Static Algorithm Research Articles

Dynamic three-way neighborhood decision model for multi-dimensional variation of incomplete hybrid data

The theory of three-way decisions, as a powerful methodology of granular computing, has been widely used in making decision under uncertainty environments. Decision tasks in incomplete hybrid data including heterogeneous and missing features are of abundance in realistic situations. To deal with these tasks, some work based on three-way decisions has been investigated. However, the losses used for evaluating objects are precise real numbers, which makes these decision models have some limitations in applications when there exist missing values in incomplete hybrid data. Thus, this paper constructs a generalized three-way neighborhood decision model by assigning the interval-valued loss function to each object and further adopting an average strategy to integrate the interval-valued loss functions of objects in each data-driven neighborhood class. Moreover, considering that the objects and attributes of incomplete hybrid data will simultaneously change over time, this paper also provides an efficient framework to dynamically maintain three-way regions of the proposed model. An approach based on matrix to compute the three-way regions is first presented by introducing the matrix operations and the matrix forms of related concepts. Then, with the simultaneous variation of objects and attributes, the matrix-based incremental mechanism and algorithm are proposed for updating the three-way regions, respectively. Experimental results on nine datasets indicate that the proposed incremental algorithm can effectively improve the computational performance for evolving data in comparison with the static algorithm.

An Improved Static Residual Force Algorithm and Its Application in Cable Damage Identification for Cable-Stayed Bridges

For cable-stayed bridges, cables are very important components to maintain the safety of the whole bridge structure. It is well-known that change in cable force reflects the health of the cable-stayed bridge. Therefore, it is necessary to detect and quantify local damage in cables prior to the occurrence of a failure. To this end, an improved residual force algorithm independent of static load vector was proposed in this study. The proposed method mainly makes use of the particularity that only a few coefficients in the residual force and static load vectors are nonzero. By combining two different static loading modes, a new damage indicator vector was defined in the method for damage localization and quantification. Compared with existing static residual force methods, the significant advantage of the proposed algorithm is that the specific value and loading position of the static load are not required in the damage identification process. This special advantage causes this method to not require special static loading, but instead uses any load vehicle. This advantage can make the operation process of structural damage identification based on static tests easier and faster. A single tower cable-stayed bridge structure was used to verify the feasibility of the proposed method in cable damage identification. It was shown that the proposed method successfully identified cable damage, even if the value and loading point of the static load were uncertain.

A Dynamic Task Allocation Algorithm for Heterogeneous UUV Swarms.

Aiming at the task allocation problem of heterogeneous unmanned underwater vehicle (UUV) swarms, this paper proposes a dynamic extended consensus-based bundle algorithm (DECBBA) based on consistency algorithm. Our algorithm considers the multi-UUV task allocation problem that each UUV can individually complete multiple tasks, constructs a “UUV-task” matching matrix and designs new marginal utility, reward and cost functions for the influence of time, path and UUV voyage. Furthermore, in view of the unfavorable factors that restrict the underwater acoustic communication range between UUVs in the real environment, our algorithm complete dynamic task allocation of UUV swarms with optimization in load balance indicator by the update of the UUV individual and the task completion status in the discrete time stage. The performance indicators (including global utility and task completion rate) of the dynamic task allocation algorithm in the scenario with communication constraints can be well close to the static algorithm in the ideal scenario without communication constraints. The simulation experiment results show that the algorithm proposed in this paper can quickly and efficiently obtain the dynamic and conflict-free task allocation assignment of UUV swarms with great performance.

PQROM: To optimize software defined network QoS-aware routing with proximal policy optimization

Currently, Quality-of-Service (QoS)-aware routing is one of the crucial challenges in Software Defined Network (SDN). The QoS performances, e.g. latency, packet loss ratio and throughput, must be optimized to improve the performance of network. Traditional static routing algorithms based on Open Shortest Path First (OSPF) could not adapt to traffic fluctuation, which may cause severe network congestion and service degradation. Central intelligence of SDN controller and recent breakthroughs of Deep Reinforcement Learning (DRL) pose a promising solution to tackle this challenge. Thus, we propose an on-policy DRL mechanism, namely the PPO-based (Proximal Policy Optimization) QoS-aware Routing Optimization Mechanism (PQROM), to achieve a general and re-customizable routing optimization. PQROM can dynamically update the routing calculation by adjusting the reward function according to different optimization objectives, and it is independent of any specific network pattern. Additionally, as a black-box one-step optimization, PQROM is qualified for both continuous and discrete action space with high-dimensional input and output. The OMNeT ++ simulation experiment results show that PQROM not only has good convergence, but also has better stability compared with OSPF, less training time and simpler hyper-parameters adjustment than Deep Deterministic Policy Gradient (DDPG) and less hardware consumption than Asynchronous Advantage Actor-Critic (A3C).

