nils-herrmann/clean_scirepeval huggingface dataset:
Fuzzy SVM Controller for Robotic Manipulator Based on GA and LS Algorithm

To improve the control precision of robotic manipulator, fuzzy support vector machines control method for robotic manipulator was presented based on genetic algorithm and least square algorithm. Fuzzy algorithm was used to decouple joints. Using support vector machines, fuzzy logical control of complete process and treatment of non-linear signal were realized. The controller parameters were optimized by hybrid learning algorithm. First, least square algorithm was used for off-line optimization to form support vector machines control system. Then, genetic algorithm was used for on-line optimization to get the optimal performance parameters of support vector machines and the optimal fuzzy proportional parameters. The simulation results of a two-link manipulator demonstrated that the control method designed gets tracking effect with high precision.
Stability analysis and control of adhesive forces of bio-inspired spines

In this paper, a mechanical model of a compliant spiny toe inspired by the features of Serica orientalis Motschulsky's tarsal system is proposed. The contact forces of the compliant spine are obtained by considering the contact asperities as random curves, and the stability of the spine adhesion system is analyzed. A single spiny toe is fabricated via fast prototyping. The adhesive forces and displacements of the spine mechanism are measured with a homebuilt apparatus using displacement-control method under different compressive deformations. The testing results verify the theoretical model analysis. A force control system of the single compliant spine mechanism structure is also prosed. The system consists of four elastic spring damping subsystems in order to control the interferences and achieve stability in attachment state. The state-feedback control strategy is applied to optimize the control parameters for the original system. The simulation results show that the steady state of the spine system can be improved with the optimized feedback algorithm.
Home Video Visual Quality Assessment With Spatiotemporal Factors

Compared with the video programs taken by professionals, home videos are always with low quality content resulted from non-professional capture skills. In this paper, we present a novel spatiotemporal quality assessment scheme in terms of low-level content features for home videos. In contrast to existing frame-level-based quality assessment approaches, a type of temporal segment of video, subshot, is selected as the basic unit for quality assessment. A set of spatiotemporal visual artifacts, regarded as the key factors affecting the overall perceived quality (i.e., unstableness and jerkiness as temporal factors; infidelity, blurring, brightness, and orientation as spatial factors), are mined from each subshot based on particular characteristics of home videos. The relationship between the overall quality metric and these factors are exploited by three different methods, including user study-based, rule-based and learning-based. To validate the proposed scheme, we present a scalable quality-based home video summarization system from a novel perspective-achieving the best visual quality while simultaneously preserving the most informative content. A comparison user study between this system and the attention model-based video skimming approach demonstrated the effectiveness of the proposed quality assessment scheme
Learning to video search rerank via pseudo preference feedback

Conventional approaches to video search reranking only care whether search results are relevant or irrelevant to the given query, while the ranking order of these results indicating the level of relevance or typicality are usually neglected. This paper presents a novel learning-based approach to video search reranking by investigating the ranking order information. The proposed approach, called pseudo preference feedback (PPF), automatically discovers an optimal set of pseudo preference pairs from the initial ranked list and learns a reranking model by ranking support vector machines (ranking SVM) based on the selected pairs. We have proved that PPF can be used for any reranking purpose such as video search and concept detection. We conducted comprehensive experiments for both automatic search and concept detection tasks over TRECVID 2006-2007 benchmark, and showed that PPF could gain significant improvements over the baselines.
EMS: Energy Minimization Based Video Scene Segmentation

This paper proposes a novel energy minimization based approach to video scene segmentation. In video content analysis, scene is defined as a set of adjacent shots related to a particular setting or a continuous action in one place. This indicates that not only the global distribution of time and content, but also the local temporal continuity should be taken into account for scene segmentation. Motivated from this fact, we formulate the segmentation procedure as a unified energy minimization framework, in which the global and local constraint is represented by content and context energy, respectively. This energy minimization problem is optimized by two steps in an iterative fashion: first find an initial estimation of scene label for content energy by a generative model; and then iterated conditional modes (ICM) is used for context energy to find the global optimization. Furthermore, a boundary voting procedure is devised to decide the optimal scene boundaries. We apply EMS on an extensive set of home videos and feature movies, and report superior performance compared with several existing key approaches to scene segmentation.
Dynamic analysis and experiment of a 3mm swimming microrobot

