Earticle

The global aging leads to a dramatic increase in the demand for hearing aid. However, the lack of hearing care institutions and hearing professionals makes high price of hearing aids, which hinders the widespread use of hearing aids, especially in the developing countries and the remote areas of developed countries. Compared to the teleaudiology, the self-fitting hearing aid can be installed, fitted, and managed by the users themselves. The whole process hardly needs any professionals and special equipments. Although the research of self-fitting hearing aids is in its infancy, it has significant theoretical value and application prospect. At present, except for some commercial attempts to the self-fitting hearing aids, there are still some problems to be imminently solved. In this survey paper, an overview of the state of the art and prototype design for the self-fitting hearing aid is provided. Then, the challenges for self-fitting hearing aids are discussed. Finally, the future trends of self-fitting hearing aids are prospected.

This paper describes a 12-bit 100kS/s successive approximation register analog-to-digital converter (SAR ADC) for biomedical system. Both top-plate sampling technique and VCM-based switching technique are applied to the capacitor digital-to-analog converter (CDAC) to implement a 12-bit SAR ADC with 10-b capacitor array DAC. To enhance the linearity of proposed ADC, thermometer decoder is used in capacitor array DAC. Switching-energy minimization technique, asynchronous control with a low-power delay circuit and true single phase clocking (TSPC) D_FF are also adopted to reduce power consumption. Simulation results show that the proposed ADC achieves the SNDR of 70.97dB, the SDFR of 80.23dB and the ENOB of 11.49b with the CMOS 0.18m technology. Total power consumption is 11.16W under the supply voltage of 1.8V at the sampling frequency of 100 kHz. And the figure of merit (FoM) is 38.79fJ/conversion-steps.

Distributed renewable energy generation is lack of intelligent information processing and decision-making section. The above problems can be solved effectively by the means of intelligent information processing technology based on the dynamic structure of multi-agent for distributed renewable energy. In order to solve the problems of less data and poor information, this paper puts forward fuzzy hyperbolic model and attempts to use dynamic intelligent information processing technology based on multi-agent to change the unreliable, inaccurate information into be full, reliable and accurate. And then the information we have obtained could be intelligent fitted, filtered and decided. The self-organizing self-learning and reasoning ability of multi-agent are considered when the information processing. The intelligent information processing section is comprised of three layers and two kinds of agents. The two kinds of agents are Bus Agents and Coordination Agents, and the three structural layers are the data layer, the filter layer and the policy making layer. Moreover the information have been processed could be utilized.

Due to the uncertainty of the green product market, the manufacturers assume enormous R&D and manufacturing risks. Under such background, the problem regarding the cooperation between the manufacture and the retailer becomes an important factor influencing the decisions and the profits of the two parties. In allusion to such problem, the revenue sharing contract is proposed in this article to promote the deep cooperation between the manufacturer and the retailer. Firstly, in consideration of such factors as product greenness and risk avoidance, the centralized decision-marking model and the manufacturer predominated Stackelberg game model are established, wherein the supply chain efficiency of the latter model is discovered to be less than that of the former model. Therefore, the revenue sharing contract is proposed to coordinate the green supply chain so as to make the overall profit and efficiency of the supply chain reach the corresponding level of the centralized decision-making model. Finally, the numerical experiment is carried out to verify the effectiveness and the reliability of the proposed revenue sharing contract. The experiment result shows that the revenue contract can well coordinate the cooperative relationship between the manufacturer and the retailer and meanwhile maximally improve the utilities and the profits for the two parties.

Within the discipline of philosophy, ideas cannot remain in a textual setting if they are to be truly effective. Examining and presenting concepts by use of visualization techniques are both not only possible but also necessary. This paper aims at representing philosophical writings in engineering-type schemata that use examples from Hume and Kant, and these can be regarded as a translation from text to drawings. The claim here is that, for computer scientists and engineers, such an effort is worth pursuing, especially since such schemata give them a familiar tool for facilitating understanding of philosophical works. The proposed diagrammatic language is introduced through examples taken from the two philosophers. Hume’s and Kant’s standing on philosophical issues is crucial for a proper understanding of modern philosophy in general. Their approaches to the theory of knowledge are first diagrammed, then Kant’s and Hume’s stands on the specific problem of Causality are introduced through examples. The resultant diagrammatic description seems to contribute to understanding of these philosophers’ thoughts.

