Increase Search Efficiency Research Articles

Fault diagnosis method based on extension case-based reasoning

Extension case-based reasoning is a kind of knowledge reasoning method which is the combination of the extenics and case-based reasoning. Firstly,the representation of extension case using compound element was introduced,and the similarity computing method of fault symptom with interval value was given.Secondly,the reasoning process of extension case was analyzed; in the retrieve of extension cases,rough set method was adopted to confirm weight of fault symptom,and search strategy guided by rules was proposed; in the reuse of extension cases,conductive transformation was adopted to fulfill the reuse of extension case according to different search results; in the revise of extension cases,increasing and decreasing transform were adopted to add or delete fault symptoms of reused extension case and change their weight; in the retain of extension cases,three retain modes of revised extension case were discussed,including adding,deleting and replacing. Thirdly,the fault diagnosis process of this method was explained by an application case,and the time complexity of extension case retrieve was analyzed. By the comparison of the proposed search strategy and global search strategy,it is known that using the proposed search strategy can increase search efficiency and improve diagnosis speed.

Parametric modeling of visual search efficiency in real scenes.

How should the efficiency of searching for real objects in real scenes be measured? Traditionally, when searching for artificial targets, e.g., letters or rectangles, among distractors, efficiency is measured by a reaction time (RT) × Set Size function. However, it is not clear whether the set size of real scenes is as effective a parameter for measuring search efficiency as the set size of artificial scenes. The present study investigated search efficiency in real scenes based on a combination of low-level features, e.g., visible size and target-flanker separation factors, and high-level features, e.g., category effect and target template. Visible size refers to the pixel number of visible parts of an object in a scene, whereas separation is defined as the sum of the flank distances from a target to the nearest distractors. During the experiment, observers searched for targets in various urban scenes, using pictures as the target templates. The results indicated that the effect of the set size in real scenes decreased according to the variances of other factors, e.g., visible size and separation. Increasing visible size and separation factors increased search efficiency. Based on these results, an RT × Visible Size × Separation function was proposed. These results suggest that the proposed function is a practicable predictor of search efficiency in real scenes.

Sum-of-Processing-Times-Based Two-Agent Single-Machine Scheduling with Aging Effects and Tardiness

We consider a two-agent single-machine scheduling problem that minimizes the total weighted tardiness of one agent under the restriction that the second agent is prohibited from having tardy jobs. The actual processing times of all jobs are affected by a sum-of-processing-times-based aging effect. After showing the NP-hardness of the problem, we design a branch-and-bound (B&B) algorithm to find an optimal solution by developing dominance properties and a lower bound for the total weighted tardiness to increase search efficiency. Because B&B takes a long time to find an optimal solution, we propose a genetic algorithm as an efficient, near optimal solution approach. Four methods for generating initial populations are considered, and edge recombination crossover is adopted as a genetic operator. Through numerical experiments, we verify the outstanding performance of the proposed genetic algorithm.

Action Planning Mediates Guidance of Visual Attention from Working Memory.

Visual search is impaired when a salient task-irrelevant stimulus is presented together with the target. Recent research has shown that this attentional capture effect is enhanced when the salient stimulus matches working memory (WM) content, arguing in favor of attention guidance from WM. Visual attention was also shown to be closely coupled with action planning. Preparing a movement renders action-relevant perceptual dimensions more salient and thus increases search efficiency for stimuli sharing that dimension. The present study aimed at revealing common underlying mechanisms for selective attention, WM, and action planning. Participants both prepared a specific movement (grasping or pointing) and memorized a color hue. Before the movement was executed towards an object of the memorized color, a visual search task (additional singleton) was performed. Results showed that distraction from target was more pronounced when the additional singleton had a memorized color. This WM-guided attention deployment was more pronounced when participants prepared a grasping movement. We argue that preparing a grasping movement mediates attention guidance from WM content by enhancing representations of memory content that matches the distractor shape (i.e., circles), thus encouraging attentional capture by circle distractors of the memorized color. We conclude that templates for visual search, action planning, and WM compete for resources and thus cause interferences.

