Several methods, like...... the delayed signal cancellation-based method (DSC), have been proposed to alleviate the detrimental effect of unbalance. This paper proposes an improvement to a delayed signal cancellation based synchronization algorithm for unbalanced grids. The proposed PLL structure employs only half of the delay required...

Improved Recommendations Based on Trust Relationships in Social Networks

Directory of Open Access Journals (Sweden)

Hao Tian

2017-03-01

Full Text Available In order to alleviate the pressure of information overload and enhance consumer satisfaction, personalization recommendation has become increasingly popular in recent years. As a result, various approaches for recommendation have been proposed in the past few years. However, traditional recommendation methods are still troubled with typical issues such as cold start, sparsity, and low accuracy. To address these problems, this paper proposed an improved recommendation method based on trust relationships in social networks to improve the performance of recommendations. In particular, we define trust relationship afresh and consider several representative factors in the formalization of trust relationships. To verify the proposed approach comprehensively, this paper conducted experiments in three ways. The experimental results show that our proposed approach leads to a substantial increase in prediction accuracy and is very helpful in dealing with cold start and sparsity.

Case base classification on digital mammograms: improving the performance of case base classifier

Science.gov (United States)

Raman, Valliappan; Then, H. H.; Sumari, Putra; Venkatesa Mohan, N.

2011-10-01

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. The aim of the research presented here is in twofold. First stage of research involves machine learning techniques, which segments and extracts features from the mass of digital mammograms. Second level is on problem solving approach which includes classification of mass by performance based case base classifier. In this paper we build a case-based Classifier in order to diagnose mammographic images. We explain different methods and behaviors that have been added to the classifier to improve the performance of the classifier. Currently the initial Performance base Classifier with Bagging is proposed in the paper and it's been implemented and it shows an improvement in specificity and sensitivity.

Facial expression recognition based on improved deep belief networks

Science.gov (United States)

Wu, Yao; Qiu, Weigen

2017-08-01

In order to improve the robustness of facial expression recognition, a method of face expression recognition based on Local Binary Pattern (LBP) combined with improved deep belief networks (DBNs) is proposed. This method uses LBP to extract the feature, and then uses the improved deep belief networks as the detector and classifier to extract the LBP feature. The combination of LBP and improved deep belief networks is realized in facial expression recognition. In the JAFFE (Japanese Female Facial Expression) database on the recognition rate has improved significantly.

Tariff-based incentives for improving coal-power-plant efficiencies in India

International Nuclear Information System (INIS)

Chikkatur, Ananth P.; Sagar, Ambuj D.; Abhyankar, Nikit; Sreekumar, N.

2007-01-01

Improving the efficiency of coal-based power plants plays an important role in improving the performance of India's power sector. It allows for increased consumer benefits through cost reduction, while enhancing energy security and helping reduce local and global pollution through more efficient coal use. A focus on supply-side efficiency also complements other ongoing efforts on end-use efficiency. The recent restructuring of the Indian electricity sector offers an important route to improving power plant efficiency, through regulatory mechanisms that allow for an independent tariff setting process for bulk purchases of electricity from generators. Current tariffs based on normative benchmarks for performance norms are hobbled by information asymmetry (where regulators do not have access to detailed performance data). Hence, we propose a new incentive scheme that gets around the asymmetry problem by setting performance benchmarks based on actual efficiency data, rather than on a normative basis. The scheme provides direct tariff-based incentives for efficiency improvements, while benefiting consumers by reducing electricity costs in the long run. This proposal might also be useful for regulators in other countries to incorporate similar incentives for efficiency improvement in power generation

Improved memristor-based relaxation oscillator

KAUST Repository

Mosad, Ahmed G.

2013-09-01

This paper presents an improved memristor-based relaxation oscillator which offers higher frequency and wider tunning range than the existing reactance-less oscillators. It also has the capability of operating on two positive supplies or alternatively a positive and negative supply. Furthermore, it has the advantage that it can be fully integrated on-chip providing an area-efficient solution. On the other hand, The oscillation concept is discussed then a complete mathematical analysis of the proposed oscillator is introduced. Furthermore, the power consumption of the new relaxation circuit is discussed and validated by the PSPICE circuit simulations showing an excellent agreement. MATLAB results are also introduced to demonstrate the resistance range and the corresponding frequency range which can be obtained from the proposed relaxation oscillator. © 2013 Elsevier Ltd.

Face-based recognition techniques: proposals for the metrological characterization of global and feature-based approaches

Science.gov (United States)

Betta, G.; Capriglione, D.; Crenna, F.; Rossi, G. B.; Gasparetto, M.; Zappa, E.; Liguori, C.; Paolillo, A.

2011-12-01

Security systems based on face recognition through video surveillance systems deserve great interest. Their use is important in several areas including airport security, identification of individuals and access control to critical areas. These systems are based either on the measurement of details of a human face or on a global approach whereby faces are considered as a whole. The recognition is then performed by comparing the measured parameters with reference values stored in a database. The result of this comparison is not deterministic because measurement results are affected by uncertainty due to random variations and/or to systematic effects. In these circumstances the recognition of a face is subject to the risk of a faulty decision. Therefore, a proper metrological characterization is needed to improve the performance of such systems. Suitable methods are proposed for a quantitative metrological characterization of face measurement systems, on which recognition procedures are based. The proposed methods are applied to three different algorithms based either on linear discrimination, on eigenface analysis, or on feature detection.

Face-based recognition techniques: proposals for the metrological characterization of global and feature-based approaches

International Nuclear Information System (INIS)

Betta, G; Capriglione, D; Crenna, F; Rossi, G B; Gasparetto, M; Zappa, E; Liguori, C; Paolillo, A

2011-01-01

Security systems based on face recognition through video surveillance systems deserve great interest. Their use is important in several areas including airport security, identification of individuals and access control to critical areas. These systems are based either on the measurement of details of a human face or on a global approach whereby faces are considered as a whole. The recognition is then performed by comparing the measured parameters with reference values stored in a database. The result of this comparison is not deterministic because measurement results are affected by uncertainty due to random variations and/or to systematic effects. In these circumstances the recognition of a face is subject to the risk of a faulty decision. Therefore, a proper metrological characterization is needed to improve the performance of such systems. Suitable methods are proposed for a quantitative metrological characterization of face measurement systems, on which recognition procedures are based. The proposed methods are applied to three different algorithms based either on linear discrimination, on eigenface analysis, or on feature detection

Personalized Multi-Student Improvement Based on Bayesian Cybernetics

Science.gov (United States)

Kaburlasos, Vassilis G.; Marinagi, Catherine C.; Tsoukalas, Vassilis Th.

2008-01-01

This work presents innovative cybernetics (feedback) techniques based on Bayesian statistics for drawing questions from an Item Bank towards personalized multi-student improvement. A novel software tool, namely "Module for Adaptive Assessment of Students" (or, "MAAS" for short), implements the proposed (feedback) techniques. In conclusion, a pilot…

Robotic excavator trajectory control using an improved GA based PID controller

Science.gov (United States)

Feng, Hao; Yin, Chen-Bo; Weng, Wen-wen; Ma, Wei; Zhou, Jun-jing; Jia, Wen-hua; Zhang, Zi-li

2018-05-01

In order to achieve excellent trajectory tracking performances, an improved genetic algorithm (IGA) is presented to search for the optimal proportional-integral-derivative (PID) controller parameters for the robotic excavator. Firstly, the mathematical model of kinematic and electro-hydraulic proportional control system of the excavator are analyzed based on the mechanism modeling method. On this basis, the actual model of the electro-hydraulic proportional system are established by the identification experiment. Furthermore, the population, the fitness function, the crossover probability and mutation probability of the SGA are improved: the initial PID parameters are calculated by the Ziegler-Nichols (Z-N) tuning method and the initial population is generated near it; the fitness function is transformed to maintain the diversity of the population; the probability of crossover and mutation are adjusted automatically to avoid premature convergence. Moreover, a simulation study is carried out to evaluate the time response performance of the proposed controller, i.e., IGA based PID against the SGA and Z-N based PID controllers with a step signal. It was shown from the simulation study that the proposed controller provides the least rise time and settling time of 1.23 s and 1.81 s, respectively against the other tested controllers. Finally, two types of trajectories are designed to validate the performances of the control algorithms, and experiments are performed on the excavator trajectory control experimental platform. It was demonstrated from the experimental work that the proposed IGA based PID controller improves the trajectory accuracy of the horizontal line and slope line trajectories by 23.98% and 23.64%, respectively in comparison to the SGA tuned PID controller. The results further indicate that the proposed IGA tuning based PID controller is effective for improving the tracking accuracy, which may be employed in the trajectory control of an actual excavator.

A proposed HTTP service based IDS

Directory of Open Access Journals (Sweden)

Mohamed M. Abd-Eldayem

2014-03-01

Full Text Available The tremendous growth of the web-based applications has increased information security vulnerabilities over the Internet. Security administrators use Intrusion-Detection System (IDS to monitor network traffic and host activities to detect attacks against hosts and network resources. In this paper IDS based on Naïve Bayes classifier is analyzed. The main objective is to enhance IDS performance through preparing the training data set allowing to detect malicious connections that exploit the http service. Results of application are demonstrated and discussed. In the training phase of the proposed IDS, at first a feature selection technique based on Naïve Bayes classifier is used, this technique identifies the most important HTTP traffic features that can be used to detect HTTP attacks. In the testing and running phases proposed IDS classifies the network traffic based on the requested service, then based on the selected features Naïve Bayes classifier is used to analyze the HTTP service based traffic and identifies the HTTP normal connections and attacks. The performance of the IDS is measured through experiments using NSL-KDD data set. The results show that the detection rate of the IDS is about 99%, the false-positive rate is about 1%, and the false-negative rate is about 0.25%; therefore, proposed IDS holds the highest detection rate and the lowest false alarm compared with other leading IDS. In addition, the proposed IDS based on Naïve Bayes is used to classify network connections as a normal or attack. And it holds a high detection rate and a low false alarm.

Coarse Alignment Technology on Moving base for SINS Based on the Improved Quaternion Filter Algorithm.

Science.gov (United States)

Zhang, Tao; Zhu, Yongyun; Zhou, Feng; Yan, Yaxiong; Tong, Jinwu

2017-06-17

Initial alignment of the strapdown inertial navigation system (SINS) is intended to determine the initial attitude matrix in a short time with certain accuracy. The alignment accuracy of the quaternion filter algorithm is remarkable, but the convergence rate is slow. To solve this problem, this paper proposes an improved quaternion filter algorithm for faster initial alignment based on the error model of the quaternion filter algorithm. The improved quaternion filter algorithm constructs the K matrix based on the principle of optimal quaternion algorithm, and rebuilds the measurement model by containing acceleration and velocity errors to make the convergence rate faster. A doppler velocity log (DVL) provides the reference velocity for the improved quaternion filter alignment algorithm. In order to demonstrate the performance of the improved quaternion filter algorithm in the field, a turntable experiment and a vehicle test are carried out. The results of the experiments show that the convergence rate of the proposed improved quaternion filter is faster than that of the tradition quaternion filter algorithm. In addition, the improved quaternion filter algorithm also demonstrates advantages in terms of correctness, effectiveness, and practicability.

Profitability diagnosis of refinery and improvement proposal; Seiyusho no shueki shindan to kaizen teian

Energy Technology Data Exchange (ETDEWEB)

Aoyama, H.

2000-07-01

Based on consulting service RPS-J under joint operation of Nikki, UOP LLC of U.S.A. and Nikki Universal, approaching ways applied for profitability improvement and improvement proposal for refineries and analyzing techniques used for resolution of process bottlenecks were described. In RPS-J, themes of (1) energy saving, (2) quality upgrading, (3) improvement of disintegrating ratio, (4) reduction of give-away, (5) improvement of equipment operation ratio, (6) reduction of maintenance cost, (7) effective utilization of catalysts, are considered for profitability improvement fields. Procedures from idea excavation for profitability improvement to realization of profitability improvement are carried out in the order of, (1) Grasping of the present state, (2) Excavation of improving items and selection, (3) Quantitative evaluation of draft profitability improvement plan and focusing, (4) Profitability improvement by operation improvement, (5) Profitability improvement by minor improvement, (6) Profitability improvement in middle- and long-term vision, (7) Final focusing by feasibility study. Afterwards, examination to economically solve bottlenecks of critical facilities, examination on bottlenecks of distillation tower and refining tower and utility analysis are carried out. RPS-J was already applied to 4 refineries including Muroran Refinery and Negishi Refinery of Nisseki Mitsubishi, and profitability improvement themes were found to improve profitability of 50 to 150 cents per barrel. (NEDO)

Methodological proposal for the definition of improvement strategies in logistics of SME

Directory of Open Access Journals (Sweden)

Yeimy Liseth Becerra

2014-12-01

Full Text Available A methodological proposal for defining strategies of improvement in logistics of SMEs is presented as a means to fulfill a specific objective of the project Methodological design on storage logistics, acquisition, ownership of information systems and communication for Colombian SMEs, baker subsector, which currently runs the research group SEPRO, of Universidad Nacional of Colombia and supported by Colciencias. The project corresponds to the completion of the last stage of the base project, and aims to implement the corresponding target, raised in the research project that has been developing the research group SEPRO. To do this, it was made a review of the methodology used during the execution of the basic project, as well as the state of the art of techniques used in similar research for the evaluation and definition of breeding strategies in SMEs logistics. Revised techniques were compared and a proposed methodology was configured, which consists of the techniques that represented the greatest advantages for the research development.

Improved Adaptive LSB Steganography Based on Chaos and Genetic Algorithm

Science.gov (United States)

Yu, Lifang; Zhao, Yao; Ni, Rongrong; Li, Ting

2010-12-01

We propose a novel steganographic method in JPEG images with high performance. Firstly, we propose improved adaptive LSB steganography, which can achieve high capacity while preserving the first-order statistics. Secondly, in order to minimize visual degradation of the stego image, we shuffle bits-order of the message based on chaos whose parameters are selected by the genetic algorithm. Shuffling message's bits-order provides us with a new way to improve the performance of steganography. Experimental results show that our method outperforms classical steganographic methods in image quality, while preserving characteristics of histogram and providing high capacity.

Improved Adaptive LSB Steganography Based on Chaos and Genetic Algorithm

Directory of Open Access Journals (Sweden)

Yu Lifang

2010-01-01

Full Text Available We propose a novel steganographic method in JPEG images with high performance. Firstly, we propose improved adaptive LSB steganography, which can achieve high capacity while preserving the first-order statistics. Secondly, in order to minimize visual degradation of the stego image, we shuffle bits-order of the message based on chaos whose parameters are selected by the genetic algorithm. Shuffling message's bits-order provides us with a new way to improve the performance of steganography. Experimental results show that our method outperforms classical steganographic methods in image quality, while preserving characteristics of histogram and providing high capacity.

Analysis and improvement of a chaos-based image encryption algorithm

International Nuclear Information System (INIS)

Xiao Di; Liao Xiaofeng; Wei Pengcheng

2009-01-01

The security of digital image attracts much attention recently. In Guan et al. [Guan Z, Huang F, Guan W. Chaos-based image encryption algorithm. Phys Lett A 2005; 346: 153-7.], a chaos-based image encryption algorithm has been proposed. In this paper, the cause of potential flaws in the original algorithm is analyzed in detail, and then the corresponding enhancement measures are proposed. Both theoretical analysis and computer simulation indicate that the improved algorithm can overcome these flaws and maintain all the merits of the original one.

Aircraft Route Recovery Based on An Improved GRASP Method

Directory of Open Access Journals (Sweden)

Yang He

2017-01-01

Full Text Available Aircrafts maintenance, temporary airport closures are common factors that disrupt normal flight schedule. The aircraft route recovery aims to recover original schedules by some strategies, including flights swaps, and cancellations, which is a NP-hard problem. This paper proposes an improved heuristic procedure based on Greedy Random Adaptive Search Procedure (GRASP to solve this problem. The effectiveness and high global optimization capability of the heuristic is illustrated through experiments based on large-scale problems. Compared to the original one, it is shown that the improved procedure can find feasible flight recovered schedules with lower cost in a short time.

Market-based transmission expansion planning by improved differential evolution

International Nuclear Information System (INIS)

Georgilakis, Pavlos S.

2010-01-01

The restructuring and deregulation has exposed the transmission planner to new objectives and uncertainties. As a result, new criteria and approaches are needed for transmission expansion planning (TEP) in deregulated electricity markets. This paper proposes a new market-based approach for TEP. An improved differential evolution (IDE) model is proposed for the solution of this new market-based TEP problem. The modifications of IDE in comparison to the simple differential evolution method are: (1) the scaling factor F is varied randomly within some range, (2) an auxiliary set is employed to enhance the diversity of the population, (3) the newly generated trial vector is compared with the nearest parent, and (4) the simple feasibility rule is used to treat the constraints. Results from the application of the proposed method on the IEEE 30-bus test system demonstrate the feasibility and practicality of the proposed IDE for the solution of TEP problem. (author)

Improving the spectral measurement accuracy based on temperature distribution and spectra-temperature relationship

Science.gov (United States)

Li, Zhe; Feng, Jinchao; Liu, Pengyu; Sun, Zhonghua; Li, Gang; Jia, Kebin

2018-05-01

Temperature is usually considered as a fluctuation in near-infrared spectral measurement. Chemometric methods were extensively studied to correct the effect of temperature variations. However, temperature can be considered as a constructive parameter that provides detailed chemical information when systematically changed during the measurement. Our group has researched the relationship between temperature-induced spectral variation (TSVC) and normalized squared temperature. In this study, we focused on the influence of temperature distribution in calibration set. Multi-temperature calibration set selection (MTCS) method was proposed to improve the prediction accuracy by considering the temperature distribution of calibration samples. Furthermore, double-temperature calibration set selection (DTCS) method was proposed based on MTCS method and the relationship between TSVC and normalized squared temperature. We compare the prediction performance of PLS models based on random sampling method and proposed methods. The results from experimental studies showed that the prediction performance was improved by using proposed methods. Therefore, MTCS method and DTCS method will be the alternative methods to improve prediction accuracy in near-infrared spectral measurement.

Short-term electricity price forecast based on the improved hybrid model

International Nuclear Information System (INIS)

Dong Yao; Wang Jianzhou; Jiang He; Wu Jie

2011-01-01

Highlights: → The proposed models can detach high volatility and daily seasonality of electricity price. → The improved hybrid forecast models can make full use of the advantages of individual models. → The proposed models create commendable improvements that are relatively satisfactorily for current research. → The proposed models do not require making complicated decisions about the explicit form. - Abstract: Half-hourly electricity price in power system are volatile, electricity price forecast is significant information which can help market managers and participants involved in electricity market to prepare their corresponding bidding strategies to maximize their benefits and utilities. However, the fluctuation of electricity price depends on the common effect of many factors and there is a very complicated random in its evolution process. Therefore, it is difficult to forecast half-hourly prices with traditional only one model for different behaviors of half-hourly prices. This paper proposes the improved forecasting model that detaches high volatility and daily seasonality for electricity price of New South Wales in Australia based on Empirical Mode Decomposition, Seasonal Adjustment and Autoregressive Integrated Moving Average. The prediction errors are analyzed and compared with the ones obtained from the traditional Seasonal Autoregressive Integrated Moving Average model. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably.

Short-term electricity price forecast based on the improved hybrid model

Energy Technology Data Exchange (ETDEWEB)

Dong Yao, E-mail: dongyao20051987@yahoo.cn [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Wang Jianzhou, E-mail: wjz@lzu.edu.cn [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Jiang He; Wu Jie [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China)

2011-08-15

Highlights: {yields} The proposed models can detach high volatility and daily seasonality of electricity price. {yields} The improved hybrid forecast models can make full use of the advantages of individual models. {yields} The proposed models create commendable improvements that are relatively satisfactorily for current research. {yields} The proposed models do not require making complicated decisions about the explicit form. - Abstract: Half-hourly electricity price in power system are volatile, electricity price forecast is significant information which can help market managers and participants involved in electricity market to prepare their corresponding bidding strategies to maximize their benefits and utilities. However, the fluctuation of electricity price depends on the common effect of many factors and there is a very complicated random in its evolution process. Therefore, it is difficult to forecast half-hourly prices with traditional only one model for different behaviors of half-hourly prices. This paper proposes the improved forecasting model that detaches high volatility and daily seasonality for electricity price of New South Wales in Australia based on Empirical Mode Decomposition, Seasonal Adjustment and Autoregressive Integrated Moving Average. The prediction errors are analyzed and compared with the ones obtained from the traditional Seasonal Autoregressive Integrated Moving Average model. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably.

Breaking a chaos-based secure communication scheme designed by an improved modulation method

Energy Technology Data Exchange (ETDEWEB)

Li Shujun [Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong (China)]. E-mail: hooklee@mail.com; Alvarez, Gonzalo [Instituto de Fisica Aplicada, Consejo Superior de Investigaciones Cientificas, Serrano 144-28006 Madrid (Spain); Chen Guanrong [Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong (China)

2005-07-01

Recently Bu and Wang [Bu S, Wang B-H. Chaos, Solitons and Fractals 2004;19(4):919-24] proposed a simple modulation method aiming to improve the security of chaos-based secure communications against return-map-based attacks. Soon this modulation method was independently cryptanalyzed by Chee et al. [Chee CY, Xu D, Bishop SR. Chaos, Solitons and Fractals 2004;21(5):1129-34], Wu et al. [Wu X, Hu H, Zhang B. Chaos, Solitons and Fractals 2004;22(2):367-73], and Alvarez et al. [Alvarez G, Montoya F, Romera M, Pastor G. Chaos, Solitons and Fractals, in press, arXiv:nlin/0406065] via different attacks. As an enhancement to the Bu-Wang method, an improving scheme was suggested by Wu et al. by removing the relationship between the modulating function and the zero-points. The present paper points out that the improved scheme proposed by Wu et al. is still insecure against a new attack. Compared with the existing attacks, the proposed attack is more powerful and can also break the original Bu-Wang scheme. Furthermore, it is pointed out that the security of the modulation-based schemes proposed by Wu et al. is not so satisfactory from a pure cryptographical point of view. The synchronization performance of this class of modulation-based schemes is also discussed.

Breaking a chaos-based secure communication scheme designed by an improved modulation method

International Nuclear Information System (INIS)

Li Shujun; Alvarez, Gonzalo; Chen Guanrong

2005-01-01

Recently Bu and Wang [Bu S, Wang B-H. Chaos, Solitons and Fractals 2004;19(4):919-24] proposed a simple modulation method aiming to improve the security of chaos-based secure communications against return-map-based attacks. Soon this modulation method was independently cryptanalyzed by Chee et al. [Chee CY, Xu D, Bishop SR. Chaos, Solitons and Fractals 2004;21(5):1129-34], Wu et al. [Wu X, Hu H, Zhang B. Chaos, Solitons and Fractals 2004;22(2):367-73], and Alvarez et al. [Alvarez G, Montoya F, Romera M, Pastor G. Chaos, Solitons and Fractals, in press, arXiv:nlin/0406065] via different attacks. As an enhancement to the Bu-Wang method, an improving scheme was suggested by Wu et al. by removing the relationship between the modulating function and the zero-points. The present paper points out that the improved scheme proposed by Wu et al. is still insecure against a new attack. Compared with the existing attacks, the proposed attack is more powerful and can also break the original Bu-Wang scheme. Furthermore, it is pointed out that the security of the modulation-based schemes proposed by Wu et al. is not so satisfactory from a pure cryptographical point of view. The synchronization performance of this class of modulation-based schemes is also discussed

A Proposal for IoT Dynamic Routes Selection Based on Contextual Information.

Science.gov (United States)

Araújo, Harilton da Silva; Filho, Raimir Holanda; Rodrigues, Joel J P C; Rabelo, Ricardo de A L; Sousa, Natanael de C; Filho, José C C L S; Sobral, José V V

2018-01-26

The Internet of Things (IoT) is based on interconnection of intelligent and addressable devices, allowing their autonomy and proactive behavior with Internet connectivity. Data dissemination in IoT usually depends on the application and requires context-aware routing protocols that must include auto-configuration features (which adapt the behavior of the network at runtime, based on context information). This paper proposes an approach for IoT route selection using fuzzy logic in order to attain the requirements of specific applications. In this case, fuzzy logic is used to translate in math terms the imprecise information expressed by a set of linguistic rules. For this purpose, four Objective Functions (OFs) are proposed for the Routing Protocol for Low Power and Loss Networks (RPL); such OFs are dynamically selected based on context information. The aforementioned OFs are generated from the fusion of the following metrics: Expected Transmission Count (ETX), Number of Hops (NH) and Energy Consumed (EC). The experiments performed through simulation, associated with the statistical data analysis, conclude that this proposal provides high reliability by successfully delivering nearly 100% of data packets, low delay for data delivery and increase in QoS. In addition, an 30% improvement is attained in the network life time when using one of proposed objective function, keeping the devices alive for longer duration.

A Proposal for IoT Dynamic Routes Selection Based on Contextual Information

Directory of Open Access Journals (Sweden)

Harilton da Silva Araújo

2018-01-01

Full Text Available The Internet of Things (IoT is based on interconnection of intelligent and addressable devices, allowing their autonomy and proactive behavior with Internet connectivity. Data dissemination in IoT usually depends on the application and requires context-aware routing protocols that must include auto-configuration features (which adapt the behavior of the network at runtime, based on context information. This paper proposes an approach for IoT route selection using fuzzy logic in order to attain the requirements of specific applications. In this case, fuzzy logic is used to translate in math terms the imprecise information expressed by a set of linguistic rules. For this purpose, four Objective Functions (OFs are proposed for the Routing Protocol for Low Power and Loss Networks (RPL; such OFs are dynamically selected based on context information. The aforementioned OFs are generated from the fusion of the following metrics: Expected Transmission Count (ETX, Number of Hops (NH and Energy Consumed (EC. The experiments performed through simulation, associated with the statistical data analysis, conclude that this proposal provides high reliability by successfully delivering nearly 100% of data packets, low delay for data delivery and increase in QoS. In addition, an 30% improvement is attained in the network life time when using one of proposed objective function, keeping the devices alive for longer duration.

An improved biometrics-based remote user authentication scheme with user anonymity.

Science.gov (United States)

Khan, Muhammad Khurram; Kumari, Saru

2013-01-01

The authors review the biometrics-based user authentication scheme proposed by An in 2012. The authors show that there exist loopholes in the scheme which are detrimental for its security. Therefore the authors propose an improved scheme eradicating the flaws of An's scheme. Then a detailed security analysis of the proposed scheme is presented followed by its efficiency comparison. The proposed scheme not only withstands security problems found in An's scheme but also provides some extra features with mere addition of only two hash operations. The proposed scheme allows user to freely change his password and also provides user anonymity with untraceability.

Referral system in rural Iran: improvement proposals

Directory of Open Access Journals (Sweden)

Mansour Naseriasl

2018-03-01

Full Text Available Because of insufficient communication between primary health care providers and specialists, which leads to inefficiencies and ineffectiveness in rural population health outcomes, to implement a well-functioning referral system is one of the most important tasks for some countries. Using purposive and snowballing sampling methods, we included health experts, policy-makers, family physicians, clinical specialists, and experts from health insurance organizations in this study according to pre-determined criteria. We recorded all interviews, transcribed and analyzed their content using qualitative methods. We extracted 1,522 individual codes initially. We also collected supplementary data through document review. From reviews and summarizations, four main themes, ten subthemes, and 24 issues emerged from the data. The solutions developed were: care system reform, education system reform, payment system reform, and improves in culture-building and public education. Given the executive experience, the full familiarity, the occupational and geographical diversity of participants, the solutions proposed in this study could positively affect the implementation and improvement of the referral system in Iran. The suggested solutions are complementary to each other and have less interchangeability.

Optimal Seamline Detection for Orthoimage Mosaicking Based on DSM and Improved JPS Algorithm

Directory of Open Access Journals (Sweden)

Gang Chen

2018-05-01

Full Text Available Based on the digital surface model (DSM and jump point search (JPS algorithm, this study proposed a novel approach to detect the optimal seamline for orthoimage mosaicking. By threshold segmentation, DSM was first identified as ground regions and obstacle regions (e.g., buildings, trees, and cars. Then, the mathematical morphology method was used to make the edge of obstacles more prominent. Subsequently, the processed DSM was considered as a uniform-cost grid map, and the JPS algorithm was improved and employed to search for key jump points in the map. Meanwhile, the jump points would be evaluated according to an optimized function, finally generating a minimum cost path as the optimal seamline. Furthermore, the search strategy was modified to avoid search failure when the search map was completely blocked by obstacles in the search direction. Comparison of the proposed method and the Dijkstra’s algorithm was carried out based on two groups of image data with different characteristics. Results showed the following: (1 the proposed method could detect better seamlines near the centerlines of the overlap regions, crossing far fewer ground objects; (2 the efficiency and resource consumption were greatly improved since the improved JPS algorithm skips many image pixels without them being explicitly evaluated. In general, based on DSM, the proposed method combining threshold segmentation, mathematical morphology, and improved JPS algorithms was helpful for detecting the optimal seamline for orthoimage mosaicking.

[Does simulator-based team training improve patient safety?].

Science.gov (United States)

Trentzsch, H; Urban, B; Sandmeyer, B; Hammer, T; Strohm, P C; Lazarovici, M

2013-10-01

Patient safety became paramount in medicine as well as in emergency medicine after it was recognized that preventable, adverse events significantly contributed to morbidity and mortality during hospital stay. The underlying errors cannot usually be explained by medical technical inadequacies only but are more due to difficulties in the transition of theoretical knowledge into tasks under the conditions of clinical reality. Crew Resource Management and Human Factors which determine safety and efficiency of humans in complex situations are suitable to control such sources of error. Simulation significantly improved safety in high reliability organizations, such as the aerospace industry.Thus, simulator-based team training has also been proposed for medical areas. As such training is consuming in cost, time and human resources, the question of the cost-benefit ratio obviously arises. This review outlines the effects of simulator-based team training on patient safety. Such course formats are not only capable of creating awareness and improvements in safety culture but also improve technical team performance and emphasize team performance as a clinical competence. A few studies even indicated improvement of patient-centered outcome, such as a reduced rate of adverse events but further studies are required in this respect. In summary, simulator-based team training should be accepted as a suitable strategy to improve patient safety.

Value-based distributed generator placements for service quality improvements

Energy Technology Data Exchange (ETDEWEB)

Teng, Jen-Hao; Chen, Chi-Fa [Department of Electrical Engineering, I-Shou University, No. 1, Section 1, Syuecheng Road, Dashu Township, Kaohsiung Country 840 (Taiwan); Liu, Yi-Hwa [Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei (Taiwan); Chen, Chia-Yen [Department of Computer Science, The University of Auckland (New Zealand)

2007-03-15

Distributed generator (DG) resources are small, self-contained electric generating plants that can provide power to homes, businesses or industrial facilities in distribution feeders. They can be used to reduce power loss and improve service reliability. However, the values of DGs are largely dependent on their types, sizes and locations as they were installed in distribution feeders. A value-based method is proposed in this paper to enhance the reliability and obtain the benefits for DG placement. The benefits of DG placement described in this paper include power cost saving, power loss reduction, and reliability enhancement. The costs of DG placement include the investment, maintenance and operating costs. The proposed value-based method tries to find the best tradeoff between the costs and benefits of DG placement and then find the optimal types of DG and their corresponding locations and sizes in distribution feeders. The derived formulations are solved by a genetic algorithm based method. Test results show that with proper types, sizes and installation site selection, DG placement can be used to improve system reliability, reduce customer interruption costs and save power cost; as well as enabling electric utilities to obtain the maximal economical benefits. (author)

AIB-OR: improving onion routing circuit construction using anonymous identity-based cryptosystems.

Science.gov (United States)

Wang, Changji; Shi, Dongyuan; Xu, Xilei

2015-01-01

The rapid growth of Internet applications has made communication anonymity an increasingly important or even indispensable security requirement. Onion routing has been employed as an infrastructure for anonymous communication over a public network, which provides anonymous connections that are strongly resistant to both eavesdropping and traffic analysis. However, existing onion routing protocols usually exhibit poor performance due to repeated encryption operations. In this paper, we first present an improved anonymous multi-receiver identity-based encryption (AMRIBE) scheme, and an improved identity-based one-way anonymous key agreement (IBOWAKE) protocol. We then propose an efficient onion routing protocol named AIB-OR that provides provable security and strong anonymity. Our main approach is to use our improved AMRIBE scheme and improved IBOWAKE protocol in onion routing circuit construction. Compared with other onion routing protocols, AIB-OR provides high efficiency, scalability, strong anonymity and fault tolerance. Performance measurements from a prototype implementation show that our proposed AIB-OR can achieve high bandwidths and low latencies when deployed over the Internet.

Betavoltaic Battery Conversion Efficiency Improvement Based on Interlayer Structures

International Nuclear Information System (INIS)

Li Da-Rang; Jiang Lan; Yin Jian-Hua; Lin Nai; Tan Yuan-Yuan

2012-01-01

Significant differences among the doping densities of PN junctions in semiconductors cause lattice mismatch and lattice defects that increase the recombination current of betavoltaic batteries. This extensively decreases the open circuit voltage and the short current, which results in low conversion efficiency. This study proposes P + PINN + -structure based betavoltaic batteries by adding an interlayer to typical PIN structures to improve conversion efficiency. Numerical simulations are conducted for the energy deposition of beta particles along the thickness direction in semiconductors. Based on this, 63 Ni-radiation GaAs batteries with PIN and P + PINN + structures are designed and fabricated to experimentally verify the proposed design. It turns out that the conversion efficiency of the betavoltaic battery with the proposed P + PINN + structure is about 1.45 times higher than that with the traditional PIN structure. (cross-disciplinary physics and related areas of science and technology)

Content-based quality evaluation of color images: overview and proposals

Science.gov (United States)

Tremeau, Alain; Richard, Noel; Colantoni, Philippe; Fernandez-Maloigne, Christine

2003-12-01

The automatic prediction of perceived quality from image data in general, and the assessment of particular image characteristics or attributes that may need improvement in particular, becomes an increasingly important part of intelligent imaging systems. The purpose of this paper is to propose to the color imaging community in general to develop a software package available on internet to help the user to select among all these approaches which is better appropriated to a given application. The ultimate goal of this project is to propose, next to implement, an open and unified color imaging system to set up a favourable context for the evaluation and analysis of color imaging processes. Many different methods for measuring the performance of a process have been proposed by different researchers. In this paper, we will discuss the advantages and shortcomings of most of main analysis criteria and performance measures currently used. The aim is not to establish a harsh competition between algorithms or processes, but rather to test and compare the efficiency of methodologies firstly to highlight strengths and weaknesses of a given algorithm or methodology on a given image type and secondly to have these results publicly available. This paper is focused on two important unsolved problems. Why it is so difficult to select a color space which gives better results than another one? Why it is so difficult to select an image quality metric which gives better results than another one, with respect to the judgment of the Human Visual System? Several methods used either in color imaging or in image quality will be thus discussed. Proposals for content-based image measures and means of developing a standard test suite for will be then presented. The above reference advocates for an evaluation protocol based on an automated procedure. This is the ultimate goal of our proposal.

Genetic algorithm-based improved DOA estimation using fourth-order cumulants

Science.gov (United States)

Ahmed, Ammar; Tufail, Muhammad

2017-05-01

Genetic algorithm (GA)-based direction of arrival (DOA) estimation is proposed using fourth-order cumulants (FOC) and ESPRIT principle which results in Multiple Invariance Cumulant ESPRIT algorithm. In the existing FOC ESPRIT formulations, only one invariance is utilised to estimate DOAs. The unused multiple invariances (MIs) must be exploited simultaneously in order to improve the estimation accuracy. In this paper, a fitness function based on a carefully designed cumulant matrix is developed which incorporates MIs present in the sensor array. Better DOA estimation can be achieved by minimising this fitness function. Moreover, the effectiveness of Newton's method as well as GA for this optimisation problem has been illustrated. Simulation results show that the proposed algorithm provides improved estimation accuracy compared to existing algorithms, especially in the case of low SNR, less number of snapshots, closely spaced sources and high signal and noise correlation. Moreover, it is observed that the optimisation using Newton's method is more likely to converge to false local optima resulting in erroneous results. However, GA-based optimisation has been found attractive due to its global optimisation capability.

An Improved Biometrics-Based Remote User Authentication Scheme with User Anonymity

Directory of Open Access Journals (Sweden)

Muhammad Khurram Khan

2013-01-01

Full Text Available The authors review the biometrics-based user authentication scheme proposed by An in 2012. The authors show that there exist loopholes in the scheme which are detrimental for its security. Therefore the authors propose an improved scheme eradicating the flaws of An’s scheme. Then a detailed security analysis of the proposed scheme is presented followed by its efficiency comparison. The proposed scheme not only withstands security problems found in An’s scheme but also provides some extra features with mere addition of only two hash operations. The proposed scheme allows user to freely change his password and also provides user anonymity with untraceability.

An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

Science.gov (United States)

Wang, Juan

2018-03-01

The iris image is easily polluted by noise and uneven light. This paper proposed an improved extreme learning machine (ELM) based iris recognition algorithm with hybrid feature. 2D-Gabor filters and GLCM is employed to generate a multi-granularity hybrid feature vector. 2D-Gabor filter and GLCM feature work for capturing low-intermediate frequency and high frequency texture information, respectively. Finally, we utilize extreme learning machine for iris recognition. Experimental results reveal our proposed ELM based multi-granularity iris recognition algorithm (ELM-MGIR) has higher accuracy of 99.86%, and lower EER of 0.12% under the premise of real-time performance. The proposed ELM-MGIR algorithm outperforms other mainstream iris recognition algorithms.

[Proposal for a media guideline to improve medical and health journalism].

Science.gov (United States)

Kojima, Masami

2012-01-01

A lot of healthcare professionals experienced annoyance with biased mass media news regarding medical and health issues. In this paper, I propose "news profiling method" and "media guideline" to improve the medical and health journalism.

Gene set analysis: limitations in popular existing methods and proposed improvements.

Science.gov (United States)

Mishra, Pashupati; Törönen, Petri; Leino, Yrjö; Holm, Liisa

2014-10-01

Gene set analysis is the analysis of a set of genes that collectively contribute to a biological process. Most popular gene set analysis methods are based on empirical P-value that requires large number of permutations. Despite numerous gene set analysis methods developed in the past decade, the most popular methods still suffer from serious limitations. We present a gene set analysis method (mGSZ) based on Gene Set Z-scoring function (GSZ) and asymptotic P-values. Asymptotic P-value calculation requires fewer permutations, and thus speeds up the gene set analysis process. We compare the GSZ-scoring function with seven popular gene set scoring functions and show that GSZ stands out as the best scoring function. In addition, we show improved performance of the GSA method when the max-mean statistics is replaced by the GSZ scoring function. We demonstrate the importance of both gene and sample permutations by showing the consequences in the absence of one or the other. A comparison of asymptotic and empirical methods of P-value estimation demonstrates a clear advantage of asymptotic P-value over empirical P-value. We show that mGSZ outperforms the state-of-the-art methods based on two different evaluations. We compared mGSZ results with permutation and rotation tests and show that rotation does not improve our asymptotic P-values. We also propose well-known asymptotic distribution models for three of the compared methods. mGSZ is available as R package from cran.r-project.org. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Proposed Doctrine Based Structure of the Armored Reconnaissance Squadron

Science.gov (United States)

2017-06-09

squadron. A new structure was proposed based on the deduced required capabilities, utilizing organizational theory and current army practices. This...squadron, which now puts greater emphasis on this analysis to link structure to doctrinally based task. Organizational Theory Since earliest...expect to find capability based discourse; there is a lack of proposed structure based on capability, task or equipment . The Armour Bulletin serves

Geometric Positioning Accuracy Improvement of ZY-3 Satellite Imagery Based on Statistical Learning Theory

Directory of Open Access Journals (Sweden)

Niangang Jiao

2018-05-01

Full Text Available With the increasing demand for high-resolution remote sensing images for mapping and monitoring the Earth’s environment, geometric positioning accuracy improvement plays a significant role in the image preprocessing step. Based on the statistical learning theory, we propose a new method to improve the geometric positioning accuracy without ground control points (GCPs. Multi-temporal images from the ZY-3 satellite are tested and the bias-compensated rational function model (RFM is applied as the block adjustment model in our experiment. An easy and stable weight strategy and the fast iterative shrinkage-thresholding (FIST algorithm which is widely used in the field of compressive sensing are improved and utilized to define the normal equation matrix and solve it. Then, the residual errors after traditional block adjustment are acquired and tested with the newly proposed inherent error compensation model based on statistical learning theory. The final results indicate that the geometric positioning accuracy of ZY-3 satellite imagery can be improved greatly with our proposed method.

A qualitative method proposal to improve environmental impact assessment

International Nuclear Information System (INIS)

Toro, Javier; Requena, Ignacio; Duarte, Oscar; Zamorano, Montserrat

2013-01-01

In environmental impact assessment, qualitative methods are used because they are versatile and easy to apply. This methodology is based on the evaluation of the strength of the impact by grading a series of qualitative attributes that can be manipulated by the evaluator. The results thus obtained are not objective, and all too often impacts are eliminated that should be mitigated with corrective measures. However, qualitative methodology can be improved if the calculation of Impact Importance is based on the characteristics of environmental factors and project activities instead on indicators assessed by evaluators. In this sense, this paper proposes the inclusion of the vulnerability of environmental factors and the potential environmental impact of project activities. For this purpose, the study described in this paper defined Total Impact Importance and specified a quantification procedure. The results obtained in the case study of oil drilling in Colombia reflect greater objectivity in the evaluation of impacts as well as a positive correlation between impact values, the environmental characteristics at and near the project location, and the technical characteristics of project activities. -- Highlights: • Concept of vulnerability has been used to calculate the importance impact assessment. • This paper defined Total Impact Importance and specified a quantification procedure. • The method includes the characteristics of environmental and project activities. • The application has shown greater objectivity in the evaluation of impacts. • Better correlation between impact values, environment and the project has been shown

A qualitative method proposal to improve environmental impact assessment

Energy Technology Data Exchange (ETDEWEB)

Toro, Javier, E-mail: jjtoroca@unal.edu.co [Institute of Environmental Studies, National University of Colombia at Bogotá (Colombia); Requena, Ignacio, E-mail: requena@decsai.ugr.es [Department of Computer Science and Artificial Intelligence, University of Granada (Spain); Duarte, Oscar, E-mail: ogduartev@unal.edu.co [National University of Colombia at Bogotá, Department of Electrical Engineering and Electronics (Colombia); Zamorano, Montserrat, E-mail: zamorano@ugr.es [Department of Civil Engineering, University of Granada (Spain)

2013-11-15

In environmental impact assessment, qualitative methods are used because they are versatile and easy to apply. This methodology is based on the evaluation of the strength of the impact by grading a series of qualitative attributes that can be manipulated by the evaluator. The results thus obtained are not objective, and all too often impacts are eliminated that should be mitigated with corrective measures. However, qualitative methodology can be improved if the calculation of Impact Importance is based on the characteristics of environmental factors and project activities instead on indicators assessed by evaluators. In this sense, this paper proposes the inclusion of the vulnerability of environmental factors and the potential environmental impact of project activities. For this purpose, the study described in this paper defined Total Impact Importance and specified a quantification procedure. The results obtained in the case study of oil drilling in Colombia reflect greater objectivity in the evaluation of impacts as well as a positive correlation between impact values, the environmental characteristics at and near the project location, and the technical characteristics of project activities. -- Highlights: • Concept of vulnerability has been used to calculate the importance impact assessment. • This paper defined Total Impact Importance and specified a quantification procedure. • The method includes the characteristics of environmental and project activities. • The application has shown greater objectivity in the evaluation of impacts. • Better correlation between impact values, environment and the project has been shown.

Fast Rotation-Free Feature-Based Image Registration Using Improved N-SIFT and GMM-Based Parallel Optimization.

Science.gov (United States)

Yu, Dongdong; Yang, Feng; Yang, Caiyun; Leng, Chengcai; Cao, Jian; Wang, Yining; Tian, Jie

2016-08-01

Image registration is a key problem in a variety of applications, such as computer vision, medical image processing, pattern recognition, etc., while the application of registration is limited by time consumption and the accuracy in the case of large pose differences. Aimed at these two kinds of problems, we propose a fast rotation-free feature-based rigid registration method based on our proposed accelerated-NSIFT and GMM registration-based parallel optimization (PO-GMMREG). Our method is accelerated by using the GPU/CUDA programming and preserving only the location information without constructing the descriptor of each interest point, while its robustness to missing correspondences and outliers is improved by converting the interest point matching to Gaussian mixture model alignment. The accuracy in the case of large pose differences is settled by our proposed PO-GMMREG algorithm by constructing a set of initial transformations. Experimental results demonstrate that our proposed algorithm can fast rigidly register 3-D medical images and is reliable for aligning 3-D scans even when they exhibit a poor initialization.

Localized probability of improvement for kriging based multi-objective optimization

Science.gov (United States)

Li, Yinjiang; Xiao, Song; Barba, Paolo Di; Rotaru, Mihai; Sykulski, Jan K.

2017-12-01

The paper introduces a new approach to kriging based multi-objective optimization by utilizing a local probability of improvement as the infill sampling criterion and the nearest neighbor check to ensure diversification and uniform distribution of Pareto fronts. The proposed method is computationally fast and linearly scalable to higher dimensions.

Fingerprint Identification Using SIFT-Based Minutia Descriptors and Improved All Descriptor-Pair Matching

Directory of Open Access Journals (Sweden)

Jiuqiang Han

2013-03-01

Full Text Available The performance of conventional minutiae-based fingerprint authentication algorithms degrades significantly when dealing with low quality fingerprints with lots of cuts or scratches. A similar degradation of the minutiae-based algorithms is observed when small overlapping areas appear because of the quite narrow width of the sensors. Based on the detection of minutiae, Scale Invariant Feature Transformation (SIFT descriptors are employed to fulfill verification tasks in the above difficult scenarios. However, the original SIFT algorithm is not suitable for fingerprint because of: (1 the similar patterns of parallel ridges; and (2 high computational resource consumption. To enhance the efficiency and effectiveness of the algorithm for fingerprint verification, we propose a SIFT-based Minutia Descriptor (SMD to improve the SIFT algorithm through image processing, descriptor extraction and matcher. A two-step fast matcher, named improved All Descriptor-Pair Matching (iADM, is also proposed to implement the 1:N verifications in real-time. Fingerprint Identification using SMD and iADM (FISiA achieved a significant improvement with respect to accuracy in representative databases compared with the conventional minutiae-based method. The speed of FISiA also can meet real-time requirements.

