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Accurate and automatic clothing pattern making is very important in personalized clothing customization and virtual fitting room applications. Clothing pattern generating as well as virtual clothing simulation is an attractive research issue both in clothing industry and computer graphics.

Physics-based method is an effective way to model dynamic process and generate realistic clothing animation. Due to conceptual simplicity and computational speed, mass-spring model is frequently used to simulate deformable and soft objects follow the natural physical rules. We present a physics-based clothing pattern generating framework by using scanned human body model. After giving a scanned human body model, first, we extract feature points, planes and curves on the 3D model by geometric analysis, and then, we construct a remeshed surface which has been formatted to connected quad meshes. Second, for each clothing piece in 3D, we construct a mass-spring model with same topological structures, and conduct a typical time integration algorithm to the mass-spring model. Finally, we get the convergent clothing pieces in 2D of all clothing parts, and we reconnected parts which are adjacent on 3D model to generate the basic clothing pattern.

The results show that the presented method is a feasible way for clothing pattern generating by use of scanned human body model.

The main contribution of this work is twofold: one is the geometric algorithm to scanned human body model, which is specially conducted for clothing pattern design to extract feature points, planes and curves. This is the crucial base for suit clothing pattern generating. Another is the physics-based pattern generating algorithm which flattens the 3D shape to 2D shape of cloth pattern pieces.

In this conceptual paper the authors aim to discuss the implications of a second-order cybernetic approach and eigenbehavior in ecosystem management. The authors argue that traditional management practices rely on the agreement of stakeholders, but this approach cannot satisfy multiple-objective realities, making radical constructivism necessary.

The authors examined this conjecture using a multi-agent simulation exercise. Agents display individual behavior consistent with the organizing principles proposed by von Foerster (eigenform and eigenbehavior), Hoffman (perception and fitness) and Maturana (domains of realization). This exercise is complemented with an ecosystem management classification exercise to show how convergence is achieved when a team of trained professionals describes entities of low versus high complexity in a spatially explicit domain.

The authors showed that eigenforms can diverge significantly depending on the complexity of the problem. The capacity of observers to stabilize an eigenform and create a common ground for understanding depends on the complexity of the problem at hand and their cognitive diversity. The authors highlighted the difficulties that arose when observation and modification through the intervention of the environment cannot be detached. The authors argue that this situation deeply permeates ecosystem resource management due to the unpredictable outcomes displayed by entities embedded into open system dynamics.

Finally, the authors propose that the association between indication and transformation can be operationally interconnected as Eigenperception, which corresponds to a novel dynamic state bridging the gap between noise and eigenform. This concept of Eigenperception encapsulates entities that arise from eigenbehavior, encompassing observations, meanings, human transformative actions and ecological processes, all in a simultaneous manner.

Intelligent prediction of node localization in wireless sensor networks (WSNs) is a major concern for researchers. The huge amount of data generated by modern sensor array systems required computationally efficient calibration techniques. This paper aims to improve localization accuracy by identifying obstacles in the optimization process and network scenarios.

The proposed method is used to incorporate distance estimation between nodes and packet transmission hop counts. This estimation is used in the proposed support vector machine (SVM) to find the network path using a time difference of arrival (TDoA)-based SVM. However, if the data set is noisy, SVM is prone to poor optimization, which leads to overlapping of target classes and the pathways through TDoA. The enhanced gray wolf optimization (EGWO) technique is introduced to eliminate overlapping target classes in the SVM.

The performance and efficacy of the model using existing TDoA methodologies are analyzed. The simulation results show that the proposed TDoA-EGWO achieves a higher rate of detection efficiency of 98% and control overhead of 97.8% and a better packet delivery ratio than other traditional methods.

The proposed method is successful in detecting the unknown position of the sensor node with a detection rate greater than that of other methods.