Parallelization of the B static traffic assignment algorithm

A widely used technique for predicting traffic flows in individual roads of a road traffic network is the user-equilibrium (UE) traffic assignment (TA). This technique assigns trips from origins to destinations in a road traffic network so that all trips use the cheapest path. The cost of the path, which consists of roads (edges), is the sum of the roads costs. These costs increase with increasing flow in these roads. In this paper, we describe the parallelization of the B algorithm – a relatively new TA algorithm with a fast convergence to a solution. Since the nature of the algorithm and the nature of its fast convergence complicate the parallelization itself, we considered and implemented three parallel variants and tested them on real road traffic networks to investigate their convergence, usability, and speed. The parallelization is intended for a shared memory parallel computing environment. The description of the parallelization along with the performed tests is the main contribution of this paper.

3D Reservoir Modeling of Buzurgan Oil Field, Southern Iraq

This study aims to set up a 3D static model to characterize and evaluate Mishrif Formation which represents the main reservoir in Buzurgan Oilfield, southern Iraq. Six wells have been selected to set up structural, facies and petrophysical models of Mishrif reservoir by using Petrel Software. The structural model has been built based on the structural contour map of the top of Mishrif Formation, which derived from seismic interpretation, and by using different static algorithms in Petrel Software. The structural model showed that the Buzurgan Oilfield represents an anticlinal fold with two domes north and south separated by a depression. The petrophysical model included the porosity model and water saturation model. The results of petrophysical models showed lateral and vertical variations in the porosity and water saturation characteristics of the Mishrif reservoir. The integration between the structural and petrophysical models illustrated that the main productive zones in Mishrif reservoir are MB21, MC1 and MC2 units.

Optimizing cardiac CT angiography minimum detectable difference via Taguchi's dynamic algorithm, a V-shaped line gauge, and three PMMA phantoms.

BACKGROUND: Radiologists widely use the minimum detectable difference (MDD) concept for inspecting the imaging quality and quantify the spatial resolution of scans.OBJECTIVE: This study adopted Taguchi’s dynamic algorithm to optimize the MDD of cardiac CT angiography (CTA) using a V-shaped line gauge and three PMMA phantoms (50, 70, and 90 kg).METHODS: The phantoms were customized in compliance with the ICRU-48 report, whereas the V-shaped line gauge was indigenous to solidify the cardiac CTA scan image quality by two adjacent peaks along the V-shaped slit. Accordingly, the six factors A-F assigned in this study were A (kVp), B (mAs), C (CT pitch), D (FOV), E (iDose), and F (reconstruction filter). Since each factor could have two or three levels, eighteen groups of factor combinations were organized according to Taguchi’s dynamic algorithm. Three welltrained radiologists ranked the CTA scan images three times for three different phantoms. Thus, 27 (3 3 3) ranked scores were summed and averaged to imply the integrated performance of one specific group, and eventually, 18 groups of CTA scan images were analyzed. The unique signal-to-noise ratio (S/N, dB) and sensitivity in the dynamic algorithm were calculated to reveal the true contribution of assigned factors and clarify the situation in routine CTA diagnosis.RESULTS: Minimizing the cross-interactions among factors, the optimal factor combination was found to be as follows: A (100 kVp), B (600 mAs), C (pitch 0.200 mm), D (FOV 280 mm), E (iDose 5), and F (filter XCA). The respective MDD values were 2.15, 2.32, and 1.87 mm for 50, 70, and 90 kg phantoms, respectively. The MDD of the 90 kg phantom had the most precise spatial resolution, while that of the 70 kg phantom was the worst.CONCLUSION: The Taguchi static and dynamic optimization algorithms were compared, and the latter’s superiority was substantiated.