Environmental impact assessment in Colombia: Critical analysis and proposals for improvement

International Nuclear Information System (INIS)

Toro, Javier; Requena, Ignacio; Zamorano, Montserrat

2010-01-01

The evaluation of Environmental Impact Assessment (EIA) systems is a highly recommended strategy for enhancing their effectiveness and quality. This paper describes an evaluation of EIA in Colombia, using the model and the control mechanisms proposed and applied in other countries by Christopher Wood and Ortolano. The evaluation criteria used are based on Principles of Environmental Impact Assessment Best Practice, such as effectiveness and control features, and they were contrasted with the opinions of a panel of Colombian EIA experts as a means of validating the results of the study. The results found that EIA regulations in Colombia were ineffective because of limited scope, inadequate administrative support and the inexistence of effective control mechanisms and public participation. This analysis resulted in a series of recommendations regarding the further development of the EIA system in Colombia with a view to improving its quality and effectiveness.

75 FR 72830 - Medicare Program; Quality Improvement Organization (QIO) Contracts: Solicitation of Proposals...

Science.gov (United States)

2010-11-26

...] Medicare Program; Quality Improvement Organization (QIO) Contracts: Solicitation of Proposals From In-State... the Social Security Act (the Act) to provide at least 6 months' advance notice of the expiration dates of contracts with out- of-State Quality Improvement Organizations (QIOs) before renewing any of those...

76 FR 19976 - Proposed Information Collection; Comment Request; Survey of EDA Grant Process Improvement

Science.gov (United States)

2011-04-11

...; Comment Request; Survey of EDA Grant Process Improvement AGENCY: Economic Development Administration.... In 2010, EDA made improvements in its grant application process. The proposed short survey of five to... improvements to the grant application process and to make any necessary adjustments. EDA would like to conduct...

Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing

International Nuclear Information System (INIS)

Chen Yang; Shi Luyao; Shu Huazhong; Luo Limin; Coatrieux, Jean-Louis; Yin Xindao; Toumoulin, Christine

2013-01-01

In abdomen computed tomography (CT), repeated radiation exposures are often inevitable for cancer patients who receive surgery or radiotherapy guided by CT images. Low-dose scans should thus be considered in order to avoid the harm of accumulative x-ray radiation. This work is aimed at improving abdomen tumor CT images from low-dose scans by using a fast dictionary learning (DL) based processing. Stemming from sparse representation theory, the proposed patch-based DL approach allows effective suppression of both mottled noise and streak artifacts. The experiments carried out on clinical data show that the proposed method brings encouraging improvements in abdomen low-dose CT images with tumors. (paper)

Proposed improvements to a model for characterizing the electrical and thermal energy performance of stirling engine micro-cogeneration devices based upon experimental observations

Energy Technology Data Exchange (ETDEWEB)

Lombardi, K. [CanmetENERGY, 1 Haanel Drive, Ottawa, Ont. (Canada); Ugursal, V.I. [Dalhousie University, Halifax, NS (Canada); Beausoleil-Morrison, I. [Carleton University, 1125 Colonel By Drive, Ottawa, Ont. (Canada)

2010-10-15

Stirling engines (SE) are a market-ready technology suitable for residential cogeneration of heat and electricity to alleviate the increasing demand on central power grids. Advantages of this external combustion engine include high cogeneration efficiency, fuel flexibility, low noise and vibration, and low emissions. To explore and assess the feasibility of using SE based cogeneration systems in the residential sector, there is a need for an accurate and practical simulation model that can be used to conduct sensitivity and what-if analyses. A simulation model for SE based residential scale micro-cogeneration systems was recently developed; however the model is impractical due to its functional form and data requirements. Furthermore, the available experimental data lack adequate diversity to assess the model's suitability. In this paper, first the existing model is briefly presented, followed by a review of the design and implementation of a series of experiments conducted to study the performance and behaviour of the SE system and to develop extensive, and hitherto unavailable, operational data. The empirical observations are contrasted with the functional form of the existing simulation model, and improvements to the structure of the model are proposed based upon these observations. (author)

An Improved Constraint-Based System for the Verification of Security Protocols

NARCIS (Netherlands)

Corin, R.J.; Etalle, Sandro

We propose a constraint-based system for the verification of security protocols that improves upon the one developed by Millen and Shmatikov [30]. Our system features (1) a significantly more efficient implementation, (2) a monotonic behavior, which also allows to detect flaws associated to partial

An Improved Constraint-based system for the verification of security protocols

NARCIS (Netherlands)

Corin, R.J.; Etalle, Sandro; Hermenegildo, Manuel V.; Puebla, German

We propose a constraint-based system for the verification of security protocols that improves upon the one developed by Millen and Shmatikov. Our system features (1) a significantly more efficient implementation, (2) a monotonic behavior, which also allows to detect aws associated to partial runs

48 CFR 452.216-71 - Base Fee and Award Fee Proposal.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 4 2010-10-01 2010-10-01 false Base Fee and Award Fee... Base Fee and Award Fee Proposal. As prescribed in 416.470, insert the following provision: Base Fee and Award Proposal (FEB 1988) For the purpose of this solicitation, offerors shall propose a base fee of...

Improved spring model-based collaborative indoor visible light positioning

Science.gov (United States)

Luo, Zhijie; Zhang, WeiNan; Zhou, GuoFu

2016-06-01

Gaining accuracy with indoor positioning of individuals is important as many location-based services rely on the user's current position to provide them with useful services. Many researchers have studied indoor positioning techniques based on WiFi and Bluetooth. However, they have disadvantages such as low accuracy or high cost. In this paper, we propose an indoor positioning system in which visible light radiated from light-emitting diodes is used to locate the position of receivers. Compared with existing methods using light-emitting diode light, we present a high-precision and simple implementation collaborative indoor visible light positioning system based on an improved spring model. We first estimate coordinate position information using the visible light positioning system, and then use the spring model to correct positioning errors. The system can be employed easily because it does not require additional sensors and the occlusion problem of visible light would be alleviated. We also describe simulation experiments, which confirm the feasibility of our proposed method.

Combining Correlation-Based and Reward-Based Learning in Neural Control for Policy Improvement

DEFF Research Database (Denmark)

Manoonpong, Poramate; Kolodziejski, Christoph; Wörgötter, Florentin

2013-01-01

Classical conditioning (conventionally modeled as correlation-based learning) and operant conditioning (conventionally modeled as reinforcement learning or reward-based learning) have been found in biological systems. Evidence shows that these two mechanisms strongly involve learning about...... associations. Based on these biological findings, we propose a new learning model to achieve successful control policies for artificial systems. This model combines correlation-based learning using input correlation learning (ICO learning) and reward-based learning using continuous actor–critic reinforcement...... learning (RL), thereby working as a dual learner system. The model performance is evaluated by simulations of a cart-pole system as a dynamic motion control problem and a mobile robot system as a goal-directed behavior control problem. Results show that the model can strongly improve pole balancing control...

Image Interpolation Scheme based on SVM and Improved PSO

Science.gov (United States)

Jia, X. F.; Zhao, B. T.; Liu, X. X.; Song, H. P.

2018-01-01

In order to obtain visually pleasing images, a support vector machines (SVM) based interpolation scheme is proposed, in which the improved particle swarm optimization is applied to support vector machine parameters optimization. Training samples are constructed by the pixels around the pixel to be interpolated. Then the support vector machine with optimal parameters is trained using training samples. After the training, we can get the interpolation model, which can be employed to estimate the unknown pixel. Experimental result show that the interpolated images get improvement PNSR compared with traditional interpolation methods, which is agrees with the subjective quality.

Simultaneous allocation of distributed resources using improved teaching learning based optimization

International Nuclear Information System (INIS)

Kanwar, Neeraj; Gupta, Nikhil; Niazi, K.R.; Swarnkar, Anil

2015-01-01

Highlights: • Simultaneous allocation of distributed energy resources in distribution networks. • Annual energy loss reduction is optimized using a multi-level load profile. • A new penalty factor approach is suggested to check node voltage deviations. • An improved TLBO is proposed by suggesting several modifications in standard TLBO. • An intelligent search is proposed to enhance the performance of solution technique. - Abstract: Active and reactive power flow in distribution networks can be effectively controlled by optimally placing distributed resources like shunt capacitors and distributed generators. This paper presents improved variant of Teaching Learning Based Optimization (TLBO) to efficiently and effectively deal with the problem of simultaneous allocation of these distributed resources in radial distribution networks while considering multi-level load scenario. Several algorithm specific modifications are suggested in the standard form of TLBO to cope against the intrinsic flaws of this technique. In addition, an intelligent search approach is proposed to restrict the problem search space without loss of diversity. This enhances the overall performance of the proposed method. The proposed method is investigated on IEEE 33-bus, 69-bus and 83-bus test distribution systems showing promising results

Facial expression recognition based on improved local ternary pattern and stacked auto-encoder

Science.gov (United States)

Wu, Yao; Qiu, Weigen

2017-08-01

In order to enhance the robustness of facial expression recognition, we propose a method of facial expression recognition based on improved Local Ternary Pattern (LTP) combined with Stacked Auto-Encoder (SAE). This method uses the improved LTP extraction feature, and then uses the improved depth belief network as the detector and classifier to extract the LTP feature. The combination of LTP and improved deep belief network is realized in facial expression recognition. The recognition rate on CK+ databases has improved significantly.

Anomaly Detection for Aviation Safety Based on an Improved KPCA Algorithm

Directory of Open Access Journals (Sweden)

Xiaoyu Zhang

2017-01-01

Full Text Available Thousands of flights datasets should be analyzed per day for a moderate sized fleet; therefore, flight datasets are very large. In this paper, an improved kernel principal component analysis (KPCA method is proposed to search for signatures of anomalies in flight datasets through the squared prediction error statistics, in which the number of principal components and the confidence for the confidence limit are automatically determined by OpenMP-based K-fold cross-validation algorithm and the parameter in the radial basis function (RBF is optimized by GPU-based kernel learning method. Performed on Nvidia GeForce GTX 660, the computation of the proposed GPU-based RBF parameter is 112.9 times (average 82.6 times faster than that of sequential CPU task execution. The OpenMP-based K-fold cross-validation process for training KPCA anomaly detection model becomes 2.4 times (average 1.5 times faster than that of sequential CPU task execution. Experiments show that the proposed approach can effectively detect the anomalies with the accuracy of 93.57% and false positive alarm rate of 1.11%.

Performance improvement of ERP-based brain-computer interface via varied geometric patterns.

Science.gov (United States)

Ma, Zheng; Qiu, Tianshuang

2017-12-01

Recently, many studies have been focusing on optimizing the stimulus of an event-related potential (ERP)-based brain-computer interface (BCI). However, little is known about the effectiveness when increasing the stimulus unpredictability. We investigated a new stimulus type of varied geometric pattern where both complexity and unpredictability of the stimulus are increased. The proposed and classical paradigms were compared in within-subject experiments with 16 healthy participants. Results showed that the BCI performance was significantly improved for the proposed paradigm, with an average online written symbol rate increasing by 138% comparing with that of the classical paradigm. Amplitudes of primary ERP components, such as N1, P2a, P2b, N2, were also found to be significantly enhanced with the proposed paradigm. In this paper, a novel ERP BCI paradigm with a new stimulus type of varied geometric pattern is proposed. By jointly increasing the complexity and unpredictability of the stimulus, the performance of an ERP BCI could be considerably improved.

Improving the performance of a filling line based on simulation

Science.gov (United States)

Jasiulewicz-Kaczmarek, M.; Bartkowiak, T.

2016-08-01

The paper describes the method of improving performance of a filling line based on simulation. This study concerns a production line that is located in a manufacturing centre of a FMCG company. A discrete event simulation model was built using data provided by maintenance data acquisition system. Two types of failures were identified in the system and were approximated using continuous statistical distributions. The model was validated taking into consideration line performance measures. A brief Pareto analysis of line failures was conducted to identify potential areas of improvement. Two improvements scenarios were proposed and tested via simulation. The outcome of the simulations were the bases of financial analysis. NPV and ROI values were calculated taking into account depreciation, profits, losses, current CIT rate and inflation. A validated simulation model can be a useful tool in maintenance decision-making process.

SIFT Based Vein Recognition Models: Analysis and Improvement

Directory of Open Access Journals (Sweden)

Guoqing Wang

2017-01-01

Full Text Available Scale-Invariant Feature Transform (SIFT is being investigated more and more to realize a less-constrained hand vein recognition system. Contrast enhancement (CE, compensating for deficient dynamic range aspects, is a must for SIFT based framework to improve the performance. However, evidence of negative influence on SIFT matching brought by CE is analysed by our experiments. We bring evidence that the number of extracted keypoints resulting by gradient based detectors increases greatly with different CE methods, while on the other hand the matching result of extracted invariant descriptors is negatively influenced in terms of Precision-Recall (PR and Equal Error Rate (EER. Rigorous experiments with state-of-the-art and other CE adopted in published SIFT based hand vein recognition system demonstrate the influence. What is more, an improved SIFT model by importing the kernel of RootSIFT and Mirror Match Strategy into a unified framework is proposed to make use of the positive keypoints change and make up for the negative influence brought by CE.

Face recognition based on improved BP neural network

Directory of Open Access Journals (Sweden)

Yue Gaili

2017-01-01

Full Text Available In order to improve the recognition rate of face recognition, face recognition algorithm based on histogram equalization, PCA and BP neural network is proposed. First, the face image is preprocessed by histogram equalization. Then, the classical PCA algorithm is used to extract the features of the histogram equalization image, and extract the principal component of the image. And then train the BP neural network using the trained training samples. This improved BP neural network weight adjustment method is used to train the network because the conventional BP algorithm has the disadvantages of slow convergence, easy to fall into local minima and training process. Finally, the BP neural network with the test sample input is trained to classify and identify the face images, and the recognition rate is obtained. Through the use of ORL database face image simulation experiment, the analysis results show that the improved BP neural network face recognition method can effectively improve the recognition rate of face recognition.

Hybrid Indoor-Based WLAN-WSN Localization Scheme for Improving Accuracy Based on Artificial Neural Network

Directory of Open Access Journals (Sweden)

Zahid Farid

2016-01-01

Full Text Available In indoor environments, WiFi (RSS based localization is sensitive to various indoor fading effects and noise during transmission, which are the main causes of localization errors that affect its accuracy. Keeping in view those fading effects, positioning systems based on a single technology are ineffective in performing accurate localization. For this reason, the trend is toward the use of hybrid positioning systems (combination of two or more wireless technologies in indoor/outdoor localization scenarios for getting better position accuracy. This paper presents a hybrid technique to implement indoor localization that adopts fingerprinting approaches in both WiFi and Wireless Sensor Networks (WSNs. This model exploits machine learning, in particular Artificial Natural Network (ANN techniques, for position calculation. The experimental results show that the proposed hybrid system improved the accuracy, reducing the average distance error to 1.05 m by using ANN. Applying Genetic Algorithm (GA based optimization technique did not incur any further improvement to the accuracy. Compared to the performance of GA optimization, the nonoptimized ANN performed better in terms of accuracy, precision, stability, and computational time. The above results show that the proposed hybrid technique is promising for achieving better accuracy in real-world positioning applications.

Logistics improvements in a cooperative of recyclable waste collectors in Belém-PA: A proposal based on NSWP

Directory of Open Access Journals (Sweden)

Emmily Caroline Cabral da Fonseca

2017-03-01

Full Text Available The increase in consumption has contributed to the growth in solid waste generation. In 2012, Brazil presented an increase in waste generation rates, which exceeded the rate of population growth. The metropolitan region of Belém-PA reflects this scenario as well. This Brazilian State has taken actions in order to combat this problem, and one of them was the creation of the National Solid Waste Policy (NSWP established by Law No. 12,305 (2010. This Law contains guidelines for handling, decreasing generation, reusing and recycling solid waste. In this sense, the cooperatives of recyclable waste collectors are an important link in this reusing and recycling network. Thus, this paper proposes improvements for the processes performed by a cooperative of waste collectors, located in Belém-PA, in the context of the NSWP by encouraging the sustainable development of this organization. For this purpose, the theoretical background was provided from journals and government sites, especially topics regarding to the said Act and the Reverse Logistics (RL. In addition, data from the cooperative activities were collected and analyzed, which resulted in the following contributions: proposition of improvements in the processes, object of this study, which followed the NSWP considerations. These results proposed actions aligned to the principles of sustainable development expected in the NSWP, as well as to generate new content focused on the area of Production Engineering, especially Logistics.

Resolution-improved in situ DNA hybridization detection based on microwave photonic interrogation.

Science.gov (United States)

Cao, Yuan; Guo, Tuan; Wang, Xudong; Sun, Dandan; Ran, Yang; Feng, Xinhuan; Guan, Bai-ou

2015-10-19

In situ bio-sensing system based on microwave photonics filter (MPF) interrogation method with improved resolution is proposed and experimentally demonstrated. A microfiber Bragg grating (mFBG) is used as sensing probe for DNA hybridization detection. Different from the traditional wavelength monitoring technique, we use the frequency interrogation scheme for resolution-improved bio-sensing detection. Experimental results show that the frequency shift of MPF notch presents a linear response to the surrounding refractive index (SRI) change over the range of 1.33 to 1.38, with a SRI resolution up to 2.6 × 10(-5) RIU, which has been increased for almost two orders of magnitude compared with the traditional fundamental mode monitoring technique (~3.6 × 10(-3) RIU). Due to the high Q value (about 27), the whole process of DNA hybridization can be in situ monitored. The proposed MPF-based bio-sensing system provides a new interrogation method over the frequency domain with improved sensing resolution and rapid interrogation rate for biochemical and environmental measurement.

A new simple technique for improving the random properties of chaos-based cryptosystems

Science.gov (United States)

Garcia-Bosque, M.; Pérez-Resa, A.; Sánchez-Azqueta, C.; Celma, S.

2018-03-01

A new technique for improving the security of chaos-based stream ciphers has been proposed and tested experimentally. This technique manages to improve the randomness properties of the generated keystream by preventing the system to fall into short period cycles due to digitation. In order to test this technique, a stream cipher based on a Skew Tent Map algorithm has been implemented on a Virtex 7 FPGA. The randomness of the keystream generated by this system has been compared to the randomness of the keystream generated by the same system with the proposed randomness-enhancement technique. By subjecting both keystreams to the National Institute of Standards and Technology (NIST) tests, we have proved that our method can considerably improve the randomness of the generated keystreams. In order to incorporate our randomness-enhancement technique, only 41 extra slices have been needed, proving that, apart from effective, this method is also efficient in terms of area and hardware resources.

Energy Efficiency Performance Improvements for Ant-Based Routing Algorithm in Wireless Sensor Networks

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Adamu Murtala Zungeru

2013-01-01

Full Text Available The main problem for event gathering in wireless sensor networks (WSNs is the restricted communication range for each node. Due to the restricted communication range and high network density, event forwarding in WSNs is very challenging and requires multihop data forwarding. Currently, the energy-efficient ant based routing (EEABR algorithm, based on the ant colony optimization (ACO metaheuristic, is one of the state-of-the-art energy-aware routing protocols. In this paper, we propose three improvements to the EEABR algorithm to further improve its energy efficiency. The improvements to the original EEABR are based on the following: (1 a new scheme to intelligently initialize the routing tables giving priority to neighboring nodes that simultaneously could be the destination, (2 intelligent update of routing tables in case of a node or link failure, and (3 reducing the flooding ability of ants for congestion control. The energy efficiency improvements are significant particularly for dynamic routing environments. Experimental results using the RMASE simulation environment show that the proposed method increases the energy efficiency by up to 9% and 64% in converge-cast and target-tracking scenarios, respectively, over the original EEABR without incurring a significant increase in complexity. The method is also compared and found to also outperform other swarm-based routing protocols such as sensor-driven and cost-aware ant routing (SC and Beesensor.

Improvement of Accuracy for Background Noise Estimation Method Based on TPE-AE

Science.gov (United States)

Itai, Akitoshi; Yasukawa, Hiroshi

This paper proposes a method of a background noise estimation based on the tensor product expansion with a median and a Monte carlo simulation. We have shown that a tensor product expansion with absolute error method is effective to estimate a background noise, however, a background noise might not be estimated by using conventional method properly. In this paper, it is shown that the estimate accuracy can be improved by using proposed methods.

Fast digital envelope detector based on generalized harmonic wavelet transform for BOTDR performance improvement

International Nuclear Information System (INIS)

Yang, Wei; Yang, Yuanhong; Yang, Mingwei

2014-01-01

We propose a fast digital envelope detector (DED) based on the generalized harmonic wavelet transform to improve the performance of coherent heterodyne Brillouin optical time domain reflectometry. The proposed DED can obtain undistorted envelopes due to the zero phase-shift ideal bandpass filter (BPF) characteristics of the generalized harmonic wavelet (GHW). Its envelope average ability benefits from the passband designing flexibility of the GHW, and its demodulation speed can be accelerated by using a fast algorithm that only analyses signals of interest within the passband of the GHW with reduced computational complexity. The feasibility and advantage of the proposed DED are verified by simulations and experiments. With an optimized bandwidth, Brillouin frequency shift accuracy improvements of 19.4% and 11.14%, as well as envelope demodulation speed increases of 39.1% and 24.9%, are experimentally attained by the proposed DED over Hilbert transform (HT) and Morlet wavelet transform (MWT) based DEDs, respectively. Spatial resolution by the proposed DED is undegraded, which is identical to the undegraded value by HT-DED with an allpass filter characteristic and better than the degraded value by MWT-DED with a Gaussian BPF characteristic. (paper)

An Improved Backstepping-Based Controller for Three-Dimensional Trajectory Tracking of a Midwater Trawl System

Directory of Open Access Journals (Sweden)

Zhao Yan

2016-01-01

Full Text Available An improved backstepping control method for three-dimensional trajectory tracking of a midwater trawl system is investigated. A new mathematical model of the trawl system while considering the horizontal expansion effect of two otter boards is presented based on the Newton Euler method. Subsequently, an active path tracking strategy of the trawl system based on the backstepping method is proposed. The nonstrict feedback characteristic of the proposed model employs a control allocation method and several parallel nonlinear PID (Proportion Integration Differentiation controllers to eliminate the high-order state variables. Then, the stability analysis by the Lyapunov Stability Theory shows that the proposed controller can maintain the stability of the trawl system even with the presence of external disturbances. To validate the proposed controller, a simulation comparison with a linear PID controller was conducted. The simulation results illustrate that the improved backstepping controller is effective for three-dimensional trajectory tracking of the midwater trawl system.

Genetic algorithm based reactive power dispatch for voltage stability improvement

Energy Technology Data Exchange (ETDEWEB)

Devaraj, D. [Department of Electrical and Electronics, Kalasalingam University, Krishnankoil 626 190 (India); Roselyn, J. Preetha [Department of Electrical and Electronics, SRM University, Kattankulathur 603 203, Chennai (India)

2010-12-15

Voltage stability assessment and control form the core function in a modern energy control centre. This paper presents an improved Genetic algorithm (GA) approach for voltage stability enhancement. The proposed technique is based on the minimization of the maximum of L-indices of load buses. Generator voltages, switchable VAR sources and transformer tap changers are used as optimization variables of this problem. The proposed approach permits the optimization variables to be represented in their natural form in the genetic population. For effective genetic processing, the crossover and mutation operators which can directly deal with the floating point numbers and integers are used. The proposed algorithm has been tested on IEEE 30-bus and IEEE 57-bus test systems and successful results have been obtained. (author)

An improved anti-leech mechanism based on session identifier

Science.gov (United States)

Zhang, Jianbiao; Zhu, Tong; Zhang, Han; Lin, Li

2012-01-01

With the rapid development of information technology and extensive requirement of network resource sharing, plenty of resource hotlinking phenomenons appear on the internet. The hotlinking problem not only harms the interests of legal websites but also leads to a great affection to fair internet environment. The anti-leech technique based on session identifier is highly secure, but the transmission of session identifier in plaintext form causes some security flaws. In this paper, a proxy hotlinking technique based on session identifier is introduced firstly to illustrate these security flaws; next, this paper proposes an improved anti-leech mechanism based on session identifier, the mechanism takes the random factor as the core and detects hotlinking request using a map table that contains random factor, user's information and time stamp; at last the paper analyzes the security of mechanism in theory. The result reveals that the improved mechanism has the merits of simple realization, high security and great flexibility.

Generalised Category Attack—Improving Histogram-Based Attack on JPEG LSB Embedding

Science.gov (United States)

Lee, Kwangsoo; Westfeld, Andreas; Lee, Sangjin

We present a generalised and improved version of the category attack on LSB steganography in JPEG images with straddled embedding path. It detects more reliably low embedding rates and is also less disturbed by double compressed images. The proposed methods are evaluated on several thousand images. The results are compared to both recent blind and specific attacks for JPEG embedding. The proposed attack permits a more reliable detection, although it is based on first order statistics only. Its simple structure makes it very fast.

An Improved Wavelet‐Based Multivariable Fault Detection Scheme

KAUST Repository

Harrou, Fouzi

2017-07-06

Data observed from environmental and engineering processes are usually noisy and correlated in time, which makes the fault detection more difficult as the presence of noise degrades fault detection quality. Multiscale representation of data using wavelets is a powerful feature extraction tool that is well suited to denoising and decorrelating time series data. In this chapter, we combine the advantages of multiscale partial least squares (MSPLSs) modeling with those of the univariate EWMA (exponentially weighted moving average) monitoring chart, which results in an improved fault detection system, especially for detecting small faults in highly correlated, multivariate data. Toward this end, we applied EWMA chart to the output residuals obtained from MSPLS model. It is shown through simulated distillation column data the significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional partial least square (PLS)‐based Q and EWMA methods and MSPLS‐based Q method.

Improved Tensor-Based Singular Spectrum Analysis Based on Single Channel Blind Source Separation Algorithm and Its Application to Fault Diagnosis

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Dan Yang

2017-04-01

Full Text Available To solve the problem of multi-fault blind source separation (BSS in the case that the observed signals are under-determined, a novel approach for single channel blind source separation (SCBSS based on the improved tensor-based singular spectrum analysis (TSSA is proposed. As the most natural representation of high-dimensional data, tensor can preserve the intrinsic structure of the data to the maximum extent. Thus, TSSA method can be employed to extract the multi-fault features from the measured single-channel vibration signal. However, SCBSS based on TSSA still has some limitations, mainly including unsatisfactory convergence of TSSA in many cases and the number of source signals is hard to accurately estimate. Therefore, the improved TSSA algorithm based on canonical decomposition and parallel factors (CANDECOMP/PARAFAC weighted optimization, namely CP-WOPT, is proposed in this paper. CP-WOPT algorithm is applied to process the factor matrix using a first-order optimization approach instead of the original least square method in TSSA, so as to improve the convergence of this algorithm. In order to accurately estimate the number of the source signals in BSS, EMD-SVD-BIC (empirical mode decomposition—singular value decomposition—Bayesian information criterion method, instead of the SVD in the conventional TSSA, is introduced. To validate the proposed method, we applied it to the analysis of the numerical simulation signal and the multi-fault rolling bearing signals.

Improved External Base Resistance Extraction for Submicrometer InP/InGaAs DHBT Models

DEFF Research Database (Denmark)

Johansen, Tom Keinicke; Krozer, Viktor; Nodjiadjim, Virginie

2011-01-01

An improved direct parameter extraction method is proposed for III–V heterojunction bipolar transistor (HBT) external base resistance $R_{\\rm bx}$ extraction from forward active $S$-parameters. The method is formulated taking into account the current dependence of the intrinsic base–collector cap......An improved direct parameter extraction method is proposed for III–V heterojunction bipolar transistor (HBT) external base resistance $R_{\\rm bx}$ extraction from forward active $S$-parameters. The method is formulated taking into account the current dependence of the intrinsic base...... factor given as the ratio of the emitter to the collector area. The determination of the parameters $I_{p}$ and $X_{0}$ from experimental $S$-parameters is described. The method is applied to high-speed submicrometer InP/InGaAs DHBT devices and leads to small-signal equivalent circuit models, which...

Cryptanalysis and improvement on a block cryptosystem based on iteration a chaotic map

International Nuclear Information System (INIS)

Wang Yong; Liao Xiaofeng; Xiang Tao; Wong, Kwok-Wo; Yang Degang

2007-01-01

Recently, a novel block encryption system has been proposed as an improved version of the chaotic cryptographic method based on iterating a chaotic map. In this Letter, a flaw of this cryptosystem is pointed out and a chosen plaintext attack is presented. Furthermore, a remedial improvement is suggested, which avoids the flaw while keeping all the merits of the original cryptosystem

Improved Taguchi method based contract capacity optimization for industrial consumer with self-owned generating units

International Nuclear Information System (INIS)

Yang, Hong-Tzer; Peng, Pai-Chun

2012-01-01

Highlights: ► We propose an improved Taguchi method to determine the optimal contract capacities with SOGUs. ► We solve the highly discrete and nonlinear optimization problem for the contract capacities with SOGUs. ► The proposed improved Taguchi method integrates PSO in Taguchi method. ► The customer using the proposed optimization approach may save up to 12.18% of power expenses. ► The improved Taguchi method can also be well applied to the other similar problems. - Abstract: Contract capacity setting for industrial consumer with self-owned generating units (SOGUs) is a highly discrete and nonlinear optimization problem considering expenditure on the electricity from the utility and operation costs of the SOGUs. This paper proposes an improved Taguchi method that combines existing Taguchi method and particle swarm optimization (PSO) algorithm to solve this problem. Taguchi method provides fast converging characteristics in searching the optimal solution through quality analysis in orthogonal matrices. The integrated PSO algorithm generates new solutions in the orthogonal matrices based on the searching experiences during the evolution process to further improve the quality of solution. To verify feasibility of the proposed method, the paper uses the real data obtained from a large optoelectronics factory in Taiwan. In comparison with the existing optimization methods, the proposed improved Taguchi method has superior performance as revealed in the numerical results in terms of the convergence process and the quality of solution obtained.

Proposal of competitive sport activities to improve the participation of children with late mental development to the systematic sport training.

Directory of Open Access Journals (Sweden)

María de la Caridad Veloso Pérez

2010-06-01

Full Text Available The proposal to the problematic solution dealt with in the present investigation is constituted by competitive sport activities, which respond to its totality to the integral diagnosis and therefore, to the individual and group characteristics of the selected students as it is shown, being of this form in the heat of correspondence with their real necessities. This activities were developed during the partaking sport time and three stages framed during the course to the competitions. Its organization was based on the same principles on which the Program of the Special Olympic Games is fomented, extracting from the quarries of the base sport the sport talent, it is for that reason so important the work of preparation and participation in the bases, as from the whole scale practice it is that the quality is obtained or the sport talent within the ample range of sport disciplines. The work's objective is to apply competitive sport activities to improve the participation of late mental development children in the systematic training. These activities, proposed as solution, were very effective, since it was obtained a favorable atmosphere in all the school in students, teachers, specialists, family, community, making possible these children to improved their participation in the systematic training, their technical level improved a lot and, mainly, they demonstrated that the sport is one of the fundamental routes to the formation of values in this population group. The results thrown by the investigation are considered valuable since it is the base for the profit of good results in the competence.

Aircraft Segmentation in SAR Images Based on Improved Active Shape Model

Science.gov (United States)

Zhang, X.; Xiong, B.; Kuang, G.

2018-04-01

In SAR image interpretation, aircrafts are the important targets arousing much attention. However, it is far from easy to segment an aircraft from the background completely and precisely in SAR images. Because of the complex structure, different kinds of electromagnetic scattering take place on the aircraft surfaces. As a result, aircraft targets usually appear to be inhomogeneous and disconnected. It is a good idea to extract an aircraft target by the active shape model (ASM), since combination of the geometric information controls variations of the shape during the contour evolution. However, linear dimensionality reduction, used in classic ACM, makes the model rigid. It brings much trouble to segment different types of aircrafts. Aiming at this problem, an improved ACM based on ISOMAP is proposed in this paper. ISOMAP algorithm is used to extract the shape information of the training set and make the model flexible enough to deal with different aircrafts. The experiments based on real SAR data shows that the proposed method achieves obvious improvement in accuracy.

Improved Cole parameter extraction based on the least absolute deviation method

International Nuclear Information System (INIS)

Yang, Yuxiang; Ni, Wenwen; Sun, Qiang; Wen, He; Teng, Zhaosheng

2013-01-01

The Cole function is widely used in bioimpedance spectroscopy (BIS) applications. Fitting the measured BIS data onto the model and then extracting the Cole parameters (R 0 , R ∞ , α and τ) is a common practice. Accurate extraction of the Cole parameters from the measured BIS data has great significance for evaluating the physiological or pathological status of biological tissue. The traditional least-squares (LS)-based curve fitting method for Cole parameter extraction is often sensitive to noise or outliers and becomes non-robust. This paper proposes an improved Cole parameter extraction based on the least absolute deviation (LAD) method. Comprehensive simulation experiments are carried out and the performances of the LAD method are compared with those of the LS method under the conditions of outliers, random noises and both disturbances. The proposed LAD method exhibits much better robustness under all circumstances, which demonstrates that the LAD method is deserving as an improved alternative to the LS method for Cole parameter extraction for its robustness to outliers and noises. (paper)

Blink Number Forecasting Based on Improved Bayesian Fusion Algorithm for Fatigue Driving Detection

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Wei Sun

2015-01-01

Full Text Available An improved Bayesian fusion algorithm (BFA is proposed for forecasting the blink number in a continuous video. It assumes that, at one prediction interval, the blink number is correlated with the blink numbers of only a few previous intervals. With this assumption, the weights of the component predictors in the improved BFA are calculated according to their prediction performance only from a few intervals rather than from all intervals. Therefore, compared with the conventional BFA, the improved BFA is more sensitive to the disturbed condition of the component predictors for adjusting their weights more rapidly. To determine the most relevant intervals, the grey relation entropy-based analysis (GREBA method is proposed, which can be used analyze the relevancy between the historical data flows of blink number and the data flow at the current interval. Three single predictors, that is, the autoregressive integrated moving average (ARIMA, radial basis function neural network (RBFNN, and Kalman filter (KF, are designed and incorporated linearly into the BFA. Experimental results demonstrate that the improved BFA obviously outperforms the conventional BFA in both accuracy and stability; also fatigue driving can be accurately warned against in advance based on the blink number forecasted by the improved BFA.

A value-based taxonomy of improvement approaches in healthcare.

Science.gov (United States)

Colldén, Christian; Gremyr, Ida; Hellström, Andreas; Sporraeus, Daniella

2017-06-19

Purpose The concept of value is becoming increasingly fashionable in healthcare and various improvement approaches (IAs) have been introduced with the aim of increasing value. The purpose of this paper is to construct a taxonomy that supports the management of parallel IAs in healthcare. Design/methodology/approach Based on previous research, this paper proposes a taxonomy that includes the dimensions of view on value and organizational focus; three contemporary IAs - lean, value-based healthcare, and patient-centered care - are related to the taxonomy. An illustrative qualitative case study in the context of psychiatric (psychosis) care is then presented that contains data from 23 interviews and focuses on the value concept, IAs, and the proposed taxonomy. Findings Respondents recognized the dimensions of the proposed taxonomy and indicated its usefulness as support for choosing and combining different IAs into a coherent management model, and for facilitating dialog about IAs. The findings also suggested that the view of value as "health outcomes" is widespread, but healthcare professionals are less likely than managers to also view value as a process. Originality/value The conceptual contribution of this paper is to delineate some important characteristics of IAs in relation to the emerging "value era". It also highlights the coexistence of different IAs in healthcare management practice. A taxonomy is proposed that can help managers choose, adapt, and combine IAs in local management models.

Improved Genetic Algorithm Based on the Cooperation of Elite and Inverse-elite

Science.gov (United States)

Kanakubo, Masaaki; Hagiwara, Masafumi

In this paper, we propose an improved genetic algorithm based on the combination of Bee system and Inverse-elitism, both are effective strategies for the improvement of GA. In the Bee system, in the beginning, each chromosome tries to find good solution individually as global search. When some chromosome is regarded as superior one, the other chromosomes try to find solution around there. However, since chromosomes for global search are generated randomly, Bee system lacks global search ability. On the other hand, in the Inverse-elitism, an inverse-elite whose gene values are reversed from the corresponding elite is produced. This strategy greatly contributes to diversification of chromosomes, but it lacks local search ability. In the proposed method, the Inverse-elitism with Pseudo-simplex method is employed for global search of Bee system in order to strengthen global search ability. In addition, it also has strong local search ability. The proposed method has synergistic effects of the three strategies. We confirmed validity and superior performance of the proposed method by computer simulations.

Improved Kalman Filter-Based Speech Enhancement with Perceptual Post-Filtering

Institute of Scientific and Technical Information of China (English)

WEIJianqiang; DULimin; YANZhaoli; ZENGHui

2004-01-01

In this paper, a Kalman filter-based speech enhancement algorithm with some improvements of previous work is presented. A new technique based on spectral subtraction is used for separation speech and noise characteristics from noisy speech and for the computation of speech and noise Autoregressive (AR) parameters. In order to obtain a Kalman filter output with high audible quality, a perceptual post-filter is placed at the output of the Kalman filter to smooth the enhanced speech spectra.Extensive experiments indicate that this newly proposed method works well.

Improved Reliability-Based Optimization with Support Vector Machines and Its Application in Aircraft Wing Design

Directory of Open Access Journals (Sweden)

Yu Wang

2015-01-01

Full Text Available A new reliability-based design optimization (RBDO method based on support vector machines (SVM and the Most Probable Point (MPP is proposed in this work. SVM is used to create a surrogate model of the limit-state function at the MPP with the gradient information in the reliability analysis. This guarantees that the surrogate model not only passes through the MPP but also is tangent to the limit-state function at the MPP. Then, importance sampling (IS is used to calculate the probability of failure based on the surrogate model. This treatment significantly improves the accuracy of reliability analysis. For RBDO, the Sequential Optimization and Reliability Assessment (SORA is employed as well, which decouples deterministic optimization from the reliability analysis. The improved SVM-based reliability analysis is used to amend the error from linear approximation for limit-state function in SORA. A mathematical example and a simplified aircraft wing design demonstrate that the improved SVM-based reliability analysis is more accurate than FORM and needs less training points than the Monte Carlo simulation and that the proposed optimization strategy is efficient.

An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts.

Science.gov (United States)

Jiang, Shouyong; Yang, Shengxiang

2016-02-01

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.

A multi-view face recognition system based on cascade face detector and improved Dlib

Science.gov (United States)

Zhou, Hongjun; Chen, Pei; Shen, Wei

2018-03-01

In this research, we present a framework for multi-view face detect and recognition system based on cascade face detector and improved Dlib. This method is aimed to solve the problems of low efficiency and low accuracy in multi-view face recognition, to build a multi-view face recognition system, and to discover a suitable monitoring scheme. For face detection, the cascade face detector is used to extracted the Haar-like feature from the training samples, and Haar-like feature is used to train a cascade classifier by combining Adaboost algorithm. Next, for face recognition, we proposed an improved distance model based on Dlib to improve the accuracy of multiview face recognition. Furthermore, we applied this proposed method into recognizing face images taken from different viewing directions, including horizontal view, overlooks view, and looking-up view, and researched a suitable monitoring scheme. This method works well for multi-view face recognition, and it is also simulated and tested, showing satisfactory experimental results.

Image Super-Resolution Algorithm Based on an Improved Sparse Autoencoder

Directory of Open Access Journals (Sweden)

Detian Huang

2018-01-01

Full Text Available Due to the limitations of the resolution of the imaging system and the influence of scene changes and other factors, sometimes only low-resolution images can be acquired, which cannot satisfy the practical application’s requirements. To improve the quality of low-resolution images, a novel super-resolution algorithm based on an improved sparse autoencoder is proposed. Firstly, in the training set preprocessing stage, the high- and low-resolution image training sets are constructed, respectively, by using high-frequency information of the training samples as the characterization, and then the zero-phase component analysis whitening technique is utilized to decorrelate the formed joint training set to reduce its redundancy. Secondly, a constructed sparse regularization term is added to the cost function of the traditional sparse autoencoder to further strengthen the sparseness constraint on the hidden layer. Finally, in the dictionary learning stage, the improved sparse autoencoder is adopted to achieve unsupervised dictionary learning to improve the accuracy and stability of the dictionary. Experimental results validate that the proposed algorithm outperforms the existing algorithms both in terms of the subjective visual perception and the objective evaluation indices, including the peak signal-to-noise ratio and the structural similarity measure.

Microgrid Restraining Strategy Based on Improved DC Grid Connected DFIG Torque Ripple

Science.gov (United States)

Fei, Xia; Yang, Zhixiong; Zongze, Xia

2017-05-01

Aiming to the voltage of the stator side is generated by the modulation of the SSC in the improved topology, especially under the circumstance with the asymmTeric fault of stator side, DFIG’s electromagnTeic torque, amplifies ripple of grid-connected power for the grid side. The novel control mTehod suitable to stator side converter and rotor side converter based on reduced-order resonant controller (RORC) is proposed in this thesis, DFIG’s torque and output power performance are improved. Under the RORC control conditions the transfer functions of stator current and torque control system are established, the amplitude characteristic and the system stability of RORC control are analysed. The simulation results in Matlab/Simulink verify the correctness and validity of the proposed mTehod.

Improvement of Power Flow Calculation with Optimization Factor Based on Current Injection Method

Directory of Open Access Journals (Sweden)

Lei Wang

2014-01-01

Full Text Available This paper presents an improvement in power flow calculation based on current injection method by introducing optimization factor. In the method proposed by this paper, the PQ buses are represented by current mismatches while the PV buses are represented by power mismatches. It is different from the representations in conventional current injection power flow equations. By using the combined power and current injection mismatches method, the number of the equations required can be decreased to only one for each PV bus. The optimization factor is used to improve the iteration process and to ensure the effectiveness of the improved method proposed when the system is ill-conditioned. To verify the effectiveness of the method, the IEEE test systems are tested by conventional current injection method and the improved method proposed separately. Then the results are compared. The comparisons show that the optimization factor improves the convergence character effectively, especially that when the system is at high loading level and R/X ratio, the iteration number is one or two times less than the conventional current injection method. When the overloading condition of the system is serious, the iteration number in this paper appears 4 times less than the conventional current injection method.

Sparsity-based shrinkage approach for practicability improvement of H-LBP-based edge extraction

Energy Technology Data Exchange (ETDEWEB)

Zhao, Chenyi [School of Physics, Northeast Normal University, Changchun 130024 (China); Qiao, Shuang, E-mail: qiaos810@nenu.edu.cn [School of Physics, Northeast Normal University, Changchun 130024 (China); Sun, Jianing, E-mail: sunjn118@nenu.edu.cn [School of Mathematics and Statistics, Northeast Normal University, Changchun 130024 (China); Zhao, Ruikun; Wu, Wei [Jilin Cancer Hospital, Changchun 130021 (China)

2016-07-21

The local binary pattern with H function (H-LBP) technique enables fast and efficient edge extraction in digital radiography. In this paper, we reformulate the model of H-LBP and propose a novel sparsity-based shrinkage approach, in which the threshold can be adapted to the data sparsity. Using this model, we upgrade fast H-LBP framework and apply it to real digital radiography. The experiments show that the method improved using the new shrinkage approach can avoid elaborately artificial modulation of parameters and possess greater robustness in edge extraction compared with the other current methods without increasing processing time. - Highlights: • An novel sparsity-based shrinkage approach for edge extraction on digital radiography is proposed. • The threshold of SS-LBP can be adaptive to the data sparsity. • SS-LBP is the development of AH-LBP and H-LBP. • Without boosting processing time and losing processing efficiency, SS-LBP can avoid elaborately artificial modulation of parameters provides. • SS-LBP has more robust performance in edge extraction compared with the existing methods.

A Novel Segment-Based Approach for Improving Classification Performance of Transport Mode Detection.

Science.gov (United States)

Guvensan, M Amac; Dusun, Burak; Can, Baris; Turkmen, H Irem

2017-12-30

Transportation planning and solutions have an enormous impact on city life. To minimize the transport duration, urban planners should understand and elaborate the mobility of a city. Thus, researchers look toward monitoring people's daily activities including transportation types and duration by taking advantage of individual's smartphones. This paper introduces a novel segment-based transport mode detection architecture in order to improve the results of traditional classification algorithms in the literature. The proposed post-processing algorithm, namely the Healing algorithm, aims to correct the misclassification results of machine learning-based solutions. Our real-life test results show that the Healing algorithm could achieve up to 40% improvement of the classification results. As a result, the implemented mobile application could predict eight classes including stationary, walking, car, bus, tram, train, metro and ferry with a success rate of 95% thanks to the proposed multi-tier architecture and Healing algorithm.

An Improved PSO-Based MPPT Control Strategy for Photovoltaic Systems

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M. Abdulkadir

2014-01-01

Full Text Available This paper presents a control strategy proposed for power maximizing which is a critical mechanism to ensure power track is maximized. Many tracking algorithms have been proposed for this purpose. One of the more commonly used techniques is the incremental conductance method. In this paper, an improved particle swarm optimization- (IPSO- based MPPT technique for photovoltaic system operating under varying environmental conditions is proposed. The approach of linearly decreasing scheme for weighting factor and cognitive and social parameter is modified. The proposed control scheme can overcome deficiency and accelerate convergence of the IPSO-based MPPT algorithm. The approach is not only capable of tracking the maximum power point under uniform insolation state, but also able to find the maximum power point under fast changing nonuniform insolation conditions. The photovoltaic systematic process with control schemes is created using MATLAB Simulink to verify the effectiveness with several simulations being carried out and then compared with the conventional incremental conductance technique. Lastly, the effectiveness of the intended techniques is proven using real data obtained form previous literature. With the change in insolation and temperature portrait, it produces exceptional MPPT maximization. This shows that optimum performance is achieved using the intended method compared to the typical method.

Ratio-based lengths of intervals to improve fuzzy time series forecasting.

Science.gov (United States)

Huarng, Kunhuang; Yu, Tiffany Hui-Kuang

2006-04-01

The objective of this study is to explore ways of determining the useful lengths of intervals in fuzzy time series. It is suggested that ratios, instead of equal lengths of intervals, can more properly represent the intervals among observations. Ratio-based lengths of intervals are, therefore, proposed to improve fuzzy time series forecasting. Algebraic growth data, such as enrollments and the stock index, and exponential growth data, such as inventory demand, are chosen as the forecasting targets, before forecasting based on the various lengths of intervals is performed. Furthermore, sensitivity analyses are also carried out for various percentiles. The ratio-based lengths of intervals are found to outperform the effective lengths of intervals, as well as the arbitrary ones in regard to the different statistical measures. The empirical analysis suggests that the ratio-based lengths of intervals can also be used to improve fuzzy time series forecasting.

