Conversion Technique Research Articles

Stress Detection using Deep Learning Techniques

Abstract: Stress is a typical component of daily life that has an impact on people in different circumstances. However, sustained or acute stress can negatively impact our health and interfere with our daily activities. Early recognition of mental stress is essential for good management and the avoidance of future health problems brought on by chronic stress. Understanding the connection between facial expressions and the accompanying emotional experiences of individuals is a topic of great interest. According to research, facial expressions and indications might offer important clues for the study and classification of stress. Notably, changes in the mouth and eyebrows are important signs of stress on the human face. This technology records live video and applies conventional conversion to capture stress levels. In order to analyse the user's stress levels, this system records live video and uses conventional conversion and image processing techniques. The technology provides more precise and effective results in stress prediction by utilising machine learning algorithms that concentrate on brow and lip motions.

Role of in situ (in contact with biomass) and ex situ (in contact with pyrolysis vapors) transition metal catalysts on pyrolysis of cherry pits

While energy dense liquid biofuels can be produced from sustainable waste feedstocks via thermochemical conversion techniques, these biofuels are plagued by high acidity and low stability, largely due to the presence of oxygenated functional groups. By utilizing transition metal catalysts during pyrolysis either in situ within the biomass, or ex situ (directly downstream supported on an alumina substrate) acting on the pyrolysis vapors, the quality of the bio-oil can be improved via deoxygenation. Four transition metals (Cu, Mn, Ni, Zn) were either wet impregnated on cherry pit biomass, or were impregnated on alumina wool fibers placed downstream of the biomass to act on the devolatilized gases. The in situ catalysts Cu, Mn and Zn increase the pyrolysis bio-oil yield. The Mn, Ni and Zn used as ex situ catalysts increase the total non-condensable gas produced, suggesting that these downstream catalysts crack the pyrolyzed molecules into smaller organics and water. The ex situ catalysts result in larger quantities of especially oxygen-containing gases like CO2, which in turn lowered the bio-oil’s O/C ratio. However, such reactions also increase the water content of the resulting bio-oil. Both Mn and Zn used in either location enhance the concentration of alcohols relative to aromatic carbons, and when used ex situ, Mn shows a decrease in the ratio of aromatic to aliphatic carbon content.

MoMusic: A Motion-Driven Human-AI Collaborative Music Composition and Performing System

The significant development of artificial neural network architectures has facilitated the increasing adoption of automated music composition models over the past few years. However, most existing systems feature algorithmic generative structures based on hard code and predefined rules, generally excluding interactive or improvised behaviors. We propose a motion based music system, MoMusic, as a AI real time music generation system. MoMusic features a partially randomized harmonic sequencing model based on a probabilistic analysis of tonal chord progressions, mathematically abstracted through musical set theory. This model is presented against a dual dimension grid that produces resulting sounds through a posture recognition mechanism. A camera captures the users' fingers' movement and trajectories, creating coherent, partially improvised harmonic progressions. MoMusic integrates several timbrical registers, from traditional classical instruments such as the piano to a new ''human voice instrument'' created using a voice conversion technique. Our research demonstrates MoMusic's interactiveness, ability to inspire musicians, and ability to generate coherent musical material with various timbrical registers. MoMusic's capabilities could be easily expanded to incorporate different forms of posture controlled timbrical transformation, rhythmic transformation, dynamic transformation, or even digital sound processing techniques.

Optimal time-frequency equivalency factor for approximate interconversion between the dynamic and relaxation moduli

The viscoelastic stiffness of asphalt concrete is commonly represented using relaxation modulus and dynamic modulus, which are functions of loading time and loading frequency, respectively. They are typically measured via experimental testing whereby only one of the moduli is determined, and interconversion techniques can be used to obtain the other modulus if needed. Although exact approaches exist for pavement modulus interconversion, they can be difficult to implement in practice, and approximate conversion techniques have therefore been developed for conventional use. A popular approach is to approximate a direct relationship between the time and frequency domains via an equivalency factor, but there is no apparent consensus on its proper value. In this paper, a new numerical technique is applied to experimental data to ascertain the optimal value of the time-frequency equivalency factor. Approximate conversions from dynamic modulus to relaxation modulus are conducted using the optimal factor, and results are compared to popular alternative approaches. The optimal factor is determined to be with 95% confidence. Using the mean value of 0.0673 produced conversion errors of 1.41% on average among 30 samples of hot mix asphalt.