Losing the predator—prey space race leads to extirpation of woodland caribou from Pukaskwa National Park

Persistence for woodland caribou (Rangifer tarandus) in Pukaskwa National Park (PNP, Ontario, Canada; 1878 km2) was tied to females finding safe calving areas on offshore islands with a water barrier created by Lake Superior. During 1975–1988, PNP caribou fluctuated around a carrying capacity of 24, but after 1988 the population declined, falling to 5 individuals by 2009. Caribou are now likely extirpated from PNP, even in the absence of any local or increased anthropogenic disturbance since the protected area was created in 1978. As moose (Alces alces) in the region declined concurrently, their relative density remained higher along the Lake Superior coastal strip than further inland, the reverse of the situation during 1975–1988; moose especially held to the coast during heavy snow years. Wolves (Canis lupus) accordingly shifted more of their hunting effort to the coast, likely encountering both moose and caribou with increasing search efficiency. These behaviours are described as a predator—prey “space race” that wolves eventually won.

Modified particle swarm optimization-based multilevel thresholding for image segmentation

Since the conventional multilevel thresholding approaches exhaustively search the optimal thresholds to optimize objective functions, they are computational expensive. In this paper, the modified particle swarm optimization (MPSO) algorithm is proposed to overcome this drawback. The MPSO employs two new strategies to improve the performance of original particle swarm optimization (PSO), which are named adaptive inertia (AI) and adaptive population (AP), respectively. With the help of AI strategy, inertia weight is variable with the searching state, which helps MPSO to increase search efficiency and convergence speed. Moreover, with the help of AP strategy, the population size of MPSO is also variable with the searching state, which mainly helps the algorithm to jump out of local optima. Here, the searching state is estimated as exploration or exploitation simply according to whether the gBest has been updated in $$k$$ k consecutive generations or not, where the gBest stands for the position with the best fitness found so far among all the particles in the swarm. The MPSO has been evaluated on 12 unimodal and multimodal Benchmark functions, and the effects of AI and AP strategies are studied. The results show that MPSO improves the performance of the PSO paradigm. The MPSO is also used to find the optimal thresholds by maximizing the Otsu's objective function, and its performance has been validated on 16 standard test images. The experimental results of 30 independent runs illustrate the better solution quality of MPSO when compared with the global particle swarm optimization and standard genetic algorithm.

Information retrieval for ecological syntheses.

Research syntheses are increasingly being conducted within the fields of ecology and environmental management. Information retrieval is crucial in any synthesis in identifying data for inclusion whilst potentially reducing biases in the dataset gathered, yet the nature of ecological information provides several challenges when compared with medicine that should be considered when planning and undertaking searches. We present ten recommendations for anyone considering undertaking information retrieval for ecological research syntheses that highlight the main differences with medicine and, if adopted, may help reduce biases in the dataset retrieved, increase search efficiency and improve reporting standards. They are as follows: (1) plan for information retrieval at an early stage, (2) identify and use sources of help, (3) clearly define the question to be addressed, (4) ensure that provisions for managing, recording and reporting the search are in place, (5) select an appropriate search type, (6) identify sources to be used, (7) identify limitations of the sources, (8) ensure that the search vocabulary is appropriate, (9) identify limits and filters that can help direct the search, and (10) test the strategy to ensure that it is realistic and manageable. These recommendations may be of value for other disciplines where search infrastructures are not yet sufficiently well developed.

Parameter fitting of PEMFC models based on adaptive differential evolution

Optimal modeling of proton exchange membrane fuel cell (PEMFC) is extremely important for the understanding and interpretation of the overall cell behavior. Intelligent optimization technique is an effective strategy to optimally model PEMFCs because the determination of model parameters is a complex optimization problem. In this paper, an adaptive differential evolution algorithm (ADE) is proposed to accurately fit parameters of PEMFC models. The main contribution is the proposal of ADE for the parameter fitting of PEMFC models. To avoid premature convergence and increase search efficiency, ADE uses two adaptation schemes to dynamically adjust its variation operators and control parameters. The effectiveness and robustness of ADE is verified by applying it to solve benchmark functions and to extract model parameters of four PEMFCs using both noise-free or noised data. Experimental results demonstrate that the proposed method produces more accurate and robust models than several other algorithms and the polarization curves obtained by ADE match well with the experimental data in all cases. This work indicates that ADE is an effective alternative to solve the PEMFC optimal modeling problem.

