Miracle-dz/UniFAR-FTUs huggingface dataset:
State of The Art of Machine Learning for Product Sustainability

Nowadays, the feasibility and life-time for every business venture is dependent on the generation of sustainable products and services. Almost all businesses and industries have endorsed Machine Learning (ML) technologies. The application of Machine Learning in determining the sustainability aspects is quite a challenging process. ML models can help in capturing the decision-making processes or predict the sustainability evaluation metrics assuming some key aspects such as triple bottom line and life-cycle factors are provided to the models. The main aim of this paper is to present a systematic review of studies related to the product sustainability assessments, framework development using Machine Learning techniques. Search strategies are applied to the most common computer science digital database libraries from 2010 to 2020 in the field of product sustainability using machine learning techniques. 15 out of 23 papers are shortlisted as was considered suitable for this research. A set of the research queries are defined and addressed by the review process. Based on the findings obtained in this literature review, future outlook in the field of Machine Learning for sustainable production is represented.
A Novel Companding Technique to Reduce High Peak to Average Power Ratio in OFDM Systems

The reduction of the high peak-to-average-power ratio (PAPR) is important to the efficiency of the orthogonal frequency division multiplexing (OFDM) technique. Excessive PAPR contributes to non-linear clipping induced harmonic distortions that reduce system reliability. In this article, a new technique for decreasing the high PAPR in OFDM with minimum effects on the system performance is proposed. The technique uses the image adjust (IMADJS) function to reduce the high PAPR of transmitted OFDM signals by compressing large signals and expanding small signals. In comparison, the IMADJS strategy has the advantage of maintaining a constant average power level before and after companding. A comparative analysis is provided between the proposed (IMADJS) technique and well-known companding techniques such as $\mu -$ law, absolute exponential (AEXP), and the new error function (NERF). The comparison is based on PAPR, bit error rate (BER), power spectral density (PSD), and average power performance metrics. Simulation results confirm that the IMADJS technique significantly improved the drawbacks of the PAPR. Furthermore, the PAPR is reduced by 2.81dB. The IMADJS technique has less impact on the original power spectrum than on other companding schemes. The average out-of-band radiation of the IMADJS technique reaches about −50dB at the frequency of 0MHz. In contrast, the average of the original OFDM signal is that the out-of-band radiation reaches around −52dB. The AEXP and NERF companding techniques reach about −46dB, while the $\mu $ -law companding technique hits about −37dB.
Millimeter-Long Silicon Photonic Antenna for Optical Phased Arrays at 2-μm Wavelength Band

Silicon photonic optical phased arrays are promising solutions for solid-state LIDAR. But the nonlinear absorption significantly saturates the optical power in the waveguides at 1.5-μm wavelengths. Besides, the high refractive index contrast of silicon grating exhibits a strong diffraction strength of the antenna, which results in a limited aperture of the device. These ultimately place a limit on the detection accuracy and reach. Here, a shallow etched two-dimensional subwavelength grating structure is proposed to improve the efficiency and aperture of the waveguide antenna. A reduction of diffraction strength is achieved via nanohole structure instead of conventional grating lines. Thus, a millimeter-long optical antenna is demonstrated with a peak efficiency of 71%. The far-field diffracted beam width of 0.035° and vertical field-of-view of 17.5° are achieved within 100 nm wavelengths range from 1.95 μm to 2.05 μm. The operation at 2-μm waveband also guarantees better eye-safety and significantly reduces the nonlinear absorption in silicon waveguide compared with conventional 1.5-μm band.
Grade Prediction From Multi-Valued Click-Stream Traces via Bayesian-Regularized Deep Neural Networks

