Heuristic Weight Research Articles

Improved A-Star Search Algorithm for Probabilistic Air Pollution Detection Using UAVs.

Recently, air pollution problems in urban areas have become serious, and unmanned aerial vehicles (UAVs) can be used to monitor air pollution because they can perform spatial movement. However, because air pollution sources are fluid, probabilistic search methods are required to identify a target through the probability of its existence. This study proposes an efficient algorithm to detect air pollution in urban areas using UAVs. An improved A-star algorithm that can efficiently perform searches based on a probabilistic search model using a UAV is designed. In particular, in the proposed improved A-star algorithm, several special weights are used to calculate the probability of target existence. For example, a heuristic weight based on the expected target, a weight based on data collected from the drone sensor, and a weight based on the prior information of obstacles presence are determined. The method and procedure for applying the proposed algorithm to the stochastic search environment of a drone are described. Finally, the superiority of the proposed improved A-star algorithm is demonstrated by comparing it with existing stochastic search algorithms through various practical simulations. The proposed method exhibited more than 45% better performance in terms of successful search rounds compared with existing methods.

Heuristic Weight Initialization for Diagnosing Heart Diseases Using Feature Ranking

The advent of Artificial Intelligence (AI) has had a broad impact on life to solve various tasks. Building AI models and integrating them with modern technologies is a central challenge for researchers. These technologies include wearables and implants in living beings, and their use is known as human augmentation, using technology to enhance human abilities. Combining human augmentation with artificial intelligence (AI), especially after the recent successes of the latter, is the most significant advancement in their applicability. In the first section, we briefly introduce these modern applications in health care and examples of their use cases. Then, we present a computationally efficient AI-driven method to diagnose heart failure events by leveraging actual heart failure data. The classifier model is designed without conventional models such as gradient descent. Instead, a heuristic is used to discover the optimal parameters of a linear model. An analysis of the proposed model shows that it achieves an accuracy of 84% and an F1 score of 0.72 with only one feature. With five features for diagnosis, the accuracy achieved is 83%, and the F1 score is 0.74. Moreover, the model is flexible, allowing experts to determine which variables are more important than others when implementing diagnostic systems.

Target Assignment Algorithm for Joint Air Defense Operation Based on Spatial Crowdsourcing Mode

Spatial crowdsourcing is a mode that uses distributed artificial computing power to solve specific function sets through Internet outsourcing. It has broad application value in the networked command and control of current joint air defense operations. In this paper, we introduce the spatial crowdsourcing theory into the field of target allocation for joint air defense operations and establish a weapon-target assignment model based on spatial crowdsourcing mode, which is more appropriate to the real situation and highlights the system cooperation capability of joint air defense operations. To solve the model, we propose a heuristic variable weight nonlinear learning factor particle swarm optimization (VWNF-PSO). This algorithm can significantly improve the efficiency and adaptability to weapon-target assignment problems under large-scale extreme conditions. Finally, we establish two kinds of joint air defense operation scenarios to verify the proposed model, then compare the proposed algorithm with variable weight PSO (VWPSO) and adaptive learning factor PSO (AFPSO), to validate the effectiveness and efficiency of the VWNF-PSO algorithm proposed in this paper.

Improved lazy theta∗ algorithm based on octree map for path planning of UAV

This paper investigates the path planning method of unmanned aerial vehicle (UAV) in three-dimensional map. Firstly, in order to keep a safe distance between UAV and obstacles, the obstacle grid in the map is expanded. By using the data structure of octree, the octree map is constructed, and the search nodes is significantly reduced. Then, the lazy theta∗ algorithm, including neighbor node search, line-of-sight algorithm and heuristics weight adjustment is improved. In the process of node search, UAV constraint conditions are considered to ensure the planned path is actually flyable. The redundant nodes are reduced by the line-of-sight algorithm through judging whether visible between two nodes. Heuristic weight adjustment strategy is employed to control the precision and speed of search. Finally, the simulation results show that the improved lazy theta∗ algorithm is suitable for path planning of UAV in complex environment with multi-constraints. The effectiveness and flight ability of the algorithm are verified by comparing experiments and real flight.

