Path Optimization Technique Research Articles

Sustainable Green City Development Project Analysis using the Critical Path Method (CPM) and the Crashing Project Method on Time and Cost Optimization

Due to the increase in population all over the world, the term city development has emerged using the method of sustainable green cities, which are cities designed taking into account the environmental impact to reduce the required input from energy, water, and food production. This study discusses this topic with three main objectives; the first is to analyze the project work network of the Sustainable Green City Development Project, the second is to investigate the impact of speeding up the project on network planning, and the third is to determine the cost differences before and after acceleration. The Critical Path Method (CPM) is valuable for managing complex projects if the activity durations are known. However, in reality, the projected durations may vary in the course of a project due to multiple factors, such as equipment breakdowns, human errors, and material shortages. This study used the CPM and crashing methods to determine the optimal project turnaround time using critical path optimization techniques. Analysis using CPM showed that the initial project duration was 1205 weeks but can be reduced to 1197 weeks. However, with acceleration, there is a slight difference, corresponding to a 0.67% decrease in the duration and approximately 8 weeks. The study showed that the total cost of the project work is US $55M at the normal duration and became US $59M after acceleration, which is a slight increase.

Effective Aggregate Data Collection and Enhanced Network Lifetime Using Energy Efficient Aggregation Data Convening Routing in Wireless Sensor Network

Wireless Technology is an integral part of today's applications. It encompasses commercial and human-oriented environmental monitoring, military applications, robotics, and healthcare monitoring systems, home or factory automation, and logistics. A wireless sensor network is a network system that uses wireless sensor nodes to monitor physical or environmental conditions such as voice, temperature, and spatial dispersive movements. Each node can locally sense its environment, process information and data, and send the data to one or more collection points within the WSN. The existing solution is categorized into member nodes and group/cluster heads (CH). The CH election process increases the overhead of the network and reduces the network lifetime. The processing and energy limitations of the nodes are considered for the CH election process. In this cluster formation methods aiming at the Cluster head selection process and providing trust in hierarchical WSN are proposed. The Energy Efficient Aggregation Data Convening Routing (E2ADCR) is used to estimate the routing path, and aggregate data collection to improve the network lifetime. The major advantage of this technique is to avoid the malicious or selfish node from becoming a dominant cluster in a group of clusters. Initially sink node selection forwards the Configuration Message (CM) to every node on the network to construct the performing node. In this, cluster selection is based on connection density, degree of the node angle, and residual energy (Quality Factor) that is evaluated from the link robustness, energy, and degree of the node. Multi-hop link transmission support path optimization technique is estimated in the path when the obstacle is present in the WSN. To introduce an Aggregated Support-based Data Collection to evaluate each packet flow monitor on the network to if any unrelated packet will eliminate to forward to the sink node. The proposed routing path of computational simplicity is achieved by a simple method. The new routing protocols, which were developed during this research, have better energy efficiency.

Mobile Robot Path Optimization Technique Based on Reinforcement Learning Algorithm in Warehouse Environment

This paper reports on the use of reinforcement learning technology for optimizing mobile robot paths in a warehouse environment with automated logistics. First, we compared the results of experiments conducted using two basic algorithms to identify the fundamentals required for planning the path of a mobile robot and utilizing reinforcement learning techniques for path optimization. The algorithms were tested using a path optimization simulation of a mobile robot in same experimental environment and conditions. Thereafter, we attempted to improve the previous experiment and conducted additional experiments to confirm the improvement. The experimental results helped us understand the characteristics and differences in the reinforcement learning algorithm. The findings of this study will facilitate our understanding of the basic concepts of reinforcement learning for further studies on more complex and realistic path optimization algorithm development.

Investigation of loops and paths as optimization tools for total annual cost in heat exchanger networks

This research investigates the effectiveness of loops and paths as embedded in a modified pinch package (Aspen Energy Analyzer), that comprises a blend of traditional pinch technique with mathematical programming, in simultaneous optimization of total annual cost (TAC) in heat exchanger network synthesis (HENS). It uses composite curves, grand composites curves, supertargeting and looping system just as in pinch, as well as linear programming and mixed integer linear programming (MILP) in the design of HENs. The tool was adopted in solving some literature problems that had earlier been solved using pinch and mathematical programming techniques. The results obtained compared well with those of various authors that used different techniques as shown in the tables of cost comparison in this paper. The loop and path optimization technique adopted in this research obtained the least TAC in four out of five problems solved in this paper. This shows that loop and path optimization as available in a software-combining pinch and mathematical programming can be more effective than various other methods that have been adopted in the literature. It further demonstrated that no particular technique can return the lowest TAC for all HENS problems.

