Fused Contextual Data with Threading Technology to Accelerate Processing in Home UbiHealth

According to the ubiquitous computing paradigm, dispersed computers within the home environment can support the residents’ health by being aware of all the developing and evolving situations. The context-awareness of the supporting computers stems from the data acquisition of the occurring events at home. In some cases, different sensors provide input of identical type, thereby raising conflict-related issues. Thus, for each type of input data, fusion methods must be applied on the raw data to obtain a dominant input value. Also, for diagnostic inference purpose, data fusion methods must be applied on the values of the available classes of multiple contextual data structures. Dempster-Shafer theory offers the algorithmic tools to efficiently fuse the data of each input type or class. The employment of threading technology accelerates the computational process and carrying out benchmarks on publicly available data set, is shown to be more efficient. Thus, threading technology proved promising for home UbiHealth applications by lowering the number of required cooperating computers.

Analysis of Environmental Variables and Carbon Input on Soil Microbiome, Metabolome and Disease Control Efficacy in Strawberry Attributable to Anaerobic Soil Disinfestation

Charcoal rot and Fusarium wilt, caused by Macrophomina phaseolina and Fusarium oxysporum f. sp. fragariae, respectively, are major soil-borne diseases of strawberry that have caused significant crop losses in California. Anaerobic soil disinfestation has been studied as an industry-level option to replace soil fumigants to manage these serious diseases. Studies were conducted to discern whether Gramineae carbon input type, incubation temperature, or incubation duration influences the efficacy of this disease control tactic. In experiments conducted using ‘low rate’ amendment applications at moderate day/night temperatures (24/18 °C), and carbon inputs (orchard grass, wheat, and rice bran) induced an initial proliferation and subsequent decline in soil density of the Fusarium wilt pathogen. This trend coincided with the onset of anaerobic conditions and a corresponding generation of various anti-fungal compounds, including volatile organic acids, hydrocarbons, and sulfur compounds. Generation of these metabolites was associated with increases in populations of Clostridium spp. Overall, carbon input and incubation temperature, but not incubation duration, significantly influenced disease suppression. All Gramineae carbon inputs altered the soil microbiome and metabolome in a similar fashion, though the timing and maximum yield of specific metabolites varied with input type. Fusarium wilt and charcoal rot suppression were superior when anaerobic soil disinfestation was conducted using standard amendment rates of 20 t ha−1 at elevated temperatures combined with a 3-week incubation period. Findings indicate that anaerobic soil disinfestation can be further optimized by modulating carbon source and incubation temperature, allowing the maximum generation of antifungal toxic volatile compounds. Outcomes also indicate that carbon input and environmental variables may influence treatment efficacy in a target pathogen-dependent manner which will require pathogen-specific optimization of treatment protocols.

Underwater Image Enhancement with a Deep Residual Framework

This paper focuses on framework developed with the goal to enhance the quality of underwater images using machine learning models for the Underwater Image enhancement system. It also covers the various technologies and language used in the development process using Python programming language. The developed system provides two major functionality such as feature to provide input as image or video and returns enhanced image or video depending upon user input type with focus on more image quality, sharpness, colour correctness etc.

Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier

The main objective of the current work was to enhance the transformer fault diagnostic accuracy based on dissolved gas analysis (DGA) data with a proposed coupled system of support vector machine (SVM)-bat algorithm (BA) and Gaussian classifiers. Six electrical and thermal fault classes were categorized based on the IEC and IEEE standard rules. The concentration of five main combustible gases (hydrogen, methane, ethane, ethylene, and acetylene) was utilized as an input vector of the two classifiers. Two types of input vectors have been tested; the first input type considered the five gases in ppm, and the second input type considered the gases introduced in the percentage of the sum of the five gases. An extensive database of 481 had been used for training and testing phases (321 data samples for training and 160 data samples for testing). The SVM model conditioning parameter “λ” and penalty margin parameter “C” were adjusted through the bat algorithm to develop a maximum accuracy rate. The SVM-BA and Gaussian classifiers’ accuracy was evaluated and compared with several DGA techniques in the literature.