A Review on Direct Digital Conversion Techniques for Biomedical Signal Acquisition

Biomedical signals such as Electrocardiogram (ECG), Electroencephalogram (EEG) and photoplethysmography (PPG) are recorded routinely to provide helpful information for early diagnosis of disease. Low power consumption is very important to allow long-term ambulatory monitoring with battery-powered systems. A direct digital conversion (DDC) technique has been proposed in recent years, which employs preamplifier and data converters, reducing the complexity of the readout chain and thus its power consumption. This paper provides a review on DDC for biopotential signals and bio-optical signal acquisition. The state-of-the-art DDC-based readout architectures together with circuit implementations are provided.

Bioconversion of CO2 and potential of gas fermentation for mainstream applications: Critical advances and engineering challenges

AbstractThe undesirable consequences of climate change are attributed to the ever‐increasing emissions of greenhouse gases, especially carbon dioxide. For the sustainable development of human society, there is an urgent need to develop novel techniques for efficient conversion and utilization of carbon dioxide. Hence, carbon capture and storage, artificial bioconversion of carbon dioxide are topics of great interest for current researchers across the globe. Here, we elucidate the different techniques of carbon fixation, which fall under the broad categories of natural fixation pathways, thermo‐catalytic conversion techniques, and synthetic carbon fixation pathways. Based on a comparative analysis, gas fermentation is a promising method for the microbial conversion of CO2‐containing gases into fuels and chemicals. However, for mainstream applications, in‐depth reviews of different reactor configurations and the various engineering aspects related to process development are still lacking. We have analyzed the published literature relating to artificial bioconversion of carbon dioxide and focused on the factors like equipment selection and reactor design that can assist in scale‐up of gas fermentation technology. The review provides an in‐depth understanding of engineering aspects related to stirred tank and bubble column reactors, focusing on the technical design parameters and discussing the conditions for optimal operation and performance during commercial gas fermentation processes.

A computer-aided feature-based encryption model with concealed access structure for medical Internet of Things

One of the Internet of Things (IoT) security issues is the secure sharing and granular management of data access. This study recommends a feature-based encryption scheme with a hidden access structure for medical IoT data security. While establishing fine-grained access control of ciphertext data, the system can guarantee clinical client data privacy. First, it is recommended to convert identity-based encryption (IBE) into a feature-based encryption model (FBEM) using a universal conversion technique that supports multi-valued attributes and gates. IBE characteristics could be inherited by the converted FBEM. The conversion method is then used to change the receiver anonymous IBE scheme into the FBEM scheme with concealed access structure. The FBEM model is then used to construct the IoT scenario for the smart medical application. Theoretical analysis and experimental findings reveal that the suggested system provides advantages over prominent systems regarding computing efficiency, storage load, and security when the access structure is disguised.

“Can my mother come?” Asylum interviews with unaccompanied and separated children seeking asylum in the Netherlands

IntroductionThe UN Convention on the Rights of the Child (CRC) accords children the right to give their views on all important decisions in their life (art.12 CRC). In the past decades increased awareness has risen among professionals who work with children in judicial and administrative proceedings, to hear their voices. The key question guiding this research was whether refugee children have the possibility to meaningfully participate in asylum proceedings, as required by international children's rights law and standards? Asylum application procedures are highly complex administrative procedures, that are often not adapted to the capacities and level of maturity of children. Recent studies suggest that the right to participation and information is insufficiently safeguarded for children involved in asylum procedures. Unaccompanied children seeking asylum as young as 6 years of age have to go through the asylum procedure in the Netherlands. Efforts have been put in making this procedure more child-friendly, by designing a child-friendly interview room and training immigration officers. The aim of this study was to explore to what extent the immigration authority takes into account children's voice, age and development, in line with international children's rights.MethodsObservations have been conducted of first instance asylum application interviews with children held by immigration officers. In total 13 interviews with children aged 7–11 have been observed, that were held between 2012 and 2019.ResultsThe results show that child-friendly conversation techniques and tools are used to some extent, however, immigration officers should be trained more extensively in order to enhance the effective participation of young children.DiscussionIt is concluded that interviews with children could be improved by giving children more information and using techniques to communicate with young children. In order to truly hear the child's voice the interviews should be better adapted to the age and level of development of unaccompanied children.