Enhanced Virtualization-Based Dynamic Bin-Packing Optimized Energy Management Solution for Heterogeneous Clouds

Cloud computing provides unprecedented advantages of using computing resources with very less efforts and cost. The energy utilization in cloud data centers has forced the cloud service providers to raise the expense of using its services and has increased the carbon footprints in the environment. Many static bin-packing algorithms exist which can reduce energy by some percentage, but with new era of digitization, advanced and dynamic techniques are required which can serve heterogeneous users and random users’ requests. Thus, in this paper, two new dynamic best-fit decreasing-based bin-packing algorithms are proposed wherein the first technique is for service providers and focuses on increasing server utilization and the second approach acts as a switcher to harness best results among all algorithms. Both techniques deliberately achieve high performance in terms of total energy consumption, resource utilization, and makespan along with serving continuous and varying requests from customers. The simulations are performed using Java. The results exhibited that DEE-BFD can escalate resource utilization by 96% and EM switcher can reduce total energy consumption by 49% and reduce makespan by 56%.

Accelerating DNN Training Through Selective Localized Learning.

Training Deep Neural Networks (DNNs) places immense compute requirements on the underlying hardware platforms, expending large amounts of time and energy. We propose LoCal+SGD, a new algorithmic approach to accelerate DNN training by selectively combining localized or Hebbian learning within a Stochastic Gradient Descent (SGD) based training framework. Back-propagation is a computationally expensive process that requires 2 Generalized Matrix Multiply (GEMM) operations to compute the error and weight gradients for each layer. We alleviate this by selectively updating some layers' weights using localized learning rules that require only 1 GEMM operation per layer. Further, since localized weight updates are performed during the forward pass itself, the layer activations for such layers do not need to be stored until the backward pass, resulting in a reduced memory footprint. Localized updates can substantially boost training speed, but need to be used judiciously in order to preserve accuracy and convergence. We address this challenge through a Learning Mode Selection Algorithm, which gradually selects and moves layers to localized learning as training progresses. Specifically, for each epoch, the algorithm identifies a Localized→SGD transition layer that delineates the network into two regions. Layers before the transition layer use localized updates, while the transition layer and later layers use gradient-based updates. We propose both static and dynamic approaches to the design of the learning mode selection algorithm. The static algorithm utilizes a pre-defined scheduler function to identify the position of the transition layer, while the dynamic algorithm analyzes the dynamics of the weight updates made to the transition layer to determine how the boundary between SGD and localized updates is shifted in future epochs. We also propose a low-cost weak supervision mechanism that controls the learning rate of localized updates based on the overall training loss. We applied LoCal+SGD to 8 image recognition CNNs (including ResNet50 and MobileNetV2) across 3 datasets (Cifar10, Cifar100, and ImageNet). Our measurements on an Nvidia GTX 1080Ti GPU demonstrate upto 1.5× improvement in end-to-end training time with ~0.5% loss in Top-1 classification accuracy.

Parallel incremental update of semi-monolayer covering rough set based on object set changes

The semi-monolayer covering rough set has the characteristics of high approximation quality and efficient computation. In the face of massive data changes, the study of rough sets will face the challenge of high complexity computation. To address the large-scale changes of object sets in set-valued decision information systems, this paper proposes a dynamic update method based on Spark framework to solve the approximation set problem of semi-monolayer covering rough set. This method is mainly embodied in four aspects: firstly, for the change of information grains in the proposed single-layer covering rough set, this paper proposes an update strategy for the change of information units; then for the update of reliable elements, an update strategy is proposed for 〖▁C_"GC0" 〗^' (X) and 〖¯C_"GC0" 〗^' (X); secondly, for the change of reliable elements and disputed elements, an RSM matrix update strategy; and finally the update strategy of the approximation set is proposed for the changes of information units. Since the incremental methods using other models will yield different approximation sets and the prerequisite of the comparison experiments requires the approximation sets to be consistent, in order to ensure the correctness of the experimental results, this paper designs the comparison experiments of static and dynamic algorithms based on the semi-monolayer covering rough set model. The experimental results with several data sets show that the incremental algorithm speeds up in 3.67~7.72 times than the static algorithm when there is an object set update.