Improved chaotic maps-based password-authenticated key agreement using smart cards

Science.gov (United States)

Lin, Han-Yu

2015-02-01

Elaborating on the security of password-based authenticated key agreement, in this paper, the author cryptanalyzes a chaotic maps-based password-authenticated key agreement proposed by Guo and Chang recently. Specifically, their protocol could not achieve strong user anonymity due to a fixed parameter and a malicious adversary is able to derive the shared session key by manipulating the property of Chebyshev chaotic maps. Additionally, the author also presents an improved scheme to eliminate the above weaknesses and still maintain the efficiency.

A GPS-Based Control Method for Load Sharing and Power Quality Improvement in Microgrids

DEFF Research Database (Denmark)

Golsorkhi, Mohammad; Lu, Dylan; Savaghebi, Mehdi

2016-01-01

This paper proposes a novel control method for accurate sharing of load current among the Distributed Energy Resources (DER) and high power quality operating in islanded ac microgrids. This control scheme is based on hierarchical structure comprising of decentralized primary controllers and a cen....... The secondary controller produces compensation signals at fundamental and dominant harmonics to improve the voltage quality at a sensitive load bus. Experimental results are presented to validate the efficacy of the proposed method.......This paper proposes a novel control method for accurate sharing of load current among the Distributed Energy Resources (DER) and high power quality operating in islanded ac microgrids. This control scheme is based on hierarchical structure comprising of decentralized primary controllers...

Chaotic improved PSO-based multi-objective optimization for minimization of power losses and L index in power systems

International Nuclear Information System (INIS)

Chen, Gonggui; Liu, Lilan; Song, Peizhu; Du, Yangwei

2014-01-01

Highlights: • New method for MOORPD problem using MOCIPSO and MOIPSO approaches. • Constrain-prior Pareto-dominance method is proposed to meet the constraints. • The limits of the apparent power flow of transmission line are considered. • MOORPD model is built up for MOORPD problem. • The achieved results by MOCIPSO and MOIPSO approaches are better than MOPSO method. - Abstract: Multi-objective optimal reactive power dispatch (MOORPD) seeks to not only minimize power losses, but also improve the stability of power system simultaneously. In this paper, the static voltage stability enhancement is achieved through incorporating L index in MOORPD problem. Chaotic improved PSO-based multi-objective optimization (MOCIPSO) and improved PSO-based multi-objective optimization (MOIPSO) approaches are proposed for solving complex multi-objective, mixed integer nonlinear problems such as minimization of power losses and L index in power systems simultaneously. In MOCIPSO and MOIPSO based optimization approaches, crossover operator is proposed to enhance PSO diversity and improve their global searching capability, and for MOCIPSO based optimization approach, chaotic sequences based on logistic map instead of random sequences is introduced to PSO for enhancing exploitation capability. In the two approaches, constrain-prior Pareto-dominance method (CPM) is proposed to meet the inequality constraints on state variables, the sorting and crowding distance methods are considered to maintain a well distributed Pareto optimal solutions, and moreover, fuzzy set theory is employed to extract the best compromise solution over the Pareto optimal curve. The proposed approaches have been examined and tested in the IEEE 30 bus and the IEEE 57 bus power systems. The performances of MOCIPSO, MOIPSO, and multi-objective PSO (MOPSO) approaches are compared with respect to multi-objective performance measures. The simulation results are promising and confirm the ability of MOCIPSO and

Numerical Analysis of Modeling Based on Improved Elman Neural Network

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Shao Jie

2014-01-01

Full Text Available A modeling based on the improved Elman neural network (IENN is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL model, Chebyshev neural network (CNN model, and basic Elman neural network (BENN model, the proposed model has better performance.

A Symmetric Chaos-Based Image Cipher with an Improved Bit-Level Permutation Strategy

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Chong Fu

2014-02-01

Full Text Available Very recently, several chaos-based image ciphers using a bit-level permutation have been suggested and shown promising results. Due to the diffusion effect introduced in the permutation stage, the workload of the time-consuming diffusion stage is reduced, and hence the performance of the cryptosystem is improved. In this paper, a symmetric chaos-based image cipher with a 3D cat map-based spatial bit-level permutation strategy is proposed. Compared with those recently proposed bit-level permutation methods, the diffusion effect of the new method is superior as the bits are shuffled among different bit-planes rather than within the same bit-plane. Moreover, the diffusion key stream extracted from hyperchaotic system is related to both the secret key and the plain image, which enhances the security against known/chosen plaintext attack. Extensive security analysis has been performed on the proposed scheme, including the most important ones like key space analysis, key sensitivity analysis, plaintext sensitivity analysis and various statistical analyses, which has demonstrated the satisfactory security of the proposed scheme

An improved recommended algorithm for network structure based on two partial graphs

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Deng Song

2017-08-01

Full Text Available In this thesis,we introduce an improved algorithm based on network structure.Based on the standard material diffusion algorithm,considering the influence of the user's score on the recommendation,the adjustment factor of the initial resource allocation vector and the resource transfer matrix in the recommendation algorithm is improved.Using the practical data set from GroupLens webite to evaluate the performance of the proposed algorithm,we performed a series of experiments.The experimental results reveal that it can yield better recommendation accuracy and has higher hitting rate than collaborative filtering,network-based inference.It can solve the problem of cold start and scalability in the standard material diffusion algorithm.And it also can make the recommendation results diversified.

Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image

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Huiyan Jiang

2013-01-01

Full Text Available A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our improved GrowCut approach is used as an initial contour for the future precise segmentation based on Snakes model. At last, several experiments are carried out to demonstrate the performance of our proposed approach and some comparisons are conducted between the traditional GrowCut algorithm. Experimental results show that the improved approach not only has a better robustness and precision but also is more efficient than the traditional GrowCut method.

Improved Collaborative Representation Classifier Based on l2-Regularized for Human Action Recognition

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Shirui Huo

2017-01-01

Full Text Available Human action recognition is an important recent challenging task. Projecting depth images onto three depth motion maps (DMMs and extracting deep convolutional neural network (DCNN features are discriminant descriptor features to characterize the spatiotemporal information of a specific action from a sequence of depth images. In this paper, a unified improved collaborative representation framework is proposed in which the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and calculated. The improved collaborative representation classifier (ICRC based on l2-regularized for human action recognition is presented to maximize the likelihood that a test sample belongs to each class, then theoretical investigation into ICRC shows that it obtains a final classification by computing the likelihood for each class. Coupled with the DMMs and DCNN features, experiments on depth image-based action recognition, including MSRAction3D and MSRGesture3D datasets, demonstrate that the proposed approach successfully using a distance-based representation classifier achieves superior performance over the state-of-the-art methods, including SRC, CRC, and SVM.

The improved business valuation model for RFID company based on the community mining method.

Science.gov (United States)

Li, Shugang; Yu, Zhaoxu

2017-01-01

Nowadays, the appetite for the investment and mergers and acquisitions (M&A) activity in RFID companies is growing rapidly. Although the huge number of papers have addressed the topic of business valuation models based on statistical methods or neural network methods, only a few are dedicated to constructing a general framework for business valuation that improves the performance with network graph (NG) and the corresponding community mining (CM) method. In this study, an NG based business valuation model is proposed, where real options approach (ROA) integrating CM method is designed to predict the company's net profit as well as estimate the company value. Three improvements are made in the proposed valuation model: Firstly, our model figures out the credibility of the node belonging to each community and clusters the network according to the evolutionary Bayesian method. Secondly, the improved bacterial foraging optimization algorithm (IBFOA) is adopted to calculate the optimized Bayesian posterior probability function. Finally, in IBFOA, bi-objective method is used to assess the accuracy of prediction, and these two objectives are combined into one objective function using a new Pareto boundary method. The proposed method returns lower forecasting error than 10 well-known forecasting models on 3 different time interval valuing tasks for the real-life simulation of RFID companies.

An Improved FCM Medical Image Segmentation Algorithm Based on MMTD

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Ningning Zhou

2014-01-01

Full Text Available Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.

A new cooperative MIMO scheme based on SM for energy-efficiency improvement in wireless sensor network.

Science.gov (United States)

Peng, Yuyang; Choi, Jaeho

2014-01-01

Improving the energy efficiency in wireless sensor networks (WSN) has attracted considerable attention nowadays. The multiple-input multiple-output (MIMO) technique has been proved as a good candidate for improving the energy efficiency, but it may not be feasible in WSN which is due to the size limitation of the sensor node. As a solution, the cooperative multiple-input multiple-output (CMIMO) technique overcomes this constraint and shows a dramatically good performance. In this paper, a new CMIMO scheme based on the spatial modulation (SM) technique named CMIMO-SM is proposed for energy-efficiency improvement. We first establish the system model of CMIMO-SM. Based on this model, the transmission approach is introduced graphically. In order to evaluate the performance of the proposed scheme, a detailed analysis in terms of energy consumption per bit of the proposed scheme compared with the conventional CMIMO is presented. Later, under the guide of this new scheme we extend our proposed CMIMO-SM to a multihop clustered WSN for further achieving energy efficiency by finding an optimal hop-length. Equidistant hop as the traditional scheme will be compared in this paper. Results from the simulations and numerical experiments indicate that by the use of the proposed scheme, significant savings in terms of total energy consumption can be achieved. Combining the proposed scheme with monitoring sensor node will provide a good performance in arbitrary deployed WSN such as forest fire detection system.

Improve the functional status of students using the proposed method recovery

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Evtukh M.I.

2012-12-01

Full Text Available Purpose - to improve the organizational and methodological foundations of physical education for the improvement of high school students in training. The study involved 152 students of the second year of the International Economics and Humanities University named after Stepan Demyanchuk. Students were divided into control (n = 76 and primary (n = 76 groups, which were similar in age and physical development. At the end of the study, through the application of the proposed technique improvement in students the core group, was able to restore the function of the respiratory and cardiovascular systems to the possibilities of healthy untrained people. A similar increase in the functionality of the core group of students registered with the definition of the index Skibinski - held a combined evaluation of functions of the respiratory and cardiovascular systems of students and determine its growth with satisfactory to good level.

The Robot Path Planning Based on Improved Artificial Fish Swarm Algorithm

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Yi Zhang

2016-01-01

Full Text Available Path planning is critical to the efficiency and fidelity of robot navigation. The solution of robot path planning is to seek a collision-free and the shortest path from the start node to target node. In this paper, we propose a new improved artificial fish swarm algorithm (IAFSA to process the mobile robot path planning problem in a real environment. In IAFSA, an attenuation function is introduced to improve the visual of standard AFSA and get the balance of global search and local search; also, an adaptive operator is introduced to enhance the adaptive ability of step. Besides, a concept of inertia weight factor is proposed in IAFSA inspired by PSO intelligence algorithm to improve the convergence rate and accuracy of IAFSA. Five unconstrained optimization test functions are given to illustrate the strong searching ability and ideal convergence of IAFSA. Finally, the ROS (robot operation system based experiment is carried out on a Pioneer 3-DX mobile robot; the experiment results also show the superiority of IAFSA.

Development of Wavelet Based Tools for Improving the γ-ray Spectrometry

International Nuclear Information System (INIS)

Hamzaoui, E-M.; El Badri, L.; Laraki, K.; Cherkaoui-Elmorsli, R.

2013-06-01

In this article, we propose a wavelet transform based tool to improve the use of gamma ray spectrometry as a nuclear technique. First, we attempt to study the problem of filtering the preamplifier's output signals of HPGe detector used in the measurements chain. Thus, we developed a nonlinear method based on discrete Coiflet transform combined to principal component analysis, which allows a significant improvement of the signal to noise ratio (SNR) at the output of the HPGe preamplifier. In a second step, the continuous wavelet transform, based on the Mexican Hat mother function, is used to achieve an automatic processing of the spectrometric data. This method permits us to get an alternative representation of the gamma energy spectrum. The results of different tests, performed in both the presence and the absence of a gamma radiation source, are illustrated. (authors)

Harmonic analysis of electrified railway based on improved HHT

Science.gov (United States)

Wang, Feng

2018-04-01

In this paper, the causes and harms of the current electric locomotive electrical system harmonics are firstly studied and analyzed. Based on the characteristics of the harmonics in the electrical system, the Hilbert-Huang transform method is introduced. Based on the in-depth analysis of the empirical mode decomposition method and the Hilbert transform method, the reasons and solutions to the endpoint effect and modal aliasing problem in the HHT method are explored. For the endpoint effect of HHT, this paper uses point-symmetric extension method to extend the collected data; In allusion to the modal aliasing problem, this paper uses the high frequency harmonic assistant method to preprocess the signal and gives the empirical formula of high frequency auxiliary harmonic. Finally, combining the suppression of HHT endpoint effect and modal aliasing problem, an improved HHT method is proposed and simulated by matlab. The simulation results show that the improved HHT is effective for the electric locomotive power supply system.

Research on Improved Control Strategy for STATCOM Based on Virtual Matrix Method

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Wang Xudong

2016-01-01

Full Text Available Fast and accurate detection of reactive current is the precondition for the realization of static synchronous compensator (STATCOM reactive power compensation and harmonic suppression. Aiming at deviation and delay of the traditional reactive current detection algorithm with phase-locked loop (PLL and low-pass filter (LPF of STATCOM, a novel improved reactive current detection algorithm without PLL is proposed, in which the virtual matrix (VM is built to replace the original PLL, and improved current average value filter is used to realize the function of LPF, so as to improve the real-time performance and robustness of reactive current detection. The realization process of VM detection method is derived in this paper, and improved control strategy for STATCOM is designed based on the VM detection method. Simulation analysis of the proposed detection algorithm and control strategy is conducted in Matlab platform so as to verify the correctness and effectiveness of the control strategy. The VM detection has the advantages of simple structure, fast response and easy for digital realization, which provides reference for the improvement of reactive power compensation precision for STATCOM.

A model-based radiography restoration method based on simple scatter-degradation scheme for improving image visibility

Science.gov (United States)

Kim, K.; Kang, S.; Cho, H.; Kang, W.; Seo, C.; Park, C.; Lee, D.; Lim, H.; Lee, H.; Kim, G.; Park, S.; Park, J.; Kim, W.; Jeon, D.; Woo, T.; Oh, J.

2018-02-01

In conventional planar radiography, image visibility is often limited mainly due to the superimposition of the object structure under investigation and the artifacts caused by scattered x-rays and noise. Several methods, including computed tomography (CT) as a multiplanar imaging modality, air-gap and grid techniques for the reduction of scatters, phase-contrast imaging as another image-contrast modality, etc., have extensively been investigated in attempt to overcome these difficulties. However, those methods typically require higher x-ray doses or special equipment. In this work, as another approach, we propose a new model-based radiography restoration method based on simple scatter-degradation scheme where the intensity of scattered x-rays and the transmission function of a given object are estimated from a single x-ray image to restore the original degraded image. We implemented the proposed algorithm and performed an experiment to demonstrate its viability. Our results indicate that the degradation of image characteristics by scattered x-rays and noise was effectively recovered by using the proposed method, which improves the image visibility in radiography considerably.

Improved Real-time Denoising Method Based on Lifting Wavelet Transform

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Liu Zhaohua

2014-06-01

Full Text Available Signal denoising can not only enhance the signal to noise ratio (SNR but also reduce the effect of noise. In order to satisfy the requirements of real-time signal denoising, an improved semisoft shrinkage real-time denoising method based on lifting wavelet transform was proposed. The moving data window technology realizes the real-time wavelet denoising, which employs wavelet transform based on lifting scheme to reduce computational complexity. Also hyperbolic threshold function and recursive threshold computing can ensure the dynamic characteristics of the system, in addition, it can improve the real-time calculating efficiency as well. The simulation results show that the semisoft shrinkage real-time denoising method has quite a good performance in comparison to the traditional methods, namely soft-thresholding and hard-thresholding. Therefore, this method can solve more practical engineering problems.

Reliability-Based Design Optimization of Trusses with Linked-Discrete Design Variables using the Improved Firefly Algorithm

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N. M. Okasha

2016-04-01

Full Text Available In this paper, an approach for conducting a Reliability-Based Design Optimization (RBDO of truss structures with linked-discrete design variables is proposed. The sections of the truss members are selected from the AISC standard tables and thus the design variables that represent the properties of each section are linked. Latin hypercube sampling is used in the evaluation of the structural reliability. The improved firefly algorithm is used for the optimization solution process. It was found that in order to use the improved firefly algorithm for efficiently solving problems of reliability-based design optimization with linked-discrete design variables; it needs to be modified as proposed in this paper to accelerate its convergence.

Risk Assessment for Distribution Systems Using an Improved PEM-Based Method Considering Wind and Photovoltaic Power Distribution

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Qingwu Gong

2017-03-01

Full Text Available The intermittency and variability of permeated distributed generators (DGs could cause many critical security and economy risks to distribution systems. This paper applied a certain mathematical distribution to imitate the output variability and uncertainty of DGs. Then, four risk indices—EENS (expected energy not supplied, PLC (probability of load curtailment, EFLC (expected frequency of load curtailment, and SI (severity index—were established to reflect the system risk level of the distribution system. For the certain mathematical distribution of the DGs’ output power, an improved PEM (point estimate method-based method was proposed to calculate these four system risk indices. In this improved PEM-based method, an enumeration method was used to list the states of distribution systems, and an improved PEM was developed to deal with the uncertainties of DGs, and the value of load curtailment in distribution systems was calculated by an optimal power flow algorithm. Finally, the effectiveness and advantages of this proposed PEM-based method for distribution system assessment were verified by testing a modified IEEE 30-bus system. Simulation results have shown that this proposed PEM-based method has a high computational accuracy and highly reduced computational costs compared with other risk assessment methods and is very effective for risk assessments.

A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

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R. Venkata Rao

2014-01-01

Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT.

Science.gov (United States)

Wen, Yintang; Jia, Yao; Zhang, Yuyan; Luo, Xiaoyuan; Wang, Hongrui

2017-10-25

This paper studies the defect detection problem of adhesive layer of thermal insulation materials. A novel detection method based on an improved particle swarm optimization (PSO) algorithm of Electrical Capacitance Tomography (ECT) is presented. Firstly, a least squares support vector machine is applied for data processing of measured capacitance values. Then, the improved PSO algorithm is proposed and applied for image reconstruction. Finally, some experiments are provided to verify the effectiveness of the proposed method in defect detection for adhesive layer of thermal insulation materials. The performance comparisons demonstrate that the proposed method has higher precision by comparing with traditional ECT algorithms.

Improving Generalization Based on l1-Norm Regularization for EEG-Based Motor Imagery Classification

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Yuwei Zhao

2018-05-01

Full Text Available Multichannel electroencephalography (EEG is widely used in typical brain-computer interface (BCI systems. In general, a number of parameters are essential for a EEG classification algorithm due to redundant features involved in EEG signals. However, the generalization of the EEG method is often adversely affected by the model complexity, considerably coherent with its number of undetermined parameters, further leading to heavy overfitting. To decrease the complexity and improve the generalization of EEG method, we present a novel l1-norm-based approach to combine the decision value obtained from each EEG channel directly. By extracting the information from different channels on independent frequency bands (FB with l1-norm regularization, the method proposed fits the training data with much less parameters compared to common spatial pattern (CSP methods in order to reduce overfitting. Moreover, an effective and efficient solution to minimize the optimization object is proposed. The experimental results on dataset IVa of BCI competition III and dataset I of BCI competition IV show that, the proposed method contributes to high classification accuracy and increases generalization performance for the classification of MI EEG. As the training set ratio decreases from 80 to 20%, the average classification accuracy on the two datasets changes from 85.86 and 86.13% to 84.81 and 76.59%, respectively. The classification performance and generalization of the proposed method contribute to the practical application of MI based BCI systems.

Shear wave velocity-based evaluation and design of stone column improved ground for liquefaction mitigation

Science.gov (United States)

Zhou, Yanguo; Sun, Zhengbo; Chen, Jie; Chen, Yunmin; Chen, Renpeng

2017-04-01

The evaluation and design of stone column improvement ground for liquefaction mitigation is a challenging issue for the state of practice. In this paper, a shear wave velocity-based approach is proposed based on the well-defined correlations of liquefaction resistance (CRR)-shear wave velocity ( V s)-void ratio ( e) of sandy soils, and the values of parameters in this approach are recommended for preliminary design purpose when site specific values are not available. The detailed procedures of pre- and post-improvement liquefaction evaluations and stone column design are given. According to this approach, the required level of ground improvement will be met once the target V s of soil is raised high enough (i.e., no less than the critical velocity) to resist the given earthquake loading according to the CRR- V s relationship, and then this requirement is transferred to the control of target void ratio (i.e., the critical e) according to the V s- e relationship. As this approach relies on the densification of the surrounding soil instead of the whole improved ground and is conservative by nature, specific considerations of the densification mechanism and effect are given, and the effects of drainage and reinforcement of stone columns are also discussed. A case study of a thermal power plant in Indonesia is introduced, where the effectiveness of stone column improved ground was evaluated by the proposed V s-based method and compared with the SPT-based evaluation. This improved ground performed well and experienced no liquefaction during subsequent strong earthquakes.

State of charge estimation of lithium-ion batteries based on an improved parameter identification method

International Nuclear Information System (INIS)

Xia, Bizhong; Chen, Chaoren; Tian, Yong; Wang, Mingwang; Sun, Wei; Xu, Zhihui

2015-01-01

The SOC (state of charge) is the most important index of the battery management systems. However, it cannot be measured directly with sensors and must be estimated with mathematical techniques. An accurate battery model is crucial to exactly estimate the SOC. In order to improve the model accuracy, this paper presents an improved parameter identification method. Firstly, the concept of polarization depth is proposed based on the analysis of polarization characteristics of the lithium-ion batteries. Then, the nonlinear least square technique is applied to determine the model parameters according to data collected from pulsed discharge experiments. The results show that the proposed method can reduce the model error as compared with the conventional approach. Furthermore, a nonlinear observer presented in the previous work is utilized to verify the validity of the proposed parameter identification method in SOC estimation. Finally, experiments with different levels of discharge current are carried out to investigate the influence of polarization depth on SOC estimation. Experimental results show that the proposed method can improve the SOC estimation accuracy as compared with the conventional approach, especially under the conditions of large discharge current. - Highlights: • The polarization characteristics of lithium-ion batteries are analyzed. • The concept of polarization depth is proposed to improve model accuracy. • A nonlinear least square technique is applied to determine the model parameters. • A nonlinear observer is used as the SOC estimation algorithm. • The validity of the proposed method is verified by experimental results.

An improved DPSO with mutation based on similarity algorithm for optimization of transmission lines loading

International Nuclear Information System (INIS)

Shayeghi, H.; Mahdavi, M.; Bagheri, A.

2010-01-01

Static transmission network expansion planning (STNEP) problem acquires a principal role in power system planning and should be evaluated carefully. Up till now, various methods have been presented to solve the STNEP problem. But only in one of them, lines adequacy rate has been considered at the end of planning horizon and the problem has been optimized by discrete particle swarm optimization (DPSO). DPSO is a new population-based intelligence algorithm and exhibits good performance on solution of the large-scale, discrete and non-linear optimization problems like STNEP. However, during the running of the algorithm, the particles become more and more similar, and cluster into the best particle in the swarm, which make the swarm premature convergence around the local solution. In order to overcome these drawbacks and considering lines adequacy rate, in this paper, expansion planning has been implemented by merging lines loading parameter in the STNEP and inserting investment cost into the fitness function constraints using an improved DPSO algorithm. The proposed improved DPSO is a new conception, collectivity, which is based on similarity between the particle and the current global best particle in the swarm that can prevent the premature convergence of DPSO around the local solution. The proposed method has been tested on the Garver's network and a real transmission network in Iran, and compared with the DPSO based method for solution of the TNEP problem. The results show that the proposed improved DPSO based method by preventing the premature convergence is caused that with almost the same expansion costs, the network adequacy is increased considerably. Also, regarding the convergence curves of both methods, it can be seen that precision of the proposed algorithm for the solution of the STNEP problem is more than DPSO approach.

An Improved Routing Optimization Algorithm Based on Travelling Salesman Problem for Social Networks

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Naixue Xiong

2017-06-01

Full Text Available A social network is a social structure, which is organized by the relationships or interactions between individuals or groups. Humans link the physical network with social network, and the services in the social world are based on data and analysis, which directly influence decision making in the physical network. In this paper, we focus on a routing optimization algorithm, which solves a well-known and popular problem. Ant colony algorithm is proposed to solve this problem effectively, but random selection strategy of the traditional algorithm causes evolution speed to be slow. Meanwhile, positive feedback and distributed computing model make the algorithm quickly converge. Therefore, how to improve convergence speed and search ability of algorithm is the focus of the current research. The paper proposes the improved scheme. Considering the difficulty about searching for next better city, new parameters are introduced to improve probability of selection, and delay convergence speed of algorithm. To avoid the shortest path being submerged, and improve sensitive speed of finding the shortest path, it updates pheromone regulation formula. The results show that the improved algorithm can effectively improve convergence speed and search ability for achieving higher accuracy and optimal results.

Improved laser-based triangulation sensor with enhanced range and resolution through adaptive optics-based active beam control.

Science.gov (United States)

Reza, Syed Azer; Khwaja, Tariq Shamim; Mazhar, Mohsin Ali; Niazi, Haris Khan; Nawab, Rahma

2017-07-20

Various existing target ranging techniques are limited in terms of the dynamic range of operation and measurement resolution. These limitations arise as a result of a particular measurement methodology, the finite processing capability of the hardware components deployed within the sensor module, and the medium through which the target is viewed. Generally, improving the sensor range adversely affects its resolution and vice versa. Often, a distance sensor is designed for an optimal range/resolution setting depending on its intended application. Optical triangulation is broadly classified as a spatial-signal-processing-based ranging technique and measures target distance from the location of the reflected spot on a position sensitive detector (PSD). In most triangulation sensors that use lasers as a light source, beam divergence-which severely affects sensor measurement range-is often ignored in calculations. In this paper, we first discuss in detail the limitations to ranging imposed by beam divergence, which, in effect, sets the sensor dynamic range. Next, we show how the resolution of laser-based triangulation sensors is limited by the interpixel pitch of a finite-sized PSD. In this paper, through the use of tunable focus lenses (TFLs), we propose a novel design of a triangulation-based optical rangefinder that improves both the sensor resolution and its dynamic range through adaptive electronic control of beam propagation parameters. We present the theory and operation of the proposed sensor and clearly demonstrate a range and resolution improvement with the use of TFLs. Experimental results in support of our claims are shown to be in strong agreement with theory.

Aerosol characteristics inversion based on the improved lidar ratio profile with the ground-based rotational Raman-Mie lidar

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Ji, Hongzhu; Zhang, Yinchao; Chen, Siying; Chen, He; Guo, Pan

2018-06-01

An iterative method, based on a derived inverse relationship between atmospheric backscatter coefficient and aerosol lidar ratio, is proposed to invert the lidar ratio profile and aerosol extinction coefficient. The feasibility of this method is investigated theoretically and experimentally. Simulation results show the inversion accuracy of aerosol optical properties for iterative method can be improved in the near-surface aerosol layer and the optical thick layer. Experimentally, as a result of the reduced insufficiency error and incoherence error, the aerosol optical properties with higher accuracy can be obtained in the near-surface region and the region of numerical derivative distortion. In addition, the particle component can be distinguished roughly based on this improved lidar ratio profile.

Proposal for the diagnosis and monitoring of quality, based on key performance indicators, that may lead to generate improvements in the Servicio de Radiologia of Hospital Mexico

International Nuclear Information System (INIS)

Brenes Acosta, Carolina; Cortes Barquero, Jorge

2014-01-01

Key performance indicators are proposed for the diagnosis, monitoring of quality and to generate improvements in the Servicio de Radiologia of Hospital Mexico. Critical performance key indicators are identified for service improvement. A method for collection of information is proposed of the individual production of medical assistants from Servicio de Radiologia of Hospital Mexico. Waiting times for patients in the ultrasound are measured in the Radiology Service, as indicator of patient experience and level of service. The satisfaction of treating physicians and their perception about the level quality of the service provided are identified with respect to the reports generated by the Departamento de Radiologia of Hospital Mexico. A peer review system is established to measure the clinical performance of the studies. A structured radiological report template is proposed for radiological practice in the Hospital Mexico, to facilitate its reading and guarantee a minimum standard of quality in information [es

An Improved Ensemble of Random Vector Functional Link Networks Based on Particle Swarm Optimization with Double Optimization Strategy.

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Ling, Qing-Hua; Song, Yu-Qing; Han, Fei; Yang, Dan; Huang, De-Shuang

2016-01-01

For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system.

HDR Pathological Image Enhancement Based on Improved Bias Field Correction and Guided Image Filter

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Qingjiao Sun

2016-01-01

Full Text Available Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR pathological image enhancement method based on improved bias field correction and guided image filter (GIF. Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work.

Dynamic Sensor Management Algorithm Based on Improved Efficacy Function

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TANG Shujuan

2016-01-01

Full Text Available A dynamic sensor management algorithm based on improved efficacy function is proposed to solve the multi-target and multi-sensory management problem. The tracking task precision requirements (TPR, target priority and sensor use cost were considered to establish the efficacy function by weighted sum the normalized value of the three factors. The dynamic sensor management algorithm was accomplished through control the diversities of the desired covariance matrix (DCM and the filtering covariance matrix (FCM. The DCM was preassigned in terms of TPR and the FCM was obtained by the centralized sequential Kalman filtering algorithm. The simulation results prove that the proposed method could meet the requirements of desired tracking precision and adjust sensor selection according to target priority and cost of sensor source usage. This makes sensor management scheme more reasonable and effective.

Improving Accuracy and Simplifying Training in Fingerprinting-Based Indoor Location Algorithms at Room Level

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Mario Muñoz-Organero

2016-01-01

Full Text Available Fingerprinting-based algorithms are popular in indoor location systems based on mobile devices. Comparing the RSSI (Received Signal Strength Indicator from different radio wave transmitters, such as Wi-Fi access points, with prerecorded fingerprints from located points (using different artificial intelligence algorithms, fingerprinting-based systems can locate unknown points with a few meters resolution. However, training the system with already located fingerprints tends to be an expensive task both in time and in resources, especially if large areas are to be considered. Moreover, the decision algorithms tend to be of high memory and CPU consuming in such cases and so does the required time for obtaining the estimated location for a new fingerprint. In this paper, we study, propose, and validate a way to select the locations for the training fingerprints which reduces the amount of required points while improving the accuracy of the algorithms when locating points at room level resolution. We present a comparison of different artificial intelligence decision algorithms and select those with better results. We do a comparison with other systems in the literature and draw conclusions about the improvements obtained in our proposal. Moreover, some techniques such as filtering nonstable access points for improving accuracy are introduced, studied, and validated.

Joint Dictionary Learning-Based Non-Negative Matrix Factorization for Voice Conversion to Improve Speech Intelligibility After Oral Surgery.

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Fu, Szu-Wei; Li, Pei-Chun; Lai, Ying-Hui; Yang, Cheng-Chien; Hsieh, Li-Chun; Tsao, Yu

2017-11-01

Objective: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). Methods: We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. Results: The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. Conclusion: The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NMF VC method in terms of STOI scores under the desired scenario. Significance: We confirmed the advantages of the proposed joint training criterion for the NMF-based VC. Moreover, we verified that the proposed JD-NMF can effectively improve the speech intelligibility scores of oral surgery patients. Objective: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient

Improving a HMM-based off-line handwriting recognition system using MME-PSO optimization

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Hamdani, Mahdi; El Abed, Haikal; Hamdani, Tarek M.; Märgner, Volker; Alimi, Adel M.

2011-01-01

One of the trivial steps in the development of a classifier is the design of its architecture. This paper presents a new algorithm, Multi Models Evolvement (MME) using Particle Swarm Optimization (PSO). This algorithm is a modified version of the basic PSO, which is used to the unsupervised design of Hidden Markov Model (HMM) based architectures. For instance, the proposed algorithm is applied to an Arabic handwriting recognizer based on discrete probability HMMs. After the optimization of their architectures, HMMs are trained with the Baum- Welch algorithm. The validation of the system is based on the IfN/ENIT database. The performance of the developed approach is compared to the participating systems at the 2005 competition organized on Arabic handwriting recognition on the International Conference on Document Analysis and Recognition (ICDAR). The final system is a combination between an optimized HMM with 6 other HMMs obtained by a simple variation of the number of states. An absolute improvement of 6% of word recognition rate with about 81% is presented. This improvement is achieved comparing to the basic system (ARAB-IfN). The proposed recognizer outperforms also most of the known state-of-the-art systems.

An Improved EMD-Based Dissimilarity Metric for Unsupervised Linear Subspace Learning

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Xiangchun Yu

2018-01-01

Full Text Available We investigate a novel way of robust face image feature extraction by adopting the methods based on Unsupervised Linear Subspace Learning to extract a small number of good features. Firstly, the face image is divided into blocks with the specified size, and then we propose and extract pooled Histogram of Oriented Gradient (pHOG over each block. Secondly, an improved Earth Mover’s Distance (EMD metric is adopted to measure the dissimilarity between blocks of one face image and the corresponding blocks from the rest of face images. Thirdly, considering the limitations of the original Locality Preserving Projections (LPP, we proposed the Block Structure LPP (BSLPP, which effectively preserves the structural information of face images. Finally, an adjacency graph is constructed and a small number of good features of a face image are obtained by methods based on Unsupervised Linear Subspace Learning. A series of experiments have been conducted on several well-known face databases to evaluate the effectiveness of the proposed algorithm. In addition, we construct the noise, geometric distortion, slight translation, slight rotation AR, and Extended Yale B face databases, and we verify the robustness of the proposed algorithm when faced with a certain degree of these disturbances.

Improved OAM-Based Radar Targets Detection Using Uniform Concentric Circular Arrays

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Mingtuan Lin

2016-01-01

Full Text Available Without any relative moves or beam scanning, the novel Orbital-Angular-Momentum- (OAM- based radar targets detection technique using uniform concentric circular arrays (UCCAs shows the azimuthal estimation ability, which provides new perspective for radar system design. However, the main estimation method, that is, Fast Fourier Transform (FFT, under this scheme suffers from low resolution. As a solution, this paper rebuilds the OAM-based radar targets detection model and introduces the multiple signal classification (MUSIC algorithm to improve the resolution for detecting targets within the main lobes. The spatial smoothing technique is proposed to tackle the coherent problem brought by the proposed model. Analytical study and simulation demonstrate the superresolution estimation capacity the MUSIC algorithm can achieve for detecting targets within the main lobes. The performance of the MUSIC algorithm to detect targets not illuminated by the main lobes is further evaluated. Despite the fact that MUSIC algorithm loses the resolution advantage under this case, its estimation is more robust than that of the FFT method. Overall, the proposed MUSIC algorithm for the OAM-based radar system demonstrates the superresolution ability for detecting targets within the main lobes and good robustness for targets out of the main lobes.

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

Science.gov (United States)

Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

2018-02-01

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

Proposals for an effective application of the continuous improvement at the ININ according to the IAEA 50-C/SG-Q new code

International Nuclear Information System (INIS)

Cardenas A, B.M.; Olivares O, L.A.

1997-01-01

This work contains the requirements of continuous improvement contained in the IAEA new code Q uality assurance for safety in nuclear power plants and other nuclear installations, code 50-C/SG-Q. Assuming that it was the base for to elaborate the review No. 5 of the Quality assurance plan at ININ, it was done an analysis to give proposals for the application of continuous improvement in effective way. The relevant points which must be taken in account at the continuous improvement process are: Direction responsibility, involucring of all personnel, process planning, education and training, elaboration of improvement projects, investigation of processes which can be improved, continuation of the improvement process and its evaluation. With the implantation of an effective continuous improvement system it will be obtained to get a better quality and a more efficient safety. (Author)

Multi-objective optimum design of fast tool servo based on improved differential evolution algorithm

International Nuclear Information System (INIS)

Zhu, Zhiwei; Zhou, Xiaoqin; Liu, Qiang; Zhao, Shaoxin

2011-01-01

The flexure-based mechanism is a promising realization of fast tool servo (FTS), and the optimum determination of flexure hinge parameters is one of the most important elements in the FTS design. This paper presents a multi-objective optimization approach to optimizing the dimension and position parameters of the flexure-based mechanism, which is based on the improved differential evolution algorithm embedding chaos and nonlinear simulated anneal algorithm. The results of optimum design show that the proposed algorithm has excellent performance and a well-balanced compromise is made between two conflicting objectives, the stroke and natural frequency of the FTS mechanism. The validation tests based on finite element analysis (FEA) show good agreement with the results obtained by using the proposed theoretical algorithm of this paper. Finally, a series of experimental tests are conducted to validate the design process and assess the performance of the FTS mechanism. The designed FTS reaches up to a stroke of 10.25 μm with at least 2 kHz bandwidth. Both of the FEA and experimental results demonstrate that the parameters of the flexure-based mechanism determined by the proposed approaches can achieve the specified performance and the proposed approach is suitable for the optimum design of FTS mechanism and of excellent performances

Improved cook stove adoption and impact assessment: A proposed methodology

International Nuclear Information System (INIS)

Troncoso, Karin; Armendáriz, Cynthia; Alatorre, Silvia

2013-01-01

Aims: Until now, the success of improved cook stoves (ICS) implementation programs has usually been measured by the number of ICS distributed. Some important research has been conducted to try to determine the effects of the use of an ICS in the user′s health, but these studies are expensive and time consuming. Moreover, no evaluations show the impact of the technology in the user′s lives. This study seeks to contribute to fill this gap. Scope: By applying cluster analysis techniques to survey data, the most relevant variables that explain adoption and impact were identified. Using these variables, two qualitative indexes are proposed: The adoption index considers the use of the new technology, the level of satisfaction, and the conditions of the stove. The impact index considers the changes in cooking practices and life quality brought about by the ICS. Both indexes are then applied to two implementation programs. The indexes show the differences between the program results and the user′s perceptions of each technology. Conclusions: The proposed indexes can be used to measure the success of an ICS implementation program in terms of the benefits perceived by the users of these technologies. -- Highlights: •Two qualitative indexes are proposed to measure the benefits perceived by ICS users. •Two implementation programs were assessed. •The approach enables determining the impact of ICS programs at a fraction of the cost. •It enables comparing the results of different implementation programs

An improved feature extraction algorithm based on KAZE for multi-spectral image

Science.gov (United States)

Yang, Jianping; Li, Jun

2018-02-01

Multi-spectral image contains abundant spectral information, which is widely used in all fields like resource exploration, meteorological observation and modern military. Image preprocessing, such as image feature extraction and matching, is indispensable while dealing with multi-spectral remote sensing image. Although the feature matching algorithm based on linear scale such as SIFT and SURF performs strong on robustness, the local accuracy cannot be guaranteed. Therefore, this paper proposes an improved KAZE algorithm, which is based on nonlinear scale, to raise the number of feature and to enhance the matching rate by using the adjusted-cosine vector. The experiment result shows that the number of feature and the matching rate of the improved KAZE are remarkably than the original KAZE algorithm.

Improved Savitzky-Golay-method-based fluorescence subtraction algorithm for rapid recovery of Raman spectra.

Science.gov (United States)

Chen, Kun; Zhang, Hongyuan; Wei, Haoyun; Li, Yan

2014-08-20

In this paper, we propose an improved subtraction algorithm for rapid recovery of Raman spectra that can substantially reduce the computation time. This algorithm is based on an improved Savitzky-Golay (SG) iterative smoothing method, which involves two key novel approaches: (a) the use of the Gauss-Seidel method and (b) the introduction of a relaxation factor into the iterative procedure. By applying a novel successive relaxation (SG-SR) iterative method to the relaxation factor, additional improvement in the convergence speed over the standard Savitzky-Golay procedure is realized. The proposed improved algorithm (the RIA-SG-SR algorithm), which uses SG-SR-based iteration instead of Savitzky-Golay iteration, has been optimized and validated with a mathematically simulated Raman spectrum, as well as experimentally measured Raman spectra from non-biological and biological samples. The method results in a significant reduction in computing cost while yielding consistent rejection of fluorescence and noise for spectra with low signal-to-fluorescence ratios and varied baselines. In the simulation, RIA-SG-SR achieved 1 order of magnitude improvement in iteration number and 2 orders of magnitude improvement in computation time compared with the range-independent background-subtraction algorithm (RIA). Furthermore the computation time of the experimentally measured raw Raman spectrum processing from skin tissue decreased from 6.72 to 0.094 s. In general, the processing of the SG-SR method can be conducted within dozens of milliseconds, which can provide a real-time procedure in practical situations.

PROPOSAL FOR IMPROVEMENT OF BUINESS CONTINUITY PLAN (BCP) BASED ON THE LESSONS OF THE GREAT EAST JAPAN EARTHQUAKE

Science.gov (United States)

Maruya, Hiroaki

For most Japanese companies and organizations, the enormous damage of the Great East Japan Earthquake was more than expected. In addition to great tsunami and earthquake motion, the lack of electricity and fuel disturbed to business activities seriously, and they should be considered important constraint factors in future earthquakes. Furthermore, disruption of supply chains also led considerable decline of production in many industries across Japan and foreign countries. Therefore it becomes urgent need for Japanese government and industries to utilize the lessons of the Great Earthquake and execute effective countermeasures, considering great earthquakes such as Tonankai & Nankai earthquakes and Tokyo Inland Earthquakes. Obviously most basic step is improving earthquake-resistant ability of buildings and facilities. In addition the spread of BCP and BCM to enterprises and organizations is indispensable. Based on the lessons, the BCM should include the point of view of the supply chain management more clearly, and emphasize "substitute strategy" more explicitly because a company should survive even if it completely loses its present production base. The central and local governments are requested, in addition to develop their own BCP, to improve related systematic conditions for BCM of the private sectors.

An Improved Physics-Based Model for Topographic Correction of Landsat TM Images

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Ainong Li

2015-05-01

Full Text Available Optical remotely sensed images in mountainous areas are subject to radiometric distortions induced by topographic effects, which need to be corrected before quantitative applications. Based on Li model and Sandmeier model, this paper proposed an improved physics-based model for the topographic correction of Landsat Thematic Mapper (TM images. The model employed Normalized Difference Vegetation Index (NDVI thresholds to approximately divide land targets into eleven groups, due to NDVI’s lower sensitivity to topography and its significant role in indicating land cover type. Within each group of terrestrial targets, corresponding MODIS BRDF (Bidirectional Reflectance Distribution Function products were used to account for land surface’s BRDF effect, and topographic effects are corrected without Lambertian assumption. The methodology was tested with two TM scenes of severely rugged mountain areas acquired under different sun elevation angles. Results demonstrated that reflectance of sun-averted slopes was evidently enhanced, and the overall quality of images was improved with topographic effect being effectively suppressed. Correlation coefficients between Near Infra-Red band reflectance and illumination condition reduced almost to zero, and coefficients of variance also showed some reduction. By comparison with the other two physics-based models (Sandmeier model and Li model, the proposed model showed favorable results on two tested Landsat scenes. With the almost half-century accumulation of Landsat data and the successive launch and operation of Landsat 8, the improved model in this paper can be potentially helpful for the topographic correction of Landsat and Landsat-like data.

Improved personalized recommendation based on a similarity network

Science.gov (United States)

Wang, Ximeng; Liu, Yun; Xiong, Fei

2016-08-01

A recommender system helps individual users find the preferred items rapidly and has attracted extensive attention in recent years. Many successful recommendation algorithms are designed on bipartite networks, such as network-based inference or heat conduction. However, most of these algorithms define the resource-allocation methods for an average allocation. That is not reasonable because average allocation cannot indicate the user choice preference and the influence between users which leads to a series of non-personalized recommendation results. We propose a personalized recommendation approach that combines the similarity function and bipartite network to generate a similarity network that improves the resource-allocation process. Our model introduces user influence into the recommender system and states that the user influence can make the resource-allocation process more reasonable. We use four different metrics to evaluate our algorithms for three benchmark data sets. Experimental results show that the improved recommendation on a similarity network can obtain better accuracy and diversity than some competing approaches.

A GPS-Based Control Framework for Accurate Current Sharing and Power Quality Improvement in Microgrids

DEFF Research Database (Denmark)

Golsorkhi, Mohammad; Savaghebi, Mehdi; Lu, Dylan

2017-01-01

This paper proposes a novel hierarchical control strategy for improvement of load sharing and power quality in ac microgrids. This control framework is composed of a droop based controller at the primary level, and a combination of distributed power sharing and voltage conditioning schemes...... consensus protocol to ensure proportional sharing of average power. The voltage conditioning scheme produces compensation signals at fundamental and dominant harmonics to improve the voltage quality at a sensitive load bus. Experimental results are presented to validate the efficacy of the proposed method....... dynamic response. The droop coefficient, which acts as a virtual resistance is adaptively changed as a function of the peak current. This strategy not only simplifies the control design but also improves the current sharing accuracy at high loading conditions. The distributed power sharing scheme uses...

A Proposed Method for Improving the Performance of P-Type GaAs IMPATTs

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H. A. El-Motaafy

2012-07-01

Full Text Available A special waveform is proposed and assumed to be the optimum waveform for p-type GaAs IMPATTs. This waveform is deduced after careful and extensive study of the performance of these devices. The results presented here indicate the superiority of the performance of the IMPATTs driven by the proposed waveform over that obtained when the same IMPATTs are driven by the conventional sinusoidal waveform. These results are obtained using a full-scale computer simulation program that takes fully into account all the physical effects pertinent to IMPATT operation.  In this paper, it is indicated that the superiority of the proposed waveform is attributed to its ability to reduce the bad effects that usually degrade the IMPATT performance such as the space-charge effect and the drift-velocity dropping below saturation effect. The superiority is also attributed to the ability of the proposed waveform to improve the phase relationship between the terminal voltage and the induced current.Key Words: Computer-Aided Design, GaAs IMPATT, Microwave Engineering

Motion Normalized Proportional Control for Improved Pattern Recognition-Based Myoelectric Control.

Science.gov (United States)

Scheme, Erik; Lock, Blair; Hargrove, Levi; Hill, Wendy; Kuruganti, Usha; Englehart, Kevin

2014-01-01

This paper describes two novel proportional control algorithms for use with pattern recognition-based myoelectric control. The systems were designed to provide automatic configuration of motion-specific gains and to normalize the control space to the user's usable dynamic range. Class-specific normalization parameters were calculated using data collected during classifier training and require no additional user action or configuration. The new control schemes were compared to the standard method of deriving proportional control using a one degree of freedom Fitts' law test for each of the wrist flexion/extension, wrist pronation/supination and hand close/open degrees of freedom. Performance was evaluated using the Fitts' law throughput value as well as more descriptive metrics including path efficiency, overshoot, stopping distance and completion rate. The proposed normalization methods significantly outperformed the incumbent method in every performance category for able bodied subjects (p < 0.001) and nearly every category for amputee subjects. Furthermore, one proposed method significantly outperformed both other methods in throughput (p < 0.0001), yielding 21% and 40% improvement over the incumbent method for amputee and able bodied subjects, respectively. The proposed control schemes represent a computationally simple method of fundamentally improving myoelectric control users' ability to elicit robust, and controlled, proportional velocity commands.

