Walking Strategy Research Articles

A New Method for Transformer Fault Prediction Based on Multifeature Enhancement and Refined Long Short-Term Memory

This research proposes a novel predictive model to improve the gas prediction accuracy in transformer oil and provide guarantees for accident prevention. First, this study constructs a cross-entropy loss function with variable thresholds and dynamic weights to reduce error transmission in the deep residual shrinkage network, enhancing the sensitivity of the normal and abnormal transformer states by the network. Second, the multiobjective particle swarm algorithm and random walk strategy are adopted to optimize the long short-term memory (LSTM) network to ensure the prediction model's objectivity. Finally, the improved subchannel threshold depth residual shrinkage network is integrated with the optimized LSTM network. The new model can identify potential abnormal conditions in advance and preproduce an approximate fault model using a feature gas vector (similar to image recognition). Experiments verify the effectiveness of the method. Compared with the existing prediction methods, the new method can avoid blind prediction and significantly improve the prediction accuracy and efficiency, which provides essential value for transformer fault prevention.

Node Embedding Research Over Temporal Graph

Compared with conventional graph data analysis methods, the graph embedding algorithm provides a new graph data analysis strategy. It aims to encode graph nodes into vectors to mine or analyze graph data more effectively using neural network related technologies. Some classic tasks have been improved significantly by graph embedding methods, such as node classification, link prediction, and traffic flow prediction. Although substantial breakthroughs have been made by former researchers in graph embedding, the nodes embedding problem over temporal graph has been seldom studied. In this study, we propose an adaptive temporal graph embedding (ATGED), attempting to encode temporal graph nodes into vectors by combining previous research and the information propagation characteristics. First, an adaptive cluster method is proposed by solving the situation that nodes active frequency varies types of graph. Then, a new node walk strategy is designed in order to store the time sequence between nodes, and also the walking list will be stored in a bidirectional multi-tree in the walking process to get complete walking lists fast. Last, based on the basic walking characteristics and graph topology, an important node sampling strategy is proposed to train the satisfied neural network as soon as possible. Sufficient experiments demonstrate that the proposed method surpasses existing embedding methods in terms of node clustering, reachability prediction, and node classification in temporal graphs.

Analytical Comparison of Resource Search Algorithms in Non-DHT Mobile Peer-to-Peer Networks

One of the key challenges in ad-hoc networks is the resource discovery problem. How efficiently & quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question? Broadcasting is a basic technique in the Mobile Ad-hoc Networks (MANETs), and it refers to sending a packet from one node to every other node within the transmission range. Flooding is a type of broadcast where the received packet is retransmitted once by every node. The naive flooding technique floods the network with query messages, while the random walk scheme operates by contacting subsets of each node’s neighbors at every step, thereby restricting the search space. Many earlier works have mainly focused on the simulation-based analysis of flooding technique, and its variants, in a wired network scenario. Although, there have been some empirical studies in peer-to-peer (P2P) networks, the analytical results are still lacking, especially in the context of mobile P2P networks. In this article, we mathematically model different widely used existing search techniques, and compare with the proposed improved random walk method, a simple lightweight approach suitable for the non-DHT architecture. We provide analytical expressions to measure the performance of the different flooding-based search techniques, and our proposed technique. We analytically derive 3 relevant key performance measures, i.e., the avg. number of steps needed to find a resource, the probability of locating a resource, and the avg. number of messages generated during the entire search process.

Timeless evolution of walking and pace strategy of women’s race walking

Background and Study Aim. The purpose of this research was to study the timeline evolution of walking, as well as the Pacing Strategy Profiles of high-level women in the 20 km of race walking. Material: The practical example of applying the theoretical basis was made during the Women’s Greek Championship (Megara 2016), in which 12 athletes aged 19 to 40 participated (28.50 ± 7.20).
 Material and Methods. The certified distance of the 20km route was divided into 10 sections of 2 km each. The same happened with the times (intermediate, final) corresponding to the individual sections (2 km) of the route. The athletes were divided into 4 groups: the first 3, those who finished 15% slower than the first, those who finished 15% - 30% slower, and those who finished more than 30% slower than the winner. Finally became comparison of the first 6 and last 6 athletes’ groups.
 Results. The individual pace strategies that describe the tactics of the athletes in this race have been calculated. It was found that the winners of the race used Even Pacing Strategy, maintaining a steady speed on most of the route. As the level of women athletes became lower, Variable Pacing Strategy was used, while the athletes who finished last did not seem to be able to maintain any particular pacing strategy.
 Conclusions. It is suggested that athletes should follow Even Pacing Strategy during the race in order to improve their performance.