Non-Coherent Detection for Ultraviolet Communications With Inter-Symbol Interference

Ultraviolet communication (UVC) serves as a promising supplement to share the responsibility for the overloads in conventional wireless communication systems. One difficulty lies in how to address the inter-symbol-interference (ISI) from the strong scattering nature and the time-varying channel response. This is more challenging for the energy-constrained scenarios (e.g., underwater UV), as existing coherent schemes (i.e., requiring exact channel information for signal detection) become less attractive given the computational and storaging complexity for repeated time-varying channel estimation and statistical signal detection. In this work, a novel non-coherent paradigm is proposed, via the exploration of the UV signal features that are insensitive to the ISI. By optimally weighting and combining the extracted features to minimize the bit error rate (BER), the optimally-weighted non-coherent detection (OWNCD) is proposed, which converts the signal detection with ISI into a binary detection framework, with a heuristic weight updating approach for time-varying channel. As such, the proposed OWNCD avoids the complex channel estimation and guarantees the detection accuracy. Compared to the state-of-the-art coherent maximum likelihood sequence detection (MLSD) in the cases of static and time-varying channel response, the proposed OWNCD can gain $\sim$ 1 dB and $\sim$ 8 dB in signal-to-noise-ratio (SNR) at the 7 $\%$ overhead forward-error-correction (FEC) limit (BER of $4.5\times 10^{-3}$ ), respectively, and can also reduce the computational complexity by 4 order of magnitude.

Two-phase A*: A real-time global motion planning method for non-holonomic unmanned ground vehicles

This paper presents a search-based global motion planning method, called the two-phase A*, with an adaptive heuristic weight. This method is suitable for planning a global path in real time for a car-like vehicle in both indoor and outdoor environments. In each planning cycle, the method first estimates a proper heuristic weight based on the hardness of the planning query. Then, it finds a nearly optimal path subject to the non-holonomic constraints using an improved A* with a weighted heuristic function. By estimating the heuristic weight dynamically, the two-phase A* is able to adjust the optimality level of its path based on the hardness of the planning query. Therefore, the two-phase A* sacrifices little planning optimality, and its computation time is acceptable in most situations. The two-phase A* has been implemented and tested in the simulations and real-world experiments over various task environments. The results show that the two-phase A* can generate a nearly optimal global path dynamically, which satisfies the non-holonomic constraints of a car-like vehicle and reduces the total navigation time.

Predictive Speed Estimation for Encoderless Control of an Induction Motor Fed by Delta Sigma Modulated Matrix Converter

Predictive control has emerged as a popular tool in the modulation of power converters and control of sensorless motor drives. However, it is associated with inherent limitations, such as increased processor burden and heuristic weight tuning, when dealing with multiple objectives. Moreover, in applications, such as modulation of matrix converters, predictive control necessitates input filter modeling, besides requiring repeated reruns of load (and input filter) models for securing the most optimum state selection. This article deals with the restricted use of predictive control as an estimation technique only, for speed sensorless control of an induction motor through delta sigma modulated matrix converter. Delta-sigma technique permits conveniently implementable modulation for the power converter, whereas predictive estimation of flux, torque, and speed ensures high precision even at near zero motor speed. Both predictive control and delta-sigma modulation work in tandem, to provide a robust drive solution, as demonstrated through a wide range of simulation and experimental results.

Semantics Based Web Ranking Using a Robust Weight Scheme

In this paper, HTML tags and attributes are used to determine different structural position of text in a web page. Tags- attributes based models are used to assign a weight to a text that exist in different structural position of web page. Genetic algorithms (GAs), harmony search (HS), and particle swarm optimization (PSO) algorithms are used to select the informative terms using a novel tags-attributes and term frequency weighting scheme. These informative terms with heuristic weight give emphasis to important terms, qualifying how well they semantically explain a webpage and distinguish them from each other. The proposed approach is developed by customizing Terrier and tested over the Clueweb09B, WT10g, .GOV2 and uncontrolled data collections. The performance of the proposed approach is found to be encouraging against five baseline ranking models. The percentage gain of approach achieved is 75-90%, 70-83% and 43-60% in P@5, P@10 and MAP, respectively.