A Trusted Computing Architecture of Embedded System Based on Improved TPM

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Wang Xiaosheng

2017-01-01

Full Text Available The Trusted Platform Module (TPM currently used by PCs is not suitable for embedded systems, it is necessary to improve existing TPM. The paper proposes a trusted computing architecture with new TPM and the cryptographic system developed by China for the embedded system. The improved TPM consists of the Embedded System Trusted Cryptography Module (eTCM and the Embedded System Trusted Platform Control Module (eTPCM, which are combined and implemented the TPM’s autonomous control, active defense, high-speed encryption/decryption and other function through its internal bus arbitration module and symmetric and asymmetric cryptographic engines to effectively protect the security of embedded system. In our improved TPM, a trusted measurement method with chain model and star type model is used. Finally, the improved TPM is designed by FPGA, and it is used to a trusted PDA to carry out experimental verification. Experiments show that the trusted architecture of the embedded system based on the improved TPM is efficient, reliable and secure.

Improving the Computational Performance of Ontology-Based Classification Using Graph Databases

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Thomas J. Lampoltshammer

2015-07-01

Full Text Available The increasing availability of very high-resolution remote sensing imagery (i.e., from satellites, airborne laser scanning, or aerial photography represents both a blessing and a curse for researchers. The manual classification of these images, or other similar geo-sensor data, is time-consuming and leads to subjective and non-deterministic results. Due to this fact, (semi- automated classification approaches are in high demand in affected research areas. Ontologies provide a proper way of automated classification for various kinds of sensor data, including remotely sensed data. However, the processing of data entities—so-called individuals—is one of the most cost-intensive computational operations within ontology reasoning. Therefore, an approach based on graph databases is proposed to overcome the issue of a high time consumption regarding the classification task. The introduced approach shifts the classification task from the classical Protégé environment and its common reasoners to the proposed graph-based approaches. For the validation, the authors tested the approach on a simulation scenario based on a real-world example. The results demonstrate a quite promising improvement of classification speed—up to 80,000 times faster than the Protégé-based approach.

Improvement of vision measurement accuracy using Zernike moment based edge location error compensation model

International Nuclear Information System (INIS)

Cui, J W; Tan, J B; Zhou, Y; Zhang, H

2007-01-01

This paper presents the Zernike moment based model developed to compensate edge location errors for further improvement of the vision measurement accuracy by compensating the slight changes resulting from sampling and establishing mathematic expressions for subpixel location of theoretical and actual edges which are either vertical to or at an angle with X-axis. Experimental results show that the proposed model can be used to achieve a vision measurement accuracy of up to 0.08 pixel while the measurement uncertainty is less than 0.36μm. It is therefore concluded that as a model which can be used to achieve a significant improvement of vision measurement accuracy, the proposed model is especially suitable for edge location of images with low contrast

An approach of point cloud denoising based on improved bilateral filtering

Science.gov (United States)

Zheng, Zeling; Jia, Songmin; Zhang, Guoliang; Li, Xiuzhi; Zhang, Xiangyin

2018-04-01

An omnidirectional mobile platform is designed for building point cloud based on an improved filtering algorithm which is employed to handle the depth image. First, the mobile platform can move flexibly and the control interface is convenient to control. Then, because the traditional bilateral filtering algorithm is time-consuming and inefficient, a novel method is proposed which called local bilateral filtering (LBF). LBF is applied to process depth image obtained by the Kinect sensor. The results show that the effect of removing noise is improved comparing with the bilateral filtering. In the condition of off-line, the color images and processed images are used to build point clouds. Finally, experimental results demonstrate that our method improves the speed of processing time of depth image and the effect of point cloud which has been built.

Location Privacy Protection Based on Improved K-Value Method in Augmented Reality on Mobile Devices

Directory of Open Access Journals (Sweden)

Chunyong Yin

2017-01-01

Full Text Available With the development of Augmented Reality technology, the application of location based service (LBS is more and more popular, which provides enormous convenience to people’s life. User location information could be obtained at anytime and anywhere. So user location privacy security suffers huge threats. Therefore, it is crucial to pay attention to location privacy protection in LBS. Based on the architecture of the trusted third party (TTP, we analyzed the advantages and shortages of existing location privacy protection methods in LBS on mobile terminal. Then we proposed the improved K-value location privacy protection method according to privacy level, which combines k-anonymity method with pseudonym method. Through the simulation experiment, the results show that this improved method can anonymize all service requests effectively. In addition to the experiment of execution time, it demonstrated that our proposed method can realize the location privacy protection more efficiently.

An Improved Test Selection Optimization Model Based on Fault Ambiguity Group Isolation and Chaotic Discrete PSO

Directory of Open Access Journals (Sweden)

Xiaofeng Lv

2018-01-01

Full Text Available Sensor data-based test selection optimization is the basis for designing a test work, which ensures that the system is tested under the constraint of the conventional indexes such as fault detection rate (FDR and fault isolation rate (FIR. From the perspective of equipment maintenance support, the ambiguity isolation has a significant effect on the result of test selection. In this paper, an improved test selection optimization model is proposed by considering the ambiguity degree of fault isolation. In the new model, the fault test dependency matrix is adopted to model the correlation between the system fault and the test group. The objective function of the proposed model is minimizing the test cost with the constraint of FDR and FIR. The improved chaotic discrete particle swarm optimization (PSO algorithm is adopted to solve the improved test selection optimization model. The new test selection optimization model is more consistent with real complicated engineering systems. The experimental result verifies the effectiveness of the proposed method.

Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings

Directory of Open Access Journals (Sweden)

Sharif Uddin

2016-01-01

Full Text Available An enhanced k-nearest neighbor (k-NN classification algorithm is presented, which uses a density based similarity measure in addition to a distance based similarity measure to improve the diagnostic performance in bearing fault diagnosis. Due to its use of distance based similarity measure alone, the classification accuracy of traditional k-NN deteriorates in case of overlapping samples and outliers and is highly susceptible to the neighborhood size, k. This study addresses these limitations by proposing the use of both distance and density based measures of similarity between training and test samples. The proposed k-NN classifier is used to enhance the diagnostic performance of a bearing fault diagnosis scheme, which classifies different fault conditions based upon hybrid feature vectors extracted from acoustic emission (AE signals. Experimental results demonstrate that the proposed scheme, which uses the enhanced k-NN classifier, yields better diagnostic performance and is more robust to variations in the neighborhood size, k.

A Novel Wide-Area Backup Protection Based on Fault Component Current Distribution and Improved Evidence Theory

Directory of Open Access Journals (Sweden)

Zhe Zhang

2014-01-01

Full Text Available In order to solve the problems of the existing wide-area backup protection (WABP algorithms, the paper proposes a novel WABP algorithm based on the distribution characteristics of fault component current and improved Dempster/Shafer (D-S evidence theory. When a fault occurs, slave substations transmit to master station the amplitudes of fault component currents of transmission lines which are the closest to fault element. Then master substation identifies suspicious faulty lines according to the distribution characteristics of fault component current. After that, the master substation will identify the actual faulty line with improved D-S evidence theory based on the action states of traditional protections and direction components of these suspicious faulty lines. The simulation examples based on IEEE 10-generator-39-bus system show that the proposed WABP algorithm has an excellent performance. The algorithm has low requirement of sampling synchronization, small wide-area communication flow, and high fault tolerance.

A Novel Wide-Area Backup Protection Based on Fault Component Current Distribution and Improved Evidence Theory

Science.gov (United States)

Zhang, Zhe; Kong, Xiangping; Yin, Xianggen; Yang, Zengli; Wang, Lijun

2014-01-01

In order to solve the problems of the existing wide-area backup protection (WABP) algorithms, the paper proposes a novel WABP algorithm based on the distribution characteristics of fault component current and improved Dempster/Shafer (D-S) evidence theory. When a fault occurs, slave substations transmit to master station the amplitudes of fault component currents of transmission lines which are the closest to fault element. Then master substation identifies suspicious faulty lines according to the distribution characteristics of fault component current. After that, the master substation will identify the actual faulty line with improved D-S evidence theory based on the action states of traditional protections and direction components of these suspicious faulty lines. The simulation examples based on IEEE 10-generator-39-bus system show that the proposed WABP algorithm has an excellent performance. The algorithm has low requirement of sampling synchronization, small wide-area communication flow, and high fault tolerance. PMID:25050399

Steps toward improving ethical evaluation in health technology assessment: a proposed framework.

Science.gov (United States)

Assasi, Nazila; Tarride, Jean-Eric; O'Reilly, Daria; Schwartz, Lisa

2016-06-06

While evaluation of ethical aspects in health technology assessment (HTA) has gained much attention during the past years, the integration of ethics in HTA practice still presents many challenges. In response to the increasing demand for expansion of health technology assessment (HTA) methodology to include ethical issues more systematically, this article reports on a multi-stage study that aimed at construction of a framework for improving the integration of ethics in HTA. The framework was developed through the following phases: 1) a systematic review and content analysis of guidance documents for ethics in HTA; 2) identification of factors influencing the integration of ethical considerations in HTA; 3) preparation of an action-oriented framework based on the key elements of the existing guidance documents and identified barriers to and facilitators of their implementation; and 4) expert consultation and revision of the framework. The proposed framework consists of three main components: an algorithmic flowchart, which exhibits the different steps of an ethical inquiry throughout the HTA process, including: defining the objectives and scope of the evaluation, stakeholder analysis, assessing organizational capacity, framing ethical evaluation questions, ethical analysis, deliberation, and knowledge translation; a stepwise guide, which focuses on the task objectives and potential questions that are required to be addressed at each step; and a list of some commonly recommended or used tools to help facilitate the evaluation process. The proposed framework can be used to support and promote good practice in integration of ethics into HTA. However, further validation of the framework through case studies and expert consultation is required to establish its utility for HTA practice.

Improving UWB-Based Localization in IoT Scenarios with Statistical Models of Distance Error.

Science.gov (United States)

Monica, Stefania; Ferrari, Gianluigi

2018-05-17

Interest in the Internet of Things (IoT) is rapidly increasing, as the number of connected devices is exponentially growing. One of the application scenarios envisaged for IoT technologies involves indoor localization and context awareness. In this paper, we focus on a localization approach that relies on a particular type of communication technology, namely Ultra Wide Band (UWB). UWB technology is an attractive choice for indoor localization, owing to its high accuracy. Since localization algorithms typically rely on estimated inter-node distances, the goal of this paper is to evaluate the improvement brought by a simple (linear) statistical model of the distance error. On the basis of an extensive experimental measurement campaign, we propose a general analytical framework, based on a Least Square (LS) method, to derive a novel statistical model for the range estimation error between a pair of UWB nodes. The proposed statistical model is then applied to improve the performance of a few illustrative localization algorithms in various realistic scenarios. The obtained experimental results show that the use of the proposed statistical model improves the accuracy of the considered localization algorithms with a reduction of the localization error up to 66%.

R and D proposals to improve outages operation. Methods, practices and tools

International Nuclear Information System (INIS)

Dionis, Francois

2014-01-01

This paper deals with outage operation improvement. It offers a number of tracks on the interactions between the operation activities and maintenance, with a methodological perspective and proposals concerning the Information System. On the methodological point of view, a clever plant systems modeling may allow representing the needed characteristics in order to optimize tagouts, alignment procedures and the schedule. Tools must be taken n into account for new tagout practices such as tags sharing. It is possible to take advantage of 2D drawings integrated into the information system in order to improve the data controls and to visualize operation activities. An integrated set of mobile applications should allow field operators to join the information system for a better and safer performance. (author)

Improved power quality based high brightness LED lamp driver

African Journals Online (AJOL)

user

consists of a PFC Cuk DC-DC converter which operates in continuous conduction mode (CCM) to improve the ... In proposed LED driver as shown in Figure 1, a Cuk buck boost AC-DC converter ... Design and Analysis of Proposed LED Driver.

A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Wanxing Sheng

2013-01-01

Full Text Available A distribution generation (DG multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper. The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. The proposed algorithm is utilized to the optimize DG injection models to maximize DG utilization while minimizing system loss and environmental pollution. A revised IEEE 33-bus system with multiple DG units was used to test the multiobjective optimization algorithm in a distribution power system. The proposed algorithm was implemented and compared with the strength Pareto evolutionary algorithm 2 (SPEA2, a particle swarm optimization (PSO algorithm, and nondominated sorting genetic algorithm II (NGSA-II. The comparison of the results demonstrates the validity and practicality of utilizing DG units in terms of economic dispatch and optimal operation in a distribution power system.

A Project Strategic Index proposal for portfolio selection in electrical company based on the Analytic Network Process

Energy Technology Data Exchange (ETDEWEB)

Smith-Perera, Aida [Universidad Metropolitana de Caracas, Departamento de Gestion Tecnologica, Caracas 1071, Edo Miranda (Venezuela); Garcia-Melon, Monica; Poveda-Bautista, Rocio; Pastor-Ferrando, Juan-Pascual [Universidad Politecnica de Valencia, Departamento de Proyectos de Ingenieria, Camino de vera s/n 46022 Valencia (Spain)

2010-08-15

In this paper a new approach to prioritize project portfolio in an efficient and reliable way is presented. It is based on strategic objectives of the company and multicriteria decision methods. The paper introduces a rigorous method with acceptable complexity which seeks to assist managers of a big Electrical Company of Venezuela to distribute the annual budget among the possible improvement actions to be conducted on the electrical network of Caracas. A total of 15 network improvement actions grouped into three clusters according to the strategic objectives of the company have been analyzed using the Project Strategic Index (PSI) proposed. The approach combines the use of the Analytic Network Process (ANP) method with the information obtained from the experts during the decision-making process. The ANP method allows the aggregation of the experts' judgments on each of the indicators used into one Project Strategic Index. In addition, ANP is based on utility ratio functions which are the most appropriate for the analysis of uncertain data, like experts' estimations. Finally, unlike the other multicriteria techniques, ANP allows the decision problem to be modelled using the relationships among dependent criteria. The participating experts coincided in the appreciation that the method proposed in this paper is useful and an improvement from traditional budget distribution techniques. They find the results obtained coherent, the process seems sufficiently rigorous and precise, and the use of resources is significantly less than in other methods. (author)

A Project Strategic Index proposal for portfolio selection in electrical company based on the Analytic Network Process

International Nuclear Information System (INIS)

Smith-Perera, Aida; Garcia-Melon, Monica; Poveda-Bautista, Rocio; Pastor-Ferrando, Juan-Pascual

2010-01-01

In this paper a new approach to prioritize project portfolio in an efficient and reliable way is presented. It is based on strategic objectives of the company and multicriteria decision methods. The paper introduces a rigorous method with acceptable complexity which seeks to assist managers of a big Electrical Company of Venezuela to distribute the annual budget among the possible improvement actions to be conducted on the electrical network of Caracas. A total of 15 network improvement actions grouped into three clusters according to the strategic objectives of the company have been analyzed using the Project Strategic Index (PSI) proposed. The approach combines the use of the Analytic Network Process (ANP) method with the information obtained from the experts during the decision-making process. The ANP method allows the aggregation of the experts' judgments on each of the indicators used into one Project Strategic Index. In addition, ANP is based on utility ratio functions which are the most appropriate for the analysis of uncertain data, like experts' estimations. Finally, unlike the other multicriteria techniques, ANP allows the decision problem to be modelled using the relationships among dependent criteria. The participating experts coincided in the appreciation that the method proposed in this paper is useful and an improvement from traditional budget distribution techniques. They find the results obtained coherent, the process seems sufficiently rigorous and precise, and the use of resources is significantly less than in other methods. (author)

A Proposal of the European Association for the Study of Obesity to Improve the ICD-11 Diagnostic Criteria for Obesity Based on the Three Dimensions Etiology, Degree of Adiposity and Health Risk

Directory of Open Access Journals (Sweden)

Johannes Hebebrand

2017-07-01

Full Text Available Diagnostic criteria for complex medical conditions caused by a multitude of both genetic and environmental factors should be descriptive and avoid any attribution of causality. Furthermore, the wording used to describe a disorder should be evidence-based and avoid stigmatization of the affected individuals. Both terminology and categorizations should be readily comprehensible for healthcare professionals and guide clinical decision making. Uncertainties with respect to diagnostic issues and their implications may be addressed to direct future clinical research. In this context, the European Association of the Study of Obesity (EASO considers it an important endeavor to review the current ICD-11 Beta Draft for the definition of overweight and obesity and to propose a substantial revision. We aim to provide an overview of the key issues that we deem relevant for the discussion of the diagnostic criteria. We first discuss the current ICD-10 criteria and those proposed in the ICD 11 Beta Draft. We conclude with our own proposal for diagnostic criteria, which we believe will improve the assessment of patients with obesity in a clinically meaningful way.

A Proposal of the European Association for the Study of Obesity to Improve the ICD-11 Diagnostic Criteria for Obesity Based on the Three Dimensions Etiology, Degree of Adiposity and Health Risk

Science.gov (United States)

Hebebrand, Johannes; Holm, Jens-Christian; Woodward, Euan; Baker, Jennifer Lyn; Blaak, Ellen; Schutz, Dominique Durrer; Farpour-Lambert, Nathalie J.; Frühbeck, Gema; Halford, Jason G.C.; Lissner, Lauren; Micic, Dragan; Mullerova, Dana; Roman, Gabriela; Schindler, Karin; Toplak, Hermann; Visscher, Tommy L.S.; Yumuk, Volkan

2017-01-01

Diagnostic criteria for complex medical conditions caused by a multitude of both genetic and environmental factors should be descriptive and avoid any attribution of causality. Furthermore, the wording used to describe a disorder should be evidence-based and avoid stigmatization of the affected individuals. Both terminology and categorizations should be readily comprehensible for healthcare professionals and guide clinical decision making. Uncertainties with respect to diagnostic issues and their implications may be addressed to direct future clinical research. In this context, the European Association of the Study of Obesity (EASO) considers it an important endeavor to review the current ICD-11 Beta Draft for the definition of overweight and obesity and to propose a substantial revision. We aim to provide an overview of the key issues that we deem relevant for the discussion of the diagnostic criteria. We first discuss the current ICD-10 criteria and those proposed in the ICD 11 Beta Draft. We conclude with our own proposal for diagnostic criteria, which we believe will improve the assessment of patients with obesity in a clinically meaningful way. PMID:28738325

Improved Ordinary Measure and Image Entropy Theory based intelligent Copy Detection Method

Directory of Open Access Journals (Sweden)

Dengpan Ye

2011-10-01

Full Text Available Nowadays, more and more multimedia websites appear in social network. It brings some security problems, such as privacy, piracy, disclosure of sensitive contents and so on. Aiming at copyright protection, the copy detection technology of multimedia contents becomes a hot topic. In our previous work, a new computer-based copyright control system used to detect the media has been proposed. Based on this system, this paper proposes an improved media feature matching measure and an entropy based copy detection method. The Levenshtein Distance was used to enhance the matching degree when using for feature matching measure in copy detection. For entropy based copy detection, we make a fusion of the two features of entropy matrix of the entropy feature we extracted. Firstly,we extract the entropy matrix of the image and normalize it. Then, we make a fusion of the eigenvalue feature and the transfer matrix feature of the entropy matrix. The fused features will be used for image copy detection. The experiments show that compared to use these two kinds of features for image detection singly, using feature fusion matching method is apparent robustness and effectiveness. The fused feature has a high detection for copy images which have been received some attacks such as noise, compression, zoom, rotation and so on. Comparing with referred methods, the method proposed is more intelligent and can be achieved good performance.

Improving the learning of clinical reasoning through computer-based cognitive representation.

Science.gov (United States)

Wu, Bian; Wang, Minhong; Johnson, Janice M; Grotzer, Tina A

2014-01-01

Clinical reasoning is usually taught using a problem-solving approach, which is widely adopted in medical education. However, learning through problem solving is difficult as a result of the contextualization and dynamic aspects of actual problems. Moreover, knowledge acquired from problem-solving practice tends to be inert and fragmented. This study proposed a computer-based cognitive representation approach that externalizes and facilitates the complex processes in learning clinical reasoning. The approach is operationalized in a computer-based cognitive representation tool that involves argument mapping to externalize the problem-solving process and concept mapping to reveal the knowledge constructed from the problems. Twenty-nine Year 3 or higher students from a medical school in east China participated in the study. Participants used the proposed approach implemented in an e-learning system to complete four learning cases in 4 weeks on an individual basis. For each case, students interacted with the problem to capture critical data, generate and justify hypotheses, make a diagnosis, recall relevant knowledge, and update their conceptual understanding of the problem domain. Meanwhile, students used the computer-based cognitive representation tool to articulate and represent the key elements and their interactions in the learning process. A significant improvement was found in students' learning products from the beginning to the end of the study, consistent with students' report of close-to-moderate progress in developing problem-solving and knowledge-construction abilities. No significant differences were found between the pretest and posttest scores with the 4-week period. The cognitive representation approach was found to provide more formative assessment. The computer-based cognitive representation approach improved the learning of clinical reasoning in both problem solving and knowledge construction.

Proposal optimization in nuclear accident emergency decision based on IAHP

International Nuclear Information System (INIS)

Xin Jing

2007-01-01

On the basis of establishing the multi-layer structure of nuclear accident emergency decision, several decision objectives are synthetically analyzed, and an optimization model of decision proposals for nuclear accident emergency based on interval analytic hierarchy process is proposed in the paper. The model makes comparisons among several emergency decision proposals quantified, and the optimum proposal is selected out, which solved the uncertain and fuzzy decision problem of judgments by experts' experiences in nuclear accidents emergency decision. Case study shows that the optimization result is much more reasonable, objective and reliable than subjective judgments, and it could be decision references for nuclear accident emergency. (authors)

An Improved Azimuth Angle Estimation Method with a Single Acoustic Vector Sensor Based on an Active Sonar Detection System.

Science.gov (United States)

Zhao, Anbang; Ma, Lin; Ma, Xuefei; Hui, Juan

2017-02-20

In this paper, an improved azimuth angle estimation method with a single acoustic vector sensor (AVS) is proposed based on matched filtering theory. The proposed method is mainly applied in an active sonar detection system. According to the conventional passive method based on complex acoustic intensity measurement, the mathematical and physical model of this proposed method is described in detail. The computer simulation and lake experiments results indicate that this method can realize the azimuth angle estimation with high precision by using only a single AVS. Compared with the conventional method, the proposed method achieves better estimation performance. Moreover, the proposed method does not require complex operations in frequencydomain and achieves computational complexity reduction.

An Improved Azimuth Angle Estimation Method with a Single Acoustic Vector Sensor Based on an Active Sonar Detection System

Directory of Open Access Journals (Sweden)

Anbang Zhao

2017-02-01

Full Text Available In this paper, an improved azimuth angle estimation method with a single acoustic vector sensor (AVS is proposed based on matched filtering theory. The proposed method is mainly applied in an active sonar detection system. According to the conventional passive method based on complex acoustic intensity measurement, the mathematical and physical model of this proposed method is described in detail. The computer simulation and lake experiments results indicate that this method can realize the azimuth angle estimation with high precision by using only a single AVS. Compared with the conventional method, the proposed method achieves better estimation performance. Moreover, the proposed method does not require complex operations in frequencydomain and achieves computational complexity reduction.

Fisheye-Based Method for GPS Localization Improvement in Unknown Semi-Obstructed Areas

Directory of Open Access Journals (Sweden)

Julien Moreau

2017-01-01

Full Text Available A precise GNSS (Global Navigation Satellite System localization is vital for autonomous road vehicles, especially in cluttered or urban environments where satellites are occluded, preventing accurate positioning. We propose to fuse GPS (Global Positioning System data with fisheye stereovision to face this problem independently to additional data, possibly outdated, unavailable, and needing correlation with reality. Our stereoscope is sky-facing with 360° × 180° fisheye cameras to observe surrounding obstacles. We propose a 3D modelling and plane extraction through following steps: stereoscope self-calibration for decalibration robustness, stereo matching considering neighbours epipolar curves to compute 3D, and robust plane fitting based on generated cartography and Hough transform. We use these 3D data with GPS raw data to estimate NLOS (Non Line Of Sight reflected signals pseudorange delay. We exploit extracted planes to build a visibility mask for NLOS detection. A simplified 3D canyon model allows to compute reflections pseudorange delays. In the end, GPS positioning is computed considering corrected pseudoranges. With experimentations on real fixed scenes, we show generated 3D models reaching metric accuracy and improvement of horizontal GPS positioning accuracy by more than 50%. The proposed procedure is effective, and the proposed NLOS detection outperforms CN0-based methods (Carrier-to-receiver Noise density.

Improving Language Models in Speech-Based Human-Machine Interaction

Directory of Open Access Journals (Sweden)

Raquel Justo

2013-02-01

Full Text Available This work focuses on speech-based human-machine interaction. Specifically, a Spoken Dialogue System (SDS that could be integrated into a robot is considered. Since Automatic Speech Recognition is one of the most sensitive tasks that must be confronted in such systems, the goal of this work is to improve the results obtained by this specific module. In order to do so, a hierarchical Language Model (LM is considered. Different series of experiments were carried out using the proposed models over different corpora and tasks. The results obtained show that these models provide greater accuracy in the recognition task. Additionally, the influence of the Acoustic Modelling (AM in the improvement percentage of the Language Models has also been explored. Finally the use of hierarchical Language Models in a language understanding task has been successfully employed, as shown in an additional series of experiments.

Rolling scheduling of electric power system with wind power based on improved NNIA algorithm

Science.gov (United States)

Xu, Q. S.; Luo, C. J.; Yang, D. J.; Fan, Y. H.; Sang, Z. X.; Lei, H.

2017-11-01

This paper puts forth a rolling modification strategy for day-ahead scheduling of electric power system with wind power, which takes the operation cost increment of unit and curtailed wind power of power grid as double modification functions. Additionally, an improved Nondominated Neighbor Immune Algorithm (NNIA) is proposed for solution. The proposed rolling scheduling model has further improved the operation cost of system in the intra-day generation process, enhanced the system’s accommodation capacity of wind power, and modified the key transmission section power flow in a rolling manner to satisfy the security constraint of power grid. The improved NNIA algorithm has defined an antibody preference relation model based on equal incremental rate, regulation deviation constraints and maximum & minimum technical outputs of units. The model can noticeably guide the direction of antibody evolution, and significantly speed up the process of algorithm convergence to final solution, and enhance the local search capability.

Research on Abnormal Detection Based on Improved Combination of K - means and SVDD

Science.gov (United States)

Hao, Xiaohong; Zhang, Xiaofeng

2018-01-01

In order to improve the efficiency of network intrusion detection and reduce the false alarm rate, this paper proposes an anomaly detection algorithm based on improved K-means and SVDD. The algorithm first uses the improved K-means algorithm to cluster the training samples of each class, so that each class is independent and compact in class; Then, according to the training samples, the SVDD algorithm is used to construct the minimum superspheres. The subordinate relationship of the samples is determined by calculating the distance of the minimum superspheres constructed by SVDD. If the test sample is less than the center of the hypersphere, the test sample belongs to this class, otherwise it does not belong to this class, after several comparisons, the final test of the effective detection of the test sample.In this paper, we use KDD CUP99 data set to simulate the proposed anomaly detection algorithm. The results show that the algorithm has high detection rate and low false alarm rate, which is an effective network security protection method.

Adaptive Wavelet Threshold Denoising Method for Machinery Sound Based on Improved Fruit Fly Optimization Algorithm

Directory of Open Access Journals (Sweden)

Jing Xu

2016-07-01

Full Text Available As the sound signal of a machine contains abundant information and is easy to measure, acoustic-based monitoring or diagnosis systems exhibit obvious superiority, especially in some extreme conditions. However, the sound directly collected from industrial field is always polluted. In order to eliminate noise components from machinery sound, a wavelet threshold denoising method optimized by an improved fruit fly optimization algorithm (WTD-IFOA is proposed in this paper. The sound is firstly decomposed by wavelet transform (WT to obtain coefficients of each level. As the wavelet threshold functions proposed by Donoho were discontinuous, many modified functions with continuous first and second order derivative were presented to realize adaptively denoising. However, the function-based denoising process is time-consuming and it is difficult to find optimal thresholds. To overcome these problems, fruit fly optimization algorithm (FOA was introduced to the process. Moreover, to avoid falling into local extremes, an improved fly distance range obeying normal distribution was proposed on the basis of original FOA. Then, sound signal of a motor was recorded in a soundproof laboratory, and Gauss white noise was added into the signal. The simulation results illustrated the effectiveness and superiority of the proposed approach by a comprehensive comparison among five typical methods. Finally, an industrial application on a shearer in coal mining working face was performed to demonstrate the practical effect.

Z-Source-Inverter-Based Flexible Distributed Generation System Solution for Grid Power Quality Improvement

DEFF Research Database (Denmark)

Blaabjerg, Frede; Vilathgamuwa, D. M.; Loh, Poh Chiang

2009-01-01

Distributed generation (DG) systems are usually connected to the grid using power electronic converters. Power delivered from such DG sources depends on factors like energy availability and load demand. The converters used in power conversion do not operate with their full capacity all the time......-stage buck-boost inverter, recently proposed Z-source inverter (ZSI) is a good candidate for future DG systems. This paper presents a controller design for a ZSI-based DG system to improve power quality of distribution systems. The proposed control method is tested with simulation results obtained using...

Maximization Network Throughput Based on Improved Genetic Algorithm and Network Coding for Optical Multicast Networks

Science.gov (United States)

Wei, Chengying; Xiong, Cuilian; Liu, Huanlin

2017-12-01

Maximal multicast stream algorithm based on network coding (NC) can improve the network's throughput for wavelength-division multiplexing (WDM) networks, which however is far less than the network's maximal throughput in terms of theory. And the existing multicast stream algorithms do not give the information distribution pattern and routing in the meantime. In the paper, an improved genetic algorithm is brought forward to maximize the optical multicast throughput by NC and to determine the multicast stream distribution by hybrid chromosomes construction for multicast with single source and multiple destinations. The proposed hybrid chromosomes are constructed by the binary chromosomes and integer chromosomes, while the binary chromosomes represent optical multicast routing and the integer chromosomes indicate the multicast stream distribution. A fitness function is designed to guarantee that each destination can receive the maximum number of decoding multicast streams. The simulation results showed that the proposed method is far superior over the typical maximal multicast stream algorithms based on NC in terms of network throughput in WDM networks.

The Improved Locating Algorithm of Particle Filter Based on ROS Robot

Science.gov (United States)

Fang, Xun; Fu, Xiaoyang; Sun, Ming

2018-03-01

This paperanalyzes basic theory and primary algorithm of the real-time locating system and SLAM technology based on ROS system Robot. It proposes improved locating algorithm of particle filter effectively reduces the matching time of laser radar and map, additional ultra-wideband technology directly accelerates the global efficiency of FastSLAM algorithm, which no longer needs searching on the global map. Meanwhile, the re-sampling has been largely reduced about 5/6 that directly cancels the matching behavior on Roboticsalgorithm.

1972 preliminary safety analysis report based on a conceptual design of a proposed repository in Kansas

International Nuclear Information System (INIS)

Blomeke, J.O.

1977-08-01

This preliminary safety analysis report is based on a proposed Federal Repository at Lyons, Kansas, for receiving, handling, and depositing radioactive solid wastes in bedded salt during the remainder of this century. The safety analysis applies to a hypothetical site in central Kansas identical to the Lyons site, except that it is free of nearby salt solution-mining operations and bore holes that cannot be plugged to Repository specifications. This PSAR contains much information that also appears in the conceptual design report. Much of the geological-hydrological information was gathered in the Lyons area. This report is organized in 16 sections: considerations leading to the proposed Repository, design requirements and criteria, a description of the Lyons site and its environs, land improvements, support facilities, utilities, different impacts of Repository operations, safety analysis, design confirmation program, operational management, requirements for eventually decommissioning the facility, design criteria for protection from severe natural events, and the proposed program of experimental investigations

1972 preliminary safety analysis report based on a conceptual design of a proposed repository in Kansas

Energy Technology Data Exchange (ETDEWEB)

Blomeke, J.O.

1977-08-01

This preliminary safety analysis report is based on a proposed Federal Repository at Lyons, Kansas, for receiving, handling, and depositing radioactive solid wastes in bedded salt during the remainder of this century. The safety analysis applies to a hypothetical site in central Kansas identical to the Lyons site, except that it is free of nearby salt solution-mining operations and bore holes that cannot be plugged to Repository specifications. This PSAR contains much information that also appears in the conceptual design report. Much of the geological-hydrological information was gathered in the Lyons area. This report is organized in 16 sections: considerations leading to the proposed Repository, design requirements and criteria, a description of the Lyons site and its environs, land improvements, support facilities, utilities, different impacts of Repository operations, safety analysis, design confirmation program, operational management, requirements for eventually decommissioning the facility, design criteria for protection from severe natural events, and the proposed program of experimental investigations. (DLC)

Improved Data-based Fault Detection Strategy and Application to Distillation Columns

KAUST Repository

Madakyaru, Muddu

2017-01-31

Chemical and petrochemical processes require continuous monitoring to detect abnormal events and to sustain normal operations. Furthermore, process monitoring enhances productivity, efficiency, and safety in process industries. Here, we propose an innovative statistical approach that exploits the advantages of multiscale partial least squares (MSPLS) models and generalized likelihood ratio (GLR) tests for fault detection in processes. Specifically, we combine an MSPLS algorithm with wavelet analysis to create our modeling framework. Then, we use GLR hypothesis testing based on the uncorrelated residuals obtained from the MSPLS model to improve fault detection. We use simulated distillation column data to evaluate the MSPLS-based GLR chart. Results show that our MSPLS-based GLR method is more powerful than the PLS-based Q and GLR method and MSPLS-based Q method, especially in early detection of small faults with abrupt or incipient behavior.

Improved Data-based Fault Detection Strategy and Application to Distillation Columns

KAUST Repository

Madakyaru, Muddu; Harrou, Fouzi; Sun, Ying

2017-01-01

Chemical and petrochemical processes require continuous monitoring to detect abnormal events and to sustain normal operations. Furthermore, process monitoring enhances productivity, efficiency, and safety in process industries. Here, we propose an innovative statistical approach that exploits the advantages of multiscale partial least squares (MSPLS) models and generalized likelihood ratio (GLR) tests for fault detection in processes. Specifically, we combine an MSPLS algorithm with wavelet analysis to create our modeling framework. Then, we use GLR hypothesis testing based on the uncorrelated residuals obtained from the MSPLS model to improve fault detection. We use simulated distillation column data to evaluate the MSPLS-based GLR chart. Results show that our MSPLS-based GLR method is more powerful than the PLS-based Q and GLR method and MSPLS-based Q method, especially in early detection of small faults with abrupt or incipient behavior.

The robust corrective action priority-an improved approach for selecting competing corrective actions in FMEA based on principle of robust design

Science.gov (United States)

Sutrisno, Agung; Gunawan, Indra; Vanany, Iwan

2017-11-01

In spite of being integral part in risk - based quality improvement effort, studies improving quality of selection of corrective action priority using FMEA technique are still limited in literature. If any, none is considering robustness and risk in selecting competing improvement initiatives. This study proposed a theoretical model to select risk - based competing corrective action by considering robustness and risk of competing corrective actions. We incorporated the principle of robust design in counting the preference score among corrective action candidates. Along with considering cost and benefit of competing corrective actions, we also incorporate the risk and robustness of corrective actions. An example is provided to represent the applicability of the proposed model.

An improved gravity compensation method for high-precision free-INS based on MEC–BP–AdaBoost

International Nuclear Information System (INIS)

Zhou, Xiao; Yang, Gongliu; Wang, Jing; Li, Jing

2016-01-01

In recent years, with the rapid improvement of inertial sensors (accelerometers and gyroscopes), gravity compensation has become more important for improving navigation accuracy in inertial navigation systems (INS), especially for high-precision INS. This paper proposes a mind evolutionary computation (MEC) back propagation (BP) AdaBoost algorithm neural-network-based gravity compensation method that estimates the gravity disturbance on the track based on measured gravity data. A MEC–BP–AdaBoost network-based gravity compensation algorithm used in the training process to establish the prediction model takes the carrier position (longitude and latitude) provided by INS as the input data and the gravity disturbance as the output data, and then compensates the obtained gravity disturbance into the INS’s error equations to restrain the position error propagation. The MEC–BP–AdaBoost algorithm can not only effectively avoid BP neural networks being trapped in local extrema, but also perfectly solve the nonlinearity between the input and output data that cannot be solved by traditional interpolation methods, such as least-square collocation (LSC) interpolation. The accuracy and feasibility of the proposed interpolation method are verified through numerical tests. A comparison of several other compensation methods applied in field experiments, including LSC interpolation and traditional BP interpolation, highlights the superior performance of the proposed method. The field experiment results show that the maximum value of the position error can reduce by 28% with the proposed gravity compensation method. (paper)

An improved principal component analysis based region matching method for fringe direction estimation

Science.gov (United States)

He, A.; Quan, C.

2018-04-01

The principal component analysis (PCA) and region matching combined method is effective for fringe direction estimation. However, its mask construction algorithm for region matching fails in some circumstances, and the algorithm for conversion of orientation to direction in mask areas is computationally-heavy and non-optimized. We propose an improved PCA based region matching method for the fringe direction estimation, which includes an improved and robust mask construction scheme, and a fast and optimized orientation-direction conversion algorithm for the mask areas. Along with the estimated fringe direction map, filtered fringe pattern by automatic selective reconstruction modification and enhanced fast empirical mode decomposition (ASRm-EFEMD) is used for Hilbert spiral transform (HST) to demodulate the phase. Subsequently, windowed Fourier ridge (WFR) method is used for the refinement of the phase. The robustness and effectiveness of proposed method are demonstrated by both simulated and experimental fringe patterns.

An improved method of continuous LOD based on fractal theory in terrain rendering

Science.gov (United States)

Lin, Lan; Li, Lijun

2007-11-01

With the improvement of computer graphic hardware capability, the algorithm of 3D terrain rendering is going into the hot topic of real-time visualization. In order to solve conflict between the rendering speed and reality of rendering, this paper gives an improved method of terrain rendering which improves the traditional continuous level of detail technique based on fractal theory. This method proposes that the program needn't to operate the memory repeatedly to obtain different resolution terrain model, instead, obtains the fractal characteristic parameters of different region according to the movement of the viewpoint. Experimental results show that the method guarantees the authenticity of landscape, and increases the real-time 3D terrain rendering speed.

Proposed Brookhaven accelerator-based neutron generator

International Nuclear Information System (INIS)

Grand, P.; Batchelor, K.; Chasman, R.; Rheaume, R.

1976-01-01

The d-Li Neutron Source concept, which includes a high-current dueteron linac, is an outgrowth of attempts made to use the BNL, 200-MeV proton linac BLIP facility to do radiation damage studies. It included a 100 mA, 30-MeV deuteron linear accelerator and a fast-flowing liquid lithium jet as the target. The latest design is not very different, except that the current is now 200 mA and the linac energy has been raised to 35 MeV. Both parameters, were changed to optimize the effectiveness of the facility with respect to flux, experimental volume and match to 14 MeV neutron-radiation-damage effects. The proposed Brookhaven Accelerator-based Neutron Generator is described with particular emphasis on the linear accelerator. The proposed facility is a practical and efficient way of producing the intense, high energy neutron beams needed for CTR material studies. The accelerator and liquid-metal technologies are well proven, state-of-the-art technologies. The fact that no new technology is required guarantees the possibility of meeting construction schedules, and more importantly, guarantees a high level of operational reliability

Supplier selection based on improved MOGA and its application in nuclear power equipment procurement

International Nuclear Information System (INIS)

Yan Zhaojun; Wang Dezhong; Zhou Lei

2007-01-01

Considering the fact that there are few objective and available methods supporting the supplier selection in nuclear power equipment purchasing process, a supplier selection method based on improved multi-objective genetic algorithm (MOGA) is proposed. The simulation results demonstrate the effectiveness and efficiency of this method for the supplier selection in nuclear power equipment procurement process. (authors)

[Automatic Sleep Stage Classification Based on an Improved K-means Clustering Algorithm].

Science.gov (United States)

Xiao, Shuyuan; Wang, Bei; Zhang, Jian; Zhang, Qunfeng; Zou, Junzhong

2016-10-01

Sleep stage scoring is a hotspot in the field of medicine and neuroscience.Visual inspection of sleep is laborious and the results may be subjective to different clinicians.Automatic sleep stage classification algorithm can be used to reduce the manual workload.However,there are still limitations when it encounters complicated and changeable clinical cases.The purpose of this paper is to develop an automatic sleep staging algorithm based on the characteristics of actual sleep data.In the proposed improved K-means clustering algorithm,points were selected as the initial centers by using a concept of density to avoid the randomness of the original K-means algorithm.Meanwhile,the cluster centers were updated according to the‘Three-Sigma Rule’during the iteration to abate the influence of the outliers.The proposed method was tested and analyzed on the overnight sleep data of the healthy persons and patients with sleep disorders after continuous positive airway pressure(CPAP)treatment.The automatic sleep stage classification results were compared with the visual inspection by qualified clinicians and the averaged accuracy reached 76%.With the analysis of morphological diversity of sleep data,it was proved that the proposed improved K-means algorithm was feasible and valid for clinical practice.

Improved metamodel-based importance sampling for the performance assessment of radioactive waste repositories

International Nuclear Information System (INIS)

Cadini, F.; Gioletta, A.; Zio, E.

2015-01-01

In the context of a probabilistic performance assessment of a radioactive waste repository, the estimation of the probability of exceeding the dose threshold set by a regulatory body is a fundamental task. This may become difficult when the probabilities involved are very small, since the classically used sampling-based Monte Carlo methods may become computationally impractical. This issue is further complicated by the fact that the computer codes typically adopted in this context requires large computational efforts, both in terms of time and memory. This work proposes an original use of a Monte Carlo-based algorithm for (small) failure probability estimation in the context of the performance assessment of a near surface radioactive waste repository. The algorithm, developed within the context of structural reliability, makes use of an estimated optimal importance density and a surrogate, kriging-based metamodel approximating the system response. On the basis of an accurate analytic analysis of the algorithm, a modification is proposed which allows further reducing the computational efforts by a more effective training of the metamodel. - Highlights: • We tackle uncertainty propagation in a radwaste repository performance assessment. • We improve a kriging-based importance sampling for estimating failure probabilities. • We justify the modification by an analytic, comparative analysis of the algorithms. • The probability of exceeding dose thresholds in radwaste repositories is estimated. • The algorithm is further improved reducing the number of its free parameters

Improving ELM-Based Service Quality Prediction by Concise Feature Extraction

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Yuhai Zhao

2015-01-01

Full Text Available Web services often run on highly dynamic and changing environments, which generate huge volumes of data. Thus, it is impractical to monitor the change of every QoS parameter for the timely trigger precaution due to high computational costs associated with the process. To address the problem, this paper proposes an active service quality prediction method based on extreme learning machine. First, we extract web service trace logs and QoS information from the service log and convert them into feature vectors. Second, by the proposed EC rules, we are enabled to trigger the precaution of QoS as soon as possible with high confidence. An efficient prefix tree based mining algorithm together with some effective pruning rules is developed to mine such rules. Finally, we study how to extract a set of diversified features as the representative of all mined results. The problem is proved to be NP-hard. A greedy algorithm is presented to approximate the optimal solution. Experimental results show that ELM trained by the selected feature subsets can efficiently improve the reliability and the earliness of service quality prediction.

Dictionary-based fiber orientation estimation with improved spatial consistency.

Science.gov (United States)

Ye, Chuyang; Prince, Jerry L

2018-02-01

Diffusion magnetic resonance imaging (dMRI) has enabled in vivo investigation of white matter tracts. Fiber orientation (FO) estimation is a key step in tract reconstruction and has been a popular research topic in dMRI analysis. In particular, the sparsity assumption has been used in conjunction with a dictionary-based framework to achieve reliable FO estimation with a reduced number of gradient directions. Because image noise can have a deleterious effect on the accuracy of FO estimation, previous works have incorporated spatial consistency of FOs in the dictionary-based framework to improve the estimation. However, because FOs are only indirectly determined from the mixture fractions of dictionary atoms and not modeled as variables in the objective function, these methods do not incorporate FO smoothness directly, and their ability to produce smooth FOs could be limited. In this work, we propose an improvement to Fiber Orientation Reconstruction using Neighborhood Information (FORNI), which we call FORNI+; this method estimates FOs in a dictionary-based framework where FO smoothness is better enforced than in FORNI alone. We describe an objective function that explicitly models the actual FOs and the mixture fractions of dictionary atoms. Specifically, it consists of data fidelity between the observed signals and the signals represented by the dictionary, pairwise FO dissimilarity that encourages FO smoothness, and weighted ℓ 1 -norm terms that ensure the consistency between the actual FOs and the FO configuration suggested by the dictionary representation. The FOs and mixture fractions are then jointly estimated by minimizing the objective function using an iterative alternating optimization strategy. FORNI+ was evaluated on a simulation phantom, a physical phantom, and real brain dMRI data. In particular, in the real brain dMRI experiment, we have qualitatively and quantitatively evaluated the reproducibility of the proposed method. Results demonstrate that

An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine.

Science.gov (United States)

Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar

2017-12-07

Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the necessity of real world diagnostic situations. In this paper, an efficient PBDS based on MR images is proposed that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) to enhance the quality of the input MR images. Thereafter, two-dimensional PCA (2DPCA) strategy is employed to extract the features and subsequently, a PCA+LDA approach is used to generate a compact and discriminative feature set. Finally, a new learning algorithm called MDE-ELM is suggested that combines modified differential evolution (MDE) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The MDE is utilized to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks (SLFN), whereas an analytical method is used for determining the output weights. The proposed algorithm performs optimization based on both the root mean squared error (RMSE) and norm of the output weights of SLFNs. The suggested scheme is benchmarked on three standard datasets and the results are compared against other competent schemes. The experimental outcomes show that the proposed scheme offers superior results compared to its counterparts. Further, it has been noticed that the proposed MDE-ELM classifier obtains better accuracy with compact network architecture than conventional algorithms.