Synthesis of Polymers with Regulated Repeating Structures by Utilizing Chain Walking Strategy

Abstract Pd-catalyzed reaction of olefins is sometimes accompanied by isomerization of the Pd center along the aliphatic chain (chain walking). The chain walking reaction enables synthesis of polyolefins with regulated structure, via C-C bond formation between vinyl or vinylene groups with the carbon atom at a remote position of the olefin monomers. The chain walking reaction also allows synthesis of poly(arylene alkenylene)s by three-component polycondensation of diiodoarenes, non-conjugated dienes and nucleophiles. Polycondensation of diols, diacids, and diamines with long alkylene chain, obtained by chain-walking alkoxycarbonylation, affords long-chain polyesters and polyamides.

A Literature Review on Walking Strategies of Legged Robots

Bu çalışmada, ayaklı hareket üzerine yapılan geçmiş dönem araştırmalara; özellikle, ayaklı robotlar literatürünün önemli kilometre taşlarına değinilmiştir. Geçmiş dönem araştırmalardan, öncelikle canlıların yürüyüşü ve bunu taklit edebilen makineler üzerine yapılan çalışmalardan bahsedilmiştir. Daha sonra, çalışmanın konusu olan ayaklı robotların tarihçesi ele alınmıştır. Ayaklı robotlar üzerine yapılan çalışmalarda, yürüyüş stratejisi olarak iki temel yaklaşım söz konusudur; statik yürüyüş ve dinamik yürüyüş. Bu sebepten dolayı ayaklı robotlar tarihçesi, Statik Yürüyen Ayaklı Robotlar ve Dinamik Yürüyen Ayaklı Robotlar olmak üzere iki temel başlık altında kronolojik sırayla anlatılmıştır. Statik Yürüyen Ayaklı Robotlar başlığında, engebeli yüzeylerde hareket ve engelleri aşma gibi arazi adaptasyonunun daha karmaşık problemleriyle başa çıkmak için çok bacaklı sistemlerin kontrol edilmesine yönelik tekniklerin geliştirilmesi üzerine yapılan araştırmalar; Dinamik Yürüyen Ayaklı Robotlar başlığında ise, koşma ve zıplama gibi oldukça gelişmiş hareket kabiliyetlerine sahip ayaklı sistemler geliştirebilmek için dinamik kontrol kavramlarının ve tekniklerinin ilerletilmesi üzerine yapılan araştırmalar sunulmuştur.

Client-side rogue access-point detection using a simple walking strategy and round-trip time analysis

Traditional rogue access-point (AP) detection mechanisms are employed in network administration to protect network infrastructure and organization; however, these mechanisms do not protect end users from connecting to a rogue-AP. In this paper, a rogue-AP detection technique on the mobile-user side is proposed. By using a simple method involving walking, the round-trip time (RTT) and the modulation and coding scheme values are obtained, and a more accurate transmission rate for particular RTT values is thereby calculated. Further, the cleansed data are classified using the k-means method and the cumulative distribution function for the detection process. The results demonstrate that a rogue-AP can be detected with an F-measure value of up to 0.9. In the future, the proposed algorithm can be implemented as an application installed on mobile devices so that nontechnical users can detect rogue-APs.

Smartphone Monitoring of Gait and Balance During Irregular Surface Walking and Obstacle Crossing.

As gait adaptation is vital for successful locomotion, the development of field-based tools to quantify gait in challenging real-world environments are crucial. The aims of this study were to assess the reliability and validity of a smartphone-based gait and balance assessment while walking on unobstructed and obstructed terrains using two phone placements. Furthermore, age-related differences in smartphone-derived gait strategies when navigating different walking conditions and environments were evaluated. By providing a method for evaluating gait in the simulated free-living environment, results of this study can elucidate the strategies young and older adults utilize to navigate obstructed and unobstructed walking paths. A total of 24 young and older adults ambulated indoors and outdoors under three conditions: level walking, irregular surface walking, and obstacle crossing. Android smartphones placed on the body and in a bag computed spatiotemporal gait (i.e., velocity, step time, step length, and cadence) and balance (i.e., center of mass (COM) displacement), with motion capture and video used to validate parameters in the laboratory and free-living environments, respectively. Reliability was evaluated using the intraclass correlation coefficient and validity was evaluated using Pearson's correlation and Bland-Altman analysis. A three-way ANOVA was used to assess outcome measures across group, condition, and environment. Results showed that smartphones were reliable and valid for measuring gait across all conditions, phone placements, and environments (ICC2,1: 0.606–0.965; Pearson's r: 0.72–1.00). Although body and bag placement demonstrated similar results for spatiotemporal parameters, accurate vertical COM displacement could only be obtained from the body placement. Older adults demonstrated a longer step time and lower cadence only during obstacle crossing, when compared to young adults. Furthermore, environmental differences in walking strategy were observed only during irregular surface walking. In particular, participants utilized a faster gait speed and a longer step length in the free-living environment, compared to the laboratory environment. In conclusion, smartphones demonstrate the potential for remote patient monitoring and home health care. Along with being easy-to-use, inexpensive, and portable, smartphones can accurately evaluate gait during both unobstructed and obstructed walking, indoors and outdoors.