Intrusion Detection in Computer Networks using Lazy Learning Algorithm

Intrusion Detection Systems (IDS) are used in computer networks to safeguard the integrity and confidentiality of sensitive data. In recent years, network traffic has become sizeable enough to be considered under the big data domain. Current machine learning based techniques used in IDS are largely defined on eager learning paradigms which lose performance efficiency by trying to generalize training data before receiving queries thereby incurring overheads for trivial computations. This paper, proposes the use of lazy learning methodologies to improve overall performance of IDS. A novel heuristic weight based indexing technique has been used to overcome the drawback of high search complexity inherent in lazy learning. IBk and LWL, two popular lazy learning algorithms have been compared and applied on the NSL-KDD dataset for simulating a real-world like scenario and comparing their relative performances with hw-IBk. The results of this paper clearly indicate lazy algorithms as a viable solution for real-world network intrusion detection.

Signal Detection in Clutter and Noise Using Well-Characterized Subspace

In scenarios involving persistent signal and clutter, where subspaces of clutter and signal of interest may be well characterized, the generalized likelihood ratio test (GLRT), also known as matched subspace detector, exploits only the component of signal that is linearly independent of the clutter subspace. This letter introduces a detector that augments the GLRT in such a situation by incorporating and capitalizing upon an assumption of homogeneous spread of clutter energy across the dimensions of whole clutter subspace. The receiving operator characteristic curves show how the advantage of proposed detector over GLRT with a heuristic weight choice.

Sistema de apoyo para la toma de decisiones entermografía de glándulas mamarias

A decision tree based system with heuristic weight factors oriented to diagnosis by thermography was developed. The proposed clinical decision support system (CDSS) includes binary decision trees for statistical classification of some diseases of the mammary gland. These include weight constant factors found by the correlation of the variables involved in the development of breast disorders according to the statistical attachment of the thermal features, as well as complementary clinical information (medical history, physical parameters, genetic load and habits, among others), regarding its diagnostic incidence. The certainty of the system for inflammatory disorders is 96%, for fibrocystic mastopathy is 78 %, for fat necrosis is 62% and for breast cancer is 86%. The 14% error is associated to microcalcifications that with the reported thermographic technique it is not possible to differentiate. Thus, it is possible to catalog the risk of a patient having a condition according to the results obtained after conducting a thermal test. Then, with the CDSS the possibility of applying early detection tests in the population suffering from contraindications to perform the others is improved, in addition it allows a comprehensive diagnosis for other patients.

Boosting Response Aware Model-Based Collaborative Filtering

Recommender systems are promising for providing personalized favorite services. Collaborative filtering (CF) technologies, making prediction of users’ preference based on users’ previous behaviors, have become one of the most successful techniques to build modern recommender systems. Several challenging issues occur in previously proposed CF methods: 1) most CF methods ignore users’ response patterns and may yield biased parameter estimation and suboptimal performance; 2) some CF methods adopt heuristic weight settings, which lacks a systematical implementation; and 3) the multinomial mixture models may weaken the computational ability of matrix factorization for generating the data matrix, thus increasing the computational cost of training. To resolve these issues, we incorporate users’ response models into the probabilistic matrix factorization (PMF), a popular matrix factorization CF model, to establish the response aware probabilistic matrix factorization (RAPMF) framework. More specifically, we make the assumption on the user response as a Bernoulli distribution which is parameterized by the rating scores for the observed ratings while as a step function for the unobserved ratings. Moreover, we speed up the algorithm by a mini-batch implementation and a crafting scheduling policy. Finally, we design different experimental protocols and conduct systematical empirical evaluation on both synthetic and real-world datasets to demonstrate the merits of the proposed RAPMF and its mini-batch implementation.

Optical Activity in the Near-IR Region: The λ=980 nm Multiplet of Chiral Yb3+ Complexes.

We used a very simplified electrostatic model based on charge and polarizability of atoms and groups on an organic ligand around a lanthanide ion to predict the near-infrared electronic circular dichroism (NIR ECD) spectra of Yb3+ (a monoelectronic ion). We tuned our method by using two widely different complexes. The first was the heterobimetallic species CsYb(hfbc)4 [hfbc=(-)-3-heptafluorobutyrylcamphorate], in which the ligand is a diketonate and, as such, is endowed with a chromophore with strong UV absorption (π-π* ). Its oxygen atoms define a square antiprism, which provides a symmetric coordination polyhedron. The second system was Yb DOTMA [DOTMA=(1R,4R,7R,10R)-α,α',α'',α'''-tetramethyl-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid], a chiral Yb analogue of Gd DOTA (DOTA=1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid), in which the ligand lacks relevant electronic transitions and provides a dissymmetric cage. The relative weights of dynamic (ligand polarization) and static contributions to Yb NIR ECD were evaluated, and the spectra appear to have been well predicted by theory through the introduction of a heuristic weight factor. To validate the approach and to confirm the value of the weight factor, we applied it to two other compounds, namely, Na3 Yb(BINOLate)3 and Yb(BINOLAM)3 [BINOLate=2,2'-dihydroxy-1,1'-binaphthyl; BINOLAM=3,3'-bis(diethylaminomethyl)-1-1'-bi-2-naphthol].