Improving the learning of clinical reasoning through computer-based cognitive representation

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Bian Wu

2014-12-01

Full Text Available Objective: Clinical reasoning is usually taught using a problem-solving approach, which is widely adopted in medical education. However, learning through problem solving is difficult as a result of the contextualization and dynamic aspects of actual problems. Moreover, knowledge acquired from problem-solving practice tends to be inert and fragmented. This study proposed a computer-based cognitive representation approach that externalizes and facilitates the complex processes in learning clinical reasoning. The approach is operationalized in a computer-based cognitive representation tool that involves argument mapping to externalize the problem-solving process and concept mapping to reveal the knowledge constructed from the problems. Methods: Twenty-nine Year 3 or higher students from a medical school in east China participated in the study. Participants used the proposed approach implemented in an e-learning system to complete four learning cases in 4 weeks on an individual basis. For each case, students interacted with the problem to capture critical data, generate and justify hypotheses, make a diagnosis, recall relevant knowledge, and update their conceptual understanding of the problem domain. Meanwhile, students used the computer-based cognitive representation tool to articulate and represent the key elements and their interactions in the learning process. Results: A significant improvement was found in students’ learning products from the beginning to the end of the study, consistent with students’ report of close-to-moderate progress in developing problem-solving and knowledge-construction abilities. No significant differences were found between the pretest and posttest scores with the 4-week period. The cognitive representation approach was found to provide more formative assessment. Conclusions: The computer-based cognitive representation approach improved the learning of clinical reasoning in both problem solving and knowledge

Improved artificial bee colony algorithm based gravity matching navigation method.

Science.gov (United States)

Gao, Wei; Zhao, Bo; Zhou, Guang Tao; Wang, Qiu Ying; Yu, Chun Yang

2014-07-18

Gravity matching navigation algorithm is one of the key technologies for gravity aided inertial navigation systems. With the development of intelligent algorithms, the powerful search ability of the Artificial Bee Colony (ABC) algorithm makes it possible to be applied to the gravity matching navigation field. However, existing search mechanisms of basic ABC algorithms cannot meet the need for high accuracy in gravity aided navigation. Firstly, proper modifications are proposed to improve the performance of the basic ABC algorithm. Secondly, a new search mechanism is presented in this paper which is based on an improved ABC algorithm using external speed information. At last, modified Hausdorff distance is introduced to screen the possible matching results. Both simulations and ocean experiments verify the feasibility of the method, and results show that the matching rate of the method is high enough to obtain a precise matching position.

Road Network Vulnerability Analysis Based on Improved Ant Colony Algorithm

Directory of Open Access Journals (Sweden)

Yunpeng Wang

2014-01-01

Full Text Available We present an improved ant colony algorithm-based approach to assess the vulnerability of a road network and identify the critical infrastructures. This approach improves computational efficiency and allows for its applications in large-scale road networks. This research involves defining the vulnerability conception, modeling the traffic utility index and the vulnerability of the road network, and identifying the critical infrastructures of the road network. We apply the approach to a simple test road network and a real road network to verify the methodology. The results show that vulnerability is directly related to traffic demand and increases significantly when the demand approaches capacity. The proposed approach reduces the computational burden and may be applied in large-scale road network analysis. It can be used as a decision-supporting tool for identifying critical infrastructures in transportation planning and management.

Optimization Based Shunt APF Controller to Mitigate Reactive Power, Burden of Neutral Conductor, Current Harmonics and Improve cosɸ

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P. Anjana

2017-03-01

Full Text Available This paper presents a Modified Gravitational Search Algorithm (MGSA to improve the performance of PI controller in varying load condition. The proposed approach is capable of mitigating reactive power, neutral current, source current THD and significant improvement in power factor nearly unity (0.997. The DC link voltage across the capacitor is controlled by PI controller which is deciding the performance of shunt APF. Hence, the robust optimization technique based integral time square error (ITSE with consideration of weight factor (α & β, maximum overshoot ((|(∆_Ve ̅〖(n〗_max | and setling time t_s-t_0, is providing the optimum solution of Kp & Ki. The robustness of proposed objective function and algorithm compared with GSA based three other error criterion techniques. The efficiency of the proposed controller has been tested over nonlinear and unbalance loading condition. The performance of ITSE based MGSA-PI controller is batter then other three error criterion techniques. The values of THD are below the mark of 5% specified in IEEE-519 standard.

An Improved Bacterial-Foraging Optimization-Based Machine Learning Framework for Predicting the Severity of Somatization Disorder

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Xinen Lv

2018-02-01

Full Text Available It is of great clinical significance to establish an accurate intelligent model to diagnose the somatization disorder of community correctional personnel. In this study, a novel machine learning framework is proposed to predict the severity of somatization disorder in community correction personnel. The core of this framework is to adopt the improved bacterial foraging optimization (IBFO to optimize two key parameters (penalty coefficient and the kernel width of a kernel extreme learning machine (KELM and build an IBFO-based KELM (IBFO-KELM for the diagnosis of somatization disorder patients. The main innovation point of the IBFO-KELM model is the introduction of opposition-based learning strategies in traditional bacteria foraging optimization, which increases the diversity of bacterial species, keeps a uniform distribution of individuals of initial population, and improves the convergence rate of the BFO optimization process as well as the probability of escaping from the local optimal solution. In order to verify the effectiveness of the method proposed in this study, a 10-fold cross-validation method based on data from a symptom self-assessment scale (SCL-90 is used to make comparison among IBFO-KELM, BFO-KELM (model based on the original bacterial foraging optimization model, GA-KELM (model based on genetic algorithm, PSO-KELM (model based on particle swarm optimization algorithm and Grid-KELM (model based on grid search method. The experimental results show that the proposed IBFO-KELM prediction model has better performance than other methods in terms of classification accuracy, Matthews correlation coefficient (MCC, sensitivity and specificity. It can distinguish very well between severe somatization disorder and mild somatization and assist the psychological doctor with clinical diagnosis.

AN IMPROVEMENT ON GEOMETRY-BASED METHODS FOR GENERATION OF NETWORK PATHS FROM POINTS

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Z. Akbari

2014-10-01

Full Text Available Determining network path is important for different purposes such as determination of road traffic, the average speed of vehicles, and other network analysis. One of the required input data is information about network path. Nevertheless, the data collected by the positioning systems often lead to the discrete points. Conversion of these points to the network path have become one of the challenges which different researchers, presents many ways for solving it. This study aims at investigating geometry-based methods to estimate the network paths from the obtained points and improve an existing point to curve method. To this end, some geometry-based methods have been studied and an improved method has been proposed by applying conditions on the best method after describing and illustrating weaknesses of them.

A Dynamic Hidden Forwarding Path Planning Method Based on Improved Q-Learning in SDN Environments

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Yun Chen

2018-01-01

Full Text Available Currently, many methods are available to improve the target network’s security. The vast majority of them cannot obtain an optimal attack path and interdict it dynamically and conveniently. Almost all defense strategies aim to repair known vulnerabilities or limit services in target network to improve security of network. These methods cannot response to the attacks in real-time because sometimes they need to wait for manufacturers releasing corresponding countermeasures to repair vulnerabilities. In this paper, we propose an improved Q-learning algorithm to plan an optimal attack path directly and automatically. Based on this path, we use software-defined network (SDN to adjust routing paths and create hidden forwarding paths dynamically to filter vicious attack requests. Compared to other machine learning algorithms, Q-learning only needs to input the target state to its agents, which can avoid early complex training process. We improve Q-learning algorithm in two aspects. First, a reward function based on the weights of hosts and attack success rates of vulnerabilities is proposed, which can adapt to different network topologies precisely. Second, we remove the actions and merge them into every state that reduces complexity from O(N3 to O(N2. In experiments, after deploying hidden forwarding paths, the security of target network is boosted significantly without having to repair network vulnerabilities immediately.

Energy and environment efficiency analysis based on an improved environment DEA cross-model: Case study of complex chemical processes

International Nuclear Information System (INIS)

Geng, ZhiQiang; Dong, JunGen; Han, YongMing; Zhu, QunXiong

2017-01-01

Highlights: •An improved environment DEA cross-model method is proposed. •Energy and environment efficiency analysis framework of complex chemical processes is obtained. •This proposed method is efficient in energy-saving and emission reduction of complex chemical processes. -- Abstract: The complex chemical process is a high pollution and high energy consumption industrial process. Therefore, it is very important to analyze and evaluate the energy and environment efficiency of the complex chemical process. Data Envelopment Analysis (DEA) is used to evaluate the relative effectiveness of decision-making units (DMUs). However, the traditional DEA method usually cannot genuinely distinguish the effective and inefficient DMU due to its extreme or unreasonable weight distribution of input and output variables. Therefore, this paper proposes an energy and environment efficiency analysis method based on an improved environment DEA cross-model (DEACM) method. The inputs of the complex chemical process are divided into energy and non-energy inputs. Meanwhile, the outputs are divided into desirable and undesirable outputs. And then the energy and environment performance index (EEPI) based on the cross evaluation is used to represent the overall performance of each DMU. Moreover, the improvement direction of energy-saving and carbon emission reduction of each inefficiency DMU is quantitatively obtained based on the self-evaluation model of the improved environment DEACM. The results show that the improved environment DEACM method has a better effective discrimination than the original DEA method by analyzing the energy and environment efficiency of the ethylene production process in complex chemical processes, and it can obtain the potential of energy-saving and carbon emission reduction of ethylene plants, especially the improvement direction of inefficient DMUs to improve energy efficiency and reduce carbon emission.

Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases

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Peng Lu

2018-01-01

Full Text Available Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively.

Multi-clues image retrieval based on improved color invariants

Science.gov (United States)

Liu, Liu; Li, Jian-Xun

2012-05-01

At present, image retrieval has a great progress in indexing efficiency and memory usage, which mainly benefits from the utilization of the text retrieval technology, such as the bag-of-features (BOF) model and the inverted-file structure. Meanwhile, because the robust local feature invariants are selected to establish BOF, the retrieval precision of BOF is enhanced, especially when it is applied to a large-scale database. However, these local feature invariants mainly consider the geometric variance of the objects in the images, and thus the color information of the objects fails to be made use of. Because of the development of the information technology and Internet, the majority of our retrieval objects is color images. Therefore, retrieval performance can be further improved through proper utilization of the color information. We propose an improved method through analyzing the flaw of shadow-shading quasi-invariant. The response and performance of shadow-shading quasi-invariant for the object edge with the variance of lighting are enhanced. The color descriptors of the invariant regions are extracted and integrated into BOF based on the local feature. The robustness of the algorithm and the improvement of the performance are verified in the final experiments.

Performance improvement of shunt active power filter based on non-linear least-square approach

DEFF Research Database (Denmark)

Terriche, Yacine

2018-01-01

. This paper proposes an improved open loop strategy which is unconditionally stable and flexible. The proposed method which is based on non-linear least square (NLS) approach can extract the fundamental voltage and estimates its phase within only half cycle, even in the presence of odd harmonics and dc offset......). The synchronous reference frame (SRF) approach is widely used for generating the RCC due to its simplicity and computation efficiency. However, the SRF approach needs precise information of the voltage phase which becomes a challenge under adverse grid conditions. A typical solution to answer this need...

A Spreadsheet-Based Visualized Mindtool for Improving Students' Learning Performance in Identifying Relationships between Numerical Variables

Science.gov (United States)

Lai, Chiu-Lin; Hwang, Gwo-Jen

2015-01-01

In this study, a spreadsheet-based visualized Mindtool was developed for improving students' learning performance when finding relationships between numerical variables by engaging them in reasoning and decision-making activities. To evaluate the effectiveness of the proposed approach, an experiment was conducted on the "phenomena of climate…

Predicting IVF Outcome: A Proposed Web-based System Using Artificial Intelligence.

Science.gov (United States)

Siristatidis, Charalampos; Vogiatzi, Paraskevi; Pouliakis, Abraham; Trivella, Marialenna; Papantoniou, Nikolaos; Bettocchi, Stefano

2016-01-01

To propose a functional in vitro fertilization (IVF) prediction model to assist clinicians in tailoring personalized treatment of subfertile couples and improve assisted reproduction outcome. Construction and evaluation of an enhanced web-based system with a novel Artificial Neural Network (ANN) architecture and conformed input and output parameters according to the clinical and bibliographical standards, driven by a complete data set and "trained" by a network expert in an IVF setting. The system is capable to act as a routine information technology platform for the IVF unit and is capable of recalling and evaluating a vast amount of information in a rapid and automated manner to provide an objective indication on the outcome of an artificial reproductive cycle. ANNs are an exceptional candidate in providing the fertility specialist with numerical estimates to promote personalization of healthcare and adaptation of the course of treatment according to the indications. Copyright © 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Proposal of Instruction Process for Improvement of Language Activities in Technology Education Course

OpenAIRE

山本, 智広; 山本, 利一

2012-01-01

This study is a proposal of instruction process for improvement of language activities in the technology education course in the junior high school in Japan. In this study, two efforts were carried out for the technology concerning material and processing. The first effort was the extraction of the learning situations that develop abilities of thinking, judgment and expression through language activities peculiar to the technology education course. The second effort was the verification o...

Analysis and Improvement of Fireworks Algorithm

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Xi-Guang Li

2017-02-01

Full Text Available The Fireworks Algorithm is a recently developed swarm intelligence algorithm to simulate the explosion process of fireworks. Based on the analysis of each operator of Fireworks Algorithm (FWA, this paper improves the FWA and proves that the improved algorithm converges to the global optimal solution with probability 1. The proposed algorithm improves the goal of further boosting performance and achieving global optimization where mainly include the following strategies. Firstly using the opposition-based learning initialization population. Secondly a new explosion amplitude mechanism for the optimal firework is proposed. In addition, the adaptive t-distribution mutation for non-optimal individuals and elite opposition-based learning for the optimal individual are used. Finally, a new selection strategy, namely Disruptive Selection, is proposed to reduce the running time of the algorithm compared with FWA. In our simulation, we apply the CEC2013 standard functions and compare the proposed algorithm (IFWA with SPSO2011, FWA, EFWA and dynFWA. The results show that the proposed algorithm has better overall performance on the test functions.

Silica aerogel threshold Cherenkov counters for the JLab Hall A spectrometers: improvements and proposed modifications

CERN Document Server

Lagamba, L; Colilli, S; Crateri, R; De Leo, R; Frullani, S; Garibaldi, F; Giuliani, F; Gricia, M; Iodice, M; Iommi, R; Leone, A; Lucentini, M; Mostarda, A; Nappi, E; Perrino, R; Pierangeli, L; Santavenere, F; Urciuoli, G M

2001-01-01

Recently approved experiments at Jefferson Lab Hall A require a clean kaon identification in a large electron, pion, and proton background environment. To this end, improved performance is required of the silica aerogel threshold Cherenkov counters installed in the focal plane of the two Hall A spectrometers. In this paper we propose two strategies to improve the performance of the Cherenkov counters which presently use a hydrophilic aerogel radiator, and convey Cherenkov photons towards the photomultipliers by means of mirrors with a parabolic shape in one direction and flat in the other. The first strategy is aerogel baking. In the second strategy we propose a modification of the counter geometry by replacing the mirrors with a planar diffusing surface and by displacing in a different way the photomultipliers. Tests at CERN with a 5 GeV/c multiparticle beam revealed that both the strategies are able to increase significantly the number of the detected Cherenkov photons and, therefore, the detector performan...

An improved approach for flow-based cloud point extraction.

Science.gov (United States)

Frizzarin, Rejane M; Rocha, Fábio R P

2014-04-11

Novel strategies are proposed to circumvent the main drawbacks of flow-based cloud point extraction (CPE). The surfactant-rich phase (SRP) was directly retained into the optical path of the spectrophotometric cell, thus avoiding its dilution previously to the measurement and yielding higher sensitivity. Solenoid micro-pumps were exploited to improve mixing by the pulsed flow and also to modulate the flow-rate for retention and removal of the SRP, thus avoiding the elution step, often carried out with organic solvents. The heat released and the increase of the salt concentration provided by an on-line neutralization reaction were exploited to induce the cloud point without an external heating device. These innovations were demonstrated by the spectrophotometric determination of iron, yielding a linear response from 10 to 200 μg L(-1) with a coefficient of variation of 2.3% (n=7). Detection limit and sampling rate were estimated at 5 μg L(-1) (95% confidence level) and 26 samples per hour, respectively. The enrichment factor was 8.9 and the procedure consumed only 6 μg of TAN and 390 μg of Triton X-114 per determination. At the 95% confidence level, the results obtained for freshwater samples agreed with the reference procedure and those obtained for digests of bovine muscle, rice flour, brown bread and tort lobster agreed with the certified reference values. The proposed procedure thus shows advantages in relation to previously proposed approaches for flow-based CPE, being a fast and environmental friendly alternative for on-line separation and pre-concentration. Copyright © 2014 Elsevier B.V. All rights reserved.

Process-based project proposal risk management

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Alok Kumar

2016-12-01

Full Text Available We all are aware of the organizational omnipresence. Projects within the organizations are ubiquitous too. Projects achieve their goals successfully if they are planned, scheduled, controlled and implemented well. The project lifecycle of initiating, planning, scheduling, controlling and implementing are very well-planned by project managers and the organizations. Successful projects have well-developed risk management plans to deal with situations impacting projects. Like any other organisation, a university does try to access funds for different purposes too. For such organisations, running a project is not the issue, rather getting a project proposal approved to fund a project is the key. Project proposal processing is done by the nodal office in every organisation. Usually, these nodal offices help in administration and submission of a project proposal for accessing funds. Seldom are these nodal project offices within the organizations facilitate a project proposal approval by proactively reaching out to the project managers. And as project managers prepare project proposals, little or no attention is made to prepare a project proposal risk plan so as to maximise project acquisition. Risk plans are submitted while preparing proposals but these risk plans cater to a requirement to address actual projects upon approval. Hence, a risk management plan for project proposal is either missing or very little effort is made to treat the risks inherent in project acquisition. This paper is an integral attempt to highlight the importance of risk treatment for project proposal stage as an extremely important step to preparing the risk management plan made for projects corresponding to their lifecycle phases. Several tools and techniques have been proposed in the paper to help and guide either the project owner (proposer or the main organisational unit responsible for project management. Development of tools and techniques to further enhance project

A proposed data base system for detection, classification and ...

African Journals Online (AJOL)

A proposed data base system for detection, classification and location of fault on electricity company of Ghana electrical distribution system. Isaac Owusu-Nyarko, Mensah-Ananoo Eugine. Abstract. No Abstract. Keywords: database, classification of fault, power, distribution system, SCADA, ECG. Full Text: EMAIL FULL TEXT ...

Improved targeted immunization strategies based on two rounds of selection

Science.gov (United States)

Xia, Ling-Ling; Song, Yu-Rong; Li, Chan-Chan; Jiang, Guo-Ping

2018-04-01

In the case of high degree targeted immunization where the number of vaccine is limited, when more than one node associated with the same degree meets the requirement of high degree centrality, how can we choose a certain number of nodes from those nodes, so that the number of immunized nodes will not exceed the limit? In this paper, we introduce a new idea derived from the selection process of second-round exam to solve this problem and then propose three improved targeted immunization strategies. In these proposed strategies, the immunized nodes are selected through two rounds of selection, where we increase the quotas of first-round selection according the evaluation criterion of degree centrality and then consider another characteristic parameter of node, such as node's clustering coefficient, betweenness and closeness, to help choose targeted nodes in the second-round selection. To validate the effectiveness of the proposed strategies, we compare them with the degree immunizations including the high degree targeted and the high degree adaptive immunizations using two metrics: the size of the largest connected component of immunized network and the number of infected nodes. Simulation results demonstrate that the proposed strategies based on two rounds of sorting are effective for heterogeneous networks and their immunization effects are better than that of the degree immunizations.

Size, shape, and topology optimization of planar and space trusses using mutation-based improved metaheuristics

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Ghanshyam G. Tejani

2018-04-01

Full Text Available In this study, simultaneous size, shape, and topology optimization of planar and space trusses are investigated. Moreover, the trusses are subjected to constraints for element stresses, nodal displacements, and kinematic stability conditions. Truss Topology Optimization (TTO removes the superfluous elements and nodes from the ground structure. In this method, the difficulties arise due to unacceptable and singular topologies; therefore, the Grubler’s criterion and the positive definiteness are used to handle such issue. Moreover, the TTO is challenging due to its search space, which is implicit, non-convex, non-linear, and often leading to divergence. Therefore, mutation-based metaheuristics are proposed to investigate them. This study compares the performance of four improved metaheuristics (viz. Improved Teaching–Learning-Based Optimization (ITLBO, Improved Heat Transfer Search (IHTS, Improved Water Wave Optimization (IWWO, and Improved Passing Vehicle Search (IPVS and four basic metaheuristics (viz. TLBO, HTS, WWO, and PVS in order to solve structural optimization problems. Keywords: Structural optimization, Mutation operator, Improved metaheuristics, Modified algorithms, Truss topology optimization

Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA

Science.gov (United States)

Ma, Xiaoqi

2015-01-01

A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA) are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM) and the classifier is established by optimized SVM. In order to verify the effectiveness of the proposed method, SVM and other classification methods have been chosen as the comparing methods. The experimental results show that the proposed method can improve the classifier performance and cost less time. PMID:26543867

Planning Optimization of the Distributed Antenna System in High-Speed Railway Communication Network Based on Improved Cuckoo Search

Directory of Open Access Journals (Sweden)

Zhaoyu Chen

2018-01-01

Full Text Available The network planning is a key factor that directly affects the performance of the wireless networks. Distributed antenna system (DAS is an effective strategy for the network planning. This paper investigates the antenna deployment in a DAS for the high-speed railway communication networks and formulates an optimization problem which is NP-hard for achieving the optimal deployment of the antennas in the DAS. To solve this problem, a scheme based on an improved cuckoo search based on dimension cells (ICSDC algorithm is proposed. ICSDC introduces the dimension cell mechanism to avoid the internal dimension interferences in order to improve the performance of the algorithm. Simulation results show that the proposed ICSDC-based scheme obtains a lower network cost compared with the uniform network planning method. Moreover, ICSDC algorithm has better performance in terms of the convergence rate and accuracy compared with the conventional cuckoo search algorithm, the particle swarm optimization, and the firefly algorithm.

Algorithm based on regional separation for automatic grain boundary extraction using improved mean shift method

Science.gov (United States)

Zhenying, Xu; Jiandong, Zhu; Qi, Zhang; Yamba, Philip

2018-06-01

Metallographic microscopy shows that the vast majority of metal materials are composed of many small grains; the grain size of a metal is important for determining the tensile strength, toughness, plasticity, and other mechanical properties. In order to quantitatively evaluate grain size in metals, grain boundaries must be identified in metallographic images. Based on the phenomenon of grain boundary blurring or disconnection in metallographic images, this study develops an algorithm based on regional separation for automatically extracting grain boundaries by an improved mean shift method. Experimental observation shows that the grain boundaries obtained by the proposed algorithm are highly complete and accurate. This research has practical value because the proposed algorithm is suitable for grain boundary extraction from most metallographic images.

A Comprehensive Strategy for Accurate Reactive Power Distribution, Stability Improvement, and Harmonic Suppression of Multi-Inverter-Based Micro-Grid

Directory of Open Access Journals (Sweden)

Henan Dong

2018-03-01

Full Text Available Among the issues of accurate power distribution, stability improvement, and harmonic suppression in micro-grid, each has been well studied as an individual, and most of the strategies about these issues aim at one inverter-based micro-grid, hence there is a need to establish a model to achieve these functions as a whole, aiming at a multi-inverter-based micro-grid. This paper proposes a comprehensive strategy which achieves this goal successfully; since the output voltage and frequency of micro-grid all consist of fundamental and harmonic components, the strategy contains two parts accordingly. On one hand, a fundamental control strategy is proposed upon the conventional droop control. The virtual impedance is introduced to solve the problem of accurate allocation of reactive power between inverters. Meanwhile, a secondary power balance controller is added to improve the stability of voltage and frequency while considering the aggravating problem of stability because of introducing virtual impedance. On the other hand, the fractional frequency harmonic control strategy is proposed. It can solve the influence of nonlinear loads, micro-grid inverters, and the distribution network on output voltage of inverters, which is focused on eliminating specific harmonics caused by the nonlinear loads, micro-grid converters, and the distribution network so that the power quality of micro-grid can be improved effectively. Finally, small signal analysis is used to analyze the stability of the multi-converter parallel system after introducing the whole control strategy. The simulation results show that the strategy proposed in this paper has a great performance on distributing reactive power, regulating and stabilizing output voltage of inverters and frequency, eliminating harmonic components, and improving the power quality of multi-inverter-based micro-grid.

An object-oriented classification method of high resolution imagery based on improved AdaTree

International Nuclear Information System (INIS)

Xiaohe, Zhang; Liang, Zhai; Jixian, Zhang; Huiyong, Sang

2014-01-01

With the popularity of the application using high spatial resolution remote sensing image, more and more studies paid attention to object-oriented classification on image segmentation as well as automatic classification after image segmentation. This paper proposed a fast method of object-oriented automatic classification. First, edge-based or FNEA-based segmentation was used to identify image objects and the values of most suitable attributes of image objects for classification were calculated. Then a certain number of samples from the image objects were selected as training data for improved AdaTree algorithm to get classification rules. Finally, the image objects could be classified easily using these rules. In the AdaTree, we mainly modified the final hypothesis to get classification rules. In the experiment with WorldView2 image, the result of the method based on AdaTree showed obvious accuracy and efficient improvement compared with the method based on SVM with the kappa coefficient achieving 0.9242

Classifying orofacial pains: a new proposal of taxonomy based on ontology

Science.gov (United States)

NIXDORF, D. R.; DRANGSHOLT, M. T.; ETTLIN, D. A.; GAUL, C.; DE LEEUW, R.; SVENSSON, P.; ZAKRZEWSKA, J. M.; DE LAAT, A.; CEUSTERS, W.

2012-01-01

SUMMARY Propose a new taxonomy model based on ontological principles for disorders that manifest themselves through the symptom of persistent orofacial pain and are commonly seen in clinical practice and difficult to manage. Consensus meeting of eight experts from various geographic areas representing different perspectives (orofacial pain, headache, oral medicine and ontology) as an initial step towards improving the taxonomy. Ontological principles were introduced, reviewed and applied during the consensus building process. Diagnostic criteria for persistent dento-alveolar pain disorder (PDAP) were formulated as an example to be used to model the taxonomical structure of all orofacial pain conditions. These criteria have the advantage of being (i) anatomically defined, (ii) in accordance with other classification systems for the provision of clinical care, (iii) descriptive and succinct, (iv) easy to adapt for applications in varying settings, (v) scalable and (vi) transferable for the description of pain disorders in other orofacial regions of interest. Limitations are that the criteria introduce new terminology, do not have widespread acceptance and have yet to be tested. These results were presented to the greater conference membership and were unanimously accepted. Consensus for the diagnostic criteria of PDAP was established within this working group. This is an initial first step towards developing a coherent taxonomy for orofacial pain disorders, which is needed to improve clinical research and care. PMID:21848527

Classifying orofacial pains: a new proposal of taxonomy based on ontology.

Science.gov (United States)

Nixdorf, D R; Drangsholt, M T; Ettlin, D A; Gaul, C; De Leeuw, R; Svensson, P; Zakrzewska, J M; De Laat, A; Ceusters, W

2012-03-01

We propose a new taxonomy model based on ontological principles for disorders that manifest themselves through the symptom of persistent orofacial pain and are commonly seen in clinical practice and difficult to manage. Consensus meeting of eight experts from various geographic areas representing different perspectives (orofacial pain, headache, oral medicine and ontology) as an initial step towards improving the taxonomy. Ontological principles were introduced, reviewed and applied during the consensus building process. Diagnostic criteria for persistent dento-alveolar pain disorder (PDAP) were formulated as an example to be used to model the taxonomical structure of all orofacial pain conditions. These criteria have the advantage of being (i) anatomically defined, (ii) in accordance with other classification systems for the provision of clinical care, (iii) descriptive and succinct, (iv) easy to adapt for applications in varying settings, (v) scalable and (vi) transferable for the description of pain disorders in other orofacial regions of interest. Limitations are that the criteria introduce new terminology, do not have widespread acceptance and have yet to be tested. These results were presented to the greater conference membership and were unanimously accepted. Consensus for the diagnostic criteria of PDAP was established within this working group. This is an initial first step towards developing a coherent taxonomy for orofacial pain disorders, which is needed to improve clinical research and care. © 2011 Blackwell Publishing Ltd.

Research on the control strategy of distributed energy resources inverter based on improved virtual synchronous generator.

Science.gov (United States)

Gao, Changwei; Liu, Xiaoming; Chen, Hai

2017-08-22

This paper focus on the power fluctuations of the virtual synchronous generator(VSG) during the transition process. An improved virtual synchronous generator(IVSG) control strategy based on feed-forward compensation is proposed. Adjustable parameter of the compensation section can be modified to achieve the goal of reducing the order of the system. It can effectively suppress the power fluctuations of the VSG in transient process. To verify the effectiveness of the proposed control strategy for distributed energy resources inverter, the simulation model is set up in MATLAB/SIMULINK platform and physical experiment platform is established. Simulation and experiment results demonstrate the effectiveness of the proposed IVSG control strategy.

An improved resource management model based on MDS

Science.gov (United States)

Yuan, Man; Sun, Changying; Li, Pengfei; Sun, Yongdong; He, Rui

2005-11-01

GRID technology provides a kind of convenient method for managing GRID resources. This service is so-called monitoring, discovering service. This method is proposed by Globus Alliance, in this GRID environment, all kinds of resources, such as computational resources, storage resources and other resources can be organized by MDS specifications. However, this MDS is a theory framework, particularly, in a small world intranet, in the case of limit of resources, the MDS has its own limitation. Based on MDS, an improved light method for managing corporation computational resources and storage resources is proposed in intranet(IMDS). Firstly, in MDS, all kinds of resource description information is stored in LDAP, it is well known although LDAP is a light directory access protocol, in practice, programmers rarely master how to access and store resource information into LDAP store, in such way, it limits MDS to be used. So, in intranet, these resources' description information can be stored in RDBMS, programmers and users can access this information by standard SQL. Secondly, in MDS, how to monitor all kinds of resources in GRID is not transparent for programmers and users. In such way, it limits its application scope, in general, resource monitoring method base on SNMP is widely employed in intranet, therefore, a kind of resource monitoring method based on SNMP is integrated into MDS. Finally, all kinds of resources in the intranet can be described by XML, and all kinds of resources' description information is stored in RDBMS, such as MySql, and retrieved by standard SQL, dynamic information for all kinds of resources can be sent to resource storage by SNMP, A prototype resource description, monitoring is designed and implemented in intranet.

An improved self-adaptive ant colony algorithm based on genetic strategy for the traveling salesman problem

Science.gov (United States)

Wang, Pan; Zhang, Yi; Yan, Dong

2018-05-01

Ant Colony Algorithm (ACA) is a powerful and effective algorithm for solving the combination optimization problem. Moreover, it was successfully used in traveling salesman problem (TSP). But it is easy to prematurely converge to the non-global optimal solution and the calculation time is too long. To overcome those shortcomings, a new method is presented-An improved self-adaptive Ant Colony Algorithm based on genetic strategy. The proposed method adopts adaptive strategy to adjust the parameters dynamically. And new crossover operation and inversion operation in genetic strategy was used in this method. We also make an experiment using the well-known data in TSPLIB. The experiment results show that the performance of the proposed method is better than the basic Ant Colony Algorithm and some improved ACA in both the result and the convergence time. The numerical results obtained also show that the proposed optimization method can achieve results close to the theoretical best known solutions at present.

Proposing a Wiki-Based Technique for Collaborative Essay Writing

Directory of Open Access Journals (Sweden)

Mabel Ortiz Navarrete

2014-10-01

Full Text Available This paper aims at proposing a technique for students learning English as a foreign language when they collaboratively write an argumentative essay in a wiki environment. A wiki environment and collaborative work play an important role within the academic writing task. Nevertheless, an appropriate and systematic work assignment is required in order to make use of both. In this paper the proposed technique when writing a collaborative essay mainly attempts to provide the most effective way to enhance equal participation among group members by taking as a base computer mediated collaboration. Within this context, the students’ role is clearly defined and individual and collaborative tasks are explained.

Improvement of Roller Bearing Diagnosis with Unlabeled Data Using Cut Edge Weight Confidence Based Tritraining

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Wei-Li Qin

2016-01-01

Full Text Available Roller bearings are one of the most commonly used components in rotational machines. The fault diagnosis of roller bearings thus plays an important role in ensuring the safe functioning of the mechanical systems. However, in most cases of bearing fault diagnosis, there are limited number of labeled data to achieve a proper fault diagnosis. Therefore, exploiting unlabeled data plus few labeled data, this paper proposed a roller bearing fault diagnosis method based on tritraining to improve roller bearing diagnosis performance. To overcome the noise brought by wrong labeling into the classifiers training process, the cut edge weight confidence is introduced into the diagnosis framework. Besides a small trick called suspect principle is adopted to avoid overfitting problem. The proposed method is validated in two independent roller bearing fault experiment vibrational signals that both include three types of faults: inner-ring fault, outer-ring fault, and rolling element fault. The results demonstrate the desirable diagnostic performance improvement by the proposed method in the extreme situation where there is only limited number of labeled data.

Optical vector network analyzer with improved accuracy based on polarization modulation and polarization pulling.

Science.gov (United States)

Li, Wei; Liu, Jian Guo; Zhu, Ning Hua

2015-04-15

We report a novel optical vector network analyzer (OVNA) with improved accuracy based on polarization modulation and stimulated Brillouin scattering (SBS) assisted polarization pulling. The beating between adjacent higher-order optical sidebands which are generated because of the nonlinearity of an electro-optic modulator (EOM) introduces considerable error to the OVNA. In our scheme, the measurement error is significantly reduced by removing the even-order optical sidebands using polarization discrimination. The proposed approach is theoretically analyzed and experimentally verified. The experimental results show that the accuracy of the OVNA is greatly improved compared to a conventional OVNA.

A fault diagnosis approach for diesel engine valve train based on improved ITD and SDAG-RVM

International Nuclear Information System (INIS)

Yu, Liu; Junhong, Zhang; Fengrong, Bi; Jiewei, Lin; Wenpeng, Ma

2015-01-01

Targeting the non-stationary characteristics of the vibration signals of a diesel engine valve train, and the limitation of the autoregressive (AR) model, a novel approach based on the improved intrinsic time-scale decomposition (ITD) and relevance vector machine (RVM) is proposed in this paper for the identification of diesel engine valve train faults. The approach mainly consists of three stages: First, prior to the feature extraction, non-uniform B-spline interpolation is introduced to the ITD method for the fitting of baseline signal, then the improved ITD is used to decompose the non-stationary signals into a set of stationary proper rotation components (PRCs). Second, the AR model is established for each PRC, and the first several AR coefficients together with the remnant variance of all PRCs are regarded as the fault feature vectors. Finally, a new separability based directed acyclic graph (SDAG) method is proposed to determine the structure of multi-class RVM, and the fault feature vectors are classified using the SDAG-RVM classifier to recognize the fault of the diesel engine valve train. The experimental results demonstrate that the proposed fault diagnosis approach can effectively extract the fault features and accurately identify the fault patterns. (paper)

Size-based scheduling to improve web performance

NARCIS (Netherlands)

Harchol-Balter, M.; Schroeder, B.; Bansal, N.; Agrawal, M.

2003-01-01

Is it possible to reduce the expected response time of every request at a web server, simply by changing the order in which we schedule the requests? That is the question we ask in this paper.This paper proposes a method for improving the performance of web servers servicing static HTTP requests.

Proposed higher order continuum-based models for an elastic ...

African Journals Online (AJOL)

Three new variants of continuum-based models for an elastic subgrade are proposed. The subgrade is idealized as a homogenous, isotropic elastic layer of thickness H overlying a firm stratum. All components of the stress tensor in the subgrade are taken into account. Reasonable assumptions are made regarding the ...

Global Maximum Power Point Tracking (MPPT of a Photovoltaic Module Array Constructed through Improved Teaching-Learning-Based Optimization

Directory of Open Access Journals (Sweden)

Kuei-Hsiang Chao

2016-11-01

Full Text Available The present study proposes a maximum power point tracking (MPPT method in which improved teaching-learning-based optimization (I-TLBO is applied to perform global MPPT of photovoltaic (PV module arrays under dissimilar shading situations to ensure the maximum power output of the module arrays. The proposed I-TLBO enables the automatic adjustment of teaching factors according to the self-learning ability of students. Incorporating smart-tracking and self-study strategies can effectively improve the tracking response speed and steady-state tracking performance. To evaluate the feasibility of the proposed I-TLBO, a HIP-2717 PV module array from Sanyo Electric was employed to compose various arrays with different serial and parallel configurations. The arrays were operated under different shading conditions to test the MPPT with double, triple, or quadruple peaks of power-voltage characteristic curves. Boost converters were employed with TMS320F2808 digital signal processors to test the proposed MPPT method. Empirical results confirm that the proposed method exhibits more favorable dynamic and static-state response tracking performance compared with that of conventional TLBO.

An Improved Method of Pose Estimation for Lighthouse Base Station Extension.

Science.gov (United States)

Yang, Yi; Weng, Dongdong; Li, Dong; Xun, Hang

2017-10-22

In 2015, HTC and Valve launched a virtual reality headset empowered with Lighthouse, the cutting-edge space positioning technology. Although Lighthouse is superior in terms of accuracy, latency and refresh rate, its algorithms do not support base station expansion, and is flawed concerning occlusion in moving targets, that is, it is unable to calculate their poses with a small set of sensors, resulting in the loss of optical tracking data. In view of these problems, this paper proposes an improved pose estimation algorithm for cases where occlusion is involved. Our algorithm calculates the pose of a given object with a unified dataset comprising of inputs from sensors recognized by all base stations, as long as three or more sensors detect a signal in total, no matter from which base station. To verify our algorithm, HTC official base stations and autonomous developed receivers are used for prototyping. The experiment result shows that our pose calculation algorithm can achieve precise positioning when a few sensors detect the signal.

Combined failure acoustical diagnosis based on improved frequency domain blind deconvolution

International Nuclear Information System (INIS)

Pan, Nan; Wu, Xing; Chi, YiLin; Liu, Xiaoqin; Liu, Chang

2012-01-01

According to gear box combined failure extraction in complex sound field, an acoustic fault detection method based on improved frequency domain blind deconvolution was proposed. Follow the frequency-domain blind deconvolution flow, the morphological filtering was firstly used to extract modulation features embedded in the observed signals, then the CFPA algorithm was employed to do complex-domain blind separation, finally the J-Divergence of spectrum was employed as distance measure to resolve the permutation. Experiments using real machine sound signals was carried out. The result demonstrate this algorithm can be efficiently applied to gear box combined failure detection in practice.

An Improved PID Algorithm Based on Insulin-on-Board Estimate for Blood Glucose Control with Type 1 Diabetes.

Science.gov (United States)

Hu, Ruiqiang; Li, Chengwei

2015-01-01

Automated closed-loop insulin infusion therapy has been studied for many years. In closed-loop system, the control algorithm is the key technique of precise insulin infusion. The control algorithm needs to be designed and validated. In this paper, an improved PID algorithm based on insulin-on-board estimate is proposed and computer simulations are done using a combinational mathematical model of the dynamics of blood glucose-insulin regulation in the blood system. The simulation results demonstrate that the improved PID algorithm can perform well in different carbohydrate ingestion and different insulin sensitivity situations. Compared with the traditional PID algorithm, the control performance is improved obviously and hypoglycemia can be avoided. To verify the effectiveness of the proposed control algorithm, in silico testing is done using the UVa/Padova virtual patient software.

Tree-based server-middleman-client architecture: improving scalability and reliability for voting-based network games in ad hoc wireless networks

Science.gov (United States)

Guo, Y.; Fujinoki, H.

2006-10-01

The concept of a new tree-based architecture for networked multi-player games was proposed by Matuszek to improve scalability in network traffic at the same time to improve reliability. The architecture (we refer it as "Tree-Based Server- Middlemen-Client architecture") will solve the two major problems in ad-hoc wireless networks: frequent link failures and significance in battery power consumption at wireless transceivers by using two new techniques, recursive aggregation of client messages and subscription-based propagation of game state. However, the performance of the TBSMC architecture has never been quantitatively studied. In this paper, the TB-SMC architecture is compared with the client-server architecture using simulation experiments. We developed an event driven simulator to evaluate the performance of the TB-SMC architecture. In the network traffic scalability experiments, the TB-SMC architecture resulted in less than 1/14 of the network traffic load for 200 end users. In the reliability experiments, the TB-SMC architecture improved the number of successfully delivered players' votes by 31.6, 19.0, and 12.4% from the clientserver architecture at high (failure probability of 90%), moderate (50%) and low (10%) failure probability.

A Simulation-Based Quality Improvement Initiative Improves Pediatric Readiness in Community Hospitals.

Science.gov (United States)

Whitfill, Travis; Gawel, Marcie; Auerbach, Marc

2017-07-17

The National Pediatric Readiness Project Pediatric Readiness Survey (PRS) measured pediatric readiness in 4149 US emergency departments (EDs) and noted an average score of 69 on a 100-point scale. This readiness score consists of 6 domains: coordination of pediatric patient care (19/100), physician/nurse staffing and training (10/100), quality improvement activities (7/100), patient safety initiatives (14/100), policies and procedures (17/100), and availability of pediatric equipment (33/100). We aimed to assess and improve pediatric emergency readiness scores across Connecticut's hospitals. The aim of this study was to compare the National Pediatric Readiness Project readiness score before and after an in situ simulation-based assessment and quality improvement program in Connecticut hospitals. We leveraged in situ simulations to measure the quality of resuscitative care provided by interprofessional teams to 3 simulated patients (infant septic shock, infant seizure, and child cardiac arrest) presenting to their ED resuscitation bay. Assessments of EDs were made based on a composite quality score that was measured as the sum of 4 distinct domains: (1) adherence to sepsis guidelines, (2) adherence to cardiac arrest guidelines, (3) performance on seizure resuscitation, and (4) teamwork. After the simulation, a detailed report with scores, comparisons to other EDs, and a gap analysis were provided to sites. Based on this report, a regional children's hospital team worked collaboratively with each ED to develop action items and a timeline for improvements. The National Pediatric Readiness Project PRS scores, the primary outcome of this study, were measured before and after participation. Twelve community EDs in Connecticut participated in this project. The PRS scores were assessed before and after the intervention (simulation-based assessment and gap analysis/report-out). The average time between PRS assessments was 21 months. The PRS scores significantly improved 12

Modeling and performance analysis of an improved movement-based location management scheme for packet-switched mobile communication systems.

Science.gov (United States)

Chung, Yun Won; Kwon, Jae Kyun; Park, Suwon

2014-01-01

One of the key technologies to support mobility of mobile station (MS) in mobile communication systems is location management which consists of location update and paging. In this paper, an improved movement-based location management scheme with two movement thresholds is proposed, considering bursty data traffic characteristics of packet-switched (PS) services. The analytical modeling for location update and paging signaling loads of the proposed scheme is developed thoroughly and the performance of the proposed scheme is compared with that of the conventional scheme. We show that the proposed scheme outperforms the conventional scheme in terms of total signaling load with an appropriate selection of movement thresholds.

Control and communication co-design: analysis and practice on performance improvement in distributed measurement and control system based on fieldbus and Ethernet.

Science.gov (United States)

Liang, Geng

2015-01-01

In this paper, improving control performance of a networked control system by reducing DTD in a different perspective was investigated. Two different network architectures for system implementation were presented. Analysis and improvement dealing with DTD for the experimental control system were expounded. Effects of control scheme configuration on DTD in the form of FB were investigated and corresponding improvements by reallocation of FB and re-arrangement of schedule table are proposed. Issues of DTD in hybrid network were investigated and corresponding approaches to improve performance including (1) reducing DTD in PLC or PAC by way of IEC61499 and (2) cascade Smith predictive control with BPNN-based identification were proposed and investigated. Control effects under the proposed methodologies were also given. Experimental and field practices validated these methodologies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

An Improved Teaching-Learning-Based Optimization with the Social Character of PSO for Global Optimization

Directory of Open Access Journals (Sweden)

Feng Zou

2016-01-01

Full Text Available An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO, which is considering the teacher’s behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods.

Interval-Valued Hesitant Fuzzy Multiattribute Group Decision Making Based on Improved Hamacher Aggregation Operators and Continuous Entropy

Directory of Open Access Journals (Sweden)

Jun Liu

2017-01-01

Full Text Available Under the interval-valued hesitant fuzzy information environment, we investigate a multiattribute group decision making (MAGDM method with continuous entropy weights and improved Hamacher information aggregation operators. Firstly, we introduce the axiomatic definition of entropy for interval-valued hesitant fuzzy elements (IVHFEs and construct a continuous entropy formula on the basis of the continuous ordered weighted averaging (COWA operator. Then, based on the Hamacher t-norm and t-conorm, the adjusted operational laws for IVHFEs are defined. In order to aggregate interval-valued hesitant fuzzy information, some new improved interval-valued hesitant fuzzy Hamacher aggregation operators are investigated, including the improved interval-valued hesitant fuzzy Hamacher ordered weighted averaging (I-IVHFHOWA operator and the improved interval-valued hesitant fuzzy Hamacher ordered weighted geometric (I-IVHFHOWG operator, the desirable properties of which are discussed. In addition, the relationship among these proposed operators is analyzed in detail. Applying the continuous entropy and the proposed operators, an approach to MAGDM is developed. Finally, a numerical example for emergency operating center (EOC selection is provided, and comparative analyses with existing methods are performed to demonstrate that the proposed approach is both valid and practical to deal with group decision making problems.

RTDS implementation of an improved sliding mode based inverter controller for PV system.

Science.gov (United States)

Islam, Gazi; Muyeen, S M; Al-Durra, Ahmed; Hasanien, Hany M

2016-05-01

This paper proposes a novel approach for testing dynamics and control aspects of a large scale photovoltaic (PV) system in real time along with resolving design hindrances of controller parameters using Real Time Digital Simulator (RTDS). In general, the harmonic profile of a fast controller has wide distribution due to the large bandwidth of the controller. The major contribution of this paper is that the proposed control strategy gives an improved voltage harmonic profile and distribute it more around the switching frequency along with fast transient response; filter design, thus, becomes easier. The implementation of a control strategy with high bandwidth in small time steps of Real Time Digital Simulator (RTDS) is not straight forward. This paper shows a good methodology for the practitioners to implement such control scheme in RTDS. As a part of the industrial process, the controller parameters are optimized using particle swarm optimization (PSO) technique to improve the low voltage ride through (LVRT) performance under network disturbance. The response surface methodology (RSM) is well adapted to build analytical models for recovery time (Rt), maximum percentage overshoot (MPOS), settling time (Ts), and steady state error (Ess) of the voltage profile immediate after inverter under disturbance. A systematic approach of controller parameter optimization is detailed. The transient performance of the PSO based optimization method applied to the proposed sliding mode controlled PV inverter is compared with the results from genetic algorithm (GA) based optimization technique. The reported real time implementation challenges and controller optimization procedure are applicable to other control applications in the field of renewable and distributed generation systems. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Virtual Impedance Based Stability Improvement for DC Microgrids with Constant Power Loads

DEFF Research Database (Denmark)

Lu, Xiaonan; Sun, Kai; Huang, Lipei

2014-01-01

DC microgrid provides an efficient way to integrate different kinds of renewable energy sources with DC couplings. In this paper, in order to improve the stability of DC microgrids with constant power loads (CPLs), a virtual impedance based method is proposed. The CPLs have inherent instability....... To validate the stability with the above stabilizers in a DC microgrid with parallel interfacing converters and CPL, the impedance matching approach is employed. The output impedance of the source converter and input impedance of the load are calculated respectively, and the influence of droop control...

