Artificial Intelegence Pemanfaatan Metode Hill Climbing Mencari Lintasan Terpendek Objek Wisata Menggunakan sistem Informasi

The search is often used to search for the shortest route, the Hill Climbing Method is a part of the test that uses heuristic functions. The problem that is often encountered is in the form of miscalculations in calculating the distance so that it requires long distances, costs a lot and takes a very long time. To solve this case, it can be solved by making a structure graph by looking at the city points from the two sides of the point to be passed. Using an algorithm can help make it easier to find a location and save time and travel costs that will be passed. This advantage is that all points will be obtained and checked from the right and left sides one by one so as to obtain effective and maximum results. The Hill Climbing method that will be used has the concept of a geographic information system as a guide and is used as a system for decision making. The heuristic search method is one of the methods commonly used in finding a way

A Motion Image Pose Contour Extraction Method Based on B-Spline Wavelet

In order to improve the accuracy of traditional motion image pose contour extraction and shorten the extraction time, a motion image pose contour extraction method based on B-spline wavelet is proposed. Moving images are acquired through the visual system, the information fusion process is used to perform statistical analysis on the images containing motion information, the location of the motion area is determined, convolutional neural network technology is used to preprocess the initial motion image pose contour, and B-spline wavelet theory is used. The preprocessed motion image pose contour is detected, combined with the heuristic search method to obtain the pose contour points, and the motion image pose contour extraction is completed. The simulation results show that the proposed method has higher accuracy and shorter extraction time in extracting motion image pose contours.

Real-Time Detection of Linear Structure Objects Using Mean Shift Segmentation and Heuristic Search

With the development of intelligent industrial production, industrial components with linear structure tend to be regular, such as TV LCD module, mobile phone screen, and electronic equipment shell. Recognition of linear structure objects by machine vision is an important aspect of intelligent industry. At present, shape matching algorithm is mostly used for arbitrary structure objects. It will be time-consuming if it is directly used to detect the linear structure objects as it needs to traverse the parameter space of the object. To solve the traversal problem and detect the linear structure objects in real time, a heuristic detection algorithm is designed according to the characteristics of linear structure objects. First, the coarse position and orientation are obtained by mean shift filtering and heuristic region grouping to reduce the searching range. Then, the heuristic search method is used to get the precise location information. The heuristic search method is designed based on the particle swarm optimization algorithm and heuristic information. The proposed method has been evaluated on two image databases of common industrial parts and backlight units which are typical linear structure objects. The experimental results showed that the proposed algorithm could reduce the detect time by more than 70% averagely while the detection accuracy is kept. It proves that the proposed algorithm can detect linear structure objects in real time and is suitable for the detection of objects with linear structures.

Distribution System Restoration Based On Optimized Heuristic Search Method

Service recovery is still among the most essential approaches to enhance the durability of their contemporary distribution system. Following the error location is identified and isolated; a correct SR program ought to be ascertained to resupply out-of-service places. 2 heuristic approaches are suggested to locate an efficient and speedy solution in contemporary power supply systems. For resolving the support recovery issue in distribution systems, change selection indices sectionalizes switch stated by an analytic plan in addition to a practicable optimized heuristic graph-based process are given. The formulation of the issue includes four different functions like optimizing the complete load restored and cutting back the amount of changing operations. Maximizing the best priority restored loading, also decreasing load decreasing. A nice evaluation of change indices is used for many player tie sticks from the apparatus to think about the ideal solution and minimize the complete quantity of shifting operations. A brand new graph-based program may be used for hunting the best sectionalizes change and diminishing the voltage fall. The precision as well as the validity of this process are analyzed in two regular electrical supply procedures. The outcomes of these suggested methods are utilized for IEEE regular bus test instance.

An Intelligent Planning-Based Modeling Method for Diagnosis and Repair

Planning and model-based diagnosis are both branches of artificial intelligence. In model-based diagnosis, multiresults may be gotten which lead to an uncertain diagnosis. We use the landmark method from planning to designing an event sequence to get a reaction. A method that uses planning to repair in local results of incremental diagnosis is proposed. Firstly, a model is established on model-based diagnosis and planning. Incremental diagnosis results are used as the initial state of planning, and the heuristic search method is used to find the solution to an unfaulty state. Two algorithms with different strategies are designed for diagnosis and repair: one is to repair all possible faults and use controllable events to repair them, and the other is to test through the feedback of controllable events and observable events to get the only solution and repair them. At the same time, the efficiency of the incremental diagnosis method is improved based on heuristics.

AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates

Structured weight pruning is a representative model compression technique of DNNs to reduce the storage and computation requirements and accelerate inference. An automatic hyperparameter determination process is necessary due to the large number of flexible hyperparameters. This work proposes AutoCompress, an automatic structured pruning framework with the following key performance improvements: (i) effectively incorporate the combination of structured pruning schemes in the automatic process; (ii) adopt the state-of-art ADMM-based structured weight pruning as the core algorithm, and propose an innovative additional purification step for further weight reduction without accuracy loss; and (iii) develop effective heuristic search method enhanced by experience-based guided search, replacing the prior deep reinforcement learning technique which has underlying incompatibility with the target pruning problem. Extensive experiments on CIFAR-10 and ImageNet datasets demonstrate that AutoCompress is the key to achieve ultra-high pruning rates on the number of weights and FLOPs that cannot be achieved before. As an example, AutoCompress outperforms the prior work on automatic model compression by up to 33× in pruning rate (120× reduction in the actual parameter count) under the same accuracy. Significant inference speedup has been observed from the AutoCompress framework on actual measurements on smartphone. We release models of this work at anonymous link: http://bit.ly/2VZ63dS.