Exploring the Relationship between Preprocessing and Hyperparameter Tuning for Vibration-Based Machine Fault Diagnosis Using CNNs

This paper demonstrates the differences between popular transformation-based input representations for vibration-based machine fault diagnosis. This paper highlights the dependency of different input representations on hyperparameter selection with the results of training different configurations of classical convolutional neural networks (CNNs) with three common benchmarking datasets. Raw temporal measurement, Fourier spectrum, envelope spectrum, and spectrogram input types are individually used to train CNNs. Many configurations of CNNs are trained, with variable input sizes, convolutional kernel sizes and stride. The results show that each input type favors different combinations of hyperparameters, and that each of the datasets studied yield different performance characteristics. The input sizes are found to be the most significant determiner of whether overfitting will occur. It is demonstrated that CNNs trained with spectrograms are less dependent on hyperparameter optimization over all three datasets. This paper demonstrates the wide range of performance achieved by CNNs when preprocessing method and hyperparameters are varied as well as their complex interaction, providing researchers with useful background information and a starting place for further optimization.

Implementasi Max Length dan Input Type Number Pada Form Login Website Untuk Mencegah Penetrasi SQL Injeksi Secara Paksa

Dalam dunia IT pada website sangat rentan akan serangan hacker dengan berbagai jenis cara agar mereka dapat membobol kemanan website target. Serangan SQL Injeksi sering dilakukan pada pembobolan website dari form login dengan menginputkan username dan password khusus injeksi sehingga website dapat dibobol dengan mudah. Dalam mengamankan sebuah website dari serangan injeksi beragam caranya salah satunya dengan menggunakan teknik maxlength dan input type number. Teknik maxlengntht dan input type number ini dibuat dalam bentuk source code php atau html yang disisipkan kedlalam source code form login pada bagian input username dan password. Salah satu keunggulan teknik maxlengnth dan input type ini akan membuat batasan inputan username dan mengubah format inputan password hanya bertipekan angka saja yang artinya akan mencegah hacker dalam melakukan penetrasi secara paksa dalam serangan SQL Injeksi pada website

The invention relates to a method and a device for detecting the drainage flow rate of a hidden water tank

The invention discloses a method and a device for detecting the drainage flow rate of a hidden water tank. The method of real-time communication between electronic scale load-bearing device and profinet is proposed to realize the detection of drainage flow. The device consists of electronic scale load-bearing device, high-speed electronic scale acquisition module, PLC lower computer, touch screen man-machine interaction layer and so on. The device achieves fast detection response time and ensures sample integrity. It also avoid the problem of slow response of the input type liquid level sensor and inconvenient installation of magnetostrictive liquid level sensor.

The Copernicus EMS Validation service as a vector for improving the emergency mapping based on Sentinel data

<p class="p1">The Copernicus Emergency Management Service (CEMS) is coordinated by the European Commission and “provides all actors involved in the management of natural disasters, man-made emergency situations, and humanitarian crises with timely and accurate geo-spatial information derived from satellite remote sensing and complemented by available in situ or open data sources”. It includes two components, Early Warning and Monitoring and Mapping. The latter provides on demand geo-spatial information derived from satellite imagery during all phases of the disaster management cycle. It includes 3 systems, Rapid Mapping (RM), Risk and Recovery Mapping (RRM), and a Validation Service. RM provides geospatial information immediately after a disaster to assess its impact; RRM in the prevention, preparation and reconstruction phases; and the Validation Service is in charge of validating and verifying the products generated by both, and of collecting and analyzing users’ feedback. The wide spectrum of activities framed in the Validation Service has allowed it to become a vector to improve the Mapping component through the testing of new methodologies, data input type, or approach for the creation of emergency cartography in the frame of the CEMS. The present paper introduces the main investigation lines based on Sentinel-1 and 2 for flood and fire monitoring that could be implemented in the CEMS services taking into consideration the characteristics of the Mapping component in terms of products to create and time constraints. The applicability of Sentinel-1 for flood monitoring based on the backscattering, the MultiTemporal Coherence (MTC), and dual polarization; and for burnt area delineation based on MTC was studied, while Sentinel-2 was used for burnt area delineation based on vegetation indices. Results indicate that proposed methodologies might be appropriate for the creation of crisis information products in large areas, due to the relative easy and fast implementation compared to classic photo interpretation, although further applicability analyses should be carried out.</p>