A population search filter for hard-to-reach populations increased search efficiency for a systematic review

ObjectivesThis article discusses how hard-to-reach population groups were conceptualized into a search filter. The objectives of this article were to (1) discuss how the authors designed a multistranded population search filter and (2) retrospectively test the effectiveness of the search filter in capturing all relevant populations (eg, homeless people, immigrants, substance misusers) in a public health systematic review. Study Design and SettingSystematic and retrospective analysis via a case study. Retrospective analysis of the search filter was conducted by comparing the MEDLINE search results retrieved without using the search filter against those retrieved with the search filter. A total of 5,465 additional results from the unfiltered search were screened to the same criteria as the filtered search. ResultsNo additional populations were identified in the unfiltered sample. The search filter reduced the volume of MEDLINE hits to screen by 64%, with no impact on inclusion of populations. ConclusionsThe results demonstrate the effectiveness of the filter in capturing all relevant UK populations for the review. This suggests that well-planned search filters can be written for reviews that analyze imprecisely defined population groups. This filter could be used in topic areas of associated comorbidities, for rapid clinical searches, or for investigating hard-to-reach populations.

Higher set sizes in pop-out search displays do not eliminate priming or enhance target selection

Previous research shows that salient stimuli do not pop out solely in virtue of their feature contrast. Rather, visual selection of a pop-out target is strongly modulated by feature priming: Repeating the target feature (e.g., red) across trials primes attention shifts to the target but delays target selection when the target feature changes (e.g., from red to green). However, it has been argued that priming modulated target selection only because the stimuli were too sparsely packed, suggesting that pop-out is still mostly determined by the target’s saliency (i.e., local feature contrast). Here, we tested these different views by measuring the observer’s eye movements in search for a colour target (Exp. 1) or size target (Exp. 2), when the target was similar versus dissimilar to the target, and when the displays contained 6 or 12 search items. The results showed that making the target less similar to the nontargets indeed eliminated priming effects in search for colour, but not in search for size. Moreover, increasing the set size neither increased search efficiency nor eliminated feature priming effects. Taken together, the results indicated that priming can still modulate target selection even in search for salient targets.

A personalized e-course composition based on a genetic algorithm with forcing legality in an adaptive learning system

This paper proposes a personalized e-course composition based on a genetic algorithm with forcing legality (called GA⁎) in adaptive learning systems, which efficiently and accurately finds appropriate e-learning materials in the database for individual learners. The forcing legality operation not only reduces the search space size and increases search efficiency but also is more explicit in finding the best e-course composition in a legal solution space. In serial experiments, the forcing legality operation is applied in Chu et al.'s the particle swarm optimization (called PSO⁎) and Dheeban et al.'s the improved particle swarm optimization (called RPSO⁎) to show the forcing legality can speed up the computational time and reduce the computational complexity of algorithm. Furthermore, GA⁎ regardless of the number of students or the number of materials in the database, to compose a personalized e-course within a limited time is much more efficient and accurate than PSO⁎ and RPSO⁎. For the experiment increasing the number of students to 1200, the average improvement ratios of errors (learning concept error, materials difficulty error, learning time error), fitness value, stability, and execution time are above 96%, 79%, 90%, and 10%, respectively. For the experiment increasing the number of materials to 500 and the execution time set to the shortest execution time of RPSO⁎, the average improvement ratios of errors (learning concept error, materials difficulty error, learning time error), fitness value, and stability are above 97%, 51%, and 80%, respectively. Therefore, GA⁎ is able to enhance the quality of personalized e-course compositions in adaptive learning environments.

A popularity-based query scheme in P2P networks using adaptive gossip sampling

As P2P networks, such as many forms of social networking have been rapidly growing, numerous efforts have been made to improve the efficiency of the search operation especially in terms of response time and hit ratio. To this end, popularity-based schemes have recently attracted attention aimed at increasing search efficiency using content popularity ranking; however, these methods suffer from high cost and overhead, or inappropriate level of accuracy in specifying the popularity. In this paper, we propose an adaptive sampling scheme to make a tradeoff between cost and accuracy. This scheme relies on exchanging File Index Table (FIT) between peers in a local neighborhood using a Gossip Exchange Method (GEM). The proposed Hybrid Adaptive Search According to Gossip Exchange Method (HAS-A-GEM) is based on smart unstructured peer to peer overlays. We apply a hybrid overlay that efficiently combines topology-aware and interest-based links instead of random or DHT invoked connections. An analytical model as well as a simulation framework is developed to illustrate the performance of this scheme. The effectiveness of the proposed scheme is demonstrated under various conditions. Simulation results reveal that HAS-A-GEM performs well for large-scale networks, exploiting local content popularity when each local area contains enough number of peers.