Organizational Resource Allocation by Mobile Edge Computing in the Context of the Internet of Things

In recent years, the number of electronic devices and users has increased sharply with the rapid development and progress of electronic information technology. The motivation of this paper is to optimize the organization’s resource allocation strategy in the Internet of Things (IoT) environment. The optimal path planning and information processing efficiency are improved through Unmanned Aerial Vehicle (UAV) technology and migration optimization algorithm. The research method is migration optimization algorithm and UAV dynamic network. The information processing capability of traditional wireless communication systems has gradually been unable to meet the actual information processing needs. Therefore, a static network migration algorithm is constructed based on multiple-user multilateral edge computing servers. It migrates each user’s information processing task to the neighboring edge confidence processing server and uses the information processing server to perform auxiliary calculations. The simulation model adds a utility function that simulates energy consumption, delay weighting, and maximum extreme value, combined with the allocation strategy of optimizing each user’s information processing task to achieve the optimization goal. The static network migration algorithm established in this simulation has better results than other benchmark algorithms. Both scenario 1 and scenario 2 in the simulation show very close performance to the optimal solution. Meanwhile, a migration algorithm that can provide wireless charging for UAVs is built by a dynamic edge computing model based on the time associated with the UAV base station and multiple end users. Combined with completing the information processing tasks in each time slot, the energy arrival is also non-directional. The dynamic network migration algorithm can optimize the number of tasks absorbed by the end-user based on the current online status of the system without knowing the global information. The optimized target equation is related to the queue stability, and the parameter V has a linear relationship with the queue backlog length. Here, the problem of computing migration is studied in Mobile Edge Computing (MEC). The results show that the utility function of the weighted sum has an approximately linear relationship with the weights. As the value of the utility function increases, so does the weight function. The optimal data throughput of the proposed model is 70,000 bits, while the optimal data throughput of the state-of-the-art model is 68,000 bits. Therefore, the data transmission performance of the model presented here is better than that of other models. MEC can be significantly improved the efficiency of organizational resource allocation. Combining UAV and wireless charging technology, the computing and communication resource allocation issues of the UAV’s edge computing system are comprehensively discussed to improve the performance and efficiency of the network.

Deep Reinforcement Learning Based Routing in IP Media Broadcast Networks: Feasibility and Performance

The Media Broadcast industry has evolved from Serial Digital Interface (SDI) based infrastructures to IP networks. While IP based video broadcast is well established in the data plane, the use of IP networks to transport media flows still poses challenges in terms of resource management and orchestration. SDN based orchestration architectures have emerged in the industry that use SDN to route the media flows of a broadcast service across the provider IP network. Several approaches to multimedia flow routing in IP based SDN networks have been proposed in the context of streaming applications over the Internet. These range from model based linear optimization solutions that have high complexity to simple shortest path based routing heuristics with either static or dynamic link costs. More recently model-free optimization methods such as Deep Reinforcement Learning (DRL) have been proposed for routing and Traffic Engineering of multimedia flows in SDN networks. The media broadcast scenario however has specific requirements, with services like Master Control Room operation and Live broadcasting of events, and it has been rarely addressed in the literature. In this work we propose a DRL based routing method for this scenario and compare it to static and dynamic link cost algorithms based on Dijkstra shortest paths. This is to our knowledge the first work to follow this approach in the context of Media Broadcast services in IP infrastructures. The algorithm is designed considering the specifications and capabilities of one of the leading SDN orchestrators in the market and considers the more common Service Level Agreement requirements in the industry. Three different DRL algorithms are implemented and compared and we evaluate them using a real service provider network topology. The results indicate that DRL based routing is applicable in real production scenarios and that it achieves considerable performance gains when compared to the static and dynamic link cost shortest path algorithms commonly used today.