Building information model (BIM) has become the mainstream in the contemporary architecture industry, which can integrate all construction project information during the life cycle, including all stages such as the survey, design, construction, delivery, use, operation, maintenance and so on. For BIM, IFC is a commonly used format which is an oriented file format with a data model to facilitate interoperability in the building industry. Besides, OA system is an efficient manager for information data while IFC standard is a kind of data expression standard for construction products. With data flow-oriented design of OA system based on IFC, the management and application of IFC data with large number and complex structure are easily worked out, which makes sharing and utilization of resources during life cycle more efficient. This article deals with a detailed analysis of the IFC data format. According to the general principle of data flow-oriented design of OA system, the framework of data flow-oriented design based on IFC is proposed, as well as the framework of storage module and the extension method of the data dictionary based on IFC, which brings great convenience for model and big data management of enterprises.

In this paper we have aimed to design an energy efficient and thermally aware Latin Unicode Reader. Our design is based on 28nm FPGA (Kintex-7) and 40nm FPGA (Artix-7). In order to test the portability of our design, we are operating our design with respective frequency of different mobile architecture. For thermal analysis of our energy efficient design, we have taken temperatures of four different regions from reference. Latin Unicode reader takes 16-bit hexadecimal code of alphabet and clock input. At the end we can conclude that the maximum power consumption is at 2.2GHz and minimum power consumption is at 1.2GHz. When we talk in terms of temperature we can see that maximum power is consumed at 329.85K and minimum power is consumed at 294.15K. And also the power dissipation is less in the case of 40nm (Artix-6) and is more in the case of 28nm (Kintex-7). Changing the parameter (Temperature) doesn’t affect the clock power in both cases (Gated and Non-gated).

The best annual performances of the world women’s pentathlons during 2005~2013 are statistically collected in this article, and the prediction of the best performance of the world women’s heptathlon in 2013 is taken as the research object. According to the best annual performances of the world women’s heptathlons during 2005~2012, the sports performance prediction model composed of GM(1,1) grey prediction model and BP neural network prediction model in serial connection is established in this article, and this model is applied to predict the best annual performance of the world women’s heptathlon in 2013. Through the comparison of the actual value of the best annual performance of the world women’s heptathlon in 2013 and the predicted value of the model, the application of the grey BP neural network prediction model in sports performance prediction is researched and analyzed in this article. The research result shows that for the sports performance prediction problem, the grey BP neural network prediction model has the features of high prediction accuracy, simple application and strong generalization performance, and this model is also superior to single GM(1,1) grey prediction model and BP neural network model.

In allusion to such problems as low accuracy and long convergence time in traditional two-echelon vehicle routing optimization algorithm, a Memetic algorithm (QDEMA) based on Q learning theory and differential evolution is proposed in this article to solve above problems. Firstly, it is necessary to research the two-echelon vehicle routing optimization problem and adopt the optimal segmentation method to obtain the relatively reasonable distribution plan for the first-echelon SDVRP problem in order to accordingly determine the distribution quantity of the transfer stations; secondly, the second-echelon MDVRP distribution scheme is solved to obtain the total distance and the total number of the distribution vehicles for the two-echelon optimization problem; thirdly, in allusion to the solution of the second-echelon MDVRP distribution scheme, Q learning theory and the differential evaluation algorithm are adopted to design new Memetic algorithm in order to globally optimize MDVRP distribution scheme; finally, the simulation experiment is carried out to verify the algorithm effectiveness.

Traditional classroom teaching and learning processes are not in full-fledged utilizing latest new technology for teaching learning system at various college levels which in high demand. This article describe valuable concept to get awareness to the principal governing educational sector in India to the vital need of shifting from traditional system to an e-learning practice system using Data centers deployments and latest technology. In advance it focuses on significance necessity of infrastructure which is high demand to use in educational institute to improve effectiveness and value of teaching and learning process through e-learning. Major issues are in Indian Educational Sector is e-learning system decentralized model and proposed model provides solution for centralized system features and illustrate its importance of Cloud Datacenter model-Dac and its significances.

This paper studies the issue of decision making on some alternatives with category preference of multiple decision makers in which interest conflicts exist and the weights are unknown. It puts forward the method to firstly calculate the attribute weight in each decision maker’s circumstance through case-based learning and thus meet subjective preferences of different decision makers. Considering the interest conflicts between multiple decision makers, the fuzzy set theory is then used to optimize the weight of each decision makers to comply with objective requirements of group decision-making process. Finally, the total order relation of the alternative which does not only conform to the subjective preference information of the decision maker but also consist with the objective fact of group decision-making is obtained based on the calculation result of overall off-target distance for the alternative. The case indicates application procedures and method feasibility.