A swimming microrobot driven by FMP (ferromagnetic polymer) actuators under magnetic field is developed. The size of the swimming microrobot is 3 mm /spl times/ 2 mm /spl times/ 0.4 mm. The robot can swim when the magnetic intensity is higher than 8 mT and the frequency is about 10 Hz, with the speed of 0.3/spl sim/1 mm/s. Dynamic analysis of the microrobot is performed, and the results fit the experiment data reasonably. The experimental results indicate that the driving method is effective and the swimming speed can be controlled by modifying the intensity of magnetic field.
Electric Field Interference Simulation of Ionization Electrodes on Ion Filter Electrodes in FAIMS

Using the method of ANSYS finite element analysis, we calculate the electric field interference between the ionization electrodes and ion filter electrodes. The effect of varying the electrodes width and distance on the electric interference field intensity has been investigated. In this paper, a method for selecting the electrodes width and distance is proposed by synthetically considering ions recombination time, electric field interference and detection precision, and shield electrodes configuration is designed for decreasing the electric field interference.
Design of a biomimetic gecko adhesion array for microrobots

In this work the design of a biomimetic gecko adhesion array for future microrobots is described. First, models for understanding the array design are issued, and based on moment equilibrium the theory of actuation mechanism of the array is discussed in detail. Then the design parameters such as length, diameter, separation distance, and density of array are determined for non-matting. Finally, the fabrication technique is proposed
Fabrication and Adhesive Force Analysis of Biomimetic Gecko Foot-Hair Array

Gecko has elaborate adhesive structure which allows it to move along vertical walls and ceilings quickly. There is strong evidence that the adhesion ability is due to van der Waals forces. Synthetic dry adhesive hairs inspired by gecko foot-hairs have been microfabricated by several groups. To obtain re-attachable dry adhesive hairs with mass production potential, ICP technique is employed to fabricate silicon molds, and flexible hair arrays in large area is fabricated by peel-off method of polydimethylsiloxane (PDMS). Experimental results demonstrate that the adhesive forces are 2.06N/cm2 for 2 mum hairs, 1.09N/cm2 for 5 mum hairs and 0.68N/cm2 for bald polymer
PID Controller Based Adaptive GA and Neural Networks

A self-tuning PID controller based on adaptive genetic algorithm (AGA) and neural networks is presented. AGA optimizes not only the initial weights of the BP neural networks (BPNN) which optimizes parameters of PID, but also the optimum values of the following radial basis function neural networks (RBFNN) parameters: centers, variance and weights of the output layer. RBFNN identifies the Jacobian information of the controlled plant. The influence on the control performance is solved which results from the initial parameters of BPNN and RBFNN. The result of the simulation shows that the method can improve the robust performance of the control system
Image search results refinement via outlier detection using deep contexts

Visual reranking has become a widely-accepted method to improve traditional text-based image search results. The main principle is to exploit the visual aggregation property of relevant images among top results so as to boost ranking scores of relevant images, by explicitly or implicitly detecting the confident relevant images, and propagating ranking scores among visually similar images. However, such a visual aggregation property does not always hold, and thus these schemes may fail. In this paper, we instead propose to filter out the most probable irrelevant images using deep contexts, which is the extra information that is not limited in the current search results. The deep contexts for each image consist of sets of images that are returned by searches using the queries formed by the textual context of this image. We compare the popularity of this image in the current search results and the deep contexts to check the irrelevance score. Then the irrelevance scores are propagated to the images whose useful textual context is missed. We formulate the two schemes together to reach a Markov random field, which is effectively solved by graph cuts. The key is that our scheme does not rely on the assumption that relevant images are visually aggregated among top results and is based on the observation that an outlier under the current query is likely to be more popular under some other query. After that, we perform graph reranking over filtered results to reorder them. Experimental results on the INRIA dataset show that our proposed method achieves significant improvements over previous approaches.
Analysis on the configuration and simulation of a new robot composed with hybrid joints

Traditionally, discrete robot's performance in flexibility and obstacle avoidance is not very outstanding, while continuum robot cannot load much and its motion space is limited. In this paper, a new robot with hybrid joints was designed based on the two kinds of robots mentioned above. First, the connection mode between discrete and continuum joints was analyzed. It was found that when the axis of the discrete joint was vertical to the tangential direction of the continuum one, the robot's motion space could be extended. Second, the driving modes of continuum and discrete joints were introduced, and it was also introduced in detail how the continuum joint was controlled by the external ropes. Then the analysis of the dynamics model and kinematics simulation of the continuum joint was carried out, and it was found: There is effect on the control by gravity, and in the same condition, the tension of ropes is relatively larger with gravity. While the continuum joint can move smoothly without gravity and the effect by gravity does not change over the increase of the degree of elastic rod.
Query Difficulty Estimation for Image Search With Query Reconstruction Error

