Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning

Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies.

Image Segmentation Using PSO-Enhanced K-Means Clustering and Region Growing Algorithms

Image segmentation is a basic image processing technique that is primarily used for finding segments that form the entire image. These segments can be then utilized in discriminative feature extraction, image retrieval, and pattern recognition. Clustering and region growing techniques are the commonly used image segmentation methods. K-Means is a heavily used clustering technique due to its simplicity and low computational cost. However, K-Means results depend on the initial centres’ values which are selected randomly, which leads to inconsistency in the image segmentation results. In addition, the quality of the isolated regions depends on the homogeneity of the resulted segments. In this paper, an improved K-Means clustering algorithm is proposed for image segmentation. The presented method uses Particle Swarm Intelligence (PSO) for determining the initial centres based on Li’s method. These initial centroids are then fed to the K-Means algorithm to assign each pixel into the appropriate cluster. The segmented image is then given to a region growing algorithm for regions isolation and edge map generation. The experimental results show that the proposed method gives high quality segments in a short processing time.

Removal of COD from petroleum refinery wastewater using electrocoagulation method

This research assessed the removability of chemical oxygen demand (COD) from petroleum effluent using aluminum-based electrocoagulation reactor. A series of batch flow studies have been conducted to evaluate the impact of current density, electrodes separation, and duration of treatment on the removal of COD from the refinery effluent. The COD levels were determined employing the remaining concentrations using spectrophotometer namely Hach-Lang and standard cuvette test (LCC 514, LCK 314, or APC 400). The findings of the current investigation indicate the capacity of the electrocoagulation technique in a relatively short processing time to reduce the COD levels. The greatest efficiency in removing COD has been determined to be 80.0%. After 100 minutes of electrolysis, a current density of 8 mA/cm2 and electrodes separation of 20 mm achieved the highest percentage removal.

Influence of the shaker mill in the properties of ZnO processed by high energy milling

This work evaluates how the High Energy Ball Milling (HEBM) in a shaker mill influences the optical, physical, and microstructural properties of ZnO. The procedure also combines Fe inclusion from the grinding medium with particle size reduction. ZnO powder was milled by 1, 2, 3, 4, and 5 h, which resulted in a particle size reduction to the nanometric scale with a mean size of around 50 nm and a crystallite size reduction by three times when processed from 4 h. Milling has proven to be an efficient process for obtaining nanoparticles with an incredibly short processing time and changed the morphology of the particles from random to spherical shapes. Results also indicate the processing progressively expanded the ZnO hexagonal structure due to the imposed strain and Fe inclusion, which can help to decrease the bandgap and slow down the recombination rate of the electron-hole pairs, improving the photocatalysis activity. The optical results showed no additional band appeared due to milling processes and diminished the bandgap from 3.37 to 3.21 eV. Milling also led to an increase in the c value from 5.2076 to 5.2112 Å, which is one of the most important factors for improved antibacterial activity. HEBM has proved to be a suitable process for obtaining ZnO nanoparticles with properties useful for various applications.

A Performance-Based Dispatching Rule for Decentralised Manufacturing Planning and Production Control System

Considering a Flow Shop production line in an Industry 4.0 setting where the Cyber-Physical System (CPS) and Internet of Things (IoTs) can be deployed, a newly Performance-based Decentralised Dispatching Rule (PDDR) is proposed. It combines known dispatching rules with the knowledge of the monitored production system state. The goal is to provide a novel dispatching rule based on production line performance oversight. The governance system considers the machine condition in terms of machine utilisation. Regarding the assessment scenario, the proposed rule has been tested and compared with the well-known Short Processing Time (SPT) and the First-In-First-Out (FIFO) rule in a higher generality way by taking into account unforeseen events that may occur in production (such as breakdowns, potential rework, micro-stops, and unplanned machine setups). The simulation results showed interesting results where the flexibility of this rule, as well as its practical use with real hypotheses are its main advantages.

Flexible Multiscale Pore Hybrid Self-Powered Sensor for Heart Sound Detection

This research introduces an idea of producing both nanoscale and microscale pores in piezoelectric material, and combining the properties of the molecular β-phase dipoles in ferroelectric material and the space charge dipoles in order to increase the sensitivity of the sensor and modulate the response frequency bandwidth of the material. Based on this idea, a bi-nano-micro porous dual ferro-electret hybrid self-powered flexible heart sound detection sensor is proposed. Acid etching and electrospinning were the fabrication processes used to produce a piezoelectric film with nanoscale and microscale pores, and corona poling was used for air ionization to produce an electret effect. In this paper, the manufacturing process of the sensor is introduced, and the effect of the porous structure and corona poling on improving the performance of the sensor is discussed. The proposed flexible sensor has an equivalent piezoelectric coefficient d33 of 3312 pC/N, which is much larger than the piezoelectric coefficient of the common piezoelectric materials. Experiments were carried out to verify the function of the flexible sensor together with the SS17L heart sound sensor (BIOPAC, Goleta, CA, USA) as a reference. The test results demonstrated its practical application for wearable heart sound detection and the potential for heart disease detection. The proposed flexible sensor in this paper could realize batch production, and has the advantages of flexibility, low production cost and a short processing time compared with the existing heart sound detection sensors.