More efficient stochastic local search for satisfiability

Uniform random satisfiability (URS) and hard random satisfiability (HRS) are two significant generalizations of random satisfiability (RS). Recently, great breakthroughs have been made on stochastic local search (SLS) algorithms for uniform RS, resulting in several state-of-the-art algorithms, e.g., Dimetheus, YalSAT, ProbSAT and Score2SAT. However, compared to the great progress of SLS on URS, the performance of SLS on HRS lags far behind. In this paper, we propose two global clause weighting schemes and a new hybrid scoring function called SA based on a linear combination of a property score and property age, and then apply a second-level-biased random walk strategy based on two clause weighting schemes and SA to develop a new SLS solver called BRSAP. To evaluate the performance of BRSAP, we conduct extensive experiments to compare BRSAP with state-of-the-art SLS solvers and complete solvers on HRS instances and URS instances from SAT Competition 2017 and SAT Competition 2018 as well as 4100 generated satisfiable large HRS and URS ones. The experiments illustrate that BRSAP obviously outperforms its competitors, indicating the effectiveness of BRSAP. We also analyze the effectiveness of the underlying ideas in BRSAP.

Identifying Similar Users Based on Their Check-in Data: A Graph Embedding Approach

In recent years, the amount of user check-in data has significantly increased on social network platforms. Such data is an ideal source for characterizing user behaviors and identifying similar users, contributing to many research areas (e.g. user-based collaborative filtering). However, existing trajectory-based user similarity analysis approaches do not distinguish the effects of geographical factors at a fine-grained level, and thus are not able to unleash the full power of semantic information that is hidden in the trajectory. In this paper, we have proposed an effective graph embedding approach to identify similar users based on their check-in data. Specifically, we firstly identify meaningful concepts of user check-in data, based on which we design two metagraphs for representing features of similar user behaviors. Then we characterize each user with a sequence of nodes that are derived through a metagraph-guided random walk strategy. Such sequences are embedded to generate meaningful user vectors for measuring user similarity and eventually identifying similar users. We have evaluated our proposal on three public datasets, the results of which show that our approach is 4% higher than the best existing approach in terms of F1-measure.

Modeling and Understanding Ethereum Transaction Records via a Complex Network Approach

As the largest public blockchain-based platform supporting smart contracts, Ethereum has accumulated a large number of user transaction records since its debut in 2014. Analysis of Ethereum transaction records, however, is still relatively unexplored till now. Modeling the transaction records as a static simple graph, existing methods are unable to accurately characterize the temporal and multiplex features of the edges. In this brief, we first model the Ethereum transaction records as a complex network by incorporating time and amount features of the transactions, and then design several flexible temporal walk strategies for random-walk based graph representation of this large-scale network. Experiments of temporal link prediction on real Ethereum data demonstrate that temporal information and multiplicity characteristic of edges are indispensable for accurate modeling and understanding of Ethereum transaction networks.

DNA walking strategy to identify unauthorized genetically modified bacteria in microbial fermentation products

Recently, unexpected contaminations of unauthorized genetically modified microorganisms (GMM) carrying antimicrobial resistance (AMR) genes were reported in microbial fermentation products commercialized on the food and feed chain. To guarantee the traceability and safety of the food and feed chain, whole-genome sequencing (WGS) has played a key role to prove GMM contaminations via the characterization of unnatural associations of sequences. However, WGS requires a prior microbial isolation of the GMM strain, which can be difficult to successfully achieve. Therefore, in order to avoid such bottleneck, a culture-independent approach was proposed in this study. First, the screening for the aadD gene, an AMR gene conferring a resistance to kanamycin, and for the pUB110 shuttle vector, carrying the aadD gene and commonly used to produce GMM, is performed. In case of a positive signal, DNA walking methods anchored on the two borders of the detected pUB110 shuttle vector are applied to characterize unknown flanking regions. Following to the sequencing of the generated amplicons, unnatural associations of sequences can be identified, allowing to demonstrate the presence of unauthorized GMM. The developed culture-independent strategy was successfully applied on commercialized microbial fermentation products, allowing to prove the presence of GMM contaminations in the food and feed chain.