Current image search engines suffer from a radical variance in retrieval performance over different queries. It is therefore desirable to identify those “difficult” queries in order to handle them properly. Query difficulty estimation is an attempt to predict the performance of the search results returned by an image search system. Most existing methods for query difficulty estimation focus on investigating statistical characteristics of the returned images only, while neglecting very important information , i.e., the query and its relationship with returned images. This relationship plays a crucial role in query difficulty estimation and should be explored further. In this paper we propose a novel query difficulty estimation method with query reconstruction error. This method is proposed based on the observation that, given the images returned for an unknown query, we can easily deduce what the query is from those images if the search results are high quality (i.e., lots of relevant images returned); otherwise, it is difficult to deduce the original query. Therefore, we propose to predict the query difficulty by measuring to what extent the original query can be recovered from the image search results. Specifically, we first reconstruct a visual query from the returned images to summarize their visual theme, and then use the reconstruction error, i.e., the distance between the original textual query and the reconstructed visual query, to estimate the query difficulty. We conduct extensive experiments on two real-world Web image datasets and demonstrate the effectiveness of the proposed method.
Dynamic event data aggregation in wireless sensor networks

Data aggregation is an efficient method to decrease the energy consumption in wireless sensor networks. The structure based data aggregation algorithm is not suitable for the application of dynamic events, because the structured sensor network requires higher communication overheads to capture the event when the data sources are changing dynamically. We propose an energy efficient data aggregation algorithm utilized for dynamic event in wireless sensor networks. In this algorithm, the sensor nodes utilize the advantage of wireless channel that the energy consumption for broadcasting is approximately equivalent to that for sending to just one neighbor. Data are aggregated among nodes by one time broadcasting. Geography based routing method is used in data aggregation and transmit the data to the remote sink node. Simulation results show that the proposed data aggregation algorithm can decrease the maximum and mean energy consumption of sensor nodes highly in dynamic events.
Retrieval-Based Face Annotation by Weak Label Regularized Local Coordinate Coding

Auto face annotation, which aims to detect human faces from a facial image and assign them proper human names, is a fundamental research problem and beneficial to many real-world applications. In this work, we address this problem by investigating a retrieval-based annotation scheme of mining massive web facial images that are freely available over the Internet. In particular, given a facial image, we first retrieve the top $(n)$ similar instances from a large-scale web facial image database using content-based image retrieval techniques, and then use their labels for auto annotation. Such a scheme has two major challenges: 1) how to retrieve the similar facial images that truly match the query, and 2) how to exploit the noisy labels of the top similar facial images, which may be incorrect or incomplete due to the nature of web images. In this paper, we propose an effective Weak Label Regularized Local Coordinate Coding (WLRLCC) technique, which exploits the principle of local coordinate coding by learning sparse features, and employs the idea of graph-based weak label regularization to enhance the weak labels of the similar facial images. An efficient optimization algorithm is proposed to solve the WLRLCC problem. Moreover, an effective sparse reconstruction scheme is developed to perform the face annotation task. We conduct extensive empirical studies on several web facial image databases to evaluate the proposed WLRLCC algorithm from different aspects. The experimental results validate its efficacy. We share the two constructed databases "WDB" (714,454 images of 6,025 people) and "ADB" (126,070 images of 1,200 people) with the public. To further improve the efficiency and scalability, we also propose an offline approximation scheme (AWLRLCC) which generally maintains comparable results but significantly reduces the annotation time.
Video Collage: A Novel Presentation of Video Sequence

This paper presents an automatic procedure for constructing a compact synthesized collage from a video sequence. The synthesized image called video collage, is a kind of static video summary - to select most representative images from video, to extract salient regions of interest (ROI) from these images and resize them according to their saliency, and to seamlessly arrange ROI on a given canvas with the temporal structure of video content preserved. We formulate the generation of video collage as an energy minimization problem in which each of above desirability is represented by an energy term. Unlike most existing video presentation schemes, video collage is more compact and visually appealing. We have applied video collage on several home videos and report superior performance in a user study compared with key existing approaches to video presentation.
Research on self-parking path planning algorithms