MBSR: MIMO Based Sink Relocation for Path Selection in IoT Based WSN

Wireless Sensor Networks have extended its functionalities by integrating with the Internet of Things and are highly proficient when it is incorporated with other technologies. This demands communication with lesser energy consumption and one such idea of low energy consumption is low energy path selection by a mobile sink node. Path selection requires a good Signal Interference Noise Ratio (SINR) which is calculated for the positioned mobile sink. Thus, a high-quality transmission path is selected by choosing a path to obtain better SINR. MBSR algorithm considers hierarchical clustering schemes and heads are elected to monitor the network. Heads of the clusters communicate to IoT applications using Multiple Input and Multiple Output Dual Antennas. The proposed MBSR scheme utilizes a path optimization technique and finds a suitable path to establish links to end user and real-time environments. The proposed MBSR mechanism employs three phases of execution. Phase 1: Cluster Creation, Phase 2: SINR Analysis for Path Selection. Phase 3: Data transmission to MIMO Devices and in turn forwards to static sinks that connect to end-user using the internet. MBSR improves transmission quality and the proposed scheme is tested for various network parameters to check transmission quality and is compared with existing methodologies.

An Efficient and Low-Power Design of the SM3 Hash Algorithm for IoT

The Internet-of-Things (IoT) has a security problem that has become increasingly significant. New architecture of SM3 which can be implemented in loT devices is proposed in this paper. The software/hardware co-design approach is put forward to implement the new architecture to achieve high performance and low costs. To facilitate software/hardware co-design, an AHB-SM3 interface controller (AHB-SIC) is designed as an AHB slave interface IP to exchange data with the embedded CPU. Task scheduling and hardware resource optimization techniques are adopted in the design of expansion modules. The task scheduling and critical path optimization techniques are utilized in the compression module design. The proposed architecture is implemented with ASIC using SMIC 130 nm technology. For the purpose of comparison, the proposed architecture is also implemented on Virtex 7 FPGA with a 36 MHz system clock. Compared with the standard implementation of SM3, the proposed architecture saves the number of registers for approximately 3.11 times, and 263 Mbps throughput is achieved under the 36 MHz clock. This design signifies an excellent trade-off between performance and the hardware area. Thus, the design accommodates the resource-limited IoT security devices very well. The proposed architecture is applied to an intelligent security gateway device.

The Spectrum Sensing and Path Optimization Techniques in Cognitive Radio Networks

Cognitive Radio (CR) is a versatile, insightful radio and system innovation that can naturally recognize accessible directs in a remote range and change transmission parameters empowering more interchanges to run simultaneously and furthermore improve radio working conduct. The primary thought of the intellectual system is to give the range band to the unlicensed clients without making any damage to the earth. By utilizing the recurrence range band, we came to utilization of these intellectual systems. Right off the bat, we have to frame the system through any methodologies .we are utilizing optimization Mechanisms for path identification in cognitive Radio.

A Path Optimization Technique with Obstacle Avoidance for an 8-DOF Robot in Bolt Looseness Detection Task

In the bolt looseness detection task of the locomotive system, the robot could be used to highly improve the working efficiency and reliability, but faces the complex static obstacles while planning the path for the robotic motion. In this paper, a path optimization technique is proposed to obtain an optimal path with obstacle avoidance for a redundant robot in the static complicated environment. There are three main contributions in this technique. The first is the solving of the inverse kinematics problem for a redundant robot based on the screw theory and the geometric description, which is general to all robots with rotational joints. The second is the modeling of the spatial constraint, where the pseudo distance is defined based on the plane description of the obstacles and calculated using the spatial analytic geometry knowledge. The third is the presentation of the whole path-planning framework based on the above two contributions, which could largely improve the generality of the presented technique. In this framework, the minimum-time path could be obtained while guaranteeing both the motion stability and obstacle avoidance. Moreover, a real setting that includes an obstacle environment and an 8-DOF robot, is taken as an example to better present the technique. Finally, the simulation experiment was performed in Isight software to verify the effectiveness of the path optimization technique.