An Improved Piecewise Linear Chaotic Map Based Image Encryption Algorithm

Directory of Open Access Journals (Sweden)

Yuping Hu

2014-01-01

Full Text Available An image encryption algorithm based on improved piecewise linear chaotic map (MPWLCM model was proposed. The algorithm uses the MPWLCM to permute and diffuse plain image simultaneously. Due to the sensitivity to initial key values, system parameters, and ergodicity in chaotic system, two pseudorandom sequences are designed and used in the processes of permutation and diffusion. The order of processing pixels is not in accordance with the index of pixels, but it is from beginning or end alternately. The cipher feedback was introduced in diffusion process. Test results and security analysis show that not only the scheme can achieve good encryption results but also its key space is large enough to resist against brute attack.

Carbon emission analysis and evaluation of industrial departments in China: An improved environmental DEA cross model based on information entropy.

Science.gov (United States)

Han, Yongming; Long, Chang; Geng, Zhiqiang; Zhang, Keyu

2018-01-01

Environmental protection and carbon emission reduction play a crucial role in the sustainable development procedure. However, the environmental efficiency analysis and evaluation based on the traditional data envelopment analysis (DEA) cross model is subjective and inaccurate, because all elements in a column or a row of the cross evaluation matrix (CEM) in the traditional DEA cross model are given the same weight. Therefore, this paper proposes an improved environmental DEA cross model based on the information entropy to analyze and evaluate the carbon emission of industrial departments in China. The information entropy is applied to build the entropy distance based on the turbulence of the whole system, and calculate the weights in the CEM of the environmental DEA cross model in a dynamic way. The theoretical results show that the new weight constructed based on the information entropy is unique and optimal globally by using the Monte Carlo simulation. Finally, compared with the traditional environmental DEA and DEA cross model, the improved environmental DEA cross model has a better efficiency discrimination ability based on the data of industrial departments in China. Moreover, the proposed model can obtain the potential of carbon emission reduction of industrial departments to improve the energy efficiency. Copyright © 2017 Elsevier Ltd. All rights reserved.

High stability vector-based direct power control for DFIG-based wind turbine

DEFF Research Database (Denmark)

Zhu, Rongwu; Chen, Zhe; Wu, Xiaojie

2015-01-01

This paper proposes an improved vector-based direct power control (DPC) strategy for the doubly-fed induction generator (DFIG)-based wind energy conversion system. Based on the small signal model, the proposed DPC improves the stability of the DFIG, and avoids the DFIG operating in the marginal...

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Directory of Open Access Journals (Sweden)

Qianqian Ba

2017-01-01

Full Text Available Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM, an information value model improved by an analytic hierarchy process (IVM-AHP and our new improved model. Approximately 70% (5905 of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530 were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.

Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms.

Science.gov (United States)

Reena Benjamin, J; Jayasree, T

2018-02-01

In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms. A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details. The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations. In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.

An Interval-Valued Intuitionistic Fuzzy TOPSIS Method Based on an Improved Score Function

Directory of Open Access Journals (Sweden)

Zhi-yong Bai

2013-01-01

Full Text Available This paper proposes an improved score function for the effective ranking order of interval-valued intuitionistic fuzzy sets (IVIFSs and an interval-valued intuitionistic fuzzy TOPSIS method based on the score function to solve multicriteria decision-making problems in which all the preference information provided by decision-makers is expressed as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by IVIFS value and the information about criterion weights is known. We apply the proposed score function to calculate the separation measures of each alternative from the positive and negative ideal solutions to determine the relative closeness coefficients. According to the values of the closeness coefficients, the alternatives can be ranked and the most desirable one(s can be selected in the decision-making process. Finally, two illustrative examples for multicriteria fuzzy decision-making problems of alternatives are used as a demonstration of the applications and the effectiveness of the proposed decision-making method.

An Improved Fast Compressive Tracking Algorithm Based on Online Random Forest Classifier

Directory of Open Access Journals (Sweden)

Xiong Jintao

2016-01-01

Full Text Available The fast compressive tracking (FCT algorithm is a simple and efficient algorithm, which is proposed in recent years. But, it is difficult to deal with the factors such as occlusion, appearance changes, pose variation, etc in processing. The reasons are that, Firstly, even if the naive Bayes classifier is fast in training, it is not robust concerning the noise. Secondly, the parameters are required to vary with the unique environment for accurate tracking. In this paper, we propose an improved fast compressive tracking algorithm based on online random forest (FCT-ORF for robust visual tracking. Firstly, we combine ideas with the adaptive compressive sensing theory regarding the weighted random projection to exploit both local and discriminative information of the object. The second reason is the online random forest classifier for online tracking which is demonstrated with more robust to the noise adaptively and high computational efficiency. The experimental results show that the algorithm we have proposed has a better performance in the field of occlusion, appearance changes, and pose variation than the fast compressive tracking algorithm’s contribution.

Comparison of proposed countermeasures for dilemma zone at signalized intersections based on cellular automata simulations.

Science.gov (United States)

Wu, Yina; Abdel-Aty, Mohamed; Ding, Yaoxian; Jia, Bin; Shi, Qi; Yan, Xuedong

2018-07-01

The Type II dilemma zone describes the road segment to a signalized intersection where drivers have difficulties to decide either stop or go at the onset of yellow signal. Such phenomenon can result in an increased crash risk at signalized intersections. Different types of warning systems have been proposed to help drivers make decisions. Although the warning systems help to improve drivers' behavior, they also have several disadvantages such as increasing rear-end crashes or red-light running (RLR) violations. In this study, a new warning system called pavement marking with auxiliary countermeasure (PMAIC) is proposed to reduce the dilemma zone and enhance the traffic safety at signalized intersections. The proposed warning system integrates the pavement marking and flashing yellow system which can provide drivers with better suggestions about stop/go decisions based on their arriving time and speed. In order to evaluate the performance of the proposed warning system, this paper presents a cellular automata (CA) simulation study. The CA simulations are conducted for four different scenarios in total, including the typical intersection without warning system, the intersection with flashing green countermeasure, the intersection with pavement marking, and the intersection with the PMAIC warning system. Before the specific CA simulation analysis, a logistic regression model is calibrated based on field video data to predict drivers' general stop/go decisions. Also, the rules of vehicle movements in the CA models under the influence by different warning systems are proposed. The proxy indicators of rear-end crash and potential RLR violations were estimated and used to evaluate safety levels for the different scenarios. The simulation results showed that the PMAIC countermeasure consistently offered best performance to reduce rear-end crash and RLR violation. Meanwhile, the results indicate that the flashing-green countermeasure could not effectively reduce either rear

Proposed radiological criteria for pre-operative determination of resectability in peritoneal-based malignancies

International Nuclear Information System (INIS)

Tan, Grace Hwei Ching; Chanyaputhipong, Jendana; Teo, Melissa Ching Ching; Kwek, Jin Wei; Hosseini, Reza; Tham, Chee Kian; Soo, Khee Chee

2016-01-01

The selection of patients for cytoreductive surgery (CRS) and hyperthermic intra-peritoneal chemotherapy infusion (HIPEC) is important, and relies heavily on imaging. However, it has been reported that Computer Tomographic (CT) scans may only achieve a low sensitivity of 33% for peritoneal disease. We propose a set of radiological criteria for pre-operative determination of resectability of peritoneal disease in peritoneal-based malignancies and validate this in our cohort of patients. A retrospective review of all patients who underwent laparotomy with a view for CRS and HIPEC, at the National Cancer Centre Singapore from January 2000 to April 2010, was performed. Intra-operative Peritoneal Cancer Index (PCI) scores were recorded. The pre-operative imaging was reviewed with a senior radiologist who was blinded, and recorded the radiological PCI scores (CT-PCI) and eight additional CT prognostic factors (CT-PF). The CT-PCI and CT-PF scores were then compared with the intra-operative findings to determine the radiological accuracy. The scores and the individual prognostic factors were then evaluated for their predictive ability for unresectability. Comparison of the CT-PCI and PCI scores showed a concordance correlation coefficient at 0.52 (95% CI 0.34–0.7). Accuracy was increased with the addition CT-PF. The presence of omental caking and ascites were predictors of unresectability. We propose a scoring system which is able to predict for unresectable disease with a specificity of 80% and a sensitivity of 62%. With our proposed criteria, and scoring system, the selection of patients for CRS and HIPEC can be improved, and unnecessary exploratory operations avoided.

Modelling and analysis of transient state during improved coupling procedure with the grid for DFIG based wind turbine generator

Science.gov (United States)

Kammoun, Soulaymen; Sallem, Souhir; Ben Ali Kammoun, Mohamed

2017-11-01

The aim of this study is to enhance DFIG based Wind Energy Conversion Systems (WECS) dynamics during grid coupling. In this paper, a system modelling and a starting/coupling procedure for this generator to the grid are proposed. The proposed non-linear system is a variable structure system (VSS) and has two different states, before and after coupling. So, two different state models are given to the system to analyse transient stability during the coupling. The given model represents well the transient state of the machine, through which, a behaviour assessment of the generator before, during and after connection is given based on simulation results. For this, a 300 kW DFIG based wind generation system model was simulated on the Matlab/SIMULINK environment. We judge the proposed procedure to be practical, smooth and stability improved.

Charge plasma based source/drain engineered Schottky Barrier MOSFET: Ambipolar suppression and improvement of the RF performance

Science.gov (United States)

Kale, Sumit; Kondekar, Pravin N.

2018-01-01

This paper reports a novel device structure for charge plasma based Schottky Barrier (SB) MOSFET on ultrathin SOI to suppress the ambipolar leakage current and improvement of the radio frequency (RF) performance. In the proposed device, we employ dual material for the source and drain formation. Therefore, source/drain is divided into two parts as main source/drain and source/drain extension. Erbium silicide (ErSi1.7) is used as main source/drain material and Hafnium metal is used as source/drain extension material. The source extension induces the electron plasma in the ultrathin SOI body resulting reduction of SB width at the source side. Similarly, drain extension also induces the electron plasma at the drain side. This significantly increases the SB width due to increased depletion at the drain end. As a result, the ambipolar leakage current can be suppressed. In addition, drain extension also reduces the parasitic capacitances of the proposed device to improve the RF performance. The optimization of length and work function of metal used in the drain extension is performed to achieve improvement in device performance. Moreover, the proposed device makes fabrication simpler, requires low thermal budget and free from random dopant fluctuations.

Improved Deep Belief Networks (IDBN Dynamic Model-Based Detection and Mitigation for Targeted Attacks on Heavy-Duty Robots

Directory of Open Access Journals (Sweden)

Lianpeng Li

2018-04-01

Full Text Available In recent years, the robots, especially heavy-duty robots, have become the hardest-hit areas for targeted attacks. These attacks come from both the cyber-domain and the physical-domain. In order to improve the security of heavy-duty robots, this paper proposes a detection and mitigation mechanism which based on improved deep belief networks (IDBN and dynamic model. The detection mechanism consists of two parts: (1 IDBN security checks, which can detect targeted attacks from the cyber-domain; (2 Dynamic model and security detection, used to detect the targeted attacks which can possibly lead to a physical-domain damage. The mitigation mechanism was established on the base of the detection mechanism and could mitigate transient and discontinuous attacks. Moreover, a test platform was established to carry out the performance evaluation test for the proposed mechanism. The results show that, the detection accuracy for the attack of the cyber-domain of IDBN reaches 96.2%, and the detection accuracy for the attack of physical-domain control commands reaches 94%. The performance evaluation test has verified the reliability and high efficiency of the proposed detection and mitigation mechanism for heavy-duty robots.

Energy and carbon emissions analysis and prediction of complex petrochemical systems based on an improved extreme learning machine integrated interpretative structural model

International Nuclear Information System (INIS)

Han, Yongming; Zhu, Qunxiong; Geng, Zhiqiang; Xu, Yuan

2017-01-01

Highlights: • The ELM integrated ISM (ISM-ELM) method is proposed. • The proposed method is more efficient and accurate than the ELM through the UCI data set. • Energy and carbon emissions analysis and prediction of petrochemical industries based ISM-ELM is obtained. • The proposed method is valid in improving energy efficiency and reducing carbon emissions of ethylene plants. - Abstract: Energy saving and carbon emissions reduction of the petrochemical industry are affected by many factors. Thus, it is difficult to analyze and optimize the energy of complex petrochemical systems accurately. This paper proposes an energy and carbon emissions analysis and prediction approach based on an improved extreme learning machine (ELM) integrated interpretative structural model (ISM) (ISM-ELM). ISM based the partial correlation coefficient is utilized to analyze key parameters that affect the energy and carbon emissions of the complex petrochemical system, and can denoise and reduce dimensions of data to decrease the training time and errors of the ELM prediction model. Meanwhile, in terms of the model accuracy and the training time, the robustness and effectiveness of the ISM-ELM model are better than the ELM through standard data sets from the University of California Irvine (UCI) repository. Moreover, a multi-inputs and single-output (MISO) model of energy and carbon emissions of complex ethylene systems is established based on the ISM-ELM. Finally, detailed analyses and simulations using the real ethylene plant data demonstrate the effectiveness of the ISM-ELM and can guide the improvement direction of energy saving and carbon emissions reduction in complex petrochemical systems.

MILITARY BASE CLOSURES: Questions Concerning the Proposed Sale of Housing at Mather Air Force Base

National Research Council Canada - National Science Library

1998-01-01

This report responds to your request that we review the proposed negotiated sale of 1,271 surplus family housing units at Mather Air Force Base, California, to the Sacramento Housing and Redevelopment Agency (SHRA...

From free energy to expected energy: Improving energy-based value function approximation in reinforcement learning.

Science.gov (United States)

Elfwing, Stefan; Uchibe, Eiji; Doya, Kenji

2016-12-01

Free-energy based reinforcement learning (FERL) was proposed for learning in high-dimensional state and action spaces. However, the FERL method does only really work well with binary, or close to binary, state input, where the number of active states is fewer than the number of non-active states. In the FERL method, the value function is approximated by the negative free energy of a restricted Boltzmann machine (RBM). In our earlier study, we demonstrated that the performance and the robustness of the FERL method can be improved by scaling the free energy by a constant that is related to the size of network. In this study, we propose that RBM function approximation can be further improved by approximating the value function by the negative expected energy (EERL), instead of the negative free energy, as well as being able to handle continuous state input. We validate our proposed method by demonstrating that EERL: (1) outperforms FERL, as well as standard neural network and linear function approximation, for three versions of a gridworld task with high-dimensional image state input; (2) achieves new state-of-the-art results in stochastic SZ-Tetris in both model-free and model-based learning settings; and (3) significantly outperforms FERL and standard neural network function approximation for a robot navigation task with raw and noisy RGB images as state input and a large number of actions. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

An improved three-dimensional non-scanning laser imaging system based on digital micromirror device

Science.gov (United States)

Xia, Wenze; Han, Shaokun; Lei, Jieyu; Zhai, Yu; Timofeev, Alexander N.

2018-01-01

Nowadays, there are two main methods to realize three-dimensional non-scanning laser imaging detection, which are detection method based on APD and detection method based on Streak Tube. However, the detection method based on APD possesses some disadvantages, such as small number of pixels, big pixel interval and complex supporting circuit. The detection method based on Streak Tube possesses some disadvantages, such as big volume, bad reliability and high cost. In order to resolve the above questions, this paper proposes an improved three-dimensional non-scanning laser imaging system based on Digital Micromirror Device. In this imaging system, accurate control of laser beams and compact design of imaging structure are realized by several quarter-wave plates and a polarizing beam splitter. The remapping fiber optics is used to sample the image plane of receiving optical lens, and transform the image into line light resource, which can realize the non-scanning imaging principle. The Digital Micromirror Device is used to convert laser pulses from temporal domain to spatial domain. The CCD with strong sensitivity is used to detect the final reflected laser pulses. In this paper, we also use an algorithm which is used to simulate this improved laser imaging system. In the last, the simulated imaging experiment demonstrates that this improved laser imaging system can realize three-dimensional non-scanning laser imaging detection.

Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs

International Nuclear Information System (INIS)

Lei, Yaguo; Zuo, Ming J

2009-01-01

A Hilbert–Huang transform (HHT) is a time–frequency technique and has been widely applied to analyzing vibration signals in the field of fault diagnosis of rotating machinery. It analyzes the vibration signals using intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). However, EMD sometimes cannot reveal the signal characteristics accurately because of the problem of mode mixing. Ensemble empirical mode decomposition (EEMD) was developed recently to alleviate this problem. The IMFs generated by EEMD have different sensitivity to faults. Some IMFs are sensitive and closely related to the faults but others are irrelevant. To enhance the accuracy of the HHT in fault diagnosis of rotating machinery, an improved HHT based on EEMD and sensitive IMFs is proposed in this paper. Simulated signals demonstrate the effectiveness of the improved HHT in diagnosing the faults of rotating machinery. Finally, the improved HHT is applied to diagnosing an early rub-impact fault of a heavy oil catalytic cracking machine set, and the application results prove that the improved HHT is superior to the HHT based on all IMFs of EMD

Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals.

Science.gov (United States)

Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

2015-01-01

In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature.

Proposal for Requirement Validation Criteria and Method Based on Actor Interaction

Science.gov (United States)

Hattori, Noboru; Yamamoto, Shuichiro; Ajisaka, Tsuneo; Kitani, Tsuyoshi

We propose requirement validation criteria and a method based on the interaction between actors in an information system. We focus on the cyclical transitions of one actor's situation against another and clarify observable stimuli and responses based on these transitions. Both actors' situations can be listed in a state transition table, which describes the observable stimuli or responses they send or receive. Examination of the interaction between both actors in the state transition tables enables us to detect missing or defective observable stimuli or responses. Typically, this method can be applied to the examination of the interaction between a resource managed by the information system and its user. As a case study, we analyzed 332 requirement defect reports of an actual system development project in Japan. We found that there were a certain amount of defects regarding missing or defective stimuli and responses, which can be detected using our proposed method if this method is used in the requirement definition phase. This means that we can reach a more complete requirement definition with our proposed method.

Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

Directory of Open Access Journals (Sweden)

Yang Li

Full Text Available In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA methods, a new rule extraction method based on extreme learning machine (ELM and an improved Ant-miner (IAM algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

Science.gov (United States)

Li, Yang; Li, Guoqing; Wang, Zhenhao

2015-01-01

In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

Design of Electronic Medical Record User Interfaces: A Matrix-Based Method for Improving Usability

Directory of Open Access Journals (Sweden)

Kushtrim Kuqi

2013-01-01

Full Text Available This study examines a new approach of using the Design Structure Matrix (DSM modeling technique to improve the design of Electronic Medical Record (EMR user interfaces. The usability of an EMR medication dosage calculator used for placing orders in an academic hospital setting was investigated. The proposed method captures and analyzes the interactions between user interface elements of the EMR system and groups elements based on information exchange, spatial adjacency, and similarity to improve screen density and time-on-task. Medication dose adjustment task time was recorded for the existing and new designs using a cognitive simulation model that predicts user performance. We estimate that the design improvement could reduce time-on-task by saving an average of 21 hours of hospital physicians’ time over the course of a month. The study suggests that the application of DSM can improve the usability of an EMR user interface.

Improved Hip-Based Individual Recognition Using Wearable Motion Recording Sensor

Science.gov (United States)

Gafurov, Davrondzhon; Bours, Patrick

In todays society the demand for reliable verification of a user identity is increasing. Although biometric technologies based on fingerprint or iris can provide accurate and reliable recognition performance, they are inconvenient for periodic or frequent re-verification. In this paper we propose a hip-based user recognition method which can be suitable for implicit and periodic re-verification of the identity. In our approach we use a wearable accelerometer sensor attached to the hip of the person, and then the measured hip motion signal is analysed for identity verification purposes. The main analyses steps consists of detecting gait cycles in the signal and matching two sets of detected gait cycles. Evaluating the approach on a hip data set consisting of 400 gait sequences (samples) from 100 subjects, we obtained equal error rate (EER) of 7.5% and identification rate at rank 1 was 81.4%. These numbers are improvements by 37.5% and 11.2% respectively of the previous study using the same data set.

An Improved Timestamp-Based Password Authentication Scheme Using Smart Cards

OpenAIRE

Pathan, Al-Sakib Khan; Hong, Choong Seon

2007-01-01

With the recent proliferation of distributed systems and networking, remote authentication has become a crucial task in many networking applications. Various schemes have been proposed so far for the two-party remote authentication; however, some of them have been proved to be insecure. In this paper, we propose an efficient timestamp-based password authentication scheme using smart cards. We show various types of forgery attacks against a previously proposed timestamp-based password authenti...

An Improved and Secure Biometric Authentication Scheme for Telecare Medicine Information Systems Based on Elliptic Curve Cryptography.

Science.gov (United States)

Chaudhry, Shehzad Ashraf; Mahmood, Khalid; Naqvi, Husnain; Khan, Muhammad Khurram

2015-11-01

Telecare medicine information system (TMIS) offers the patients convenient and expedite healthcare services remotely anywhere. Patient security and privacy has emerged as key issues during remote access because of underlying open architecture. An authentication scheme can verify patient's as well as TMIS server's legitimacy during remote healthcare services. To achieve security and privacy a number of authentication schemes have been proposed. Very recently Lu et al. (J. Med. Syst. 39(3):1-8, 2015) proposed a biometric based three factor authentication scheme for TMIS to confiscate the vulnerabilities of Arshad et al.'s (J. Med. Syst. 38(12):136, 2014) scheme. Further, they emphasized the robustness of their scheme against several attacks. However, in this paper we establish that Lu et al.'s scheme is vulnerable to numerous attacks including (1) Patient anonymity violation attack, (2) Patient impersonation attack, and (3) TMIS server impersonation attack. Furthermore, their scheme does not provide patient untraceability. We then, propose an improvement of Lu et al.'s scheme. We have analyzed the security of improved scheme using popular automated tool ProVerif. The proposed scheme while retaining the plusses of Lu et al.'s scheme is also robust against known attacks.

An improved AE detection method of rail defect based on multi-level ANC with VSS-LMS

Science.gov (United States)

Zhang, Xin; Cui, Yiming; Wang, Yan; Sun, Mingjian; Hu, Hengshan

2018-01-01

In order to ensure the safety and reliability of railway system, Acoustic Emission (AE) method is employed to investigate rail defect detection. However, little attention has been paid to the defect detection at high speed, especially for noise interference suppression. Based on AE technology, this paper presents an improved rail defect detection method by multi-level ANC with VSS-LMS. Multi-level noise cancellation based on SANC and ANC is utilized to eliminate complex noises at high speed, and tongue-shaped curve with index adjustment factor is proposed to enhance the performance of variable step-size algorithm. Defect signals and reference signals are acquired by the rail-wheel test rig. The features of noise signals and defect signals are analyzed for effective detection. The effectiveness of the proposed method is demonstrated by comparing with the previous study, and different filter lengths are investigated to obtain a better noise suppression performance. Meanwhile, the detection ability of the proposed method is verified at the top speed of the test rig. The results clearly illustrate that the proposed method is effective in detecting rail defects at high speed, especially for noise interference suppression.

Underwater image quality enhancement of sea cucumbers based on improved histogram equalization and wavelet transform

Directory of Open Access Journals (Sweden)

Xi Qiao

2017-09-01

Full Text Available Sea cucumbers usually live in an environment where lighting and visibility are generally not controllable, which cause the underwater image of sea cucumbers to be distorted, blurred, and severely attenuated. Therefore, the valuable information from such an image cannot be fully extracted for further processing. To solve the problems mentioned above and improve the quality of the underwater images of sea cucumbers, pre-processing of a sea cucumber image is attracting increasing interest. This paper presents a new method based on contrast limited adaptive histogram equalization and wavelet transform (CLAHE-WT to enhance the sea cucumber image quality. CLAHE was used to process the underwater image for increasing contrast based on the Rayleigh distribution, and WT was used for de-noising based on a soft threshold. Qualitative analysis indicated that the proposed method exhibited better performance in enhancing the quality and retaining the image details. For quantitative analysis, the test with 120 underwater images showed that for the proposed method, the mean square error (MSE, peak signal to noise ratio (PSNR, and entropy were 49.2098, 13.3909, and 6.6815, respectively. The proposed method outperformed three established methods in enhancing the visual quality of sea cucumber underwater gray image.

An Innovative Control Strategy to Improve the Fault Ride-Through Capability of DFIGs Based on Wind Energy Conversion Systems

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Vandai Le

2016-01-01

Full Text Available An innovative control strategy is proposed for enhancing the low voltage ride-through (LVRT capability of a doubly fed induction generator based on wind energy conversion systems (DFIG-WECS. Within the proposed control method, the current control loops of the rotor side converter (RSC are developed based on passivity theory. The control scheme for the grid side converter (GSC is designed based on a two-term approach to keep the DC-link voltage close to a given value. The first term based on the maximal voltage of GSC is introduced in the GSC control loops as a reference reactive current. The second one reflecting the instantaneous unbalanced power flow between the RSC and GSC is also introduced in the GSC control loops as a disturbance considering the instantaneous power of the grid filter to compensate the instantaneous rotor power. The effectiveness of the proposed control strategy is verified via time domain simulation of a 2.0 MW-575 V DFIG-WECS using PSCAD/EMTP. Simulation results show that the control of the DFIG with the proposed approach can improve the LVRT capability better than with the conventional one.

An Improved Image Encryption Algorithm Based on Cyclic Rotations and Multiple Chaotic Sequences: Application to Satellite Images

Directory of Open Access Journals (Sweden)

MADANI Mohammed

2017-10-01

Full Text Available In this paper, a new satellite image encryption algorithm based on the combination of multiple chaotic systems and a random cyclic rotation technique is proposed. Our contribution consists in implementing three different chaotic maps (logistic, sine, and standard combined to improve the security of satellite images. Besides enhancing the encryption, the proposed algorithm also focuses on advanced efficiency of the ciphered images. Compared with classical encryption schemes based on multiple chaotic maps and the Rubik's cube rotation, our approach has not only the same merits of chaos systems like high sensitivity to initial values, unpredictability, and pseudo-randomness, but also other advantages like a higher number of permutations, better performances in Peak Signal to Noise Ratio (PSNR and a Maximum Deviation (MD.

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging

International Nuclear Information System (INIS)

Martin, Spencer; Rodrigues, George; Gaede, Stewart; Brophy, Mark; Barron, John L; Beauchemin, Steven S; Palma, David; Louie, Alexander V; Yu, Edward; Yaremko, Brian; Ahmad, Belal

2015-01-01

This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development. (paper)

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging

Science.gov (United States)

Martin, Spencer; Brophy, Mark; Palma, David; Louie, Alexander V.; Yu, Edward; Yaremko, Brian; Ahmad, Belal; Barron, John L.; Beauchemin, Steven S.; Rodrigues, George; Gaede, Stewart

2015-02-01

This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development.

Fixed-pattern noise correction method based on improved moment matching for a TDI CMOS image sensor.

Science.gov (United States)

Xu, Jiangtao; Nie, Huafeng; Nie, Kaiming; Jin, Weimin

2017-09-01

In this paper, an improved moment matching method based on a spatial correlation filter (SCF) and bilateral filter (BF) is proposed to correct the fixed-pattern noise (FPN) of a time-delay-integration CMOS image sensor (TDI-CIS). First, the values of row FPN (RFPN) and column FPN (CFPN) are estimated and added to the original image through SCF and BF, respectively. Then the filtered image will be processed by an improved moment matching method with a moving window. Experimental results based on a 128-stage TDI-CIS show that, after correcting the FPN in the image captured under uniform illumination, the standard deviation of row mean vector (SDRMV) decreases from 5.6761 LSB to 0.1948 LSB, while the standard deviation of the column mean vector (SDCMV) decreases from 15.2005 LSB to 13.1949LSB. In addition, for different images captured by different TDI-CISs, the average decrease of SDRMV and SDCMV is 5.4922/2.0357 LSB, respectively. Comparative experimental results indicate that the proposed method can effectively correct the FPNs of different TDI-CISs while maintaining image details without any auxiliary equipment.

An improvement of the filter diagonalization-based post-processing method applied to finite difference time domain calculations of three-dimensional phononic band structures

International Nuclear Information System (INIS)

Su Xiaoxing; Zhang Chuanzeng; Ma Tianxue; Wang Yuesheng

2012-01-01

When three-dimensional (3D) phononic band structures are calculated by using the finite difference time domain (FDTD) method with a relatively small number of iterations, the results can be effectively improved by post-processing the FDTD time series (FDTD-TS) based on the filter diagonalization method (FDM), instead of the classical fast Fourier transform. In this paper, we propose a way to further improve the performance of the FDM-based post-processing method by introducing a relatively large number of observing points to record the FDTD-TS. To this end, the existing scheme of FDTD-TS preprocessing is modified. With the new preprocessing scheme, the processing efficiency of a single FDTD-TS can be improved significantly, and thus the entire post-processing method can have sufficiently high efficiency even when a relatively large number of observing points are used. The feasibility of the proposed method for improvement is verified by the numerical results.

Multilevel Image Segmentation Based on an Improved Firefly Algorithm

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Kai Chen

2016-01-01

Full Text Available Multilevel image segmentation is time-consuming and involves large computation. The firefly algorithm has been applied to enhancing the efficiency of multilevel image segmentation. However, in some cases, firefly algorithm is easily trapped into local optima. In this paper, an improved firefly algorithm (IFA is proposed to search multilevel thresholds. In IFA, in order to help fireflies escape from local optima and accelerate the convergence, two strategies (i.e., diversity enhancing strategy with Cauchy mutation and neighborhood strategy are proposed and adaptively chosen according to different stagnation stations. The proposed IFA is compared with three benchmark optimal algorithms, that is, Darwinian particle swarm optimization, hybrid differential evolution optimization, and firefly algorithm. The experimental results show that the proposed method can efficiently segment multilevel images and obtain better performance than the other three methods.

An Improved Algorithm Research on the PrefixSpan Based on the Server Session Constraint

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Cai Hong-Guo

2017-01-01

Full Text Available When we mine long sequential pattern and discover knowledge by the PrefixSpan algorithm in Web Usage Mining (WUM.The elements and the suffix sequences are much more may cause the problem of the calculation, such as the space explosion. To further solve the problem a more effective way is that. Firstly, a server session-based server log file format is proposed. Then the improved algorithm on the PrefixSpan based on server session constraint is discussed for mining frequent Sequential patterns on the website. Finally, the validity and superiority of the method are presented by the experiment in the paper.

Cryptanalysis on the improved multiparty quantum secret sharing protocol based on the GHZ state

International Nuclear Information System (INIS)

Chen Xiubo; Yang Shuai; Su Yuan; Yang Yixian

2012-01-01

Recently, Liu et al (2011 Phys. Scr. 84045015) pointed out that the multiparty quantum secret sharing (MQSS) protocol based on the GHZ state (Hwang et al 2011 Phys. Scr. 83045004) is insecure. They found that an inside participant can deduce half of the sender's secret information directly just by his piece of the secret. In order to resist this attack, an improvement was put forward. However, in this paper, we find that Liu et al's improved protocol is still insecure. We give details of three attack strategies to steal the secret information. It is shown that the eavesdropper can steal half or all of the secret information. Furthermore, a simple and ingenious MQSS protocol is proposed. We perform explicit cryptanalysis to prove that our improved protocol can resist the attacks from both the outside attackers and the inside participants, even the collusion attack.

Architecture for Improving Terrestrial Logistics Based on the Web of Things

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Antonio Skarmeta

2012-05-01

Full Text Available Technological advances for improving supply chain efficiency present three key challenges for managing goods: tracking, tracing and monitoring (TTM, in order to satisfy the requirements for products such as perishable goods where the European Legislations requires them to ship within a prescribed temperature range to ensure freshness and suitability for consumption. The proposed system integrates RFID for tracking and tracing through a distributed architecture developed for heavy goods vehicles, and the sensors embedded in the SunSPOT platform for monitoring the goods transported based on the concept of the Internet of Things. This paper presents how the Internet of Things is integrated for improving terrestrial logistics offering a comprehensive and flexible architecture, with high scalability, according to the specific needs for reaching an item-level continuous monitoring solution. The major contribution from this work is the optimization of the Embedded Web Services based on RESTful (Web of Things for the access to TTM services at any time during the transportation of goods. Specifically, it has been extended the monitoring patterns such as observe and blockwise transfer for the requirements from the continuous conditional monitoring, and for the transfer of full inventories and partial ones based on conditional queries. In definitive, this work presents an evolution of the previous TTM solutions, which were limited to trailer identification and environment monitoring, to a solution which is able to provide an exhaustive item-level monitoring, required for several use cases. This exhaustive monitoring has required new communication capabilities through the Web of Things, which has been optimized with the use and improvement of a set of communications patterns.

Architecture for improving terrestrial logistics based on the Web of Things.

Science.gov (United States)

Castro, Miguel; Jara, Antonio J; Skarmeta, Antonio

2012-01-01

Technological advances for improving supply chain efficiency present three key challenges for managing goods: tracking, tracing and monitoring (TTM), in order to satisfy the requirements for products such as perishable goods where the European Legislations requires them to ship within a prescribed temperature range to ensure freshness and suitability for consumption. The proposed system integrates RFID for tracking and tracing through a distributed architecture developed for heavy goods vehicles, and the sensors embedded in the SunSPOT platform for monitoring the goods transported based on the concept of the Internet of Things. This paper presents how the Internet of Things is integrated for improving terrestrial logistics offering a comprehensive and flexible architecture, with high scalability, according to the specific needs for reaching an item-level continuous monitoring solution. The major contribution from this work is the optimization of the Embedded Web Services based on RESTful (Web of Things) for the access to TTM services at any time during the transportation of goods. Specifically, it has been extended the monitoring patterns such as observe and blockwise transfer for the requirements from the continuous conditional monitoring, and for the transfer of full inventories and partial ones based on conditional queries. In definitive, this work presents an evolution of the previous TTM solutions, which were limited to trailer identification and environment monitoring, to a solution which is able to provide an exhaustive item-level monitoring, required for several use cases. This exhaustive monitoring has required new communication capabilities through the Web of Things, which has been optimized with the use and improvement of a set of communications patterns.

Improving the cooling performance of electrical distribution transformer using transformer oil – Based MEPCM suspension

OpenAIRE

Mushtaq Ismael Hasan

2017-01-01

In this paper the electrical distribution transformer has been studied numerically and the effect of outside temperature on its cooling performance has been investigated. The temperature range studied covers the hot climate regions. 250 KVA distribution transformer is chosen as a study model. A novel cooling fluid is proposed to improve the cooling performance of this transformer, transformer oil-based microencapsulated phase change materials suspension is used with volume concentration (5–25...

Numerical solution of the unsteady diffusion-convection-reaction equation based on improved spectral Galerkin method

Science.gov (United States)

Zhong, Jiaqi; Zeng, Cheng; Yuan, Yupeng; Zhang, Yuzhe; Zhang, Ye

2018-04-01

The aim of this paper is to present an explicit numerical algorithm based on improved spectral Galerkin method for solving the unsteady diffusion-convection-reaction equation. The principal characteristics of this approach give the explicit eigenvalues and eigenvectors based on the time-space separation method and boundary condition analysis. With the help of Fourier series and Galerkin truncation, we can obtain the finite-dimensional ordinary differential equations which facilitate the system analysis and controller design. By comparing with the finite element method, the numerical solutions are demonstrated via two examples. It is shown that the proposed method is effective.

Improving medication adherence: a framework for community pharmacy-based interventions

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Pringle J

2015-11-01

Full Text Available Janice Pringle,1 Kim C Coley2 1Program Evaluation and Research Unit, Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA; 2Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA Abstract: Evidence supports that patient medication adherence is suboptimal with patients typically taking less than half of their prescribed doses. Medication nonadherence is associated with poor health outcomes and higher downstream health care costs. Results of studies evaluating pharmacist-led models in a community pharmacy setting and their impact on medication adherence have been mixed. Community pharmacists are ideally situated to provide medication adherence interventions, and effective strategies for how they can consistently improve patient medication adherence are necessary. This article suggests a framework to use in the community pharmacy setting that will significantly improve patient adherence and provides a strategy for how to apply this framework to develop and test new medication adherence innovations. The proposed framework is composed of the following elements: 1 defining the program's pharmacy service vision, 2 using evidence-based, patient-centered communication and intervention strategies, 3 using specific implementation approaches that ensure fidelity, and 4 applying continuous evaluation strategies. Within this framework, pharmacist interventions should include those services that capitalize on their specific skill sets. It is also essential that the organization's leadership effectively communicates the pharmacy service vision. Medication adherence strategies that are evidence-based and individualized to each patient's adherence problems are most desirable. Ideally, interventions would be delivered repeatedly over time and adjusted when patient's adherence circumstances change. Motivational interviewing principles are particularly well

Novel stability criteria for fuzzy Hopfield neural networks based on an improved homogeneous matrix polynomials technique

International Nuclear Information System (INIS)

Feng Yi-Fu; Zhang Qing-Ling; Feng De-Zhi

2012-01-01

The global stability problem of Takagi—Sugeno (T—S) fuzzy Hopfield neural networks (FHNNs) with time delays is investigated. Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism. Firstly, using both Finsler's lemma and an improved homogeneous matrix polynomial technique, and applying an affine parameter-dependent Lyapunov—Krasovskii functional, we obtain the convergent LMI-based stability criteria. Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique. Secondly, to further reduce the conservatism, a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs, which is suitable to the homogeneous matrix polynomials setting. Finally, two illustrative examples are given to show the efficiency of the proposed approaches

Parameter Improved Particle Swarm Optimization Based Direct-Current Vector Control Strategy for Solar PV System

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NAMMALVAR, P.

2018-02-01

Full Text Available This paper projects Parameter Improved Particle Swarm Optimization (PIPSO based direct current vector control technology for the integration of photovoltaic array in an AC micro-grid to enhance the system performance and stability. A photovoltaic system incorporated with AC micro-grid is taken as the pursuit of research study. The test system features two power converters namely, PV side converter which consists of DC-DC boost converter with Perturbation and Observe (P&O MPPT control to reap most extreme power from the PV array, and grid side converter which consists of Grid Side-Voltage Source Converter (GS-VSC with proposed direct current vector control strategy. The gain of the proposed controller is chosen from a set of three values obtained using apriori test and tuned through the PIPSO algorithm so that the Integral of Time multiplied Absolute Error (ITAE between the actual and the desired DC link capacitor voltage reaches a minimum and allows the system to extract maximum power from PV system, whereas the existing d-q control strategy is found to perform slowly to control the DC link voltage under varying solar insolation and load fluctuations. From simulation results, it is evident that the proposed optimal control technique provides robust control and improved efficiency.

Face Recognition Performance Improvement using a Similarity Score of Feature Vectors based on Probabilistic Histograms

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SRIKOTE, G.

2016-08-01

Full Text Available This paper proposes an improved performance algorithm of face recognition to identify two face mismatch pairs in cases of incorrect decisions. The primary feature of this method is to deploy the similarity score with respect to Gaussian components between two previously unseen faces. Unlike the conventional classical vector distance measurement, our algorithms also consider the plot of summation of the similarity index versus face feature vector distance. A mixture of Gaussian models of labeled faces is also widely applicable to different biometric system parameters. By comparative evaluations, it has been shown that the efficiency of the proposed algorithm is superior to that of the conventional algorithm by an average accuracy of up to 1.15% and 16.87% when compared with 3x3 Multi-Region Histogram (MRH direct-bag-of-features and Principal Component Analysis (PCA-based face recognition systems, respectively. The experimental results show that similarity score consideration is more discriminative for face recognition compared to feature distance. Experimental results of Labeled Face in the Wild (LFW data set demonstrate that our algorithms are suitable for real applications probe-to-gallery identification of face recognition systems. Moreover, this proposed method can also be applied to other recognition systems and therefore additionally improves recognition scores.

Accuracy Improvement Capability of Advanced Projectile Based on Course Correction Fuze Concept

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Ahmed Elsaadany

2014-01-01

Full Text Available Improvement in terminal accuracy is an important objective for future artillery projectiles. Generally it is often associated with range extension. Various concepts and modifications are proposed to correct the range and drift of artillery projectile like course correction fuze. The course correction fuze concepts could provide an attractive and cost-effective solution for munitions accuracy improvement. In this paper, the trajectory correction has been obtained using two kinds of course correction modules, one is devoted to range correction (drag ring brake and the second is devoted to drift correction (canard based-correction fuze. The course correction modules have been characterized by aerodynamic computations and flight dynamic investigations in order to analyze the effects on deflection of the projectile aerodynamic parameters. The simulation results show that the impact accuracy of a conventional projectile using these course correction modules can be improved. The drag ring brake is found to be highly capable for range correction. The deploying of the drag brake in early stage of trajectory results in large range correction. The correction occasion time can be predefined depending on required correction of range. On the other hand, the canard based-correction fuze is found to have a higher effect on the projectile drift by modifying its roll rate. In addition, the canard extension induces a high-frequency incidence angle as canards reciprocate at the roll motion.

Accuracy improvement capability of advanced projectile based on course correction fuze concept.

Science.gov (United States)

Elsaadany, Ahmed; Wen-jun, Yi

2014-01-01

Improvement in terminal accuracy is an important objective for future artillery projectiles. Generally it is often associated with range extension. Various concepts and modifications are proposed to correct the range and drift of artillery projectile like course correction fuze. The course correction fuze concepts could provide an attractive and cost-effective solution for munitions accuracy improvement. In this paper, the trajectory correction has been obtained using two kinds of course correction modules, one is devoted to range correction (drag ring brake) and the second is devoted to drift correction (canard based-correction fuze). The course correction modules have been characterized by aerodynamic computations and flight dynamic investigations in order to analyze the effects on deflection of the projectile aerodynamic parameters. The simulation results show that the impact accuracy of a conventional projectile using these course correction modules can be improved. The drag ring brake is found to be highly capable for range correction. The deploying of the drag brake in early stage of trajectory results in large range correction. The correction occasion time can be predefined depending on required correction of range. On the other hand, the canard based-correction fuze is found to have a higher effect on the projectile drift by modifying its roll rate. In addition, the canard extension induces a high-frequency incidence angle as canards reciprocate at the roll motion.

On Improving Reliability of SRAM-Based Physically Unclonable Functions

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Arunkumar Vijayakumar

2017-01-01

Full Text Available Physically unclonable functions (PUFs have been touted for their inherent resistance to invasive attacks and low cost in providing a hardware root of trust for various security applications. SRAM PUFs in particular are popular in industry for key/ID generation. Due to intrinsic process variations, SRAM cells, ideally, tend to have the same start-up behavior. SRAM PUFs exploit this start-up behavior. Unfortunately, not all SRAM cells exhibit reliable start-up behavior due to noise susceptibility. Hence, design enhancements are needed for improving reliability. Some of the proposed enhancements in literature include fuzzy extraction, error-correcting codes and voting mechanisms. All enhancements involve a trade-off between area/power/performance overhead and PUF reliability. This paper presents a design enhancement technique for reliability that improves upon previous solutions. We present simulation results to quantify improvement in SRAM PUF reliability and efficiency. The proposed technique is shown to generate a 128-bit key in ≤0.2 μ s at an area estimate of 4538 μ m 2 with error rate as low as 10 − 6 for intrinsic error probability of 15%.

Improvement of the Provisions of Innovative Development of Enterprises Based on Electronic Business Technologies

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Vilkhivska Olga V.

2018-02-01

Full Text Available The article proposes improved conceptual provisions of innovative enterprise development based on the introduction of e-business technologies. The peculiarities of modern conditions of economic development under the influence of the new information paradigm are considered, the essence of which is the development and implementation of innovative network technologies (technologies of e-business in all spheres and branches of the economy. An analysis of the author’s research on the trends in the development of e-business, e-commerce, marketing, the impact of Internet technologies on business practices has been carried out, which has made it possible to achieve the goal, that was set, namely, to improve the conceptual foundations of innovative enterprise development based on e-business technologies. A special feature of the developed provisions is the emphasis on the introduction or full replacement of business processes in the enterprise by the appropriate technologies of electronic business and establishing the relationship between innovation development and their use.

improvement of digital image watermarking techniques based on FPGA implementation

International Nuclear Information System (INIS)

EL-Hadedy, M.E

2006-01-01

digital watermarking provides the ownership of a piece of digital data by marking the considered data invisibly or visibly. this can be used to protect several types of multimedia objects such as audio, text, image and video. this thesis demonstrates the different types of watermarking techniques such as (discrete cosine transform (DCT) and discrete wavelet transform (DWT) and their characteristics. then, it classifies these techniques declaring their advantages and disadvantages. an improved technique with distinguished features, such as peak signal to noise ratio ( PSNR) and similarity ratio (SR) has been introduced. the modified technique has been compared with the other techniques by measuring heir robustness against differ attacks. finally, field programmable gate arrays (FPGA) based implementation and comparison, for the proposed watermarking technique have been presented and discussed

Oceanic Flights and Airspace: Improving Efficiency by Trajectory-Based Operations

Science.gov (United States)

Fernandes, Alicia Borgman; Rebollo, Juan; Koch, Michael

2016-01-01

Oceanic operations suffer from multiple inefficiencies, including pre-departure planning that does not adequately consider uncertainty in the proposed trajectory, restrictions on the routes that a flight operator can choose for an oceanic crossing, time-consuming processes and procedures for amending en route trajectories, and difficulties exchanging data between Flight Information Regions (FIRs). These inefficiencies cause aircraft to fly suboptimal trajectories, burning fuel and time that could be conserved. A concept to support integration of existing and emerging capabilities and concepts is needed to transition to an airspace system that employs Trajectory Based Operations (TBO) to improve efficiency and safety in oceanic operations. This paper describes such a concept and the results of preliminary activities to evaluate the concept, including a stakeholder feedback activity, user needs analysis, and high level benefits analysis.