Objective: Under SCL index system, through the comparative study, this paper investigates whether there are significant differences in mental illness tendency between Chinese coal mine workers underground and on the ground. Methods: This paper uses computer to retrieve in Medline, Sciencedirect, Web of Science, the Cochrane Library, Springerlink, CNKI, Wanfang and Weipu database. The retrieval time is from the very beginning of building database to the March of 2015. After the extraction of available data, Stata 10.1 analysis software was applied to heterogeneity test on the research. Then the paper uses the random effects model to calculate and combine odds ratio as well as 95% confidence interval. Results: 15 articles were finally included by using Meta analysis. Collating data of the experimental group and control group, this paper carries on forest figure mapping, publication bias test, sensitivity analysis and cumulative Meta analysis, and one study result is found to have great deviation impact on the overall conclusion. Eliminating the above research result, the results are greatly improved. Conclusion: Through the result of the Meta analysis, this paper concludes that there are significant differences in mental illness tendency between Chinese coal mine workers underground and on the ground.

Natural dyeing is an eco-friendly and non-toxic. And it has the various functional properties. Obangsaek refers to the five colors such as blue, red, white, black, and yellow as a unique color of Korean nation. Obanggansaek is additional five colors which mixed together the five colors of the Obangsaek. The final aim of this study is to develop cultural crafted products using functional fabric dyed in Obangsaek and Obanggansaek using natural materials. For the purpose, in this study, surface color measurement, the fastness, UVprotection, deodorization, and antimicrobial activity of the cotton and silk dyed with red pigment extract from safflower was done. The results were as follows. All of the dyed cotton and silk showed the brightness of the middle. The dyed cotton tinged red with blue. The dyed silk appeared yellowish red. Chroma of the dyed cotton and silk was very low as 2.2. All of the dyed cotton and silk appeared a grade 4 to 5 in dry cleaning fastness. Cotton was better than silk in rub fastness. Also in acidity perspiration fastness, cotton was better than silk. Acidity perspiration fastness was better than alkalinity perspiration fastness in all of the cotton and silk dyed. Cotton was better than silk in functionality such as UVprotection, deodorization, and antimicrobial activity.

The medium and long term load forecasting is the basis of power planning, investment, production, scheduling and trade, which plays an important role in electric power safety and economic operation. In China, it has the increasing uncertainty and the uncertainty of random variation to forecast the medium and long term load. Thus we can regard it as a typical grey system. However, the traditional grey prediction method cannot be adapt to the needs of the load forecasting gradually. It need to be rich and perfect with the continuous improvement of power system complexity and power marketization degree. This paper studied the modelling mechanism of grey prediction model. Then we analyzed the problems existing in the model, including the boundary value problem, the background value structure problem and the least squares parameter identification problem. This paper put forward an optimization method to directly identify the boundary value x(0)(1), the developing coefficient a and grey coefficient b using ant colony algorithm according to the time response expression of GM(1,1) model, so that it established an optimized GM(1,1) prediction model based on ant colony algorithm. This model can fix the impact of boundary value, and also avoid the errors brought by the background value construction and the least squares parameter estimation. It can verify the effectiveness of the proposed optimization model through the load data simulation. And it can improve the prediction accuracy effectively.

With innovations in technology, alternate energies like solar and wind are becoming more popular. Utilities in some parts of the world rely on Wind energy over coal or gas fired power. Wind energy output is highly mercurial in nature, it changes rapidly even within the course of minutes. Hence, power system operators have to predict changes of the wind power production in order to schedule the Spinning reserve capacity and to manage the grid operations. To reduce the reserve capacity and increase the wind power penetration, the accurate forecasting of wind speed is needed. In addition, wind power forecasting plays an important role in the allocation of balancing power. Although the prediction accuracy of wind power forecasting is lower than the prediction accuracy of load forecasting, wind power forecasts still play a key role to address the operation challenges in electricity supply. This paper deals with Long-term forecast of wind energy for the state grid of Karnataka by employing a simple time series multiplicative modelling. Wind energy production is predicted for the year 2015 using the data collected from 2011-2014.

It is shown that output under financing constraints and principal-agent can be modeled as a one-side deviation from a frictionless output level. The effects of financing constraints and principal-agent can be identified and quantified by imposing a distribution assumption on the effects. Panel data on Chinese listed firms between 2008 and 2013 are used in the estimation. It is found that: (1) the output efficiency of Chinese listed firms is significantly affected by financing constraints and principal-agent; (2) the severity of financing constraints and principal-agent of firms in different regions varies; (3) firms’ output efficiency in east China varies greatly, and the mean efficiency is the lowest of the three regions.

