12895493
Improvement of HITS-based algorithms on web documents
In this paper, we present two ways to improve the precision of HITS-based algorithms on Web documents. First, by analyzing the limitations of current HITS-based algorithms, we propose a new weighted HITS-based method that assigns appropriate weights to in-links of root documents. Then, we combine content analysis with HITS-based algorithms and study the effects of four representative relevance scoring methods, VSM, Okapi, TLS, and CDR, using a set of broad topic queries. Our experimental results show that our weighted HITS-based method performs significantly better than Bharat's improved HITS algorithm. When we combine our weighted HITS-based method or Bharat's HITS algorithm with any of the four relevance scoring methods, the combined methods are only marginally better than our weighted HITS-based method. Between the four relevance-scoring methods, there is no significant quality difference when they are combined with a HITS-based algorithm.
17633305
The Impact of Cache Organization in Optimizing Microprocessor Power Consumption
In the recent years, power consumption has become increasingly an important design concern as silicon area and performance in modern computer systems design. Several factors have contributed to this trend. Perhaps the most visible have been the remarkable success and growth of Personal Digital Assistants (PDA’s), Cellular phones and pagers, etc .This work is an attempt to explore the impact of feature size shrinkage and cache configuration on optimizing power consumption in modern processors. The work studies two commercially known RISC microprocessors. The StrongARM and the Alpha-21064 microprocessors. The latter is the ancestor of the former and is therefore used here as the baseline in our analysis. Our analysis has illustrated quantitatively great power saving when technology is downsized and cache is well organized
18652922
An Area-Efficient Approach to Improving Register File Reliability against Transient Errors
This paper studies approaches to exploiting the space both within or across registers efficiently for improving the register file reliability against transient errors. The idea of our approach is based on the fact that a large number of register values are narrow (i.e., less than or equal to 16 bits for a 32-bit architecture); therefore, the upper 16 bits of the registers can be used to replicate the short operands for enhancing register integrity. This paper also adapts a prior register replication approach by selectively copying register values (i.e., long operands only) to the unused physical registers for enhancing reliability without incurring significant hardware cost. Our experiments indicate that on average, 993% register reads (regardless of short or long operands) can find their replicas available, implying significant improvement of register file integrity against transient errors.
17966983
Flexible Workflow Driven Job Shop Manufacturing Execution and Automation Based on Multi Agent System
Manufacturing automation with adoptability to the changing environment is a competitive advantage for shop floors. This paper presents a flexible workflow enacted job shop manufacturing execution and automation system. The manufacturing process sequence of each part is modeled as an abstract workflow and each manufacturing job resulting in a batch of the part items is recognized as corresponding workflow instance. The production schedule is generated to establish executable workflow instances to be enacted by the workflow engine and executed by the resources in the shop floor. Agent technology is applied to construct the system to take the advantage of distributed system architecture, reactivity, adaptability and cooperation. The proposed system can help to achieve production efficiency without huge investment on industrial automation system. The system architecture and implementation methods are discussed with demonstration and the performance of manufacturing shop floor is enhanced after the application.
9354212
High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network
Job-shop scheduling is an important task for manufacturing industries. We are interested in the particular task of scheduling payload processing for NASA's space shuttle program. This paper summarizes our previous work on formulating this task for solution by the reinforcement learning algorithm T D(). A shortcoming of this previous work was its reliance on hand-engineered input features. This paper shows how to extend the time-delay neural network (TDNN) architecture to apply it to irregular-length schedules. Experimental tests show that this TDNN-TD() network can match the performance of our previous hand-engineered system. The tests also show that both neural network approaches signiicantly out-perform the best previous (non-learning) solution to this problem in terms of the quality of the resulting schedules and the number of search steps required to construct them.
28591822
Multicast Routing with Minimum Energy Cost and Minimum Delay in Wireless Sensor Networks
In this paper, we consider the multicast routing with minimum energy cost and minimum delay (MEMD) in wireless sensor networks under the access control scheme of Spatial Time Division Multiple Access (STDMA). We formulate and explore both MEMD multicast and ME multicast, and show that the latter is just the Maximum Leaf Spanning Tree (MLST) problem, which is NP-complete. A 2-approximation algorithm is proposed for the MLST problem through improving a known one. Based on this algorithm, an approximation algorithm is obtained for the MEMD multicast problem. To further improve the delay result, we provide another approximation algorithm for our main problem using a different approach. These algorithms are all near linear in the size of the network graph, and also are shown to have good performance by simulation results.