Path planning is one of the fundamental aspects of intelligent vehicle navigation. Based on the kinematic model and the actual vehicle parking process, we proposed two improved geometriclly minimum radius and unequal radius path planning algorithms. The computer simulation results showed that two path planning algorithms had expanded significantly the scope of application, meanwhile the combination of two algorithms proposed above had optimized the whole process of parking. The algorithms proposed in this paper have a certain reference value on the study of self-parking.
Research on wide range localization for driverless vehicle in outdoor environment based on particle filter

The localization information is the premise of robot autonomous navigation. In this paper, intelligent vehicle is used as a platform for the present study. We perceive motion information and environment signpost information through wheel speed sensor and laser infrared radar SICK respectively, and realize the global localization of the vehicle with the fusion of the particle filter algorithm. The experimental result shows that when the environment signpost position is known, the global positioning error can be less than 1m.
A New Vehicle Intelligent Navigation Alarm System Based on SB5227 Microprocessor

The vehicle navigation alarm system has played an important role in avoiding traffic accidents, but the function singleness, structure faultiness and price highness are open questions for the existing products. A new multifunction intelligent navigation alarm system based on ultrasonic ranging microprocessor SB5227 is developed to caution the driver with speech and LED for over acceleration, hard braking, up/downhill, backing, high-speed, bad weather, etc. This system has the characteristics of better practicability, higher reliability and higher cost performance. It can be used to prevent traffic accident effectively when combined with GPS.
Personalized image recommendation for web search engine users

We introduce and investigate a novel problem of image recommendation for web search engine users. Modern web search engines have become a critical assistant for people's daily life. Through interacting with web search engines, users exhibit personalized information needs in various aspects. While this information is critical to improve user experience, it is mostly used only in the web search domain. In this paper, we propose to leverage web search engine users' behavior data to perform image recommendation. To this end, we have developed a two-stage method to label users' preferences for images through crowdsourcing techniques. The two-stage annotation consists of 1) inferring a user's general interests and 2) estimating if this user will be interested in an image. In addition, we implement a baseline algorithm to demonstrate the promise of the proposed cross-domain recommendation framework.
Multigraph-Based Query-Independent Learning for Video Search

Most of the existing learning-based methods for video search take query examples as ¿positive¿ and build a model for each query. These methods, referred to as query-dependent, only achieve limited success as users are mostly reluctant to provide enough query examples. To address this problem, we propose a novel query-independent learning approach based on multigraph to video search, which learns the relevance information existing in the query-shot pairs. The proposed approach, named MG-QIL, is more general and suitable for a real-world video search system as the learned relevance is independent of any queries. Specifically, MG-QIL constructs multiple graphs, including a main-graph covering all the pairs and a set of subgraphs covering the pairs within the same query. The pairs in the main-graph are connected in terms of relational similarity, while the pairs in the subgraphs for the same query are connected in terms of attributional similarity. The relevance labels are then propagated in the multiple graphs until convergence. We conducted extensive experiments on automatic search tasks over the TRECVID 2005-2007 benchmark and the results show a superior performance to state-of-the-art approaches to video search. Furthermore, when applied to video search reranking, MG-QIL can also achieve significant and consistent improvement over a text search baseline.
Personalized Travel Sequence Recommendation on Multi-Source Big Social Media

Big data increasingly benefit both research and industrial area such as health care, finance service and commercial recommendation. This paper presents a personalized travel sequence recommendation from both travelogues and community contributed photos and the heterogeneous metadata (e.g., tags, geo-location, and date taken) associated with these photos. Unlike most existing travel recommendation approaches, our approach is not only personalized to user's travel interest but also able to recommend a travel sequence rather than individual Points of Interest (POIs). Topical package space including representative tags, the distributions of cost, visiting time and visiting season of each topic, is mined to bridge the vocabulary gap between user travel preference and travel routes. We take advantage of the complementary of two kinds of social media: travelogue and community contributed photos. We map both user's and routes' textual descriptions to the topical package space to get user topical package model and route topical package model (i.e., topical interest, cost, time and season). To recommend personalized POI sequence, first, famous routes are ranked according to the similarity between user package and route package. Then top ranked routes are further optimized by social similar users' travel records. Representative images with viewpoint and seasonal diversity of POIs are shown to offer a more comprehensive impression. We evaluate our recommendation system on a collection of 7 million Flickr images uploaded by 7,387 users and 24,008 travelogues covering 864 travel POIs in nine famous cities, and show its effectiveness. We also contribute a new dataset with more than 200 K photos with heterogeneous metadata in nine famous cities.