An Improved Trusted Greedy Perimeter Stateless Routing for Wireless Sensor Networks

In this paper, an improvement over Trusted Greedy Perimeter Stateless Routing (T-GPSR) is presented. T-GPSR employs heuristic weight values to evaluate total trust value of neighboring nodes. However, heuristic assignment of weights provide flexibility but it is not suitable in presence of several security attacks such as Grey hole, selfish behavior, on-off attack etc., are launched in the network in different proportions. To overcome this limitation, an improvement is suggested with an emphasis on trust update, lightweight trust computation and storage to reduce communication and storage overhead. The simulation study indicates that the packet delivery ratio of the improved T-GPSR has improved by 10% over T-GPSR in the presence of 50% of malicious nodes in the network.

Structural-parametric optimization of the technological processes for the assurance of part’s service properties

Manufacturing application of the Product Lifecycle Management System (PLM) requires functional-oriented technology engineering production. The main feature of functionally-oriented process is application of a complex system qualimetric versatility indicator at the stages of pre-production. This will allow to significantly affect to the provision of working, exploitation, utilization, and other functional properties of engineering products. Method of structural and parametric optimization of functional-oriented machining products is described in the article. This method is based on an analysis of prognostic rheological modeling of the parts stressstrain and thermodynamic state in the process of formation. The formalization of recursive relations between structural and parametric results of technological preproduction planning and product’s operating conditions will provide the best range of qualimetric indicators by the concurrent engineering facilities. Qualimetric integral criterion of technical product is a objective function in making decisions about the optimal structure and parameters of functionally-oriented process. This parameter characterizes the wear resistance, fatigue strength, corrosion resistance and so most loaded surfaces of the product. The integral qualimetric criterion formed as a result of the analysis microtopography surface layer, residual stresses and strains in the formation of these surfaces.The algorithm of the complex system qualimetric versatility indicator accounting for the engineering products in their potential or critical operation conditions as part of a machine or technological system is described in this article. This algorithm uses accounting heuristic weight coefficients, calculation of normalized local optimization criteria for the most commonly used in engineering practice working indicators

Joint-MAP Tomographic Reconstruction with Patch Similarity Based Mixture Prior Model

Tomographic reconstruction from noisy projections do not yield adequate results. Mathematically, this tomographic reconstruction represents an ill-posed problem due to information missing caused by the presence of noise. Maximum a posteriori (MAP) or Bayesian reconstruction methods offer possibilities to improve the image quality as compared with analytical methods in particular by introducing a prior to guide the reconstruction and regularize the noise. With an aim to achieve robust utilization of continuity/connectivity information and overcome the heuristic weight update for other nonlocal prior methods, this paper proposes a novel patch similarity based mixture (PSM) prior model for tomographic reconstruction. This prior is defined by a weighted Gaussian distance between neighborhood intensities. The weight quantifies the similarity between local neighborhoods and is computed using a maximization entropy constraint. This prior is then introduced within a joint image/weight MAP computed tomography reconstruction algorithm. Several acceleration trials including Compute Unified Device Architecture (CUDA) parallelization is applied to alleviate the intensive patch distance computation involved in the joint algorithm. The method was tested with both synthetic phantoms and clinical computed tomography data and compared in accuracy with five other reconstruction algorithms which are filtered back-projection and Bayesian-based. Reconstruction results show that the proposed reconstructions are able to produce high-quality images with ensured iteration convergence.