Learning activities that have been designed linearly in small private online courses and thematic massive open online courses often result in learners undergoing similar learning journeys. While most learners perform activities leading to similar click-stream sequences, they achieve contrasting grades. This phenomenon of similar online learning behavior resulting in different learning outcomes is termed as the multi-valued (MV) problem. Existing grade prediction models are not designed to handle MV inputs and this problem is further aggravated because the MV characteristic cannot be expressed analytically due to the complexity of these sequential inputs. In this work, the detrimental impact of MV inputs on grade prediction is highlighted. A new MV-sensitive grade prediction model is proposed to quantify the MV characteristic of the dataset. As opposed to existing deep neural network based grade prediction models, the proposed algorithm is able to capture the uncertainty associated with each click-stream sequence. This results in the model organizing its feature space to discriminate the MV inputs. With the newly formulated surrogate measures, and by removing these identified MV inputs, experiment results show that the proposed models achieve lower prediction error compared to existing algorithms when evaluated on two online-learning datasets.
A Knowledge Graph-based Sensitive Feature Selection for Android Malware Classification

The rapid increase in Android malware has brought great challenges to malware analysis. To deal with such a severe situation, it has been proposed an effective way which groups malware with common behaviors into the same malware family. Although there are many methods for malware family classification, the most critical and primary step is always the definition of sensitive behavior in an application, which will be beneficial for the later classification task. Much existing literature has manually selected sensitive features, such as permission, or even designed graph-based features via the control flow graph. They heavily depend on expert knowledge and time-consuming malware application analysis, which means it has to focus on the mal ware itself to dig out valuable security knowledge at first. However, the zooming malware overwhelms such expensive feature definition methods. To overcome such a problem, we adopt a knowledge graph-based sensitive feature selection method for Android mal ware classification. Based on the Android Developer documentation, an Android API knowledge graph is constructed at first. We can obtain not only permission but also related critical API from this graph. Note that both hyperlink relation and similarity relation are used to find out the critical API. With the knowledge graph-based sensitive features, we represent each Android malware as a boolean feature vector and send it in to a machine learning classifier for malware classification. We evaluate our proposed methods on three well-known Android malware datasets, such as Genome, Drebin, and AMD. The experimental results show that: 1) our proposed sensitive API is advantageous for malware detection; 2) API chosen by similarity relation can marginally improve performance; 3) different permission groups also make an influence for classification.
MLS: An MAE-Aware LiDAR Sampling Framework for On-Road Environments Using Spatio-Temporal Information

In recent years, light detection and ranging (LiDAR) sensors have been widely utilized in various applications, including robotics and autonomous driving. However, LiDAR sensors have relatively low resolutions, take considerable time to acquire laser range measurements, and require significant resources to process and store large-scale point clouds. To tackle these issues, many depth image sampling algorithms have been proposed, but their performances are unsatisfactory in complex on-road environments, especially when the sampling rate of measuring equipment is relatively low. Although region-of-interest (ROI)-based sampling has achieved some promising results for LiDAR sampling in on-road environments, the rate of ROI sampling has not been thoroughly investigated, which has limited reconstruction performance. To address this problem, this article proposes a solution to the budget distribution optimization problem to find optimal sampling rates according to the characteristics of each region. A simple yet effective mean absolute error (MAE)-aware model of reconstruction errors was developed and employed to analytically derive optimal sampling rates. In addition, a practical LiDAR sampling framework for autonomous driving was developed. Experimental results demonstrate that the proposed method outperforms all previous approaches in terms of both the object and overall scene reconstruction performances.
Device-Free Localization: A Review of Non-RF Techniques for Unobtrusive Indoor Positioning