Artificial intelligence-driven malware detection framework for internet of things environment.

The Internet of Things (IoT) environment demands a malware detection (MD) framework for protecting sensitive data from unauthorized access. The study intends to develop an image-based MD framework. The authors apply image conversion and enhancement techniques to convert malware binaries into RGB images. You only look once (Yolo V7) is employed for extracting the key features from the malware images. Harris Hawks optimization is used to optimize the DenseNet161 model to classify images into malware and benign. IoT malware and Virusshare datasets are utilized to evaluate the proposed framework's performance. The outcome reveals that the proposed framework outperforms the current MD framework. The framework generates the outcome at an accuracy and F1-score of 98.65 and 98.5 and 97.3 and 96.63 for IoT malware and Virusshare datasets, respectively. In addition, it achieves an area under the receiver operating characteristics and the precision-recall curve of 0.98 and 0.85 and 0.97 and 0.84 for IoT malware and Virusshare datasets, accordingly. The study's outcome reveals that the proposed framework can be deployed in the IoT environment to protect the resources.

A Novel Photopharmacological Tool: Dual-Step Luminescence for Biological Tissue Penetration of Light and the Selective Activation of Photodrugs.

Conventional pharmacology lacks spatial and temporal selectivity in terms of drug action. This leads to unwanted side effects, such as damage to healthy cells, as well as other less obvious effects, such as environmental toxicity and the acquisition of resistance to drugs, especially antibiotics, by pathogenic microorganisms. Photopharmacology, based on the selective activation of drugs by light, can contribute to alleviating this serious problem. However, many of these photodrugs are activated by light in the UV-visible spectral range, which does not propagate through biological tissues. In this article, to overcome this problem, we propose a dual-spectral conversion technique, which simultaneously makes use of up-conversion (using rare earth elements) and down-shifting (using organic materials) techniques in order to modify the spectrum of light. Near-infrared light (980 nm), which penetrates tissue fairly well, can provide a "remote control" for drug activation. Once near-IR light is inside the body, it is up-converted to the UV-visible spectral range. Subsequently, this radiation is down-shifted in order to accurately adjust to the excitation wavelengths of light which can selectively activate hypothetical and specific photodrugs. In summary, this article presents, for the first time, a "dual tunable light source" which can penetrate into the human body and deliver light of specific wavelengths; thus, it can overcome one of the main limitations of photopharmacology. It opens up promising possibilities for the moving of photodrugs from the laboratory to the clinic.

Ultrahigh-resolution full-color micro-LED array with enhanced efficiency based on a color conversion technique

Micro-light emitting diodes (μLEDs) with remarkable advantages are becoming mainstream in next-generation display technologies such as augmented reality/virtual reality displays. However, further development of μLEDs still faces significant challenges, including mass transfer yield. Here, we report color conversion layer (CCL)-based, full-color, highly efficient μLED arrays as a solution to address this challenging issue. Further amalgamating CCL with color purity enhancement film enables us to realize 7 μm×7 μm subpixel fluorescent arrays with a color gamut of 95.34% in DCI-P3, resulting in an overall 1588 PPI (pixel per inch). Our results provide a promising technique for fabricating higher PPI-based next-generation display technology.

Exploring Dynamic P-Q Capability and Abnormal Operations Associated with PMSG Wind Turbines

With the proliferation of large-scale grid-connected wind farms, subsynchronous oscillation (SSO) incidents associated with Type-4 wind turbines (WTs) with a permanent magnet synchronous generator (PMSG) have occurred frequently. These incidents have caused severe reliability risks to the power grid. Conventionally, P-Q capability charts are utilized to ensure the safety operating region of a synchronous generator. However, a PMSG WT exhibits completely different and dynamic P-Q capability characteristics due to the difference in energy conversion technique and several critical factors related to the WT power converters. This paper presents a comprehensive dynamic P-Q capability study of a PMSG WT with sufficient and accurate considerations of the WT control and operation in the dq reference frame, its power converter constraints, and grid dynamics. Models of a PMSG WT are first developed based on its control principle in the dq reference frame. Then, algorithms for obtaining the P-Q capability charts of the WT are developed with the considerations of complete WT constraints in different aspects. The study analyzes the root cause of many abnormal operations of grid-connected PMSG WTs, reported in the literature, from the dynamic P-Q capability perspectives. The proposed study is verified via an electromagnetic transient (EMT) model of a grid-connected Type-4 WT.