Accelerating Aging of White and Red Wines by the Application of Hydrostatic High Pressure and Maceration with Holm Oak (Quercus ilex) Chips. Influence on Physicochemical and Sensory Characteristics

Background: The use of holm oak (Quercus ilex) chips as a potential alternative wood and the application of hydrostatic high pressure (HHP) as an alternative technique to accelerate the release to the wine of wood-related compounds within a short processing time were evaluated. Methods: Five treatments were investigated: (i) bottling without any treatment (B); (ii) and (iii) bottling after maceration (5 g/L) of holm oak chips with HHP treatments (400 MPa, 5 and 30 min) (HHP5, HHP30); (iv) bottling after maceration during 45 days with chips (M), and; (v) maceration in tanks without chips (T). The effects of treatments on general parameters, polyphenols, color, and sensorial characteristics of red and white wines were investigated over 180 days. Results: HHP5, HHP30, and M increased the polyphenols content, thus modified the chromatic characteristics regarding B and M treatments of white wines, also the tasters differentiated HHP5, HHP30, and M from B and T. However, these effects were not observed in red wines. Thus, the effect of the wood depends on the type of wine in which it is used. Conclusions: This research contributes to better knowledge about these chips as a new alternative wood species and the use of HHP as a useful technology to accelerate the aging of wines.

The Application of genetic algorithms for the selection of WSE companies in Warsaw for the investment portfolio.

Portfolio analysis is a tool particularly intended for investors. Risk assessment and risk specification make the investor able to properly diversify and offset the portfolio. Broadly speaking, there are multiple tools destined for building up an efficient set of portfolios.One of them is Markowitz’s model theory postulating building up a portfolio determined on the basis of equilibrium between expected profit level as well as accepted level of risk assessment.In the context of this paper, the objective is to shed some light on creating investment portfolios based on either Markowitz's portfolio theory or evolutionary algorithm. The simulation based methods for building up a portfolio of approximately 40-50 companies listed out in the primary marketof the Warsaw Stock Exchange using the selection function proposed in the BA thesis were presented.Portfolio profit values have been evaluated in a dynamically shifted time window. The conducted analysis showed shifts in the economy at certain periods of time. The implemented genetic algorithms smoothly handled the optimization with a relatively short processing time of the task result.

Use of UHPH to Obtain Juices With Better Nutritional Quality and Healthier Wines With Low Levels of SO2

Ultra-high pressure homogenization (UHPH) is a high pressure technique in which a fluid is pressurized by pumping at higher than 200 MPa and instantaneously depressurized at atmospheric pressure across a special valve. The full process takes <0.2 s and the in-valve time is <0.02 s. In the valve, extremely intense impacts and shear forces produce the nanofragmentation of biological tissue at a range of 100–300 nm. The antimicrobial effect is highly effective, reaching easily inactivation levels higher than 6-log cycles even at low in-valve temperatures. At in-valve temperatures of 140–150°C (0.02 s) the destruction of thermoresistant spores is possible. Even when the temperature in-valve can be elevated (70–150°C), it can be considered a gentle technology because of the tremendously short processing time. It is easy to get outlet temperatures after valve of 20–25°C by the expansion and assisted by heat exchangers. Thermal markers as hydroxymethylfurfural (HMF) are not formed, nor are deleterious effects observed in sensitive compounds as terpenes or anthocyanins, probably because of the low effect in covalent bonds of small molecules of the high-pressure techniques compared with thermal technologies. Additionally, intense inactivation of oxidative enzymes is observed, therefore protecting the sensory and nutritional quality of fruit juices and avoiding or reducing the use of antioxidants as sulphites. UHPH can be consider a powerful and highly effective continuous and sterilizing technology without thermal repercussions, able to keep fresh juices with most of their initial sensory and nutritional quality and allowing high-quality and natural fermented derivatives as wine.

Liberating microfossils from indurated carbonates: comparison of three disaggregation methods

Three disaggregation methods, i.e. Calgon, acetic acid and electric pulse fragmentation (EPF), have been applied to a range of heavily lithified, carbonate-rich sedimentary rock samples of Paleogene age. Samples are predominantly from the carbonate-rich, shallow water domain (<250 m palaeo-water depth) of Tanzania, Malta and the United Arab Emirates (Paleogene Tethys Ocean). The effectiveness and efficiency of each method has been compared, in addition to the preservation of the resultant liberated microfossil material (primarily larger foraminifera; LF). Of the three methods, the most efficient and effective was EPF, which liberated the largest number of LF in a very short processing time and resulted in the best preservation. Samples with calcitic, silicic, and clay matrices and cements were successfully disaggregated using EPF. In this study, recovered microfossils were largely >500 µm, suggesting this technique may be more appropriate for liberating larger microfossils (e.g. LFs); however, we discuss nuances to the method that would allow for more effective recovery of smaller microfossil specimens. The more traditional acetic acid method was also able to disaggregate a number of the samples; however, preservation of the LF was compromised. We suggest a best-practice methodology for implementing EPF in micropalaeontological studies.