Static Call Graph Combination to Simulate Dynamic Call Graph Behavior

Call graphs are fundamental for many higher-level code analyses. The selection of the most appropriate call graph construction tool for an analysis is not always straightforward and depends on the purpose of the results’ further usage. The choice of call graph construction tool has a great effect on the following tasks’ execution time, memory usage, and result quality. This research compares the resulting static and dynamic Java call graphs to assist in the selection of the most appropriate tools. Static call graphs, as their name suggests, are constructed by static analysis, based on the source code or the bytecode, without executing tests or any code parts. This means that the project can be analyzed in its early stages and with fewer resources, but there is concern that this will result in less accurate, noisier graphs since the dynamic behavior of the programs will be estimated by static algorithms. Inaccuracies can greatly affect analyses based on call graphs. On the other hand, dynamic call graphs are created during the actual execution of the program. The calls that are included as edges in the graph are exactly those that were executed during the run, so you can expect the result to be more accurate. However, dynamic analysis requires more resources and the execution of code via test cases which provide high test coverage. In this work, we investigated the relationship between dynamic and static call graphs. Is the graph generated by dynamic analysis really better? Can static graphs approximate or even complement dynamic call graphs with sound results? In order to find the answers to these questions, we compared the results of five static and one dynamic analyzer. They were evaluated on three projects of different sizes and test coverage. We included in the comparison a merged graph created by ourselves by combining different static analyzer outputs. Not only did we compare static graphs to the dynamic results, we also validated the calls in a dynamic graph and found that these graphs could mislead the user. The results show that dynamic graphs should be considered good, although not a golden standard since they contain phantom calls, calls that are not present in the source code. Such calls are not limited to synthetic calls. Static analyzers could not be applied without consideration either, but a combination of static call graphs does tend to contain similar calls to the dynamic graphs with no phantom calls.

Обнаружение ошибок взаимоисключающей блокировки в программах на языке С# при помощи методов статического анализа

The paper describes static analysis algorithms aimed at finding three types of errors related to the concept of a synchronizing monitor: redefinition of a variable of mutually exclusive locking inside a critical section; use of an incorrect variable type when entering the monitor; blocking involving an object that has methods that use a reference to an instance (this) to lock. Developed algorithms rely on symbolic execution technology and involve interprocedural analysis via summary of functions, which ensures scalability, field-, context-, and flow-sensivity. Proposed methods were implemented in the infrastructure of a static analyzer in the form of three separate detectors. Testing on the set of open source projects revealed 23 errors and the true positive ratio of 88.5% was obtained, while the time consumption only made up from 0.1 to 0.7% of the total analysis time. The errors that these detectors were designed to find are difficult to detect by testing or dynamic analysis because of their multithreading nature. At the same time, it is necessary to find them: just one such defect can lead to incorrectness of the program and even make it vulnerable to intruders.

Towards Data-Informed Motion Artifact Reduction in Quantitative CT Using Piecewise Linear Interpolation

We propose to use piecewise linear interpolation (PLI) in time to reduce motion artifacts in transmission computed tomography (CT). PLI is motivated by the natural occurrence of piecewise-linear evolution of voxel values during object motion. The method is specifically examined in the context of high-accuracy quantitative measurements that are compromised by small motions, and particularly sub-pixel motion. Compared to existing methods, the proposed approach offers three advantages: (i) the flexibility in the interpolation parameters provides a framework for joint optimization and data-informed dynamic CT, (ii) both continuous motion and sudden changes in voxel values can be represented while preserving the continuity of the interpolated solution, and (iii) the compactness of the interpolation functions reduces the increase in algorithmic cost. Total variation regularization is used with a second-order accurate discretization, and the resulting formulation is solved with the Chambolle-Pock proximal algorithm. The applicability of the method in practical cases is demonstrated using synchrotron data, with an algorithmic cost of two to four times that of equivalent static reconstruction algorithms.

Robust Hand Gesture Recognition Based on RGB-D Data for Natural Human–Computer Interaction

To naturally interact with virtual environment by hand gesture, this paper presents a robust RGB-D data based recognition method of static and dynamic hand gesture. Firstly, for static hand gesture recognition, starting from the hand gesture contour extraction, the palm center is identified by Distance Transform (DT) algorithm. The fingertips are localized by employing the K-Curvature-Convex Defects Detection algorithm (K-CCD). On the basis, the distances of the pixels on hand gesture contour to palm center and the angle between fingertips are considered as the auxiliary features to construct a multimodal feature vector, and then recognition algorithm is presented to robustly recognize the static hand gestures. Secondly, combining Euclidean distance between hand joints and shoulder center joint with the modulus ratios of skeleton features, this paper generates a unifying feature descriptor for each dynamic hand gesture and proposes an improved dynamic time warping (IDTW) algorithm to obtain recognition results of dynamic hand gestures. Finally, we conduct extensive experiments to test and verify the static and dynamic hand gesture recognition algorithm and realize a low-cost real-time application of natural interaction with virtual environment by hand gestures.

