Integrated circuit and system design for renewable energy inverters

This study proposes that a novel integrated circuit (IC) and system design for renewable energy inverters can harvest renewable energy to power direct current (DC) and alternating current (AC) loads. In addition, an intelligent synthesis and management tool is developed to design the proposed system and to judge the system’s operational maintenance decisions. Finally, a renewable energy inverter’s information is posted to an online system. Users can obtain the proposed system’s information at any time and place. The accurate and superior performance of the proposed IC and system is confirmed by computer simulations and experimental results.     

Graph-based video fingerprinting using double optimal projection

A double optimal projection method that involves projections for intra-cluster and inter-cluster dimensionality reduction are proposed for video fingerprinting. The video is initially set as a graph with frames as its vertices in a high-dimensional space. A similarity measure that can compute the weights of the edges is then proposed. Subsequently, the video frames are partitioned into different clusters based on the graph model. Double optimal projection is used to explore the optimal mapping points in a low-dimensional space to reduce the video dimensions. The statistics and geometrical fingerprints are generated to determine whether a query video is copied from one of the videos in the database. During matching, the video can be roughly matched by utilizing the statistics fingerprint. Further matching is thereafter performed in the corresponding group using geometrical fingerprints. Experimental results show the good performance of the proposed video fingerprinting method in robustness and discrimination.     

Modular finite state machines: Development and application to reconfigurable manufacturing cell controller generation

This paper presents a new framework developed for logic control of manufacturing systems based on finite state machines (FSMs) and a method to automatically generate the control logic for manufacturing cell controllers using this framework. The framework is modular; FSMs are encapsulated into modules with well-defined communication between them. Events in the finite state machines are of two types, triggers and responses, allowing FSMs to react to events and to force events to occur. The automatic generation method is based on a library of predefined modules. Its application to and implementation on a manufacturing testbed is described.     

An efficient face segmentation algorithm based on binary partition tree

This paper presents an efficient face segmentation algorithm based on binary partition tree. Skin-like regions are first obtained by integrating the results of pixel classification and watershed segmentation. Facial features are extracted by the techniques of valley detection and entropic thresholding, and are used to refine the skin-like regions. In order to segment the facial regions from the skin-like regions, a novel region merging algorithm is proposed by considering the impact of the common border ratio between adjacent regions, and the binary partition tree is used to represent the whole region merging process. Then the facial likeness of each node in the binary partition tree is evaluated using a set of fuzzy membership functions devised for a number of facial primitives of geometrical, elliptical and facial features. Finally, an efficient algorithm of node selecting in the binary partition tree is proposed for the final face segmentation, which can exactly segment the faces without any underlying assumption. The performance of the proposed face segmentation algorithm is demonstrated by experimental results carried out on a variety of images in different scenarios.     

Radio resource management for a mobile network with TD-CDMA

In the paper, we proposed a resource allocation method employing maximal compatible cliques to make use of channel bandwidth as efficiently as possible in a TD-CDMA system. Furthermore, the strength of beacon signals was also measured to decide whether or not to reserve resources for hand-off users, in order to reduce their dropping probabilities. Finally, we conducted several experiments to validate that our method could make use of the finite resources efficiently, thereby revealing better blocking probabilities and dropping probabilities than other methods.     

A fuzzy neural network approach for online fault detection in waste water treatment process

In this paper, an effective strategy for fault detection of sludge volume index (SVI) sensor is proposed and tested on an experimental hardware setup in waste water treatment process (WWTP). The main objective of this fault detection strategy is to design a system which consists of the online sensors, the SVI predicting plant and fault diagnosis method. The SVI predicting plant is designed utilizing a fuzzy neural network (FNN), which is trained by a historical set of data collected during fault-free operation of WWTP. The fault diagnosis method, based on the difference between the measured concentration values and FNN predictions, allows a quick revealing of the faults. Then this proposed fault detection method is applied to a real WWTP and compared with other approaches. Experimental results show that the proposed fault detection strategy can obtain the fault signals of the SVI sensor online.     

Approximations to auctions of digital goods with share-averse bidders

We consider the case of a digital product for share-averse bidders, where the product can be sold to multiple buyers who experience some disutility from other firms or consumers owning the same product. We model the problem of selling a digital product to share-averse bidders as an auction and apply a Bayesian optimal mechanism design. We also design constant-approximation algorithms in the prior-free setting including both average- and worst-case analyses.     