Accurate, reliable indoor localization or positioning is the key enabler for location-based services. Indoor localization can be broadly classified into two distinct categories. Active localization entails tracking a tag attached to or carried by the target. Passive localization, on the other hand, involves positioning a device-free or untagged target. While passive or device-free localization is comparatively more difficult to achieve, it is the preferred option for many applications. Vision-based techniques can accurately localize an untagged target. However, privacy is a significant concern and thus have limited usability for many applications, especially in noncommercial and residential settings. Passive localization using radio frequency (RF) or wireless sensing is an unobtrusive option and has seen extensive research efforts in recent years leading to a saturated research field but no consensus solution. Researchers have been investigating alternative solutions that can facilitate robust passive localization. The rapid proliferation of Internet of Things (IoT) is bringing ubiquitous networked devices and ambient sensors into modern buildings. The consequential pervasive ambient intelligence and signals of opportunity can enable unobtrusive device-free positioning. This article presents a comprehensive review of non-RF solutions covering visible light-, infrared-, physical excitation-and electric field sensing-based techniques. Limitations of the state-of-the-art and potential future research directions are also outlined.
Wideband High-Gain Double-Sided Dielectric Lens Integrated With a Dual-Bowtie Antenna

A dual-bowtie antenna loaded with a double-sided dielectric lens is presented for wideband and high gain performance. The dielectric lens is a combination of the elliptical and conical frustum lenses formed on two sides (top and bottom) of a rectangular dielectric block, respectively. The conical frustum lens is mounted at the backside of the bowtie radiator to collect the radiated energy toward the elliptical lens. The bowtie antenna is fed by a coplanar strip (CPS) taper connected to a CPS-to-microstrip line (MSL) transition. The proposed lens-integrated antenna is fabricated and measured to have an operating bandwidth of 20.5–40.0 GHz. The antenna offers a peak gain of 15.1 dB. Also, the use of the double-sided dielectric lens helps to reduce the lens size of the equivalent gain by 31%. The overall size of the proposed lens antenna is 940 × 940 × 1008 mil.
Software Design and Implementation of Training Simulator for Operation and Maintenance of an Electrical Control System

The electrical control system of a certain equipment mainly completes the operation process control of the whole vehicle. It adopts a closed-loop interlocking control method. Its signals are cross-transmitted and the conversion nodes are complicated. In addition, the equipment is often in a field condition with turbulent transportation and the working environment is harsh, which causes a great loss of equipment and greatly affects the quality of teaching and training. In order to effectively solve this problem, this article develops an operation and maintenance training simulator for an electrical control system, using a combination of software and hardware, using industrial personal computer as a platform, using computer technology, automatic testing technology, microelectronics technology, and bus technology, Multimedia technology, computer software development technology, etc., to achieve simulation training for the electrical control system of a certain type of equipment. The operation training part of the system is designed according to the actual operation interface, and the equipment action process and results are demonstrated through the computer monitor (or projector) in the form of animation or equipment demonstration video. The hardware circuit part of the system mainly provides the actual working environment for the tested equipment, applies various excitation signals to the tested equipment, collects its response signals to judge its performance, and provides corresponding results according to the operation process. The system software mainly realizes the operation program control, animation demonstration and data saving functions of the simulated trainer. This article mainly introduces the software design and realization method of the system.
A novel method for making a one-piece tight-fitting garment

We propose a method for making a one-piece tight-fitting garment that can place flexible sensors for a certain individual. We scan a certain individual’s body and calculate the body shape parameters to generate corresponding Skinned Multi-Person Linear model (SMPL) model. We segment the SMPL model and use the ARAP parametrization method to flatten the segmented model into a 2D plane. Finally, we print the flattened model out for a tailor to sew a one-piece tightfitting garment. The result shows that the garment we manufactured fits human skin with high comfort and the sensors are placed precisely in the desired position.
LogSpy: System Log Anomaly Detection for Distributed Systems

Log analysis is an important part of distributed system management. System log records the running status of the system and contains a lot of important and valuable information. This paper proposes an anomaly detection method, LogSpy, for distributed systems. It uses the combination of natural language processing technology and clustering algorithm for log template mining and feature extraction. In anomaly detection, it is found that there are a large number of remote calls in the distributed systems and traditional CNN has certain limitations on this small amount of negative sample data. LogSpy introduces the attention mechanism in detection algorithm and optimizes the detection window and computational complexity. Experiments conducted on the OpenStack test platform show that LogSpy can perform excellent anomaly detection on distributed systems compared to traditional anomaly detection methods.
Design of MPPT Based Dual Axis Solar PV Array