43120880
Principle component analysis and its variants for biometrics
Principle component analysis (PCA) has been widely used for analyzing the statistics of data. While applied to biometrics as a classification scheme, PCA faces certain challenges. We present a number of modifications to PCA in order to meet these challenges. Using face recognition as an example, we show how eigenflow, PCA applied to optimal flow, enables us to measure the difference between two images while allowing expression changes and registration error. We show how PCA can be updated to model time-varying statistics. We also show that PCA can be used to model the surface reflectance of human faces and reduce illumination variation that defeats most existing face recognition algorithms. Finally, we present distinguishing component analysis (DCA) and apply it to multimodal biometric authentication.
8019143
An average-case analysis of graph search
Many problems in real-world applications require searching graphs. Understanding the performance of search algorithms has been one of the eminent tasks of heuristic search research. Despite the importance of graph search algorithms, the research of analyzing their performance is limited, and most work on search algorithm analysis has been focused on tree search algorithms. One of the major obstacles to analyzing graph search is that no single graph is an appropriate representative of graph search problems. In this paper, we propose one possible approach to analyzing graph search: Analyzing the performance of graph search algorithms on a representative graph of a cluster of problems. We specifically consider job-sequencing problems in which a set of jobs must be sequenced on a machine such that a penalty function is minimized. We analyze the performance of A* graph search algorithm on an abstract model that closely represents job sequencing problems. It is an extension to a model widely used previously for analyzing tree search. One of the main results of our analysis is the existence of a gap of computational cost between two classes of job sequencing problems, one with exponential and the other with polynomial complexity. We provide experimental results showing that real job sequencing problems indeed have a huge difference on computational costs under different conditions.
16936043
Complete Anytime Beam Search
Beam search executes a state-space search, but may abandon nonpromising search avenues in order to reduce complexity. Although it has existed for more than two decades and has been applied to many real-world problems, beam search still suffers from the drawback of possible termination with no solution or a solution of unsatisfactory quality. In this paper, we first propose a domain-independent heuristic for node pruning, and a method to reduce the possibility that beam search will fail. We then develop a complete beam search algorithm. The new algorithm can not only find an optimal solution, but can also reach better solutions sooner than its underlying search method. We apply complete beam search to the maximum boolean satisfiability and the symmetric and asymmetric Traveling Salesman Problems. Our experimental results show that the domain-independent pruning heuristic is effective and the new algorithm significantly improves the performance of its underlying search algorithm.
205692410
Average-case analysis of best-first search in two representative directed acyclic graphs
Many problems that arise in the real world have search spaces that are graphs rather than trees. Understanding the properties of search algorithms and analyzing their performance have been major objectives of research in AI. But most published work on the analysis of search algorithms has been focused on tree search, and comparatively little has been reported on graph search. One of the major obstacles in analyzing average-case complexity of graph search is that no single graph can serve as a suitable representative of graph search problems. In this paper we propose one possible approach to analyzing graph search. We take two problem domains for which the search graphs are directed acyclic graphs of similar structure, and determine the average case performance of the best-first search algorithm A* on these graphs. The first domain relates to one-machine job sequencing problems in which a set of jobs must be sequenced on a machine in such a way that a penalty function is minimized. The second domain concerns the Traveling Salesman Problem. Our mathematical formulation extends a technique that has been used previously for analyzing tree search. We demonstrate the existence of a gap in computational cost between two classes of problem instances. One class has exponential complexity and the other has polynomial complexity. For the job sequencing domain we provide supporting experimental evidence showing that problems exhibit a huge difference in computational cost under different conditions. For the Traveling Salesman Problem, our theoretical results reflect on the long-standing debate on the expected complexity of branch-and-bound algorithms for solving the problem, indicating that the complexity can be polynomial or exponential, depending on the accuracy of the heuristic function used.
27074889
On pilot pattern design for PSAM-OFDM system
Pilot symbol assisted modulation (PSAM) of subcarriers is an attractive scheme for high data-rate OFDM transmission systems and has been widely used in broadcasting systems like DVB-T. In this paper, a new and simple pilot pattern for PSAM-OFDM system operated in the mobile communication environment is proposed and analyzed in terms of its bit error rate. Analytical and simulation results show that the proposed clustered pilot pattern for OFDM channel estimation gives a better performance than the conventional equally spaced pilot pattern in fading channel, yet without sacrificing the bandwidth efficiency.