On Application of Carbon-Containing Electrode Materials in Technology of Electrospark Alloying: Part 1. Peculiarities of Coating Formation Using Electrospark Treatment of Titanium Alloy OT4-1

Electrospark treatment of OT4-1 titanium alloy was performed sequentially with a STIM-20N hard-alloy electrode (TiC–20% Ni) and carbon-containing material (graphite and carbon-based composite materials). Kinetics of the mass transfer of the hard-alloy electrode was studied. The cathode mass loss during the first minute of the treatment was established. The kinetics results were processed using the methods of mathematical statistics. The erosion resistance of the applied carbon-containing materials was determined. Phase composition and relief of the coatings formed were analyzed. It was found that the application of the carbon-containing material increases the content of refractory phases in the coatings. Increase in the time of the treatment using the carbon-containing materials decreases the roughness of the coatings.     

Medicine Meets Thermal Spray Technology: A Review of Patents

The patent literature concerning thermal spraying for biomedical applications is reviewed in this contribution. The patents were compiled from multiple databases search spanning the 2005-2018 period. For clarity and ease of reading, the results have been grouped into sections according to four individual material groups (apatites, titanium, oxide ceramics, other), with the secondary sorting criterion being related to the specific bioapplication areas (maxillofacial, orthopedic, methods). Lastly, the patents are grouped according to the selected thermal spray method within the individual subsections. In the paper, recent R&D trends in this field are further identified and briefly commented.     

Assessment of Dam Overtopping Reliability using SUFI Based Overtopping Threshold Curve

Estimation of earth-fill dam height and dimension of spillway are strongly depend on accurate evaluation of dam overtopping reliability. Ideally, whole random and effective variables on overtopping should be considered for dam overtopping reliability assessment. However, taking into account all affecting random variables as well as overtopping reliability assessment using algorithms such as Monte Carlo (MC) could be excessively time consuming and impossible in some cases. In this study, new approach has been introduced as conditional reliability method and it has been tried to reduce the simulations time significantly by using combination of rainfall threshold theory, SUFI (Sequential Uncertainty Fitting) and MC methods. According two defined indexes, the obtain results shown, the relative error of new approach in computation of Jamishan dam overtopping reliability is less than 0.23%. Also, time saving in new method against to regular MC method is more than 87%. So, the new introduced method has more efficiency with acceptable accuracy for assessment of dam overtopping reliability. In addition, among hydrological and hydraulic uncertainties, hydrological uncertainties have more effect on overtopping reliability.     

Optimal weighed Holoentropy for video compression: an advanced HEVC

ABSTRACT

In recent times, the applications of multimedia are rising in a greedy mode and hence the amount of video transactions is also increasing exponentially. This has shouted great demands on effective models on video encoding and also for reducing the transmission channel congestion. This research work introduces a managing technique termed weighted encoding for High-Efficiency Video Coding (HEVC). HEVC, also termed as MPEG-H Part 2 and H.265 is a video compression standard that is widely utilized AVC (H.264 or MPEG-4 Part 10). When compared to AVC, HEVC grants double the ratio of data compression at a similar level of quality of the video or considerably enhanced video quality at a similar bit rate. This work intends to optimize the weight that adopted in HEVC for encoding. For this, this paper proposes a new Iterative based propagation update in the water wave Optimization Algorithm (IPU-WWO), which is the improved form of Water wave Optimization (WWO). The performance of proposed IPU-WWO is compared over other conventional methods like Artificial Bee Colony (ABC), Firefly (FF), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) with respect to Peak Signal to Noise Ratio (PSNR). By doing the encoding process, it minimizes the video size with perceptually better quality video or PSNR.     

Reoptimization framework and policy analysis for maritime inventory routing under uncertainty

We study a maritime inventory routing problem, in which shipments between production and consumption nodes are carried out by a fleet of vessels. The vessels have specific capacities and can be chartered under different agreements. The inventory levels of all consumption nodes and some production nodes should be maintained within specified bounds; for the remaining production nodes, orders should be picked up within pre-defined time windows. We propose a discrete-time mixed-integer programming model. In the face of new information and uncertainty, this optimization model has to be re-solved, as the horizon is rolled forward. We discuss how to account for different sources of uncertainty. We present a rolling-horizon reoptimization framework that allows us to study different policies that impact the quality of the implemented solution, so we can identify the optimal set of policies.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.