영상 색인용 VP-tree의 검색 범위 압축법의 개선에 관한 연구

멀티미디어 데이터베이스에서는 검색 효율을 높이기 위해 다차원 공간에 기초한 색인 방법이 사용되고 있다. 그러나 이 방법은 거리 계산의 척도로 유클리드 거리를 이용하여야 한다는 전제가 있어 범용성이 떨어진다. 한편, 거리 공리의 성립을 전제로 하는 거리 공간에 기반한 색인 방법은 유클리드 거리 이외의 거리 척도를 이용할 수 있기 때문에 범용성이 높다. 본 논문에서는 거리 공간을 색인화하는 방법 중 하나인 VP-tree의 방법을 개선하고자 한다. VP-tree는 검색 시에 루트 노드로부터 검색 범위에 적합한 노드를 따라 최종에 이르는 리프 노드에 링크되어 있는 오브젝트와의 거리를 계산하고, 검색 범위에 적합한가를 검사한다. 그러나 리프 노드에서 거리 계산 횟수가 증가하면 검색 속도가 떨어지기 때문에 리프 노드에서 삼각 부등식을 이용한 범위 압축 방법에 주목하고 그 개량 방법으로서 질의 오브젝트에 대한 최근접점을 삼각 부등식의 기준점으로 이용하는 방법을 제안한다. 이 개량 방법에 의해 검색 범위를 크게 좁힐 수 있으며, 또한 거리 계산의 횟수도 꽤 줄일 수 있다. 실제로 10,000 건의 영상 데이터를 이용하여 시스템의 성능 평가를 진행해 본 결과 기존 방법에 비해 유사 영상의 검색 시간을 5%~12%까지 절감할 수 있었다. In multimedia database, a multidimensional space-based indexing has been used to increase search efficiency. However, this method is inefficient in terms of ubiquity because it uses Euclidean distance as a scale of distance calculation. On the contrary, a metric space-based indexing method, in which metric axiom is prerequisite is widely available because a metric scale other than Euclidean distance could be used. This paper is attempted to propose a way of improving VP-tree, one of the metric space indexing methods. The VP-tree calculates the distance with an object which is ultimately linked to the a leaf node depending on the node fit for the search range from a root node and examines if it is appropriate with the search range. Because search speed decreases as the number of distance calculations at the leaf node increases, however, this paper has proposed a method which uses the latest interface on query object as the base point of trigonometric inequality for improvement after focusing on the trigonometric inequality-based range compression method in a leaf node. This improvement method would be able to narrow the search range and reduce the number of distance calculations. According to a system performance test using 10,000 video data, the new method reduced search time for similar videos by 5-12%, compared to a conventional method.

Finding orthogonal latin squares using finite model searching tools

An important class of problems in combinatorics is to find orthogonal latin squares with certain properties. Computer search is a promising approach for solving such problems. But generally its worst-case complexity is high. This paper describes how to use a general-purpose model searching program to find orthogonal latin squares. New techniques for problem representation and symmetry breaking are proposed to increase search efficiency.

Host song selection by an acoustically orienting parasitoid fly exploiting a multispecies assemblage of cricket hosts

Specialization by predators, parasites and parasitoids is often viewed as adaptive because specialization will usually increase search efficiency for suitable hosts (prey). Although many studies have documented interspecific variation in the degree of host specialization, few studies have examined intraspecific variation in specialization among different populations exploiting different numbers of hosts, and even fewer have specifically examined variation in behavioural specialization. Here we examine the acoustic preferences of a parasitoid fly, Ormia ochracea, which attacks several Gryllus species of field crickets by using the male cricket song to locate hosts. All previous studies with this system have examined fly song preferences in populations of flies that primarily exploit a single host species of cricket. Here we tested a population of flies that naturally parasitizes multiple cricket species. We found significant interspecific host song preferences; however, as predicted, the strength of preference was reduced compared to single-host populations of flies. We also examined fly song selection for intraspecific variants of two of the cricket host species; flies showed significant preference for intraspecific song variants of one of the two cricket species. Our results demonstrate that the degree of behavioural specialization, in a very highly specialized fly, nevertheless shows interpopulation variation consistent with the numbers of host species utilized.