Image segmentation based partial differential equation is very popular in image analysis and computer vision. This article presents a geometric active contour model based on local entropy for segmentation. In the proposed method, energy functional item of this model contains local energy and global energy items, and uses local entropy of each point on the contour line to determine the proportion of each kind of energy. The combined method allows the contour for the rapid evolution and convergence to the final edge after iterations. Experiment results on both synthetic and real images demonstrate the efficiency and accuracy of this model.

Students’ performance on learning is based on the Self-Efficacy and mental efforts. Learning style of a person will be changing based on the environment. Our objective is to improve the self-efficacy and provide the learning content based on the learning style adopted by the learner. This study attempt has been made to classify the e-learning user based on their opinion on a set of parameters. e-learners before commencing their learning process are asked to record their opinion on 16 parameters of a five point scale. 367 e-learners were provided the responses. Based on the responses, the users were categorized to different categories on a statically using factor analysis. The factor analysis provides five components. The components were named based on variables. Thus users were grouped as Multimedia, Modality,Contiguity, Redundancy and personalization learners. A real time agent has also been developed to categorize e-learners based on the 16 parameters thus used in statistical method has also been used. This agent will provide the suitable e-learning content based on the current learning style of the users. This helps in better understanding of the concept. The real time agent categorized the user and the results were compared with statistical method. Moreover, the proposed system analyses the error rate on learning style identified by the system and the one who posses naturally. Out of the 367 users, the real time system identified 357 learners same as in the statistical method with the error rate of 0.046296. Since the error rate was minimal the system thus developed was reliable. Further erroneously categorized by the real time systems were identified and compared with the statistical method. The content of e-learning was delivered to the user by the server according to the type of users based on statistical method. For the remaining 10 users the content were reorganized and delivered to the learner statistically categorized and as well as the real time system category.

This paper analyzes the shortcomings of traditional hidden Markov crawler, makes some improvements on the clustering strategy of web pages and the judgment algorithm for determining the correlation of pages or hyperlinks with the topic; and brings forward an AHMM (Adaptive Hidden Markov Model) modeling method. The experimental results shows that the improved AHMM is much more efficient than the traditional HMM.

With the arrival of global economic integration, the enterprise gradually realized the joint and downstream enterprises form a complementary advantage on its importance to enhance the overall competitiveness of the supply chain. To deal with the corresponding challenges, in this paper, we conduct research on pricing and the advertising coordination of multi retailer supply chain from the general perspectives of common interest maximization. For the two stage supply chain consisting of a single manufacturer and single retailer as the background, study retailers to establish online channel and manufacturers to establish the online channel and applicable condition, the difference between two kinds of the channel model and double channel of supply chain coordination mechanism. The core idea of supply chain management is focus on supply chain node enterprise specializing in their core business, and outsources non-core business to other enterprises, so as to achieve supply chain of all enterprises focus on their good at business. Our research optimizes the traditional game theory framework to achieve the better model for the issues. The experimental analysis verifies the feasibility and the effectiveness of our approach compared with the others.

The aim of this paper is to analyze Korea Telecom’s media business. For this, a theoretical logic is approached and it analyzes KT’s media business activities chronologically in conjunction with the related type of the business model innovation. KT’s first stage of business model innovation is the launch of satellite TV, but it suffers from fierce competition, even if KT tries media activities like pseudo-interactivity and HD quality. KT’s second stage of business innovation is IPTV. As KT has the problem of key channel sourcing, KT’s third stage of business model innovation is the launch of hybrid platform focusing on analog cable TV market, which results in its earnings and pay TV “monopsony” in Korea. During this time, KT plans to prepare for new competition with global players like Google, Apple, and Netflix. In order to utilize external resources like User Generated Content (UGC), in fourth stage of business model innovation KT launches ‘Open store’ on VOD section of IPTV and gives software development toolkits (SDKs) to long tail content creators. Based on the hybrid platform, KT utilizes not only the technical advantages of digital TV, but also invests in UHD quality content and channels. It is the fifth stage of integrated business model. Lastly, KT utilizes its previous business models and leverages its strong bundling power in front door to maintain the co-opetition (cooperation + competition) relationship with Over-The-Tops (OTTs).

Distribution delay is a common problem that causes logistics distribution plan difficult to implement. This paper using the customer satisfaction degree, the vehicle drivers’ satisfaction degree, the logistics company deviation cost and the customer future selection lost cost to measure the disturbing degree of disruption events. In this paper, a Genetic Algorithm based on improved elitist strategy is designed, and using re-scheduling and disruption management to solve the instance respectively, it demonstrated the effectiveness and feasibility of disruption management methods.