Selective Photocatalytic C–C Bond Cleavage of Lignin Models and Conversion to High-Value Chemical by Polyoxometalate Under a Mild Water-Based Environment

Selective bond cleavage using a photoredox reaction is a powerful technique in the chemical conversion of biomass such as lignin since it enables us to produce value-added aromatic compounds by controlling the activation of certain chemical bonds. Also, a water-based environment would be preferred for the sake of an eco-friendly reaction, additionally having the advantage of utilizing water-originated species such as •OH and H2O2. However, a direct cleavage of the C–C bond is still challenging due to the difficulties in tailoring redox potentials of catalysts relevant to covalent bond decoupling. Herein, we investigate a selective C–C bond cleavage of the β-O-4 lignin model compound using giant Keplerate polyoxometalates as photocatalysts. The redox potentials of photocatalysts are varied by changing metal compositions, and the correlation between redox potential and covalent bond decoupling is elucidated, probing each set of active radicals by HOMO and LUMOs. Especially, Mo132 shows a highly selective C–C bond cleavage with hydroxyl groups in the lignin model and produces phenyl formate─a effective CO surrogate for the synthesis of various organic compounds─as the main product (97.8% of product selectivity) in a water-based condition. Furthermore, Mo132 decomposes other aromatic polymer models, indicating the potential of photocatalytic C–C bond decoupling of polymers under a benign environment.

Waste to energy conversion: a perspective from adult learners in South-East Nigeria

The study identified waste categories that can be turned into energy, energy conversion techniques, and ways that these techniques could support a circular economy in South East Nigeria from the viewpoint of adult learners. In the study, a descriptive survey design was employed. The study was directed by three research questions and two hypotheses. There were 67731 adult learners in the population. The study included a sample size of 605 participants. Mean, t-test, and Pearson correlation were used to examine the data, which was collected using questionnaires. The results showed that wood, sawdust leaves and bark of trees, paper and cardboard, food remains like vegetable stock, yam peels, among others, were the types of waste that can be converted to energy. The study found out that combustion technology, gasified technology, and anaerobic digestion, among others, were some of the waste to energy conversion techniques. The study also noted that waste to conversion techniques foster circular economy. The paper recommends that the government should display political will by designing and enacting laws that facilitate the development of indigenous waste-to-energy conversion technology. This way, even at the household level, these technologies will be inexpensive and accessible.

Energy Autonomous Wake-Up Detectors

With the growing need to monitor, understand and manage our surroundings, the demand for wireless sensor networks has been constantly increasing. To make practical application of wireless sensor networks possible, their sensor nodes utilize specialized low-power always-on wake-up detectors to increase their energy efficiency. Advances in energy conversion and harvesting techniques and utilization of micro- and nano-electromechanical systems allow for development of autonomous wake-up detectors, powered by ambient or event energy.

Assessment of Multi-Phase Conversion and Modified PWM Strategy for Power Converters of Medium-Voltage Induction Motors Drive

Multi pulse and multilevel converters show great potential for use in medium voltage induction motor drive systems due to cascaded design and high-quality output. However, variable-speed medium voltage drives have demanding requirements in enhancement number of pulses with less number of DC sources and converter topologies with reduced component counts. This paper utilizes the phase displacement technique as well as the three-phase to five-phase conversion technique so that the drive system extracts 30-pulse current from the grid supply, which helps to enhance the power quality at the grid end. In this paper, a 30-pulse AC-DC converter based on a 10-pulse converter is designed and implemented to feed a power 4-level cascaded inverter through three DC sources. Apart from this, a modified carrier-based LSPWM technique is used to enhance the performance of the MLI at a low switching frequency. An indirect field-oriented control (IFOC) method is utilized to address the issues of the drive system during dynamic operations. Performance analysis through theoretical and simulation by using the full-scale model is evaluated in terms of voltage and current quality, speed and torque response, losses, and efficiency. Experimental verification is also carried out on a 7.5kW, 415V laboratory prototype using a digital signal controller for implementation of the control scheme.