Proposed Hydro-Quebec development plan, 1993: Proposal

International Nuclear Information System (INIS)

1992-01-01

The Quebec government now requires Hydro-Quebec to submit a development plan every three years instead of annually, in order to permit more in-depth studies and a broader consultation with interested parties. In the first of such three-year plans, a series of plan proposals is presented which was developed after a year of consultation with various groups on four fundamental matters: energy efficiency, means of generation, electro-intensive industries, and electricity exports. Options for meeting future demand at Hydro-Quebec are assessed, including the construction of new generation and transmission facilities, rehabilitation of existing facilities, improving electrical energy efficiency, and conservation strategies. These options are considered while applying the principle of sustainable development that respects the environment. Hydroelectricity will continue to be emphasized as the main source of generation since hydroelectric facilities offer distinct advantages in terms of costs, environmental impacts, and economic spinoffs. The proposed plan also presents objectives and strategies for improving the quality of service and internal operations. Financial forecasts for Hydro-Quebec are proposed which take into account the forecast changes in the utility's cost and revenue factors and its self-financing requirements. 5 figs., 15 tabs

Improved Traceability of Mission Concept to Requirements Using Model Based Systems Engineering

Science.gov (United States)

Reil, Robin

2014-01-01

Model Based Systems Engineering (MBSE) has recently been gaining significant support as a means to improve the traditional document-based systems engineering (DBSE) approach to engineering complex systems. In the spacecraft design domain, there are many perceived and propose benefits of an MBSE approach, but little analysis has been presented to determine the tangible benefits of such an approach (e.g. time and cost saved, increased product quality). This thesis presents direct examples of how developing a small satellite system model can improve traceability of the mission concept to its requirements. A comparison of the processes and approaches for MBSE and DBSE is made using the NASA Ames Research Center SporeSat CubeSat mission as a case study. A model of the SporeSat mission is built using the Systems Modeling Language standard and No Magics MagicDraw modeling tool. The model incorporates mission concept and requirement information from the missions original DBSE design efforts. Active dependency relationships are modeled to analyze the completeness and consistency of the requirements to the mission concept. Overall experience and methodology are presented for both the MBSE and original DBSE design efforts of SporeSat.

A novel single-phase phase space-based voltage mode controller for distributed static compensator to improve voltage profile of distribution systems

International Nuclear Information System (INIS)

Shokri, Abdollah; Shareef, Hussain; Mohamed, Azah; Farhoodnea, Masoud; Zayandehroodi, Hadi

2014-01-01

Highlights: • A new phase space based voltage mode controller for D-STATCOM was proposed. • The proposed compensator was tested to mitigate voltage disturbances in distribution systems. • Voltage fluctuation, voltage sag and voltage swell are considered to evaluate the performance of the proposed compensator. - Abstract: Distribution static synchronous compensator (D-STATCOM) has been developed and attained a great interest to compensate the power quality disturbances of distribution systems. In this paper, a novel single-phase control scheme for D-STATCOM is proposed to improve voltage profile at the Point of Common Coupling (PCC). The proposed voltage mode (VM) controller is based on the phase space algorithm, which is able to rapidly detect and mitigate any voltage deviations from reference voltage including voltage sags and voltage swells. To investigate the efficiency and accuracy of the proposed compensator, a system is modeled using Matlab/Simulink. The simulation results approve the capability of the proposed VM controller to provide a regulated and disturbance-free voltage for the connected loads at the PCC

Reconstructing Sessions from Data Discovery and Access Logs to Build a Semantic Knowledge Base for Improving Data Discovery

Directory of Open Access Journals (Sweden)

Yongyao Jiang

2016-04-01

Full Text Available Big geospatial data are archived and made available through online web discovery and access. However, finding the right data for scientific research and application development is still a challenge. This paper aims to improve the data discovery by mining the user knowledge from log files. Specifically, user web session reconstruction is focused upon in this paper as a critical step for extracting usage patterns. However, reconstructing user sessions from raw web logs has always been difficult, as a session identifier tends to be missing in most data portals. To address this problem, we propose two session identification methods, including time-clustering-based and time-referrer-based methods. We also present the workflow of session reconstruction and discuss the approach of selecting appropriate thresholds for relevant steps in the workflow. The proposed session identification methods and workflow are proven to be able to extract data access patterns for further pattern analyses of user behavior and improvement of data discovery for more relevancy data ranking, suggestion, and navigation.

Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models.

Science.gov (United States)

Zhang, Hua; Kurgan, Lukasz

2014-12-01

Knowledge of protein flexibility is vital for deciphering the corresponding functional mechanisms. This knowledge would help, for instance, in improving computational drug design and refinement in homology-based modeling. We propose a new predictor of the residue flexibility, which is expressed by B-factors, from protein chains that use local (in the chain) predicted (or native) relative solvent accessibility (RSA) and custom-derived amino acid (AA) alphabets. Our predictor is implemented as a two-stage linear regression model that uses RSA-based space in a local sequence window in the first stage and a reduced AA pair-based space in the second stage as the inputs. This method is easy to comprehend explicit linear form in both stages. Particle swarm optimization was used to find an optimal reduced AA alphabet to simplify the input space and improve the prediction performance. The average correlation coefficients between the native and predicted B-factors measured on a large benchmark dataset are improved from 0.65 to 0.67 when using the native RSA values and from 0.55 to 0.57 when using the predicted RSA values. Blind tests that were performed on two independent datasets show consistent improvements in the average correlation coefficients by a modest value of 0.02 for both native and predicted RSA-based predictions.

Some ideas for improving quality of the index tracking based on cointegration

Directory of Open Access Journals (Sweden)

Damián Pastor

2016-11-01

Full Text Available Cointegration approach to the passive portfolio management enables to replicate the selected stock index and to construct a portfolio with profitability and risk similar to market. This paper analyzes several options for improving this method. It focuses on one of the key tasks, which is an estimate of long-run equilibrium relationship. Five different methods were proposed and compared. The results confirmed the relevance of using the Engle-Granger methodology in all previous surveys, but it also suggested some interesting properties related to the estimate of regression coefficients based on different variants of the Minkowski metric or to estimate regression equation without intercept.

Does GEM-Encoding Clinical Practice Guidelines Improve the Quality of Knowledge Bases? A Study with the Rule-Based Formalism

Science.gov (United States)

Georg, Gersende; Séroussi, Brigitte; Bouaud, Jacques

2003-01-01

The aim of this work was to determine whether the GEM-encoding step could improve the representation of clinical practice guidelines as formalized knowledge bases. We used the 1999 Canadian recommendations for the management of hypertension, chosen as the knowledge source in the ASTI project. We first clarified semantic ambiguities of therapeutic sequences recommended in the guideline by proposing an interpretative framework of therapeutic strategies. Then, after a formalization step to standardize the terms used to characterize clinical situations, we created the GEM-encoded instance of the guideline. We developed a module for the automatic derivation of a rule base, BR-GEM, from the instance. BR-GEM was then compared to the rule base, BR-ASTI, embedded within the critic mode of ASTI, and manually built by two physicians from the same Canadian guideline. As compared to BR-ASTI, BR-GEM is more specific and covers more clinical situations. When evaluated on 10 patient cases, the GEM-based approach led to promising results. PMID:14728173

Does GEM-encoding clinical practice guidelines improve the quality of knowledge bases? A study with the rule-based formalism.

Science.gov (United States)

Georg, Georg; Séroussi, Brigitte; Bouaud, Jacques

2003-01-01

The aim of this work was to determine whether the GEM-encoding step could improve the representation of clinical practice guidelines as formalized knowledge bases. We used the 1999 Canadian recommendations for the management of hypertension, chosen as the knowledge source in the ASTI project. We first clarified semantic ambiguities of therapeutic sequences recommended in the guideline by proposing an interpretative framework of therapeutic strategies. Then, after a formalization step to standardize the terms used to characterize clinical situations, we created the GEM-encoded instance of the guideline. We developed a module for the automatic derivation of a rule base, BR-GEM, from the instance. BR-GEM was then compared to the rule base, BR-ASTI, embedded within the critic mode of ASTI, and manually built by two physicians from the same Canadian guideline. As compared to BR-ASTI, BR-GEM is more specific and covers more clinical situations. When evaluated on 10 patient cases, the GEM-based approach led to promising results.

How to improve the teaching of clinical reasoning: a narrative review and a proposal.

Science.gov (United States)

Schmidt, Henk G; Mamede, Sílvia

2015-10-01

The development of clinical reasoning (CR) in students has traditionally been left to clinical rotations, which, however, often offer limited practice and suboptimal supervision. Medical schools begin to address these limitations by organising pre-clinical CR courses. The purpose of this paper is to review the variety of approaches employed in the teaching of CR and to present a proposal to improve these practices. We conducted a narrative review of the literature on teaching CR. To that end, we searched PubMed and Web of Science for papers published until June 2014. Additional publications were identified in the references cited in the initial papers. We used theoretical considerations to characterise approaches and noted empirical findings, when available. Of the 48 reviewed papers, only 24 reported empirical findings. The approaches to teaching CR were shown to vary on two dimensions. The first pertains to the way the case information is presented. The case is either unfolded to students gradually - the 'serial-cue' approach - or is presented in a 'whole-case' format. The second dimension concerns the purpose of the exercise: is its aim to help students acquire or apply knowledge, or is its purpose to teach students a way of thinking? The most prevalent approach is the serial-cue approach, perhaps because it tries to directly simulate the diagnostic activities of doctors. Evidence supporting its effectiveness is, however, lacking. There is some empirical evidence that whole-case, knowledge-oriented approaches contribute to the improvement of students' CR. However, thinking process-oriented approaches were shown to be largely ineffective. Based on research on how expertise develops in medicine, we argue that students in different phases of their training may benefit from different approaches to the teaching of CR. © 2015 John Wiley & Sons Ltd.

ASCS online fault detection and isolation based on an improved MPCA

Science.gov (United States)

Peng, Jianxin; Liu, Haiou; Hu, Yuhui; Xi, Junqiang; Chen, Huiyan

2014-09-01

Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling ( T 2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T 2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

An Improved Rotary Interpolation Based on FPGA

Directory of Open Access Journals (Sweden)

Mingyu Gao

2014-08-01

Full Text Available This paper presents an improved rotary interpolation algorithm, which consists of a standard curve interpolation module and a rotary process module. Compared to the conventional rotary interpolation algorithms, the proposed rotary interpolation algorithm is simpler and more efficient. The proposed algorithm was realized on a FPGA with Verilog HDL language, and simulated by the ModelSim software, and finally verified on a two-axis CNC lathe, which uses rotary ellipse and rotary parabolic as an example. According to the theoretical analysis and practical process validation, the algorithm has the following advantages: firstly, less arithmetic items is conducive for interpolation operation; and secondly the computing time is only two clock cycles of the FPGA. Simulations and actual tests have proved that the high accuracy and efficiency of the algorithm, which shows that it is highly suited for real-time applications.

Development-based Trust: Proposing and Validating a New Trust Measurement Model for Buyer-Seller Relationships

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José Mauro da Costa Hernandez

2010-04-01

Full Text Available This study proposes and validates a trust measurement model for buyer-seller relationships. Baptized as development-based trust, the model encompasses three dimensions of trust: calculus-based, knowledge-based and identification-based. In addition to recognizing that trust is a multidimensional construct, the model also assumes that trust can evolve to take on a different character depending on the stage of the relationship. In order to test the proposed model and compare it to the characteristic-based trust measurement model, the measure most frequently used in the buyer-seller relationship literature, data were collected from 238 clients of an IT product wholesaler. The results show that the scales are valid and reliable and the proposed development-based trust measurement model is superior to the characteristic-based trust measurement model in terms of its ability to explain certain variables of interest in buyer-seller relationships (long-term relationship orientation, information sharing, behavioral loyalty and future intentions. Implications for practice, limitations and suggestions for future studies are discussed.

False star detection and isolation during star tracking based on improved chi-square tests.

Science.gov (United States)

Zhang, Hao; Niu, Yanxiong; Lu, Jiazhen; Yang, Yanqiang; Su, Guohua

2017-08-01

The star sensor is a precise attitude measurement device for a spacecraft. Star tracking is the main and key working mode for a star sensor. However, during star tracking, false stars become an inevitable interference for star sensor applications, which may result in declined measurement accuracy. A false star detection and isolation algorithm in star tracking based on improved chi-square tests is proposed in this paper. Two estimations are established based on a Kalman filter and a priori information, respectively. The false star detection is operated through adopting the global state chi-square test in a Kalman filter. The false star isolation is achieved using a local state chi-square test. Semi-physical experiments under different trajectories with various false stars are designed for verification. Experiment results show that various false stars can be detected and isolated from navigation stars during star tracking, and the attitude measurement accuracy is hardly influenced by false stars. The proposed algorithm is proved to have an excellent performance in terms of speed, stability, and robustness.

Improvement of grid frequency dynamic characteristic with novel wind turbine based on electromagnetic coupler

DEFF Research Database (Denmark)

You, Rui; Barahona, Braulio; Chai, Jianyun

2017-01-01

. Additional power should be generated in response to a grid frequency drop in order to improve the dynamic characteristic of the grid frequency. In this paper, a novel control strategy for WT-EMC to improve the dynamic characteristic of grid frequency is proposed. The principle is to detect active power...... torque to stabilize the rotor speed, therefore directly improving the grid frequency. The proposed control strategy effectiveness is firstly tested through simulations and then validated on a specially built experimental platform....

Planning of distributed generation in distribution network based on improved particle swarm optimization algorithm

Science.gov (United States)

Li, Jinze; Qu, Zhi; He, Xiaoyang; Jin, Xiaoming; Li, Tie; Wang, Mingkai; Han, Qiu; Gao, Ziji; Jiang, Feng

2018-02-01

Large-scale access of distributed power can improve the current environmental pressure, at the same time, increasing the complexity and uncertainty of overall distribution system. Rational planning of distributed power can effectively improve the system voltage level. To this point, the specific impact on distribution network power quality caused by the access of typical distributed power was analyzed and from the point of improving the learning factor and the inertia weight, an improved particle swarm optimization algorithm (IPSO) was proposed which could solve distributed generation planning for distribution network to improve the local and global search performance of the algorithm. Results show that the proposed method can well reduce the system network loss and improve the economic performance of system operation with distributed generation.

Single image super-resolution based on compressive sensing and improved TV minimization sparse recovery

Science.gov (United States)

Vishnukumar, S.; Wilscy, M.

2017-12-01

In this paper, we propose a single image Super-Resolution (SR) method based on Compressive Sensing (CS) and Improved Total Variation (TV) Minimization Sparse Recovery. In the CS framework, low-resolution (LR) image is treated as the compressed version of high-resolution (HR) image. Dictionary Training and Sparse Recovery are the two phases of the method. K-Singular Value Decomposition (K-SVD) method is used for dictionary training and the dictionary represents HR image patches in a sparse manner. Here, only the interpolated version of the LR image is used for training purpose and thereby the structural self similarity inherent in the LR image is exploited. In the sparse recovery phase the sparse representation coefficients with respect to the trained dictionary for LR image patches are derived using Improved TV Minimization method. HR image can be reconstructed by the linear combination of the dictionary and the sparse coefficients. The experimental results show that the proposed method gives better results quantitatively as well as qualitatively on both natural and remote sensing images. The reconstructed images have better visual quality since edges and other sharp details are preserved.

Metric-based method of software requirements correctness improvement

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Yaremchuk Svitlana

2017-01-01

Full Text Available The work highlights the most important principles of software reliability management (SRM. The SRM concept construes a basis for developing a method of requirements correctness improvement. The method assumes that complicated requirements contain more actual and potential design faults/defects. The method applies a newer metric to evaluate the requirements complexity and double sorting technique evaluating the priority and complexity of a particular requirement. The method enables to improve requirements correctness due to identification of a higher number of defects with restricted resources. Practical application of the proposed method in the course of demands review assured a sensible technical and economic effect.

Does an outcome-based approach to continuing medical education improve physicians' competences in rational prescribing?

Science.gov (United States)

Esmaily, Hamideh M; Savage, Carl; Vahidi, Rezagoli; Amini, Abolghasem; Dastgiri, Saeed; Hult, Hakan; Dahlgren, Lars Owe; Wahlstrom, Rolf

2009-11-01

Continuing medical education (CME) is compulsory in Iran, and traditionally it is lecture-based, which is mostly not successful. Outcome-based education has been proposed for CME programs. To evaluate the effectiveness of an outcome-based educational intervention with a new approach based on outcomes and aligned teaching methods, on knowledge and skills of general physicians (GPs) working in primary care compared with a concurrent CME program in the field of "Rational prescribing". The method used was cluster randomized controlled design. All GPs working in six cities in one province in Iran were invited to participate. The cities were matched and randomly divided into an intervention arm for education on rational prescribing with an outcome-based approach, and a control arm for a traditional program on the same topic. Knowledge and skills were assessed using a pre- and post-test, including case scenarios. In total, 112 GPs participated. There were significant improvements in knowledge and prescribing skills after the training in the intervention arm as well as in comparison with the changes in the control arm. The overall intervention effect was 26 percentage units. The introduction of an outcome-based approach in CME appears to be effective when creating programs to improve GPs' knowledge and skills.

Improved chaos-based video steganography using DNA alphabets

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Nirmalya Kar

2018-03-01

Full Text Available DNA based steganography plays a vital role in the field of privacy and secure communication. Here, we propose a DNA properties-based mechanism to send data hidden inside a video file. Initially, the video file is converted into image frames. Random frames are then selected and data is hidden in these at random locations by using the Least Significant Bit substitution method. We analyze the proposed architecture in terms of peak signal-to-noise ratio as well as mean squared error measured between the original and steganographic files averaged over all video frames. The results show minimal degradation of the steganographic video file. Keywords: Chaotic map, DNA, Linear congruential generator, Video steganography, Least significant bit

A Method on Dynamic Path Planning for Robotic Manipulator Autonomous Obstacle Avoidance Based on an Improved RRT Algorithm.

Science.gov (United States)

Wei, Kun; Ren, Bingyin

2018-02-13

In a future intelligent factory, a robotic manipulator must work efficiently and safely in a Human-Robot collaborative and dynamic unstructured environment. Autonomous path planning is the most important issue which must be resolved first in the process of improving robotic manipulator intelligence. Among the path-planning methods, the Rapidly Exploring Random Tree (RRT) algorithm based on random sampling has been widely applied in dynamic path planning for a high-dimensional robotic manipulator, especially in a complex environment because of its probability completeness, perfect expansion, and fast exploring speed over other planning methods. However, the existing RRT algorithm has a limitation in path planning for a robotic manipulator in a dynamic unstructured environment. Therefore, an autonomous obstacle avoidance dynamic path-planning method for a robotic manipulator based on an improved RRT algorithm, called Smoothly RRT (S-RRT), is proposed. This method that targets a directional node extends and can increase the sampling speed and efficiency of RRT dramatically. A path optimization strategy based on the maximum curvature constraint is presented to generate a smooth and curved continuous executable path for a robotic manipulator. Finally, the correctness, effectiveness, and practicability of the proposed method are demonstrated and validated via a MATLAB static simulation and a Robot Operating System (ROS) dynamic simulation environment as well as a real autonomous obstacle avoidance experiment in a dynamic unstructured environment for a robotic manipulator. The proposed method not only provides great practical engineering significance for a robotic manipulator's obstacle avoidance in an intelligent factory, but also theoretical reference value for other type of robots' path planning.

Can training improve human performance

International Nuclear Information System (INIS)

Waylett, W.J. Jr.

1986-01-01

The nuclear industry has made a significant commitment to improve training through the implementation of performance-based training programs. Senior management expects that human performance improvement will result from this significant resource allocation. The author examines this hypothesis and discusses other issues that may interfere with enhancing human performance through training. The integration of quality improvement concepts to support training is also discussed by the author, who was a pioneer facilitator during the development of Florida Power and Light Company's Quality Improvement Program. Critical success factors are proposed based on the author's experience as a plant manager, training manager and quality facilitator

Improving product development practice: An action-research based approach

DEFF Research Database (Denmark)

Harmsen, Hanne

In studies of new product development it has often been concluded that to a large extent new product suc-cess is tunder the influence of companies and long lists of direct norma-tive guide-lines have been formulated. Nevertheless descriptive studi that deve-lopment practice is still far from...... studies both purely descriptive and studies identifying success and failure factors, but almost no studies of how companies actually undertake improve-ments, which problems they encounter,, and how/whether they overcome these problems. Action research is proposed as a suitable method for studying...

Improved dynamic-programming-based algorithms for segmentation of masses in mammograms

International Nuclear Information System (INIS)

Dominguez, Alfonso Rojas; Nandi, Asoke K.

2007-01-01

In this paper, two new boundary tracing algorithms for segmentation of breast masses are presented. These new algorithms are based on the dynamic programming-based boundary tracing (DPBT) algorithm proposed in Timp and Karssemeijer, [S. Timp and N. Karssemeijer, Med. Phys. 31, 958-971 (2004)] The DPBT algorithm contains two main steps: (1) construction of a local cost function, and (2) application of dynamic programming to the selection of the optimal boundary based on the local cost function. The validity of some assumptions used in the design of the DPBT algorithm is tested in this paper using a set of 349 mammographic images. Based on the results of the tests, modifications to the computation of the local cost function have been designed and have resulted in the Improved-DPBT (IDPBT) algorithm. A procedure for the dynamic selection of the strength of the components of the local cost function is presented that makes these parameters independent of the image dataset. Incorporation of this dynamic selection procedure has produced another new algorithm which we have called ID 2 PBT. Methods for the determination of some other parameters of the DPBT algorithm that were not covered in the original paper are presented as well. The merits of the new IDPBT and ID 2 PBT algorithms are demonstrated experimentally by comparison against the DPBT algorithm. The segmentation results are evaluated with base on the area overlap measure and other segmentation metrics. Both of the new algorithms outperform the original DPBT; the improvements in the algorithms performance are more noticeable around the values of the segmentation metrics corresponding to the highest segmentation accuracy, i.e., the new algorithms produce more optimally segmented regions, rather than a pronounced increase in the average quality of all the segmented regions

Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data.

Directory of Open Access Journals (Sweden)

Jianxin Shi

2016-12-01

Full Text Available Recent heritability analyses have indicated that genome-wide association studies (GWAS have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS, a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner's-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner's curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25-50% increase in the prediction R2 for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner's curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017 and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10-5. Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.

An improved visualization-based force-measurement technique for short-duration hypersonic facilities

Energy Technology Data Exchange (ETDEWEB)

Laurence, Stuart J.; Karl, Sebastian [Institute of Aerodynamics and Flow Technology, Spacecraft Section, German Aerospace Center (DLR), Goettingen (Germany)

2010-06-15

This article is concerned with describing and exploring the limitations of an improved version of a recently proposed visualization-based technique for the measurement of forces and moments in short-duration hypersonic wind tunnels. The technique is based on tracking the motion of a free-flying body over a sequence of high-speed visualizations; while this idea is not new in itself, the use of high-speed digital cinematography combined with a highly accurate least-squares tracking algorithm allows improved results over what have been previously possible with such techniques. The technique precision is estimated through the analysis of artificially constructed and experimental test images, and the resulting error in acceleration measurements is characterized. For wind-tunnel scale models, position measurements to within a few microns are shown to be readily attainable. Image data from two previous experimental studies in the T5 hypervelocity shock tunnel are then reanalyzed with the improved technique: the uncertainty in the mean drag acceleration is shown to be reduced to the order of the flow unsteadiness, 2-3%, and time-resolved acceleration measurements are also shown to be possible. The response time of the technique for the configurations studied is estimated to be {proportional_to}0.5 ms. Comparisons with computations using the DLR TAU code also yield agreement to within the overall experimental uncertainty. Measurement of the pitching moment for blunt geometries still appears challenging, however. (orig.)

Algorithms to analyze the quality test parameter values of seafood in the proposed ontology based seafood quality analyzer and miner (ONTO SQAM model

Directory of Open Access Journals (Sweden)

Vinu Sherimon

2017-07-01

Full Text Available Ensuring the quality of food, particularly seafood has increasingly become an important issue nowadays. Quality Management Systems empower any organization to identify, measure, control and improve the quality of the products manufactured that will eventually lead to improved business performance. With the advent of new technologies, now intelligent systems are being developed. To ensure the quality of seafood, an ontology based seafood quality analyzer and miner (ONTO SQAM model is proposed. The knowledge is represented using ontology. The domain concepts are defined using ontology. This paper presents the initial part of the proposed model – the analysis of quality test parameter values. Two algorithms are proposed to do the analysis – Comparison Algorithm and Data Store Updater algorithm. The algorithms ensure that the values of various quality tests are in the acceptable range. The real data sets taken from different seafood companies in Kerala, India, and validated by the Marine Product Export Development Authority of India (MPEDA are used for the experiments. The performance of the algorithms is evaluated using standard performance metrics such as precision, recall, and accuracy. The results obtained show that all the three measures achieved good results.

Improving Conductivity Image Quality Using Block Matrix-based Multiple Regularization (BMMR Technique in EIT: A Simulation Study

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Tushar Kanti Bera

2011-06-01

Full Text Available A Block Matrix based Multiple Regularization (BMMR technique is proposed for improving conductivity image quality in EIT. The response matrix (JTJ has been partitioned into several sub-block matrices and the highest eigenvalue of each sub-block matrices has been chosen as regularization parameter for the nodes contained by that sub-block. Simulated boundary data are generated for circular domain with circular inhomogeneity and the conductivity images are reconstructed in a Model Based Iterative Image Reconstruction (MoBIIR algorithm. Conductivity images are reconstructed with BMMR technique and the results are compared with the Single-step Tikhonov Regularization (STR and modified Levenberg-Marquardt Regularization (LMR methods. It is observed that the BMMR technique reduces the projection error and solution error and improves the conductivity reconstruction in EIT. Result show that the BMMR method also improves the image contrast and inhomogeneity conductivity profile and hence the reconstructed image quality is enhanced. ;doi:10.5617/jeb.170 J Electr Bioimp, vol. 2, pp. 33-47, 2011

Simulating train movement in an urban railway based on an improved car-following model

International Nuclear Information System (INIS)

Ye Jing-Jing; Jin Xin-Min; Li Ke-Ping

2013-01-01

Based on the optimal velocity car-following model, in this paper, we propose an improved model for simulating train movement in an urban railway in which the regenerative energy of a train is considered. Here a new additional term is introduced into a traditional car-following model. Our aim is to analyze and discuss the dynamic characteristics of the train movement when the regenerative energy is utilized by the electric locomotive. The simulation results indicate that the improved car-following model is suitable for simulating the train movement. Further, some qualitative relationships between regenerative energy and dynamic characteristics of a train are investigated, such as the measurement data of regenerative energy presents a power-law distribution. Our results are useful for optimizing the design and plan of urban railway systems. (general)

Improving Night Time Driving Safety Using Vision-Based Classification Techniques.

Science.gov (United States)

Chien, Jong-Chih; Chen, Yong-Sheng; Lee, Jiann-Der

2017-09-24

The risks involved in nighttime driving include drowsy drivers and dangerous vehicles. Prominent among the more dangerous vehicles around at night are the larger vehicles which are usually moving faster at night on a highway. In addition, the risk level of driving around larger vehicles rises significantly when the driver's attention becomes distracted, even for a short period of time. For the purpose of alerting the driver and elevating his or her safety, in this paper we propose two components for any modern vision-based Advanced Drivers Assistance System (ADAS). These two components work separately for the single purpose of alerting the driver in dangerous situations. The purpose of the first component is to ascertain that the driver would be in a sufficiently wakeful state to receive and process warnings; this is the driver drowsiness detection component. The driver drowsiness detection component uses infrared images of the driver to analyze his eyes' movements using a MSR plus a simple heuristic. This component issues alerts to the driver when the driver's eyes show distraction and are closed for a longer than usual duration. Experimental results show that this component can detect closed eyes with an accuracy of 94.26% on average, which is comparable to previous results using more sophisticated methods. The purpose of the second component is to alert the driver when the driver's vehicle is moving around larger vehicles at dusk or night time. The large vehicle detection component accepts images from a regular video driving recorder as input. A bi-level system of classifiers, which included a novel MSR-enhanced KAZE-base Bag-of-Features classifier, is proposed to avoid false negatives. In both components, we propose an improved version of the Multi-Scale Retinex (MSR) algorithm to augment the contrast of the input. Several experiments were performed to test the effects of the MSR and each classifier, and the results are presented in experimental results section

Dielectric oil-based polymer actuator for improved thickness strain and breakdown voltage

International Nuclear Information System (INIS)

Cho, Min Sung; Yamamoto, Akio

2016-01-01

Dielectric elastomer actuators (DEAs) have been increasingly investigated as alternative actuators to conventional ones. However, DEAs suffer from high rates of premature failure. Therefore, this study proposes a dielectric oil-based polymer actuator, also called a Dielectric liquid actuator (DLA), to compensate for the drawbacks of DEAs. DLA was experimentally compared with conventional DEAs. Results showed that DLA successfully prevented thermal runaway at defects in the electrode and excessive thinning of the film, resulting in increased breakdown voltage. Consequently, premature failure was inhibited, and the performance was improved. The breakdown voltages of DLA and DEA were 6000 and 2000 V, respectively, and their maximum thickness strains were 49.5% and 37.5%, respectively

Dielectric oil-based polymer actuator for improved thickness strain and breakdown voltage

Energy Technology Data Exchange (ETDEWEB)

Cho, Min Sung; Yamamoto, Akio [Dept. of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo (Japan)

2016-09-15

Dielectric elastomer actuators (DEAs) have been increasingly investigated as alternative actuators to conventional ones. However, DEAs suffer from high rates of premature failure. Therefore, this study proposes a dielectric oil-based polymer actuator, also called a Dielectric liquid actuator (DLA), to compensate for the drawbacks of DEAs. DLA was experimentally compared with conventional DEAs. Results showed that DLA successfully prevented thermal runaway at defects in the electrode and excessive thinning of the film, resulting in increased breakdown voltage. Consequently, premature failure was inhibited, and the performance was improved. The breakdown voltages of DLA and DEA were 6000 and 2000 V, respectively, and their maximum thickness strains were 49.5% and 37.5%, respectively.

Deep Learning-Based Noise Reduction Approach to Improve Speech Intelligibility for Cochlear Implant Recipients.

Science.gov (United States)

Lai, Ying-Hui; Tsao, Yu; Lu, Xugang; Chen, Fei; Su, Yu-Ting; Chen, Kuang-Chao; Chen, Yu-Hsuan; Chen, Li-Ching; Po-Hung Li, Lieber; Lee, Chin-Hui

2018-01-20

for the key speech envelope information, thus, improving speech recognition more effectively for Mandarin CI recipients. The results suggest that the proposed deep learning-based NR approach can potentially be integrated into existing CI signal processors to overcome the degradation of speech perception caused by noise.

Speed and Displacement Control System of Bearingless Brushless DC Motor Based on Improved Bacterial Foraging Algorithm

Directory of Open Access Journals (Sweden)

Diao Xiaoyan

2016-01-01

Full Text Available To solve the deficiencies of long optimization time and poor precision existing in conventional bacterial foraging algorithm (BFA in the process of parameter optimization, an improved bacterial foraging algorithm (IBFA is proposed and applied to speed and displacement control system of bearingless brushless DC (Bearingless BLDC motors. To begin with the fundamental principle of BFA, the proposed method is introduced and the individual intelligence is efficiently used in the process of parameter optimization, and then the working principle of bearingless BLDC motors is expounded. Finally, modeling and simulation of the speed and displacement control system of bearingless BLDC motors based on the IBFA are carried out by taking the software of MATLAB/Simulink as a platform. Simulation results show that, speed overshoot, torque ripple and rotor position oscillation are dramatically reduced, thus the proposed method has good application prospects in the field of bearingless motors.

A Scorecard Framework Proposal for Improving Software Factories’ Sustainability: A Case Study of a Spanish Firm in the Financial Sector

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César Álvarez

2015-12-01

Full Text Available Financial institutions and especially banks have always been at the forefront of innovation in management policies in order to improve their performance, and banking is probably one of the sectors that more effectively measures productivity and efficiency in virtually all aspects of its business. However, there is one area that still fails: the productivity of its software development projects. For years banking institutions have chosen to outsource their software projects using software firms created by them for this purpose, but up until a few years ago, the deadline for the delivery of the projects was more important than the efficiency with which they were developed. The last economic crisis has forced financial institutions to review and improve the software development efficiency related to their software factories to achieve a sustainable and feasible model. The sustainability of these software factories can be achieved by improving their strategic management, and the Balanced Scorecard (BSC framework can be very useful in order to obtain this. Based on the concepts and practices of the BSC, this paper proposes a specific model to establish this kind of software factory as a way of improving their sustainability and applies it to a large Spanish firm specializing in financial sector software. We have included a preliminary validation plan as well as the first monitoring results. The adoption is still very recent and more data are needed to measure all the perspectives so no definitive conclusions can be drawn.

A chaos-based digital image encryption scheme with an improved diffusion strategy.

Science.gov (United States)

Fu, Chong; Chen, Jun-jie; Zou, Hao; Meng, Wei-hong; Zhan, Yong-feng; Yu, Ya-wen

2012-01-30

Chaos-based image cipher has been widely investigated over the last decade or so to meet the increasing demand for real-time secure image transmission over public networks. In this paper, an improved diffusion strategy is proposed to promote the efficiency of the most widely investigated permutation-diffusion type image cipher. By using the novel bidirectional diffusion strategy, the spreading process is significantly accelerated and hence the same level of security can be achieved with fewer overall encryption rounds. Moreover, to further enhance the security of the cryptosystem, a plain-text related chaotic orbit turbulence mechanism is introduced in diffusion procedure by perturbing the control parameter of the employed chaotic system according to the cipher-pixel. Extensive cryptanalysis has been performed on the proposed scheme using differential analysis, key space analysis, various statistical analyses and key sensitivity analysis. Results of our analyses indicate that the new scheme has a satisfactory security level with a low computational complexity, which renders it a good candidate for real-time secure image transmission applications.

PROPOSAL OF ANTI-TUBERCULOSIS REGIMENS BASED ON SUSCEPTIBILITY TO ISONIAZID AND RIFAMPICIN

Science.gov (United States)

Mendoza-Ticona, Alberto; Moore, David AJ; Alarcón, Valentina; Samalvides, Frine; Seas, Carlos

2014-01-01

Objective To elaborate optimal anti-tuberculosis regimens following drug susceptibility testing (DST) to isoniazid (H) and rifampicin (R). Design 12 311 M. tuberculosis strains (National Health Institute of Peru 2007-2009) were classified in four groups according H and R resistance. In each group the sensitivity to ethambutol (E), pirazinamide (Z), streptomycin (S), kanamycin (Km), capreomycin (Cm), ciprofloxacin (Cfx), ethionamide (Eto), cicloserine (Cs) and p-amino salicilic acid (PAS) was determined. Based on resistance profiles, domestic costs, and following WHO guidelines, we elaborated and selected optimal putative regimens for each group. The potential efficacy (PE) variable was defined as the proportion of strains sensitive to at least three or four drugs for each regimen evaluated. Results Selected regimes with the lowest cost, and highest PE of containing 3 and 4 effective drugs for TB sensitive to H and R were: HRZ (99,5%) and HREZ (99,1%), respectively; RZECfx (PE=98,9%) and RZECfxKm (PE=97,7%) for TB resistant to H; HZECfx (96,8%) and HZECfxKm (95,4%) for TB resistant to R; and EZCfxKmEtoCs (82.9%) for MDR-TB. Conclusion Based on resistance to H and R it was possible to select anti-tuberculosis regimens with high probability of success. This proposal is a feasible alternative to tackle tuberculosis in Peru where the access to rapid DST to H and R is improving progressively. PMID:23949502

IMPROVING CAR NAVIGATION WITH A VISION-BASED SYSTEM

Directory of Open Access Journals (Sweden)

H. Kim

2015-08-01

Full Text Available The real-time acquisition of the accurate positions is very important for the proper operations of driver assistance systems or autonomous vehicles. Since the current systems mostly depend on a GPS and map-matching technique, they show poor and unreliable performance in blockage and weak areas of GPS signals. In this study, we propose a vision oriented car navigation method based on sensor fusion with a GPS and in-vehicle sensors. We employed a single photo resection process to derive the position and attitude of the camera and thus those of the car. This image georeferencing results are combined with other sensory data under the sensor fusion framework for more accurate estimation of the positions using an extended Kalman filter. The proposed system estimated the positions with an accuracy of 15 m although GPS signals are not available at all during the entire test drive of 15 minutes. The proposed vision based system can be effectively utilized for the low-cost but high-accurate and reliable navigation systems required for intelligent or autonomous vehicles.

Improving Car Navigation with a Vision-Based System

Science.gov (United States)

Kim, H.; Choi, K.; Lee, I.

2015-08-01

The real-time acquisition of the accurate positions is very important for the proper operations of driver assistance systems or autonomous vehicles. Since the current systems mostly depend on a GPS and map-matching technique, they show poor and unreliable performance in blockage and weak areas of GPS signals. In this study, we propose a vision oriented car navigation method based on sensor fusion with a GPS and in-vehicle sensors. We employed a single photo resection process to derive the position and attitude of the camera and thus those of the car. This image georeferencing results are combined with other sensory data under the sensor fusion framework for more accurate estimation of the positions using an extended Kalman filter. The proposed system estimated the positions with an accuracy of 15 m although GPS signals are not available at all during the entire test drive of 15 minutes. The proposed vision based system can be effectively utilized for the low-cost but high-accurate and reliable navigation systems required for intelligent or autonomous vehicles.

Robustness Improvement of Superconducting Magnetic Energy Storage System in Microgrids Using an Energy Shaping Passivity-Based Control Strategy

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Rui Hou

2017-05-01

Full Text Available Superconducting magnetic energy storage (SMES systems, in which the proportional-integral (PI method is usually used to control the SMESs, have been used in microgrids for improving the control performance. However, the robustness of PI-based SMES controllers may be unsatisfactory due to the high nonlinearity and coupling of the SMES system. In this study, the energy shaping passivity (ESP-based control strategy, which is a novel nonlinear control based on the methodology of interconnection and damping assignment (IDA, is proposed for robustness improvement of SMES systems. A step-by-step design of the ESP-based method considering the robustness of SMES systems is presented. A comparative analysis of the performance between ESP-based and PI control strategies is shown. Simulation and experimental results prove that the ESP-based strategy achieves the stronger robustness toward the system parameter uncertainties than the conventional PI control. Besides, the use of ESP-based control method can reduce the eddy current losses of a SMES system due to the significant reduction of 2nd and 3rd harmonics of superconducting coil DC current.

Improvement on LEACH Agreement of Mine Wireless Sensor Network

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Yun-xiang Liu

2017-05-01

Full Text Available Based on the characteristics of wireless sensor network communication in mine, LEACH protocol clustering is optimized, and the factors of energy and distance are considered fully. The selection of cluster head nodes is optimized, and a routing algorithm based on K-means ++ clustering is proposed. The problem of uneven distribution of cluster head nodes, uneven energy consumption and network stability in LEACH algorithm is improved effectively. Simulation results show that the proposed algorithm can improve the energy consumption of the whole network and improve the energy utilization rate, extending the network life cycle effectively.

A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain-computer interface

Science.gov (United States)

Chen, Yi-Feng; Atal, Kiran; Xie, Sheng-Quan; Liu, Quan

2017-08-01

Objective. Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain-computer interface (BCI) applications. Approach. Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts. This paper presents an improved method to detect the frequency component associated with SSVEP using multivariate empirical mode decomposition (MEMD) and CCA (MEMD-CCA). EEG signals from nine healthy volunteers were recorded to evaluate the performance of the proposed method for SSVEP recognition. Main results. We compared our method with CCA and temporally local multivariate synchronization index (TMSI). The results suggest that the MEMD-CCA achieved significantly higher accuracy in contrast to standard CCA and TMSI. It gave the improvements of 1.34%, 3.11%, 3.33%, 10.45%, 15.78%, 18.45%, 15.00% and 14.22% on average over CCA at time windows from 0.5 s to 5 s and 0.55%, 1.56%, 7.78%, 14.67%, 13.67%, 7.33% and 7.78% over TMSI from 0.75 s to 5 s. The method outperformed the filter-based decomposition (FB), empirical mode decomposition (EMD) and wavelet decomposition (WT) based CCA for SSVEP recognition. Significance. The results demonstrate the ability of our proposed MEMD-CCA to improve the performance of SSVEP-based BCI.

Pheromone based alternative route planning

Directory of Open Access Journals (Sweden)

Liangbing Feng

2016-08-01

Full Text Available In this work, we propose an improved alternative route calculation based on alternative figures, which is suitable for practical environments. The improvement is based on the fact that the main traffic route is the road network skeleton in a city. Our approach using nodes may generate a higher possibility of overlapping. We employ a bidirectional Dijkstra algorithm to search the route. To measure the quality of an Alternative Figures (AG, three quotas are proposed. The experiment results indicate that the improved algorithm proposed in this paper is more effective than others.

Framework and implementation for improving physics essential skills via computer-based practice: Vector math

Science.gov (United States)

Mikula, Brendon D.; Heckler, Andrew F.

2017-06-01

We propose a framework for improving accuracy, fluency, and retention of basic skills essential for solving problems relevant to STEM introductory courses, and implement the framework for the case of basic vector math skills over several semesters in an introductory physics course. Using an iterative development process, the framework begins with a careful identification of target skills and the study of specific student difficulties with these skills. It then employs computer-based instruction, immediate feedback, mastery grading, and well-researched principles from cognitive psychology such as interleaved training sequences and distributed practice. We implemented this with more than 1500 students over 2 semesters. Students completed the mastery practice for an average of about 13 min /week , for a total of about 2-3 h for the whole semester. Results reveal large (>1 SD ) pretest to post-test gains in accuracy in vector skills, even compared to a control group, and these gains were retained at least 2 months after practice. We also find evidence of improved fluency, student satisfaction, and that awarding regular course credit results in higher participation and higher learning gains than awarding extra credit. In all, we find that simple computer-based mastery practice is an effective and efficient way to improve a set of basic and essential skills for introductory physics.

Improved Satellite-based Crop Yield Mapping by Spatially Explicit Parameterization of Crop Phenology

Science.gov (United States)

Jin, Z.; Azzari, G.; Lobell, D. B.

2016-12-01

Field-scale mapping of crop yields with satellite data often relies on the use of crop simulation models. However, these approaches can be hampered by inaccuracies in the simulation of crop phenology. Here we present and test an approach to use dense time series of Landsat 7 and 8 acquisitions data to calibrate various parameters related to crop phenology simulation, such as leaf number and leaf appearance rates. These parameters are then mapped across the Midwestern United States for maize and soybean, and for two different simulation models. We then implement our recently developed Scalable satellite-based Crop Yield Mapper (SCYM) with simulations reflecting the improved phenology parameterizations, and compare to prior estimates based on default phenology routines. Our preliminary results show that the proposed method can effectively alleviate the underestimation of early-season LAI by the default Agricultural Production Systems sIMulator (APSIM), and that spatially explicit parameterization for the phenology model substantially improves the SCYM performance in capturing the spatiotemporal variation in maize and soybean yield. The scheme presented in our study thus preserves the scalability of SCYM, while significantly reducing its uncertainty.

Improving Indicators in a Brazilian Hospital Through Quality-Improvement Programs Based on STS Database Reports

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Pedro Gabriel Melo de Barros e Silva

2015-12-01

Full Text Available ABSTRACT OBJECTIVE: To report the initial changes after quality-improvement programs based on STS-database in a Brazilian hospital. METHODS: Since 2011 a Brazilian hospital has joined STS-Database and in 2012 multifaceted actions based on STS reports were implemented aiming reductions in the time of mechanical ventilation and in the intensive care stay and also improvements in evidence-based perioperative therapies among patients who underwent coronary artery bypass graft surgeries. RESULTS: All the 947 patients submitted to coronary artery bypass graft surgeries from July 2011 to June 2014 were analyzed and there was an improvement in all the three target endpoints after the implementation of the quality-improvement program but the reduction in time on mechanical ventilation was not statistically significant after adjusting for prognostic characteristics. CONCLUSION: The initial experience with STS registry in a Brazilian hospital was associated with improvement in most of targeted quality-indicators.

Improved Mesh_Based Image Morphing ‎

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Mohammed Abdullah Taha

2017-11-01

Full Text Available Image morphing is a multi-step process that generates a sequence of transitions between two images. The thought is to get a ₔgrouping of middle pictures which, when ₔassembled with the first pictures would represent the change from one picture to the other.  The process of morphing requires time and attention to detail in order to get good results. Morphing image requires at least two processes warping and cross dissolve. Warping is the process of geometric transformation of images. The cross dissolve is the process interpolation of color of eachₔ pixel from the first image value to theₔ corresponding second imageₔ value over the time. Image morphing techniques differ from in the approach of image warping procedure. This work presents a survey of different techniques to construct morphing images by review the different warping techniques. One of the predominant approaches of warping process is mesh warping which suffers from some problems including ghosting. This work proposed and implements an improved mesh warping technique to construct morphing images. The results show that the proposed approach can overcome the problems of the traditional mesh technique

An improved fault-tolerant control scheme for PWM inverter-fed induction motor-based EVs.

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Tabbache, Bekheïra; Benbouzid, Mohamed; Kheloui, Abdelaziz; Bourgeot, Jean-Matthieu; Mamoune, Abdeslam

2013-11-01

This paper proposes an improved fault-tolerant control scheme for PWM inverter-fed induction motor-based electric vehicles. The proposed strategy deals with power switch (IGBTs) failures mitigation within a reconfigurable induction motor control. To increase the vehicle powertrain reliability regarding IGBT open-circuit failures, 4-wire and 4-leg PWM inverter topologies are investigated and their performances discussed in a vehicle context. The proposed fault-tolerant topologies require only minimum hardware modifications to the conventional off-the-shelf six-switch three-phase drive, mitigating the IGBTs failures by specific inverter control. Indeed, the two topologies exploit the induction motor neutral accessibility for fault-tolerant purposes. The 4-wire topology uses then classical hysteresis controllers to account for the IGBT failures. The 4-leg topology, meanwhile, uses a specific 3D space vector PWM to handle vehicle requirements in terms of size (DC bus capacitors) and cost (IGBTs number). Experiments on an induction motor drive and simulations on an electric vehicle are carried-out using a European urban driving cycle to show that the proposed fault-tolerant control approach is effective and provides a simple configuration with high performance in terms of speed and torque responses. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Design of SVC Controller Based on Improved Biogeography-Based Optimization Algorithm

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Feifei Dong

2014-01-01

Full Text Available Considering that common subsynchronous resonance controllers cannot adapt to the characteristics of the time-varying and nonlinear behavior of a power system, the cosine migration model, the improved migration operator, and the mutative scale of chaos and Cauchy mutation strategy are introduced into an improved biogeography-based optimization (IBBO algorithm in order to design an optimal subsynchronous damping controller based on the mechanism of suppressing SSR by static var compensator (SVC. The effectiveness of the improved controller is verified by eigenvalue analysis and electromagnetic simulations. The simulation results of Jinjie plant indicate that the subsynchronous damping controller optimized by the IBBO algorithm can remarkably improve the damping of torsional modes and thus effectively depress SSR, and ensure the safety and stability of units and power grid operation. Moreover, the IBBO algorithm has the merits of a faster searching speed and higher searching accuracy in seeking the optimal control parameters over traditional algorithms, such as BBO algorithm, PSO algorithm, and GA algorithm.