Research on Two-layer Location-Routing Problem and Optimization Algorithm

Location-routing problem (LRP) is a combinational optimization problem in a logistics system. Most heuristic methods employed for LRP divide the problem into location assignment and vehicle routing with a two-phase method, but this method often does not lead to a satisfactory result for the information can not be compressed from one phase to the other efficiently. In this paper we are concerned with a particular type of facility location problem in which there exists two echelons of facilities, and use a hybrid genetic algorithm to solve the problem regarding its solution as a whole, and thus easily obtain the optimal solution. Genetic algorithm uses a three-level chromosome coding method composed of binary code and integer code. Improving crossover and mutation with tabu search algorithm to increase search efficiency. The numerical results indicate that the method is feasible for practical applications.

Improvement of PubMed Literature Searching using Biomedical Ontology

AbstractPubMed articles are annotated using the Medical Subject Headings (MeSH) to increase search efficiency. However, MeSH contains limited information on many biomedical domains (e.g., vaccine). Biomedical ontologies may be used to improve PubMed searching capability. This study demonstrates that Vaccine Ontology (VO) can be used to significantly improve PubMed searching efficacy in the vaccine domain. The recall and precision of the ontology-based literature mining approach are analyzed and discussed.

Improvement of PubMed Literature Searching using Biomedical Ontology

PubMed articles are annotated using the Medical Subject Headings (MeSH) to increase search efficiency. However, MeSH contains limited information on many biomedical domains (e.g., vaccine). Biomedical ontologies may be used to improve PubMed searching capability. This study demonstrates that Vaccine Ontology (VO) can be used to significantly improve PubMed searching efficacy in the vaccine domain. The recall and precision of the ontology-based literature mining approach are analyzed and discussed.

Improving Success Ratio of Object Search in Highly-Dynamic Mobile P2P Networks

Mobile Ad Hoc Networks (MANETs) are temporal and infrastructure-independent wireless networks that consist of mobile nodes. For instance, a MANET can be used as an emergent network for communication among people when a disaster occurred. Since there is no central server in the network, each node has to find out its desired information (objects) by itself. Constructing a mobile Peer-to-Peer (P2P) network over the MANET can support the object search. Some researchers proposed construction schemes of mobile P2P networks, such as Ekta and MADPastry. They integrated DHT-based application-layer routing and network-layer routing to increase search efficiency. Furthermore, MADPastry proposed a clustering method which groups the overlay nodes according to their physical distance. However, it has also been pointed out that the search efficiency deteriorates in highly dynamic environments where nodes quickly move around. In this paper, we focus on route disappearances in the network layer which cause the deterioration of search efficiency. We describe the detail of this problem and evaluate quantitatively it through simulation experiments. We extend MADPastry by introducing a method sharing objects among nodes in a cluster. Through simulation experiments, we show that the proposed method can achieve up to 2.5 times larger success rate of object search than MADPastry.

Functional brain organization for visual search in ASD

Although previous studies have shown that individuals with autism spectrum disorder (ASD) excel at visual search, underlying neural mechanisms remain unknown. This study investigated the neurofunctional correlates of visual search in children with ASD and matched typically developing (TD) children, using an event-related functional magnetic resonance imaging design. We used a visual search paradigm, manipulating search difficulty by varying set size (6, 12, or 24 items), distractor composition (heterogeneous or homogeneous) and target presence to identify brain regions associated with efficient and inefficient search. While the ASD group did not evidence accelerated response time (RT) compared with the TD group, they did demonstrate increased search efficiency, as measured by RT by set size slopes. Activation patterns also showed differences between ASD group, which recruited a network including frontal, parietal, and occipital cortices, and the TD group, which showed less extensive activation mostly limited to occipito-temporal regions. Direct comparisons (for both homogeneous and heterogeneous search conditions) revealed greater activation in occipital and frontoparietal regions in ASD than in TD participants. These results suggest that search efficiency in ASD may be related to enhanced discrimination (reflected in occipital activation) and increased top-down modulation of visual attention (associated with frontoparietal activation).