Transformation of Sound Energy into Electrical Energy Using IoT

Abstract: The world is expanding more and more rapidly and we have a lot of energy in bondage. Energy is in distinct forms i.e noise energy, electrical energy, mechanical energy, etc. Energy cannot be generated or demolished but it can be transformed from one energy formation into further. However, we are rapidly expanding and finding a solution to this situation. Now a days noise energy is considered one of the biggest problems in big cities and it cannot be controlled or reduced but they can be used as energy resources. And this noise energy can be used to bring about energy. Factories, industries, vehicles, airports, etc. cause a lot of sound energy. We modify this sound energy into electrical energy. Now a days, Electrical energy source play a foremost role. So, we require additional electrical energy as world population increasing rapidly, especially renewable resources, pure and clean energy sources. Non-renewable energy sources are in short supply. So, we need to utilise the renewable energy resource very carefully. And one of the main mottos of this project is the produced electrical energy is used to reduce the accidents in the deep curve hill station areas by detecting the vehicles. And also, we can use this produced electrical energy in street lights. This project describes work done on conversion techniques and methods used to convert noise energy to corresponding electrical energy

Skills necessary to conduct a competent conversation with the ward in social rehabilitation work

The article presents an analysis of decision-making issues regarding the selection of appropriate skills during an interview. An important issue in the area of justice and social rehabilitation is the skilful conversation. It is necessary to train the personnel in such a way as to obtain precise and complete information through all elements of the social rehabilitation system. By improving conversation techniques, the functioning of the entire social rehabilitation system will be improved, because it largely consists in collecting and providing information in a direct way.Innovative methods of conducting interviews should be implemented in social rehabilitation, should be based on empirical evidence, and the information contained in them should be presented in a practical way.

Direct Urea/H2O2 Fuel Cell with a Hierarchical Porous Nanoglass Anode for High-Efficiency Energy Conversion

Direct urea/H2O2 fuel cells (DUFCs) constitute a sustainable bifunctional energy conversion technique devoted to simultaneously eliminating environmental wastewater with urea and generating clean energy. However, exploring an efficient anode material for DUFCs still remains a huge challenge. In this work, a Ni-P hierarchical porous nanoglass (HPNG) catalytic electrode was developed via a low-cost, industrially available electrodeposition technique, which exhibits one of the best performances reported so far in the urea oxidation reaction (UOR), with a potential of 1.330 V at a current density of 10 mA cm-2 and a Tafel slope of 9.77 mV dec-1. The superior UOR performance of the HPNG electrode is attributed to the excellent intrinsic catalytic activity of NG with a high-energy state and an extremely enlarged surface area from the unique 3D hierarchical porous structure. Furthermore, a DUFC system with the HPNG anode shows a performance breakthrough as indicated by the maximum power density of 38.15 mW cm-2 for 0.5 M urea, representing one of the best yet reported DUFCs. Our work demonstrates the feasibility of the scalable production of HPNG electrodes and is expected to be a great contribution to the development of the practical use of DUFCs in the near future for bifunctional energy conversion.

Evaluation of Single and Dual image Object Detection through Image Segmentation Using ResNet18 in Robotic Vision Applications

This study presents a method for enhancing the accuracy of object detection in industrial automation applications using ResNet18-based image segmentation. The objective is to extract object images from the background image accurately and efficiently. The study includes three experiments, RGB to grayscale conversion, single image processing, and dual image processing. The results of the experiments show that dual image processing is superior to both RGB to grayscale conversion and single image processing techniques in accurately identifying object edges, determining CG values, and cutting background images and gripper heads. The program achieved a 100% success rate for objects located in the workpiece tray, while also identifying the color and shape of the object using ResNet-18. However, single image processing may have advantages in certain scenarios with sufficient image information and favorable lighting conditions. Both methods have limitations, and future research could focus on further improvements and optimization of these methods, including separating objects into boxes of each type and converting image coordinate data into robot working area coordinates. Overall, this study provides valuable insights into the strengths and limitations of different object recognition techniques for industrial automation applications.