Research on Service Function Chain Orchestrating Algorithm Based on SDN and NFV

Software defined network (SDN) and network function virtualization (NFV) have become a new paradigm of a new generation of network architecture. SDN and NFV can effectively improve the flexibility of deploying and managing service function chains (SFCs). By combining SDN and NFV and applying them to the resource orchestration problem of SFC deployment, the three-tier architecture consisting of SDN controller, network function virtualization and physical underlying computing resource layer in the process of heterogeneous network resource mapping is considered. And an optimization algorithm for active control resources based on SDN and NFV is proposed. Firstly, the user’s utility is modeled by the multi-standard aggregated multi-criteria utility algorithm, and the optimization goal is transformed into the problem of maximizing the user’s utility. Then the controller, based on the algorithm’s prediction of the future state and real-time monitoring of the network utilization, makes decisions and issues control commands for the arriving SFC requests, based on which it occupies the underlying resources held by the virtualized network function (VNF). The simulation results show that, compared with the static timing resource allocation algorithm, the active control resource deployment algorithm proposed in the article has better performance in terms of resource utilization, acceptance rate, and user creation utility.

New Algorithm to Handle Routing with Load Balancing in Wireless Networks Using EERNN

In this paper, static wireless network load balance algorithm is proposed, that use only optimal paths from point-to-point to achieve good load balance. This algorithm based on Enhanced version of Elman Recurrent Neural Network (EERNN) to make load balance decision depended on two metrics (traffic load on node and probability of link failure). This algorithm make good work in terms of both metrics simultaneously. Also, this algorithm use only local information. The execution of the proposed algorithm is compared with ERNN based on (traffic load on node and probability of link failure). © 201x JASET, International Scholars and Researchers Association

Research on Ultra-Wideband (UWB) Accurate Positioning under Signal Interference Based on Deep Learning

UWB (Ultra-Wideband) technology is also called "Ultra-Wideband", also known as pulse radio technology. UWB-based positioning technology has real-time indoor and outdoor accurate tracking capabilities, with high positioning accuracy, which can achieve centimeter-level or even millimeter-level positioning. Based on the provided anchor point ranging information, this paper analyzes normal and abnormal data and establishes an accurate positioning model. For task 1, preprocess the data, export the file, delete invalid data, and fill in missing values. For task 2, it is required to establish models for normal and abnormal data respectively. For task 3, it is consistent with the model obtained in task 2. The only difference is that the coordinates of the four anchor points used for the test data have changed. When the target coordinates are calculated, the anchor point coordinates can be replaced to obtain the model required by task 3. For task 4, use the processed data in task one to establish a mathematical model, and train the model through the integrated learning method to determine whether the collected signal is interference. For task 5, first use the integrated learning model in task four to eliminate the interference data in the data, import the eliminated data into the positioning model of task two for positioning, and add Kalman based on interference recognition to the static estimation algorithm.

Incremental updating probabilistic approximations under multi-level and multi-dimensional variations in hybrid incomplete decision systems

The data types of practical application systems are various (for example, Boolean, categorical, numerical, set-valued, interval-valued and incomplete, etc.), and such complex data systems exist widely in the real world; In addition, the data is dynamic in the process of collection and screening, not only the number of objects will change, but also the number of features will vary, which leads to the knowledge being constantly changed and needing to be updated with the collation process. In this paper, aiming at the dynamic change of data in the hybrid incomplete decision system (HIDS), we mainly focus on researching the incremental updating theory and method of probabilistic approximations under the multi-level and multi-dimensional variations of objects and attributes. Firstly, for the different binary relations of multiple data types in HIDS, a normalized combination relationship-based probabilistic rough set model is proposed. Next, multi-level and multi-dimensional variations (MLMDV) of objects and attributes are analyzed; for MLMDV of the object set and the attribute set in HIDS, dynamic knowledge updating mechanisms are researched, and a matrix-based incremental algorithm for updating probabilistic approximations is designed to avoid the repeated calculation of the static algorithm and improve efficiency. Finally, a series of experiments are conducted to evaluate the efficiency of the proposed method. The experimental results of 9 data sets show that the proposed incremental algorithm can effectively update the knowledge for the multi-level and multi-dimensional variants of objects and attributes, and is superior to the static knowledge acquisition method.