电磁领域中复超越方程的遗传算法求解方法

将遗传算法与参数跟踪策略有效结合,跟踪过程中进行搜索域压缩与位移操作,形成了一种功能强大的新算法,可成功应用于电磁领域中各种各样复超越方程的高精度求解问题.在算法实现过程中,使用参数跟踪策略有效地缩小了搜索区域,保证了解的单一性,提高了运算速度;使用动态搜索域提高了解的精度;应用三阶差商公式预估新的搜索中心,使运算速度得以进一步提高.应用本文发展的算法详细求解了终端短路法测量材料的复介电常数时得到的复超越方程和部分填充矩形波导的特征方程,计算结果表明该算法能够轻松地解决复超越方程中的多值问题,解集完备性好,算法鲁棒性强.     

Which algorithm should I choose: An evolutionary algorithm portfolio approach

Many good evolutionary algorithms have been proposed in the past. However, frequently, the question arises that given a problem, one is at a loss of which algorithm to choose. In this paper, we propose a novel algorithm portfolio approach to address the above problem for single objective optimization. A portfolio of evolutionary algorithms is first formed. Covariance Matrix Adaptation Evolution Strategy (CMA-ES), History driven Evolutionary Algorithm (HdEA), Particle Swarm Optimization (PSO2011) and Self adaptive Differential Evolution (SaDE) are chosen as component algorithms. Each algorithm runs independently with no information exchange. At any point in time, the algorithm with the best predicted performance is run for one generation, after which the performance is predicted again. The best algorithm runs for the next generation, and the process goes on. In this way, algorithms switch automatically as a function of the computational budget. This novel algorithm is named Multiple Evolutionary Algorithm (MultiEA). The predictor we introduced has the nice property of being parameter-less, and algorithms switch automatically as a function of budget. The following contributions are made: (1) experimental results on 24 benchmark functions show that MultiEA outperforms (i) Multialgorithm Genetically Adaptive Method for Single Objective Optimization (AMALGAM-SO); (ii) Population-based Algorithm Portfolio (PAP); (iii) a multiple algorithm approach which chooses an algorithm randomly (RandEA); and (iv) a multiple algorithm approach which divides the computational budget evenly and execute all algorithms in parallel (ExhEA). This shows that it outperforms existing portfolio approaches and the predictor is functioning well. (2) Moreover, a neck to neck comparison of MultiEA with CMA-ES, HdEA, PSO2011, and SaDE is also made. Experimental results show that the performance of MultiEA is very competitive. In particular, MultiEA, being a portfolio algorithm, is sometimes even better than all its individual algorithms, and has more robust performance. (3) Furthermore, a positive synergic effect is discovered, namely, MultiEA can sometimes perform better than the sum of its individual EAs. This gives interesting insights into why an algorithm portfolio is a good approach. (4) It is found that MultiEA scales as well as the best algorithm in the portfolio. This suggests that MultiEA scales up nicely, which is a desirable algorithmic feature. (5) Finally, the performance of MultiEA is investigated on a real world problem. It is found that MultiEA can select the most suitable algorithm for the problem and is much better than choosing algorithms randomly.     

Computer vision techniques for construction safety and health monitoring

For construction safety and health, continuous monitoring of unsafe conditions and action is essential in order to eliminate potential hazards in a timely manner. As a robust and automated means of field observation, computer vision techniques have been applied for the extraction of safety related information from site images and videos, and regarded as effective solutions complementary to current time-consuming and unreliable manual observational practices. Although some research efforts have been directed toward computer vision-based safety and health monitoring, its application in real practice remains premature due to a number of technical issues and research challenges in terms of reliability, accuracy, and applicability. This paper thus reviews previous attempts in construction applications from both technical and practical perspectives in order to understand the current status of computer vision techniques, which in turn suggests the direction of future research in the field of computer vision-based safety and health monitoring. Specifically, this paper categorizes previous studies into three groups—object detection, object tracking, and action recognition—based on types of information required to evaluate unsafe conditions and acts. The results demonstrate that major research challenges include comprehensive scene understanding, varying tracking accuracy by camera position, and action recognition of multiple equipment and workers. In addition, we identified several practical issues including a lack of task-specific and quantifiable metrics to evaluate the extracted information in safety context, technical obstacles due to dynamic conditions at construction sites and privacy issues. These challenges indicate a need for further research in these areas. Accordingly, this paper provides researchers insights into advancing knowledge and techniques for computer vision-based safety and health monitoring, and offers fresh opportunities and considerations to practitioners in understanding and adopting the techniques.