이동 시퀀스의 빈발도를 이용한 최적 이동 패턴 탐사 기법

기존의 패턴 탐사 기법들은 제한된 시간 및 공간영역에서 발생하는 다양한 이동 패턴들 중 단순히 사용자 요구에 적합할 것으로 추정되는 불특정한 빈발 이동 패턴만을 탐사하기 때문에 특정지점들 간의 최적 이동 경로나 정해진 시간 내의 스케줄링 경로 탐색과 같은 복합적인 시간 및 공간 제약 조건을 갖는 최적 이동 패턴을 탐사하는 문제에는 적용하기 어렵다. 이에 본 논문에서는 방대한 이동 객체의 이력 데이터 집합으로부터 복합적인 시간 및 공간 제약을 갖는 최적 이동 패턴을 탐사하는 문제를 보이고, 적용 가능한 위치 기반 서비스로서 최적 이동 경로에 해당하는 패턴을 탐색하기 위한 새로운 패턴 탐사 기법인 STOMP-F를 제안한다. 제안된 기법은 특정한 지점들 사이를 이동한 객체의 패턴들 중 객체가 가장 빈번하게 이동한 경로를 탐색하여 최적 경로로 결정하는 패턴 빈발도를 이용한 탐색 방법으로, 최적 이동 패턴 탐사 과정의 이동 시퀀스 생성 단계에서 객체의 위치 값과 공간영역 간의 위상 관계를 고려하여 이동 객체의 위치 속성에 대한 최하위 수준에서의 공간 일반화를 통해 보다 효율적으로 패턴 탐사를 수행할 수 있다. 제안 방법을 Dijkstra 알고리즘과 <TEX>$A^*$</TEX> 알고리즘을 대상으로 실험 평가한 결과 <TEX>$A^*$</TEX> 알고리즘의 휴리스틱 가중치에 따라 차이는 있으나 연산 처리 시간을 기준으로 타 알고리즘들 보다 효과적임을 알 수 있다. Since the traditional pattern mining methods only probe unspecified moving patterns that seem to satisfy users' requests among diverse patterns within the limited scopes of time and space, they are not applicable to problems involving the mining of optimal moving patterns, which contain complex time and space constraints, such as 1) searching the optimal path between two specific points, and 2) scheduling a path within the specified time. Therefore, in this paper, we illustrate some problems on mining the optimal moving patterns with complex time and space constraints from a vast set of historical data of numerous moving objects, and suggest a new moving pattern mining method that can be used to search patterns of an optimal moving path as a location-based service. The proposed method, which determines the optimal path(most frequently used path) using pattern frequency retrieved from historical data of moving objects between two specific points, can efficiently carry out pattern mining tasks using by space generalization at the minimum level on the moving object's location attribute in consideration of topological relationship between the object's location and spatial scope. Testing the efficiency of this algorithm was done by comparing the operation processing time with Dijkstra algorithm and <TEX>$A^*$</TEX> algorithm which are generally used for searching the optimal path. As a result, although there were some differences according to heuristic weight on <TEX>$A^*$</TEX> algorithm, it showed that the proposed method is more efficient than the other methods mentioned.

Limitations of Network Flow Algorithms in River Basin Modeling

A number of computer models for river basin planning and management have been developed by various agencies and used extensively since the mid-1970s. Most of the early developments have been based on the use of heuristic weight factors to represent priorities of allocation, and specialized optimization algorithms that were based on the use of network flow algorithms (NFAs). While these algorithms were at first considerably faster than the standard Simplex solvers, their handling of flow constraints was simplistic, which eventually led to the use of iterative schemes for handling nonnetwork constraints. This paper critically examines the notion that iterations applied in combination with NFA are a good vehicle for handling nonnetwork constraints. The failures are demonstrated on several variants of a simple problem with two reservoirs in series.

A proposed method for learning rule weights in fuzzy rule-based classification systems

In fuzzy rule-based classification systems (FRBCSs), rule weighting has often been used as a simple mechanism to tune the classifier. In past research, a number of heuristic rule weight specification methods have been proposed for this purpose. A learning algorithm based on reward and punishment has also been proposed to adjust the weights of each fuzzy rule in the rule-base. In this paper, a new method of learning rule weight in FRBCSs is proposed. The method can be used when single winner or weighted vote methods of reasoning is used. Compared with reward and punishment scheme, the proposed method is much faster and more effective. Another advantage of the proposed method is that, during the learning process, redundant rules are removed (i.e., by setting their weights to zero). The final rule-base usually contains much fewer rules than the initial one. This feature is very useful since a compact rule-base is usually desired from the point of efficiency and interpretability. A number of UCI data sets are used to assess the performance of the proposed method in comparison with reward and punishment scheme.

ファジィ識別システムにおけるファジィルールの重みの設定方法

ファジィ識別システムにおけるファジィルールの重みの設定方法