Path Optimization Algorithm in Wireless Sensor Network with Obstacle

<p>Clustering is used for prolonging the network lifetime in WSN. It groups sensor nodes into different groups and selects a single node as a cluster head (CH) for all the groups. CHS collect the data from consonant clusters and forward the data to base station. In this paper, we proposed path optimization algorithm in Wireless Sensor Network with an obstacle (POAWSNO) that periodically selects the cluster heads according to quality factor. The quality factor is estimated by three criteria including quality of the link, remaining energy and degree of the node. Path optimization technique determines the shortest path during obstacle present in the WSN. POT is used to reduce the hop count and packet delay. The simulation results demonstrate that this approach improves the throughput and reduce the loss of packets and energy consumption in the network using network simulator.</p>

Secure and Energy Efficient Path Optimization Technique in Wireless Sensor Networks Using DH Method

Wireless sensor networks (WSNs) consist of autonomous sensor nodes, which can predict the ambiance and act accordingly to transfer the data in adverse conditions. However, limited energy and security issues restrict the efficient communication in such networks. Art of the work presented energy-based approaches using cluster heads and security is provided using encryption. However, cluster head methodologies result in congestion, and security using encryption is difficult due to the self-organized structure. So, it degrades the performance of the network. In this paper, secure and energy-efficient method of optimization is being proposed using the Dij-Huff Method. The presented approach is validated by a network simulator.

Computing similarity distances between rankings

We address the problem of computing distances between permutations that take into account similarities between elements of the ground set dictated by a graph. The problem may be summarized as follows: Given two permutations and a positive cost function on transpositions that depends on the similarity of the elements involved, find a smallest cost sequence of transpositions that converts one permutation into another. Our focus is on costs that may be described via special metric-tree structures. The presented results include a linear-time algorithm for finding a minimum cost decomposition for simple cycles and a linear-time 4∕3-approximation algorithm for permutations that contain multiple cycles. The proposed methods rely on investigating a newly introduced balancing property of cycles embedded in trees, cycle-merging methods, and shortest path optimization techniques.

MOBILE ROBOT PATH OPTIMIZATION ALGORITHM USING VECTOR CALCULUS AND MAPPING OF 2 DIMENSIONAL SPACE

This research explores path integration in mobile robot navigation and path optimization technique using vector calculus. A simulated robot in a simulated environment is used to test the algorithm that is to be developed. The simulated robot is equipped with a sonar sensor and several infrared sensors on its chassis. Mobile robot navigation in an unknown environment is very crucial as It not only has to be concerned about reaching its destination but also to avoid obstacles that may be in the way. This algorithm can effectively allow a mobile robot to navigate an unknown environment without collision into obstacles.

Solution to Holes Machining Path Planning by Intelligence Computation

The rapid development of electronic information industry has posed serious challenges to the electronics manufacturing technology and intense competition in the market. For the most basic parts of electronic components - holes of the printed circuit board processing efficiency issues need to be resolved, the machining tool path optimization techniques. Optimize holes machining path reducing tool movement time is an effective way to improve production efficiency. Machining path optimization problem is transformed into a traveling salesman problem, shortest processing path established path optimization mathematical model on the basis of the model, the analysis of a variety of typical optimization algorithm, simulated annealing algorithm, genetic algorithm, ant colony algorithm, particle swarm optimization, comparing the various algorithms, based on theoretical analysis and experimental research, for the complexity of the algorithm evaluation, flexibility, local optimum probability, feedback information utilization and weight.

A CAD/ CAM system for vector-based layered manufacturing systems

We have developed a framework for process planning for vector-based layered manufacturing (LM) technologies. The framework proposes the use of computational geometry and optimization tools at two levels to reduce process time and material wastage. The proposed architecture is tested by an implementation for laminated object manufacturing (LOM) systems. Geometric techniques are used for planning at the 3D part level. A genetic algorithm (GA) based path optimization technique is used for path planning optimization at the layer level. The use of a direct slice format is shown to reduce the system time as well as storage requirements. Results of the implementation of the open-architecture LOM planner are presented.