Design and analysis of a novel octopod platform with a reconfigurable trunk

In order to enhance the terrain mobility of the close-chain leg mechanism and maximize its advantages, a novel dual degree-of-freedom (DoF) octopod platform with a reconfigurable trunk is proposed. The walking strategy of the close-chain multi-legged mechanism is adjusted through the spatial close-chain trunk mechanism. These two single-DoF close-chain mechanisms are designed and coupled to constitute whole close-chain mechanism as the walking platform. The locomotion modes of the trunk are discussed with the mobility analysis. In steering mode, the motion characteristic is analyzed under the combined action of steering linkage and elastic deformation. In climbing mode, the kinematic analysis and dimensional optimization are carried out, and the obstacle-climbing ability is evaluated through the probability model. To enhance climbing smoothness and efficiency, the assisted appendage is developed with the inner loop of Watt-Ⅰ leg linkage. A series of dynamic simulations is conducted and analyzed to test the platform performance, and a prototype is fabricated to testify the walking speed, steering property and obstacle-climbing capability. This study provides a new reference for close-chain legged platform development.

Flows of walking and running pedestrians in a corridor through exits of different widths

Human safety in crowded environments has been recognized as an important and rapidly growing research area due to its significant implications for public safety. In this study, a series of experiments were performed in a 10 m long corridor to investigate the walking and running pedestrian flows. A piecewise linear decreasing trend is found between the pedestrian density and the corresponding speed difference with the critical density of 1.65 m−2. In the relatively free phase (density < 1.65 m−2), the speed difference decreases significantly with the slope of 0.77. Whereas, the difference remains small with a slope of 0.15 in the constrained phase (density > 1.65 m−2). In the corridor (excluding the 2 m area around the exit), running pedestrians show longer distance headways than the walking ones under relatively low-density situation. Meanwhile, the running flow is more continuous with a lower probability of clogging at the exit in the free phase. In reality, pedestrian density should be an important factor for a decision maker when deciding to take the walking or running evacuation strategy. The data also can be used as validation benchmarks for models that intend to simulate pedestrians evacuating through a corridor in the running way.

Adversarial Path Sampling for Recommender Systems

Generative adversarial networks (GANs) have achieved a big success in collaborative filtering (CF). However, existing GAN-based methods in CF still suffer from the high-sparsity and cold-start problems; in addition, they also undergo the issues of excessive space complexity or inadequate training. In this article, we propose <i>path2rec </i> a novel adversarial path-based recommendation model to address these limitations of existing GAN-based methods in recommendation task by naturally incorporating auxiliary information (e.g., social networks and item attributes). It is composed of two modules, 1) pathGAN and 2) path2vec. In pathGAN, we consider both explicit and implicit friends, as well as item attributes by regarding them as the source of graph construction. Then, we propose a smart walk strategy to automatically generate an optimizing path, which can effectively learn the semantic distribution of users and items. In path2vec, to fully exploit context features of the generated path, we use the Continuous Bag of Words (CBOW) model to fine-tune nodes representations learned by pathGAN. Through extensive experiments on real-world datasets, we demonstrate the effectiveness of the proposed <i>path2rec</i> by applying it into top-<i>n</i> item recommendation, which reaches better performance than other counterparts.

BTWalk: Branching Tree Random Walk for Multi-Order Structured Network Embedding

Multi-order proximity is useful for effective network embedding. In contrast to many previous works that only consider order-level weights, this paper proposes to explore a more expressive node-level weighting mechanism to encode the diverse local structure, with a scalable and theoretically justified sampling strategy for its learning. Specifically, we start with a formal definition of multi-order proximity matrix which leads to our new multi-order objective based on Laplacian Eigenmaps and Skip-Gram. Then we instantiate the node-specific multi-order weights in the objective with the help of neighborhood size estimation, which indicates node-specific multi-order information. For objective learning, it is implicitly fulfilled with our proposed branching tree-like random walk strategy termed by BTWalk, which differs from the dominant chain-like walk in existing sampling techniques. BTWalk is designed by a synergetic combination of BFS (breadth-first search) and DFS (depth-first search), which is modulated according to the weights of the considered proximity orders. We theoretically analyze its cost-efficiency, and further propose the so-called Vec4Cross framework that incorporates joint node embedding and network alignment for two partially overlapped networks based on the seed matchings, whereby BTWalk is also adopted for embedding. Promising experimental results are obtained on real-world datasets across popular tasks.