77 FR 13367 - General Electric-Hitachi Global Laser Enrichment, LLC, Proposed Laser-Based Uranium Enrichment...

Science.gov (United States)

2012-03-06

... NUCLEAR REGULATORY COMMISSION [NRC-2009-0157] General Electric-Hitachi Global Laser Enrichment, LLC, Proposed Laser-Based Uranium Enrichment Facility, Wilmington, NC AGENCY: Nuclear Regulatory... Impact Statement (EIS) for the proposed General Electric- Hitachi Global Laser Enrichment, LLC (GLE...

An Improved Quantum Information Hiding Protocol Based on Entanglement Swapping of χ-type Quantum States

International Nuclear Information System (INIS)

Xu Shu-Jiang; Wang Lian-Hai; Ding Qing-Yan; Zhang Shu-Hui; Chen Xiu-Bo

2016-01-01

In 2011, Qu et al. proposed a quantum information hiding protocol based on the entanglement swapping of χ-type quantum states. Because a χ-type state can be described by the 4-particle cat states which have good symmetry, the possible output results of the entanglement swapping between a given χ-type state and all of the 16 χ-type states are divided into 8 groups instead of 16 groups of different results when the global phase is not considered. So it is difficult to read out the secret messages since each result occurs twice in each line (column) of the secret messages encoding rule for the original protocol. In fact, a 3-bit instead of a 4-bit secret message can be encoded by performing two unitary transformations on 2 particles of a χ-type quantum state in the original protocol. To overcome this defect, we propose an improved quantum information hiding protocol based on the general term formulas of the entanglement swapping among χ-type states. (paper)

SQIMSO: Quality Improvement for Small Software Organizations

OpenAIRE

Rabih Zeineddine; Nashat Mansour

2005-01-01

Software quality improvement process remains incomplete if it is not initiated and conducted through a wide improvement program that considers process quality improvement, product quality improvement and evolution of human resources. But, small software organizations are not capable of bearing the cost of establishing software process improvement programs. In this work, we propose a new software quality improvement model for small organizations, SQIMSO, based on three ...

Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model

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Lokesh Selvaraj

2014-01-01

Full Text Available Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO is suggested. The suggested methodology contains four stages, namely, (i denoising, (ii feature mining (iii, vector quantization, and (iv IPSO based hidden Markov model (HMM technique (IP-HMM. At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC, mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.

Systematic review of proposed definitions of nocturnal polyuria and population-based evidence of their diagnostic accuracy.

Science.gov (United States)

Olesen, Tine Kold; Denys, Marie-Astrid; Vande Walle, Johan; Everaert, Karel

2018-02-06

Background Evidence of diagnostic accuracy for proposed definitions of nocturnal polyuria is currently unclear. Purpose Systematic review to determine population-based evidence of the diagnostic accuracy of proposed definitions of nocturnal polyuria based on data from frequency-volume charts. Methods Seventeen pre-specified search terms identified 351 unique investigations published from 1990 to 2016 in BIOSIS, Embase, Embase Alerts, International Pharmaceutical Abstract, Medline, and Cochrane. Thirteen original communications were included in this review based on pre-specified exclusion criteria. Data were extracted from each paper regarding subject age, sex, ethnicity, health status, sample size, data collection methods, and diagnostic discrimination of proposed definitions including sensitivity, specificity, positive and negative predictive value. Results The sample size of study cohorts, participant age, sex, ethnicity, and health status varied considerably in 13 studies reporting on the diagnostic performance of seven different definitions of nocturnal polyuria using frequency-volume chart data from 4968 participants. Most study cohorts were small, mono-ethnic, including only Caucasian males aged 50 or higher with primary or secondary polyuria that were compared to a control group of healthy men without nocturia in prospective or retrospective settings. Proposed definitions had poor discriminatory accuracy in evaluations based on data from subjects independent from the original study cohorts with findings being similar regarding the most widely evaluated definition endorsed by ICS. Conclusions Diagnostic performance characteristics for proposed definitions of nocturnal polyuria show poor to modest discrimination and are not based on sufficient level of evidence from representative, multi-ethnic population-based data from both females and males of all adult ages.

Experience with low-cost telemedicine in three different settings. Recommendations based on a proposed framework for network performance evaluation

Science.gov (United States)

Wootton, Richard; Vladzymyrskyy, Anton; Zolfo, Maria; Bonnardot, Laurent

2011-01-01

Background Telemedicine has been used for many years to support doctors in the developing world. Several networks provide services in different settings and in different ways. However, to draw conclusions about which telemedicine networks are successful requires a method of evaluating them. No general consensus or validated framework exists for this purpose. Objective To define a basic method of performance measurement that can be used to improve and compare teleconsultation networks; to employ the proposed framework in an evaluation of three existing networks; to make recommendations about the future implementation and follow-up of such networks. Methods Analysis based on the experience of three telemedicine networks (in operation for 7–10 years) that provide services to doctors in low-resource settings and which employ the same basic design. Findings Although there are many possible indicators and metrics that might be relevant, five measures for each of the three user groups appear to be sufficient for the proposed framework. In addition, from the societal perspective, information about clinical- and cost-effectiveness is also required. The proposed performance measurement framework was applied to three mature telemedicine networks. Despite their differences in terms of activity, size and objectives, their performance in certain respects is very similar. For example, the time to first reply from an expert is about 24 hours for each network. Although all three networks had systems in place to collect data from the user perspective, none of them collected information about the coordinator's time required or about ease of system usage. They had only limited information about quality and cost. Conclusion Measuring the performance of a telemedicine network is essential in understanding whether the network is working as intended and what effect it is having. Based on long-term field experience, the suggested framework is a practical tool that will permit

Experience with low-cost telemedicine in three different settings. Recommendations based on a proposed framework for network performance evaluation

Directory of Open Access Journals (Sweden)

Richard Wootton

2011-12-01

Full Text Available Telemedicine has been used for many years to support doctors in the developing world. Several networks provide services in different settings and in different ways. However, to draw conclusions about which telemedicine networks are successful requires a method of evaluating them. No general consensus or validated framework exists for this purpose.To define a basic method of performance measurement that can be used to improve and compare teleconsultation networks; to employ the proposed framework in an evaluation of three existing networks; to make recommendations about the future implementation and follow-up of such networks.Analysis based on the experience of three telemedicine networks (in operation for 7–10 years that provide services to doctors in low-resource settings and which employ the same basic design.Although there are many possible indicators and metrics that might be relevant, five measures for each of the three user groups appear to be sufficient for the proposed framework. In addition, from the societal perspective, information about clinical- and cost-effectiveness is also required. The proposed performance measurement framework was applied to three mature telemedicine networks. Despite their differences in terms of activity, size and objectives, their performance in certain respects is very similar. For example, the time to first reply from an expert is about 24 hours for each network. Although all three networks had systems in place to collect data from the user perspective, none of them collected information about the coordinator's time required or about ease of system usage. They had only limited information about quality and cost.Measuring the performance of a telemedicine network is essential in understanding whether the network is working as intended and what effect it is having. Based on long-term field experience, the suggested framework is a practical tool that will permit organisations to assess the performance of

Improvements on nonlinear gyrokinetic particle simulations based on δf-discretization scheme

International Nuclear Information System (INIS)

Zorat, R.; Tessarotto, M.

1998-01-01

In this work various issues regarding the definition of improved theoretical models appropriate to describe the dynamics of confined magnetoplasmas by particle simulation methods are proposed. These concern in particular an improved non linear δf discretization scheme and the treatment of binary, i.e. Coulomb, and collective interactions. (orig.)

Improved nuclear power plant operations through performance-based safety regulation

International Nuclear Information System (INIS)

Golay, M.W.

1998-01-01

The US Nuclear Regulatory Commission (NRC) has recently instituted use of Risk-Informed, Performance-Based Regulation (RIPBR) for protecting public safety in the use of nuclear power. This was done most importantly during June 1997 in issuance of revised Regulatory Guides and Standard Review Plan (SRP) guidance to licensees and the NRC staff. The propose of RIPBR is to replace the previously-used system of prescriptive regulation, which focuses upon what licensees must do, to a system which focuses upon what they must achieve. RIPBR is goals-oriented and the previous system is means-oriented. This regulatory change is potentially revolutionary, and offers many opportunities for improving the efficiency of improving both nuclear power operations and safety. However, it must be nurtured carefully if is to be successful. The work reported in this paper is concerned with showing how RIPBR can be implemented successfully, with benefits in both areas being attained. It is also concerned with how several of the practical barriers to establishing a workable new regulatory system can be overcome. This work, sponsored by the US Dept. of Energy, is being performed in collaboration with Northeast Utilities Services Crop. and the Idaho National Engineering Laboratory. In our work we have examined a practical safety-related example at the Millstone 3 nuclear power station for implementation of RIPBR. In this examination we have formulated a set of modifications to the plant's technical specifications, and are in the process of investigating their bases and refining the modifications. (author)

76 FR 77548 - Notice of Submission of Proposed Information Collection to OMB Self-Help Homeownership...

Science.gov (United States)

2011-12-13

... Proposed Information Collection to OMB Self-Help Homeownership Opportunity Program (SHOP) AGENCY: Office of... proposal. SHOP provides for funds to purchase home sites and develop/improve infrastructure to support sweat equity and volunteer-based homeownership programs for low-income persons and families. This...

An Improved Privacy-Preserving Framework for Location-Based Services Based on Double Cloaking Regions with Supplementary Information Constraints

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Li Kuang

2017-01-01

Full Text Available With the rapid development of location-based services in the field of mobile network applications, users enjoy the convenience of location-based services on one side, while being exposed to the risk of disclosure of privacy on the other side. Attacker will make a fierce attack based on the probability of inquiry, map data, point of interest (POI, and other supplementary information. The existing location privacy protection techniques seldom consider the supplementary information held by attackers and usually only generate single cloaking region according to the protected location point, and the query efficiency is relatively low. In this paper, we improve the existing LBSs system framework, in which we generate double cloaking regions by constraining the supplementary information, and then k-anonymous task is achieved by the cooperation of the double cloaking regions; specifically speaking, k dummy points of fixed dummy positions in the double cloaking regions are generated and the LBSs query is then performed. Finally, the effectiveness of the proposed method is verified by the experiments on real datasets.

Retinal Identification Based on an Improved Circular Gabor Filter and Scale Invariant Feature Transform

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Xiaoming Xi

2013-07-01

Full Text Available Retinal identification based on retinal vasculatures in the retina provides the most secure and accurate means of authentication among biometrics and has primarily been used in combination with access control systems at high security facilities. Recently, there has been much interest in retina identification. As digital retina images always suffer from deformations, the Scale Invariant Feature Transform (SIFT, which is known for its distinctiveness and invariance for scale and rotation, has been introduced to retinal based identification. However, some shortcomings like the difficulty of feature extraction and mismatching exist in SIFT-based identification. To solve these problems, a novel preprocessing method based on the Improved Circular Gabor Transform (ICGF is proposed. After further processing by the iterated spatial anisotropic smooth method, the number of uninformative SIFT keypoints is decreased dramatically. Tested on the VARIA and eight simulated retina databases combining rotation and scaling, the developed method presents promising results and shows robustness to rotations and scale changes.

Accuracy improvement in the TDR-based localization of water leaks

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Andrea Cataldo

Full Text Available A time domain reflectometry (TDR-based system for the localization of water leaks has been recently developed by the authors. This system, which employs wire-like sensing elements to be installed along the underground pipes, has proven immune to the limitations that affect the traditional, acoustic leak-detection systems.Starting from the positive results obtained thus far, in this work, an improvement of this TDR-based system is proposed. More specifically, the possibility of employing a low-cost, water-absorbing sponge to be placed around the sensing element for enhancing the accuracy in the localization of the leak is addressed.To this purpose, laboratory experiments were carried out mimicking a water leakage condition, and two sensing elements (one embedded in a sponge and one without sponge were comparatively used to identify the position of the leak through TDR measurements. Results showed that, thanks to the water retention capability of the sponge (which maintains the leaked water more localized, the sensing element embedded in the sponge leads to a higher accuracy in the evaluation of the position of the leak. Keywords: Leak localization, TDR, Time domain reflectometry, Water leaks, Underground water pipes

Drone-based Object Counting by Spatially Regularized Regional Proposal Network

OpenAIRE

Hsieh, Meng-Ru; Lin, Yen-Liang; Hsu, Winston H.

2017-01-01

Existing counting methods often adopt regression-based approaches and cannot precisely localize the target objects, which hinders the further analysis (e.g., high-level understanding and fine-grained classification). In addition, most of prior work mainly focus on counting objects in static environments with fixed cameras. Motivated by the advent of unmanned flying vehicles (i.e., drones), we are interested in detecting and counting objects in such dynamic environments. We propose Layout Prop...

SAFETY AND SECURITY IMPROVEMENT IN PUBLIC TRANSPORTATION BASED ON PUBLIC PERCEPTION IN DEVELOPING COUNTRIES

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Tri Basuki JOEWONO

2006-01-01

Three aspects of an improvement agenda are proposed based on the perception data, namely technology, management, and institution. This agenda is clarified by a set of action plans incorporating the responsible parties and a time frame. The action plan is divided into three terms to define a clear goal for each step. The short-term action focuses on the hardware and on preparing further steps, whereas the medium-term action focuses on developing and improving the standard of safety and security. The long-term action focuses on advancing safety and security practices. The effectiveness of this agenda and action plan rests upon a set of assumptions, such as the degree of seriousness from the authoritative institution, fair distribution of information, the availability of reasonable resources, and coordinated and collaborative action from all parties involved to reach the objective.

Improving performance of breast cancer risk prediction using a new CAD-based region segmentation scheme

Science.gov (United States)

Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Qiu, Yuchen; Zheng, Bin

2018-02-01

Objective of this study is to develop and test a new computer-aided detection (CAD) scheme with improved region of interest (ROI) segmentation combined with an image feature extraction framework to improve performance in predicting short-term breast cancer risk. A dataset involving 570 sets of "prior" negative mammography screening cases was retrospectively assembled. In the next sequential "current" screening, 285 cases were positive and 285 cases remained negative. A CAD scheme was applied to all 570 "prior" negative images to stratify cases into the high and low risk case group of having cancer detected in the "current" screening. First, a new ROI segmentation algorithm was used to automatically remove useless area of mammograms. Second, from the matched bilateral craniocaudal view images, a set of 43 image features related to frequency characteristics of ROIs were initially computed from the discrete cosine transform and spatial domain of the images. Third, a support vector machine model based machine learning classifier was used to optimally classify the selected optimal image features to build a CAD-based risk prediction model. The classifier was trained using a leave-one-case-out based cross-validation method. Applying this improved CAD scheme to the testing dataset, an area under ROC curve, AUC = 0.70+/-0.04, which was significantly higher than using the extracting features directly from the dataset without the improved ROI segmentation step (AUC = 0.63+/-0.04). This study demonstrated that the proposed approach could improve accuracy on predicting short-term breast cancer risk, which may play an important role in helping eventually establish an optimal personalized breast cancer paradigm.

Theoretical and Empirical Analyses of an Improved Harmony Search Algorithm Based on Differential Mutation Operator

Directory of Open Access Journals (Sweden)

Longquan Yong

2012-01-01

Full Text Available Harmony search (HS method is an emerging metaheuristic optimization algorithm. In this paper, an improved harmony search method based on differential mutation operator (IHSDE is proposed to deal with the optimization problems. Since the population diversity plays an important role in the behavior of evolution algorithm, the aim of this paper is to calculate the expected population mean and variance of IHSDE from theoretical viewpoint. Numerical results, compared with the HSDE, NGHS, show that the IHSDE method has good convergence property over a test-suite of well-known benchmark functions.

Proposing an adaptive mutation to improve XCSF performance to classify ADHD and BMD patients

Science.gov (United States)

Sadatnezhad, Khadijeh; Boostani, Reza; Ghanizadeh, Ahmad

2010-12-01

There is extensive overlap of clinical symptoms observed among children with bipolar mood disorder (BMD) and those with attention deficit hyperactivity disorder (ADHD). Thus, diagnosis according to clinical symptoms cannot be very accurate. It is therefore desirable to develop quantitative criteria for automatic discrimination between these disorders. This study is aimed at designing an efficient decision maker to accurately classify ADHD and BMD patients by analyzing their electroencephalogram (EEG) signals. In this study, 22 channels of EEGs have been recorded from 21 subjects with ADHD and 22 individuals with BMD. Several informative features, such as fractal dimension, band power and autoregressive coefficients, were extracted from the recorded signals. Considering the multimodal overlapping distribution of the obtained features, linear discriminant analysis (LDA) was used to reduce the input dimension in a more separable space to make it more appropriate for the proposed classifier. A piecewise linear classifier based on the extended classifier system for function approximation (XCSF) was modified by developing an adaptive mutation rate, which was proportional to the genotypic content of best individuals and their fitness in each generation. The proposed operator controlled the trade-off between exploration and exploitation while maintaining the diversity in the classifier's population to avoid premature convergence. To assess the effectiveness of the proposed scheme, the extracted features were applied to support vector machine, LDA, nearest neighbor and XCSF classifiers. To evaluate the method, a noisy environment was simulated with different noise amplitudes. It is shown that the results of the proposed technique are more robust as compared to conventional classifiers. Statistical tests demonstrate that the proposed classifier is a promising method for discriminating between ADHD and BMD patients.

A method proposal for cumulative environmental impact assessment based on the landscape vulnerability evaluation

International Nuclear Information System (INIS)

Pavlickova, Katarina; Vyskupova, Monika

2015-01-01

Cumulative environmental impact assessment deals with the occasional use in practical application of environmental impact assessment process. The main reasons are the difficulty of cumulative impact identification caused by lack of data, inability to measure the intensity and spatial effect of all types of impacts and the uncertainty of their future evolution. This work presents a method proposal to predict cumulative impacts on the basis of landscape vulnerability evaluation. For this purpose, qualitative assessment of landscape ecological stability is conducted and major vulnerability indicators of environmental and socio-economic receptors are specified and valuated. Potential cumulative impacts and the overall impact significance are predicted quantitatively in modified Argonne multiple matrixes while considering the vulnerability of affected landscape receptors and the significance of impacts identified individually. The method was employed in the concrete environmental impact assessment process conducted in Slovakia. The results obtained in this case study reflect that this methodology is simple to apply, valid for all types of impacts and projects, inexpensive and not time-consuming. The objectivity of the partial methods used in this procedure is improved by quantitative landscape ecological stability evaluation, assignment of weights to vulnerability indicators based on the detailed characteristics of affected factors, and grading impact significance. - Highlights: • This paper suggests a method proposal for cumulative impact prediction. • The method includes landscape vulnerability evaluation. • The vulnerability of affected receptors is determined by their sensitivity. • This method can increase the objectivity of impact prediction in the EIA process

76 FR 14034 - Proposed Collection; Comment Request; NCI Cancer Genetics Services Directory Web-Based...

Science.gov (United States)

2011-03-15

... DEPARTMENT OF HEALTH AND HUMAN SERVICES National Institutes of Health Proposed Collection; Comment Request; NCI Cancer Genetics Services Directory Web-Based Application Form and Update Mailer Summary: In... Cancer Genetics Services Directory Web-based Application Form and Update Mailer. [[Page 14035

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.

Science.gov (United States)

Ye, Fei; Lou, Xin Yuan; Sun, Lin Fu

2017-01-01

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm's performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

 Higher Order Improvements for Approximate Estimators

DEFF Research Database (Denmark)

Kristensen, Dennis; Salanié, Bernard

Many modern estimation methods in econometrics approximate an objective function, through simulation or discretization for instance. The resulting "approximate" estimator is often biased; and it always incurs an efficiency loss. We here propose three methods to improve the properties of such appr......Many modern estimation methods in econometrics approximate an objective function, through simulation or discretization for instance. The resulting "approximate" estimator is often biased; and it always incurs an efficiency loss. We here propose three methods to improve the properties...... of such approximate estimators at a low computational cost. The first two methods correct the objective function so as to remove the leading term of the bias due to the approximation. One variant provides an analytical bias adjustment, but it only works for estimators based on stochastic approximators......, such as simulation-based estimators. Our second bias correction is based on ideas from the resampling literature; it eliminates the leading bias term for non-stochastic as well as stochastic approximators. Finally, we propose an iterative procedure where we use Newton-Raphson (NR) iterations based on a much finer...

Improved hybrid information filtering based on limited time window

Science.gov (United States)

Song, Wen-Jun; Guo, Qiang; Liu, Jian-Guo

2014-12-01

Adopting the entire collecting information of users, the hybrid information filtering of heat conduction and mass diffusion (HHM) (Zhou et al., 2010) was successfully proposed to solve the apparent diversity-accuracy dilemma. Since the recent behaviors are more effective to capture the users' potential interests, we present an improved hybrid information filtering of adopting the partial recent information. We expand the time window to generate a series of training sets, each of which is treated as known information to predict the future links proven by the testing set. The experimental results on one benchmark dataset Netflix indicate that by only using approximately 31% recent rating records, the accuracy could be improved by an average of 4.22% and the diversity could be improved by 13.74%. In addition, the performance on the dataset MovieLens could be preserved by considering approximately 60% recent records. Furthermore, we find that the improved algorithm is effective to solve the cold-start problem. This work could improve the information filtering performance and shorten the computational time.

Finite-time adaptive sliding mode force control for electro-hydraulic load simulator based on improved GMS friction model

Science.gov (United States)

Kang, Shuo; Yan, Hao; Dong, Lijing; Li, Changchun

2018-03-01

This paper addresses the force tracking problem of electro-hydraulic load simulator under the influence of nonlinear friction and uncertain disturbance. A nonlinear system model combined with the improved generalized Maxwell-slip (GMS) friction model is firstly derived to describe the characteristics of load simulator system more accurately. Then, by using particle swarm optimization (PSO) algorithm ​combined with the system hysteresis characteristic analysis, the GMS friction parameters are identified. To compensate for nonlinear friction and uncertain disturbance, a finite-time adaptive sliding mode control method is proposed based on the accurate system model. This controller has the ability to ensure that the system state moves along the nonlinear sliding surface to steady state in a short time as well as good dynamic properties under the influence of parametric uncertainties and disturbance, which further improves the force loading accuracy and rapidity. At the end of this work, simulation and experimental results are employed to demonstrate the effectiveness of the proposed sliding mode control strategy.

The Parameters Optimization of MCR-WPT System Based on the Improved Genetic Simulated Annealing Algorithm

Directory of Open Access Journals (Sweden)

Sheng Lu

2015-01-01

Full Text Available To solve the problem of parameter selection during the design of magnetically coupled resonant wireless power transmission system (MCR-WPT, this paper proposed an improved genetic simulated annealing algorithm. Firstly, the equivalent circuit of the system is analysis in this study and a nonlinear programming mathematical model is built. Secondly, in place of the penalty function method in the genetic algorithm, the selection strategy based on the distance between individuals is adopted to select individual. In this way, it reduces the excess empirical parameters. Meanwhile, it can improve the convergence rate and the searching ability by calculating crossover probability and mutation probability according to the variance of population’s fitness. At last, the simulated annealing operator is added to increase local search ability of the method. The simulation shows that the improved method can break the limit of the local optimum solution and get the global optimum solution faster. The optimized system can achieve the practical requirements.

How Quality Improvement Practice Evidence Can Advance the Knowledge Base.

Science.gov (United States)

OʼRourke, Hannah M; Fraser, Kimberly D

2016-01-01

Recommendations for the evaluation of quality improvement interventions have been made in order to improve the evidence base of whether, to what extent, and why quality improvement interventions affect chosen outcomes. The purpose of this article is to articulate why these recommendations are appropriate to improve the rigor of quality improvement intervention evaluation as a research endeavor, but inappropriate for the purposes of everyday quality improvement practice. To support our claim, we describe the differences between quality improvement interventions that occur for the purpose of practice as compared to research. We then carefully consider how feasibility, ethics, and the aims of evaluation each impact how quality improvement interventions that occur in practice, as opposed to research, can or should be evaluated. Recommendations that fit the evaluative goals of practice-based quality improvement interventions are needed to support fair appraisal of the distinct evidence they produce. We describe a current debate on the nature of evidence to assist in reenvisioning how quality improvement evidence generated from practice might complement that generated from research, and contribute in a value-added way to the knowledge base.

Improving mobile robot localization: grid-based approach

Science.gov (United States)

Yan, Junchi

2012-02-01

Autonomous mobile robots have been widely studied not only as advanced facilities for industrial and daily life automation, but also as a testbed in robotics competitions for extending the frontier of current artificial intelligence. In many of such contests, the robot is supposed to navigate on the ground with a grid layout. Based on this observation, we present a localization error correction method by exploring the geometric feature of the tile patterns. On top of the classical inertia-based positioning, our approach employs three fiber-optic sensors that are assembled under the bottom of the robot, presenting an equilateral triangle layout. The sensor apparatus, together with the proposed supporting algorithm, are designed to detect a line's direction (vertical or horizontal) by monitoring the grid crossing events. As a result, the line coordinate information can be fused to rectify the cumulative localization deviation from inertia positioning. The proposed method is analyzed theoretically in terms of its error bound and also has been implemented and tested on a customary developed two-wheel autonomous mobile robot.

An improved Hough transform-based fingerprint alignment approach

CSIR Research Space (South Africa)

Mlambo, CS

2014-11-01

Full Text Available An improved Hough Transform based fingerprint alignment approach is presented, which improves computing time and memory usage with accurate alignment parameter (rotation and translation) results. This is achieved by studying the strengths...

An improved Corten-Dolan's model based on damage and stress state effects

International Nuclear Information System (INIS)

Gao, Huiying; Huang, Hong Zhong; Lv, Zhiqiang; Zuo, Fang Jun; Wang, Hai Kun

2015-01-01

The value of exponent d in Corten-Dolan's model is generally considered to be a constant. Nonetheless, the results predicted on the basis of this statement deviate significantly from the real values. In consideration of the effects of damage and stress state on fatigue life prediction, Corten-Dolan's model is improved by redefining the exponent d used in the traditional model. The improved model performs better than the traditional one with respect to the demonstration of a fatigue failure mechanism. Predictions of fatigue life on the basis of investigations into three metallic specimens indicate that the errors caused by the improved model are significantly smaller than those induced by the traditional model. Meanwhile, predictions derived according to the improved model fall into a narrower dispersion zone than those made as per Miner's rule and the traditional model. This finding suggests that the proposed model improves the life prediction accuracy of the other two models. The predictions obtained using the improved Corten-Dolan's model differ slightly from those derived according to a model proposed in previous literature; a few life predictions obtained on the basis of the former are more accurate than those derived according to the latter. Therefore, the improved model proposed in this paper is proven to be rational and reliable given the proven validity of the existing model. Therefore, the improved model can be feasibly and credibly applied to damage accumulation and fatigue life prediction to some extent.

An improved Corten-Dolan's model based on damage and stress state effects

Energy Technology Data Exchange (ETDEWEB)

Gao, Huiying; Huang, Hong Zhong; Lv, Zhiqiang; Zuo, Fang Jun; Wang, Hai Kun [University of Electronic Science and Technology of China, Chengdu (China)

2015-08-15

The value of exponent d in Corten-Dolan's model is generally considered to be a constant. Nonetheless, the results predicted on the basis of this statement deviate significantly from the real values. In consideration of the effects of damage and stress state on fatigue life prediction, Corten-Dolan's model is improved by redefining the exponent d used in the traditional model. The improved model performs better than the traditional one with respect to the demonstration of a fatigue failure mechanism. Predictions of fatigue life on the basis of investigations into three metallic specimens indicate that the errors caused by the improved model are significantly smaller than those induced by the traditional model. Meanwhile, predictions derived according to the improved model fall into a narrower dispersion zone than those made as per Miner's rule and the traditional model. This finding suggests that the proposed model improves the life prediction accuracy of the other two models. The predictions obtained using the improved Corten-Dolan's model differ slightly from those derived according to a model proposed in previous literature; a few life predictions obtained on the basis of the former are more accurate than those derived according to the latter. Therefore, the improved model proposed in this paper is proven to be rational and reliable given the proven validity of the existing model. Therefore, the improved model can be feasibly and credibly applied to damage accumulation and fatigue life prediction to some extent.

Basic study of the plant maintenance model considering plant improvement/modification

International Nuclear Information System (INIS)

Tsumaya, Akira; Inoue, Kazuya; Mochizuki, Masahito; Wakamatsu, Hidefumi; Arai, Eiji

2007-01-01

This paper proposes a maintenance activity model that considers not only routine maintenance activity but also functional maintenance including improvement/modification. Required maintenance types are categorized, and limitation of Activity Domain Integration Diagram (ADID) proposed by ISO18435 are discussed based on framework for life cycle maintenance management of manufacturing assets. Then, we proposed extension ADID model for plant maintenance activity model considering functional improvement/modification. (author)

RBMK nuclear reactors: Proposals for instrumentation and control improvements to enhanced safety and availability. IEC technical report of type 3. Working material

International Nuclear Information System (INIS)

1995-01-01

The present material presents a CD+V draft report ''RBMK nuclear reactors: Proposals for instrumentation and control improvements to enhance safety and availability'' prepared by the Joint IEC/IAEA team during 1993-1995. Experience has demonstrated the need to improve the safety instrumentation of the RBMK type reactors using well proven modern technology. The working group identified the upgrades and changes of the highest priority based on the evaluation of the RBMK systems and the events where the instrumentation was found to be inadequate for safe operation. The subjects discussed in this document were not selected on a systematic basis but were selected by the IEC and IAEA experts as considered to be appropriate to the activities of the IEC and for which technical experience was available. The items identified therefore do not reflect any ranking of the safety issues or any priority or impact on safety of any of the measures were they to be implemented. Many important safety issued and areas where physical measures are required to improve safety have been omitted and indeed not even acknowledged in this document. The recommendations presented in the document differ from those normally produced by the IEC in the form of standards as they are of a transitory nature and some have already been overtaken by the continuing process of improvements to plant safety. Figs and tabs

Trust regions in Kriging-based optimization with expected improvement

Science.gov (United States)

Regis, Rommel G.

2016-06-01

The Kriging-based Efficient Global Optimization (EGO) method works well on many expensive black-box optimization problems. However, it does not seem to perform well on problems with steep and narrow global minimum basins and on high-dimensional problems. This article develops a new Kriging-based optimization method called TRIKE (Trust Region Implementation in Kriging-based optimization with Expected improvement) that implements a trust-region-like approach where each iterate is obtained by maximizing an Expected Improvement (EI) function within some trust region. This trust region is adjusted depending on the ratio of the actual improvement to the EI. This article also develops the Kriging-based CYCLONE (CYClic Local search in OptimizatioN using Expected improvement) method that uses a cyclic pattern to determine the search regions where the EI is maximized. TRIKE and CYCLONE are compared with EGO on 28 test problems with up to 32 dimensions and on a 36-dimensional groundwater bioremediation application in appendices supplied as an online supplement available at http://dx.doi.org/10.1080/0305215X.2015.1082350. The results show that both algorithms yield substantial improvements over EGO and they are competitive with a radial basis function method.

Activity-Based Costing Using Multicriteria Drivers: An Accounting Proposal to Boost Companies Toward Sustainability

Directory of Open Access Journals (Sweden)

Heitor F. Marinho Neto

2018-05-01

Full Text Available Recognizing that natural environment is reaching its maximum limits in providing resources and diluting the waste generated by human production systems, efforts toward more sustainable production systems are mandatory to secure the development of future generations. For this purpose, changing the productivity model adopted by companies that are almost exclusively rooted on circulating money to generate profit, named business as usual, is an important issue. In this sense, an alternative would be establishing the relationship of stocks and flows of energy, material, and information with environmental, economic and social outcomes, thus resulting in new accounting approaches. This work aims to propose an activity-based costing (ABC based on multicriteria drivers including economic, emissions, and emergy (with an “m” values. The proposed ABC costing allocates each one of the multicriteria drivers into a specific part of the sustainability conceptual model, in an attempt to embrace a holistic perspective and allow for a sustainable-based decision, rather than considering purely economic drivers. The goal programming (GP method is considered so as to support a decision based on multicriteria aspects. Results show that the proposed accounting approach known as ABCsustain allows for decisions toward a company's sustainability by acting on both the amount and kind of a company's product that should be managed, as well as on the effective increase of a specific company's activity or process. The proposed ABCsustain could make the insertion of environmental issues into companies strategic planning more effective. It is expected that environmental issues go beyond a simple diagnoses and begin to be considered as action in factum in the companies' decisions toward achieving a more sustainable world system.

Improved vibration-based energy harvesting by annular mass configuration of piezoelectric circular diaphragms

Science.gov (United States)

Yang, Yangyiwei; Li, Yuanbo; Guo, Yaqian; Xu, Bai-Xiang; Yang, Tongqing

2018-03-01

Vibration-based energy harvesting using piezoelectric circular diaphragms (PCDs) with a structure featuring the central mass (C-mass) configuration has drawn much attention in recent decades. In this work, we propose a new configuration with the annular proof mass (A-mass) where an improved energy harvesting is promised. The numerical analysis was employed using the circuit-coupled piezoelectric simulation, and the experimental validation was implemented using PCDs with the even-width annular electrodes. Samples with the different mass configurations as well as structural parameters ϖ 1 and ϖ 2, which indicate the ratio between the inner boundary radius and piezoelectric ceramic radius as well as the ratio between outer boundary radius and the substrate radius, respectively, were prepared and tested. The impedance-matched output power of full-electrode PCDs was also collected, and some distinct improvement was measured on samples with the certain structural parameters. The power increases from 14.1 mW to 19.0 mW after changing the configuration from C-mass to A-mass with the same parameters (ϖ 1, ϖ 2) = (0.16, 0.9), showing the considerable improvement in energy harvesting by using A-mass configuration.

Applying tensor-based morphometry to parametric surfaces can improve MRI-based disease diagnosis.

Science.gov (United States)

Wang, Yalin; Yuan, Lei; Shi, Jie; Greve, Alexander; Ye, Jieping; Toga, Arthur W; Reiss, Allan L; Thompson, Paul M

2013-07-01

Many methods have been proposed for computer-assisted diagnostic classification. Full tensor information and machine learning with 3D maps derived from brain images may help detect subtle differences or classify subjects into different groups. Here we develop a new approach to apply tensor-based morphometry to parametric surface models for diagnostic classification. We use this approach to identify cortical surface features for use in diagnostic classifiers. First, with holomorphic 1-forms, we compute an efficient and accurate conformal mapping from a multiply connected mesh to the so-called slit domain. Next, the surface parameterization approach provides a natural way to register anatomical surfaces across subjects using a constrained harmonic map. To analyze anatomical differences, we then analyze the full Riemannian surface metric tensors, which retain multivariate information on local surface geometry. As the number of voxels in a 3D image is large, sparse learning is a promising method to select a subset of imaging features and to improve classification accuracy. Focusing on vertices with greatest effect sizes, we train a diagnostic classifier using the surface features selected by an L1-norm based sparse learning method. Stability selection is applied to validate the selected feature sets. We tested the algorithm on MRI-derived cortical surfaces from 42 subjects with genetically confirmed Williams syndrome and 40 age-matched controls, multivariate statistics on the local tensors gave greater effect sizes for detecting group differences relative to other TBM-based statistics including analysis of the Jacobian determinant and the largest eigenvalue of the surface metric. Our method also gave reasonable classification results relative to the Jacobian determinant, the pair of eigenvalues of the Jacobian matrix and volume features. This analysis pipeline may boost the power of morphometry studies, and may assist with image-based classification. Copyright © 2013

An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas.

Directory of Open Access Journals (Sweden)

Jing Shao

Full Text Available China is facing ecological and environmental challenges as its urban growth rate continues to rise, and zoning protected ecological areas is recognized as an effective response measure. Zoning inherently involves both site attributes and aggregation attributes, and the combination of mathematical models and heuristic algorithms have proven advantageous. In this article, an improved artificial bee colony (IABC-based approach is proposed for zoning protected ecological areas at a regional scale. Three main improvements were made: the first is the use of multiple strategies to generate the initial bee population of a specific quality and diversity, the second is an exploitation search procedure to generate neighbor solutions combining "replace" and "alter" operations, and the third is a "swap" strategy to enable a local search for the iterative optimal solution. The IABC algorithm was verified using simulated data. Then it was applied to define an optimum scheme of protected ecological areas of Sanya (in the Hainan province of China, and a reasonable solution was obtained. Finally, a comparison experiment with other methods (agent-based land allocation model, ant colony optimization, and density slicing was conducted and demonstrated that the IABC algorithm was more effective and efficient than the other methods. Through this study, we aimed to provide a scientifically sound, practical approach for zoning procedures.

Reduction of image-based ADI-to-AEI overlay inconsistency with improved algorithm

Science.gov (United States)

Chen, Yen-Liang; Lin, Shu-Hong; Chen, Kai-Hsiung; Ke, Chih-Ming; Gau, Tsai-Sheng

2013-04-01

In image-based overlay (IBO) measurement, the measurement quality of various measurement spectra can be judged by quality indicators and also the ADI-to-AEI similarity to determine the optimum light spectrum. However we found some IBO measured results showing erroneous indication of wafer expansion from the difference between the ADI and the AEI maps, even after their measurement spectra were optimized. To reduce this inconsistency, an improved image calculation algorithm is proposed in this paper. Different gray levels composed of inner- and outer-box contours are extracted to calculate their ADI overlay errors. The symmetry of intensity distribution at the thresholds dictated by a range of gray levels is used to determine the particular gray level that can minimize the ADI-to-AEI overlay inconsistency. After this improvement, the ADI is more similar to AEI with less expansion difference. The same wafer was also checked by the diffraction-based overlay (DBO) tool to verify that there is no physical wafer expansion. When there is actual wafer expansion induced by large internal stress, both the IBO and the DBO measurements indicate similar expansion results. The scanning white-light interference microscope was used to check the variation of wafer warpage during the ADI and AEI stages. It predicts a similar trend with the overlay difference map, confirming the internal stress.

Stable adaptive PI control for permanent magnet synchronous motor drive based on improved JITL technique.

Science.gov (United States)

Zheng, Shiqi; Tang, Xiaoqi; Song, Bao; Lu, Shaowu; Ye, Bosheng

2013-07-01

In this paper, a stable adaptive PI control strategy based on the improved just-in-time learning (IJITL) technique is proposed for permanent magnet synchronous motor (PMSM) drive. Firstly, the traditional JITL technique is improved. The new IJITL technique has less computational burden and is more suitable for online identification of the PMSM drive system which is highly real-time compared to traditional JITL. In this way, the PMSM drive system is identified by IJITL technique, which provides information to an adaptive PI controller. Secondly, the adaptive PI controller is designed in discrete time domain which is composed of a PI controller and a supervisory controller. The PI controller is capable of automatically online tuning the control gains based on the gradient descent method and the supervisory controller is developed to eliminate the effect of the approximation error introduced by the PI controller upon the system stability in the Lyapunov sense. Finally, experimental results on the PMSM drive system show accurate identification and favorable tracking performance. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Performance improvement of per-user threshold based multiuser switched scheduling system

KAUST Repository

Nam, Haewoon; Alouini, Mohamed-Slim

2013-01-01

may occur with a non-negligible probability, the proposed scheme employs post user selection in order to further improve the ergodic capacity, where the user with the highest potential for a higher channel quality than other users is selected

Improving electronic customers' profile in recommender systems using data mining techniques

Directory of Open Access Journals (Sweden)

Mohammad Julashokri

2011-10-01

Full Text Available Recommender systems are tools for realization one to one marketing. Recommender systems are systems, which attract, retain, and develop customers. Recommender systems use several ways to make recommendations. Two ways are using more than the others: collaborative filtering and content-based filtering. In this study, a recommender system model based on collaborative filtering has proposed. Proposed model was endeavored to improve the customer profile in collaborative systems to enhance the recommender system efficiency. This improvement was done using time context and group preferences. Experimental results show that the proposed model has a better recommendation performance than existing models.

Construction Projects Assessment Based on the Sustainable Development Criteria by an Integrated Fuzzy AHP and Improved GRA Model

Directory of Open Access Journals (Sweden)

Seyed Morteza Hatefi

2018-03-01

Full Text Available Due to the increasing population and earth pollution, managing construction and infrastructure projects with less damage to the environment and less pollution is very important. Sustainable development aims at reducing damage to the environment, making projects economical, and increasing comfort and social justice. This study proposes fuzzy analytic hierarchy process (AHP and improved grey relational analysis (GRA to assess construction projects based on the sustainable development criteria. For doing so, sustainable development criteria are first identified in economic, social, and environmental dimensions using literature review, and are then customized for urban construction projects using experts’ opinions. After designing questionnaires and collecting data, fuzzy AHP is used for determining the importance of sustainable development criteria and their subcriteria. Then, improved GRA is employed for assessing six recreational, commercial, and official centers in Isfahan regarding the weights of criteria and subcriteria. The proposed fuzzy AHP-improved GRA help us to prioritize construction projects based on the sustainable development criteria. The results of applying fuzzy AHP show that the weights of economic, social, and environmental criteria are equal to 0.330, 0.321, and 0.349, respectively, which are close to each other. This means that the importance of all three aspects of sustainability is almost equal to each other. Furthermore, “Having profits for the society”, “Increasing social justice”, and “Adherence to environmental policies” are identified as the most important indicators of sustainable development in terms of economic, social, and environmental aspects, respectively. Finally, the results of employing improved GRA determine Negin Chaharbagh recreational and commercial complex as the best project.

Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification.

Science.gov (United States)

Hariharan, M; Sindhu, R; Vijean, Vikneswaran; Yazid, Haniza; Nadarajaw, Thiyagar; Yaacob, Sazali; Polat, Kemal

2018-03-01

Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. The experimental

76 FR 21372 - Medicare Program; Solicitation for Proposals for the Medicare Community-Based Care Transitions...

Science.gov (United States)

2011-04-15

...] Medicare Program; Solicitation for Proposals for the Medicare Community-Based Care Transitions Program... interested parties of an opportunity to apply to participate in the Medicare Community-based Care Transitions.... 111-148, enacted on March 23, 2010) (Affordable Care Act) authorized the Medicare Community-based Care...

Mixing Matrix Estimation of Underdetermined Blind Source Separation Based on Data Field and Improved FCM Clustering

Directory of Open Access Journals (Sweden)

Qiang Guo

2018-01-01

Full Text Available In modern electronic warfare, multiple input multiple output (MIMO radar has become an important tool for electronic reconnaissance and intelligence transmission because of its anti-stealth, high resolution, low intercept and anti-destruction characteristics. As a common MIMO radar signal, discrete frequency coding waveform (DFCW has a serious overlap of both time and frequency, so it cannot be directly used in the current radar signal separation problems. The existing fuzzy clustering algorithms have problems in initial value selection, low convergence rate and local extreme values which will lead to the low accuracy of the mixing matrix estimation. Consequently, a novel mixing matrix estimation algorithm based on data field and improved fuzzy C-means (FCM clustering is proposed. First of all, the sparsity and linear clustering characteristics of the time–frequency domain MIMO radar signals are enhanced by using the single-source principal value of complex angular detection. Secondly, the data field uses the potential energy information to analyze the particle distribution, thus design a new clustering number selection scheme. Then the particle swarm optimization algorithm is introduced to improve the iterative clustering process of FCM, and finally get the estimated value of the mixing matrix. The simulation results show that the proposed algorithm improves both the estimation accuracy and the robustness of the mixing matrix.

Dynamic Obstacle Avoidance for Unmanned Underwater Vehicles Based on an Improved Velocity Obstacle Method

Directory of Open Access Journals (Sweden)

Wei Zhang

2017-11-01

Full Text Available In view of a dynamic obstacle environment with motion uncertainty, we present a dynamic collision avoidance method based on the collision risk assessment and improved velocity obstacle method. First, through the fusion optimization of forward-looking sonar data, the redundancy of the data is reduced and the position, size and velocity information of the obstacles are obtained, which can provide an accurate decision-making basis for next-step collision avoidance. Second, according to minimum meeting time and the minimum distance between the obstacle and unmanned underwater vehicle (UUV, this paper establishes the collision risk assessment model, and screens key obstacles to avoid collision. Finally, the optimization objective function is established based on the improved velocity obstacle method, and a UUV motion characteristic is used to calculate the reachable velocity sets. The optimal collision speed of UUV is searched in velocity space. The corresponding heading and speed commands are calculated, and outputted to the motion control module. The above is the complete dynamic obstacle avoidance process. The simulation results show that the proposed method can obtain a better collision avoidance effect in the dynamic environment, and has good adaptability to the unknown dynamic environment.

Stability improvement of induction generator-based wind turbine systems

DEFF Research Database (Denmark)

Chen, Zhe; Hu, Y.; Blaabjerg, Frede

2007-01-01

The stability improvement of induction-generator-based wind turbine systems under power system fault conditions has been studied. Two types of generators are considered, namely rotor short-circuited induction generators and dynamic slip-controlled wound rotor induction generators. The factors...... affecting the stability are analysed. The characteristics of the induction-generator-based wind turbines are described, and possible methods of improving stability of the wind generators are discussed. The system modelling is presented, and then the discussed methods of improving stability are investigated...

Hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) and its application to predicting key process variables.

Science.gov (United States)

He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong

2016-03-01

In this paper, a hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) is proposed. Firstly, an improved functional link neural network with small norm of expanded weights and high input-output correlation (SNEWHIOC-FLNN) was proposed for enhancing the generalization performance of FLNN. Unlike the traditional FLNN, the expanded variables of the original inputs are not directly used as the inputs in the proposed SNEWHIOC-FLNN model. The original inputs are attached to some small norm of expanded weights. As a result, the correlation coefficient between some of the expanded variables and the outputs is enhanced. The larger the correlation coefficient is, the more relevant the expanded variables tend to be. In the end, the expanded variables with larger correlation coefficient are selected as the inputs to improve the performance of the traditional FLNN. In order to test the proposed SNEWHIOC-FLNN model, three UCI (University of California, Irvine) regression datasets named Housing, Concrete Compressive Strength (CCS), and Yacht Hydro Dynamics (YHD) are selected. Then a hybrid model based on the improved FLNN integrating with partial least square (IFLNN-PLS) was built. In IFLNN-PLS model, the connection weights are calculated using the partial least square method but not the error back propagation algorithm. Lastly, IFLNN-PLS was developed as an intelligent measurement model for accurately predicting the key variables in the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. Simulation results illustrated that the IFLNN-PLS could significant improve the prediction performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.