In order to improve the prediction accuracy of reverse returned logistics, considering it has the characteristics of high volatility and uncertainty, the paper used the theory of Markov Chain to modify the result of Grey prediction. And a Grey-Markov prediction model was established. Several parallel region has been divided used the prediction curve of Grey prediction model as symmetric center. And each region was a state interval. A practical example show that the average relative error rate and the variance ratio of Grey-Markov prediction model was smaller, and the prediction accuracy is higher comparing with the Grey prediction model. The model is effective and feasible.

In this paper the different sorting algorithms execution time has been examined with different number of elements. Through this experimental result concluded that the algorithm which is working best. The analysis, execution time has analyzed, tabulated in Microsoft Excel. The sorting problem has attracted a great deal of study, possibly due to the complexity of solving it proficiently despite its simple, familiar statement. Sorting algorithms are established in opening computer science classes, where the abundance of algorithms for the problem provides a gentle beginning to variety of core algorithm concepts. Objective of this paper is finding the best sorting algorithm.

In the increasingly fierce market competition, the survival environment that enterprises are facing is increasingly harsh. This makes the supply chain is facing more and more uncertainly factors gradually on the operating environment. In addition, this increases the likelihood of the risk. Therefore, the research on the supply chain risk assessment has the important practical significance. In this paper, we put forward the assessment indexes of the supply chain risk. Then, we use the improved ACO method to optimize the parameters of the LSSVM method and we propose the improved LSSVM method. After that, we use the method to assess and study the risk of the supply chain. Finally, the experiment indicates that the reliability and validity of the evaluation system.

In allusion to the production and marketing problems among the supply chain enterprises, the multi-agent technology is applied in the two-stage supply chain to establish a concurrent negotiation model under incomplete information constraint. The coordination strategy based on particle swarm optimization can update the belief value of the negotiating agents during the negotiation process, thus to further support the continuous negotiation. The simulation result not only verifies the feasibility and the effectiveness of this model, but also shows that this model is superior to other concurrent negotiation models in the aspects of negotiation result utility, negotiation time and negotiation success rate.

The sales forecast in the tobacco sales management plays a great role in the production and operation activities of an enterprise as a bridge between the production activity and the economic benefit. Forecasting system based on time series is put forward in accordance with the characteristics of tobacco sales by knowing about market supply and demand as well as development tendency at home and abroad through the sales forecast, with the help of studies on the sales forecasting technology. Time series is a sequence of random variables in chronological order and the time series analysis technique includes the moving average forecasting, exponential smoothing and regression analysis. According to the characteristics of tobacco sales data, the moving average forecasting model, exponential smoothing model and regression analysis model are established. Through programming, a forecasting system of tobacco sales is set up, which is used in combination with examples to simulate the forecasting model, and then model tests are conducted on the simulation results to get the appropriate model, which is used for sales forecast. The establishment of the system has an important influence on the management of tobacco companies. According to the forecast of tobacco sales, they will know the sales of the next period, so that they can take effective sales management strategies to improve the accuracy and efficiency of the decisions on the company's production and operation.

Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Experimental methods for the identification of essential proteins are always costly, time-consuming, and laborious. High throughput technologies have resulted in a large number of protein-protein interaction data, which provided a stepping stone for predicting essential proteins using computational approaches. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to noise of network. In this paper, we propose a naive essential protein discovery method, named PMN, based on the integration of weighted interactome network and functional modules. The performance of PMN is validated based on the PPI network of Saccharomyces cerevisiae. Experimental results show that PMN significantly outperforms the classical centrality measures. The results also uncover relationship between the modularity and essentiality of proteins.

The bullwhip effect is one of the core problems in supply chain management. Research on it has made a great progress over the years. However, little research has discussed it combining trend demand with order-up-to level replenishment policy in a multi-echelon supply chain. The fluctuation of the bullwhip effect itself is also seldom analyzed. This paper studies the two problems from the view of agent-based simulation. An agent-based model with complex calculation is developed. One highlight is that the players in the supply chain dynamically and intelligently estimate corresponding parameters at every tick. The results indicate that both the bullwhip effect and its fluctuation aggravate with the increase of leadtime. The trend size of customer demand has little influence on the bullwhip effect. The noise of customer demand does not affect the bullwhip effect significantly when it accords with normal distribution. The mean of the bullwhip effect changes differently with the increase of noise autocorrelation coefficient when noise is an AR(1) process. Meanwhile, the standard deviation of the bullwhip effect roughly changes in line with the mean. This paper also presents one bullwhip effect measurement suitable for trend demand.