Control strategy of stable walking for a hexapod wheel-legged robot

This paper provides a legged stable walking control strategy based on multi-sensor information feedback about BIT-NAZA-II, a large load parallel hexapod wheel-legged robot developing for the problem of vertical contact impact and horizontal sliding of heavy leg robot in complex terrain environments. The BIT-NAZA-II robot has six legs and six wheels, and the wheels are installed on the foot-end. The wheels of each foot-end for the legs of the robot are locked when walking with legs. In order to realize the smooth transition between swing phase and stance phase, the leg motion is divided into different stages for control by state machine switching controller based on event (SMSCE). In the Z-direction, in order to avoid the shaking of the body caused by the contact impact at the moment of contact between the foot-end and the ground during the walking of the robot, an active compliance controller (ACC) based on impedance control (IC) is applied to solve the problem of contact impact. Moreover, in the X-direction, the swing leg retraction (SLR) based on Bezier curve (BC) is introduced to generate the foot-end trajectory of the robot, which solves the slip problem of the heavy leg robot and improves the horizontal stability. Finally, the control strategy of stable walking is respectively verified by the simulations and experiments. The results show that the ACC based on IC can effectively reduce the contact impact between the foot-end and the ground in the Z-direction and improve the stability of body. Besides, the anti-sliding ability is realized after introducing SLR based on BC in the X-direction, and we also verify that stable walking control strategy is effective, which provides a reference value for the stable walking of heavy leg robot in complex terrain.

Three-dimensional analysis of the effect of human movement on indoor airflow patterns.

Human activity is known to leave significant effects on indoor airflow patterns. These patterns are carefully designed for many facilities such as cleanrooms, pharmaceutical settings, and healthcare environments, where human-induced wakes contribute to the transport of contaminants. Therefore, the knowledge about these wakes as it relates to indoor air quality is critical. As a result, a series of experiments were conducted in a controlled chamber to study the three-dimensional effects of true human walking on airflow. Experiments were designed to capture the effect of human walking under three different flow conditions, and for two different walking schemes. The results show that the effect of walking on the airflow is not negligible and can sustain up to 10seconds after the moving body has passed. Walking on a straight line creates significant change in the velocity normal to the walking path and vertical to the plane of walking movement. These changes were detectable till 1.0m away from the walking track. Also, the similarity between airflow patterns of walking once and twice illustrated a promising opportunity of predicting the flow patterns of random walk from a set of base cases.

Extending Voronoi-diagram based modeling of oil slick spreading to surface tension-viscous spreading regime

An earlier paper demonstrated a methodology for modeling the spreading process with a Gaussian random walk procedure, but was limited to the gravity-viscous spreading regime. Here we extend the methodology of representing spread and transport of oil slicks on calm sea surface by updated Voronoi diagrams to account for the surface tension-viscous spreading regime as well. We have utilized the analogy between diffusion and spreading processes by defining a step length for the particle-based random walk scheme. In this study, calculation of the diffusive length is improved by including the surface tension term in the numerical solution method. The results from the numerical simulation of the spreading oil slick agree very well with the analytical solutions. The solution is robust in that good agreement is achieved for a large range of model and numerical solution parameters. This modeling procedure remains valid only for passive, quiescent spreading. The inclusion of spreading due to important horizontal and vertical turbulent shear processes in the Voronoi diagram paradigm remains a challenge for future work.

DIFFERENCES IN GAIT PATTERNS BETWEEN PATIENTS WITH SWEDD AND NORMAL SUBJECTS

Whether gait assessment can distinguish patients with scans without evidence of dopaminergic deficit (SWEDD) from normal individuals remains unclear. In the present study, the gait patterns in SWEDD patients and normal individuals were investigated. 10 patients with SWEDD and 10 healthy normal subjects were recruited. A GaitRite linear walkway was used to quantitatively assess level walking. Eight spatiotemporal variables were selected for comparison between two groups. For statistical analysis, independent sample [Formula: see text]-tests were performed to compare between normal individuals and SWEDD patients. Compared with normal individuals, SWEDD patients exhibited a shorter step length ([Formula: see text]). In addition, SWEDD patients showed a wider distribution of heel-to-heel (HH) base of support compared with normal individuals ([Formula: see text]). SWEDD patients had a tendency to deteriorated gait speed ([Formula: see text]) and double-limb support cycle ([Formula: see text]). These results indicate that SWEDD patients adopted a walking strategy that utilized increased step length and width to compensate for deteriorated dynamic balance. These results indicate that quantitative gait assessment could be useful for more accurate diagnosis of SWEDD patients. In addition, understanding clinical features of SWEDD patients might contribute to efficient interventions.