Since last few decades, a rapid change in the generation of electricity has been seen with the world shifting from nonrenewable sources of energy towards the renewable sources with solar as the major source of electricity. In order to increase the amount of generation and reliability, this work aims to design a Maximum Power Point Tracking (MPPT) algorithm and dual axis based Solar Photo voltaic array (SPVA) which has proved to be a better solution as compared to MPPT based SPVA. All the modeling and simulation has been done in MATLAB/ Simulink.
Comparative Investigation of High Temperature Superconducting Linear Flux-Switching Motor and High Temperature Superconducting Linear Switched Reluctance Motor for Urban Railway Transit

Both Linear Switched Reluctance Motor (LSRM) and Linear Flux-Switching Motor (LFSM) have the merits of none-adhesive thrust force and simple and robust secondary structures. Therefore, LSRM and LFSM are quite suitable for applications such as vertical lifting, wave power generation and urban railway transit. To further improve the power density of them, the field windings of LFSM and DC windings of LSRM can be replaced with High Temperature Superconducting (HTS) coils, and thus forming High Temperature Superconducting Linear Flux-Switching Motor (HTS-LFSM) and High Temperature Superconducting Linear Switched Reluctance Motor (HTS-LSRM). Despite their similar structure, they have different working principles and electromagnetic performances. To reveal their merits and drawbacks for urban railway transit system, a comparative investigation is conducted in this paper using Finite Element Method (FEM).
Nature of the Catalyst Particles in CCVD Synthesis of Multiwalled Carbon Nanotubes Revealed by the Cooling Step Study

The true chemical nature and physical state of the catalyst particles in Catalytic Chemical Vapor Deposition (CCVD) synthesis of carbon nanotubes are the subject of intense discussions, as it is one of the keys to understand their growth mechanisms. The CCVD method considered in this article involves pyrolysis of mixed liquid aerosols and leads to the synthesis of large carpets of multiwalled nanotubes (MWNTs) partially filled with iron-based materials. The experimental approach consists in studying the influence of the cooling procedure applied at the end of the synthesis. Both slow standard cooling or quenching were performed, and the structure and chemical state of the iron-based particles were compared through complementary local and global investigations involving X-ray diffraction, electron microscopy, electron diffraction, as well as electron energy loss spectroscopy. We clearly demonstrate that iron-based catalyst particles are carbon-rich and oxygen-free in quenched samples, and that they oxidize during the slow cooling step. It is inferred that they are very probably molten supersaturated carbon-metal particles during the NT growth.
The Sustained Oscillation Modeling and Its Quantitative Suppression Methodology for GaN Devices

Gallium nitride (GaN) devices are widely used due to their excellent performance when integrated into power electronics applications. However, owing to their low parasitic parameters and fast switching speed, they are more prone to instability than the silicon devices. The sustained oscillation discussed in this article is one of the instability problems, which may result in overshoot or even more serious device breakdown; thus, it is pretty necessary to suppress it effectively in practical applications. It is the first time to adopt an RC snubber to suppress the sustained oscillation in this article, which is very simple and cheap but effective. First, the sustained oscillation modeling based on a double pulse circuit is carried out. Then, the RC region is established quantitatively according to the root locus analysis results, which makes up for the defects of these qualitative methods before. Furthermore, it is first found that the sustained oscillation can be fully suppressed due to the existence of dipoles. Finally, the oscillation suppression effect within the RC region is compared with that outside the region by the simulation and experimental results, which are consistent with the theoretical analysis. Additionally, the possible negative influence of the added RC snubber is also discussed in this article, which shows that it has a little impact on the switching speed and energy loss, while the sustained oscillation is well suppressed.
Quadruple Boost Multilevel Inverter (QB-MLI) Topology With Reduced Switch Count

Multilevel inverter concepts (MLIs) with switched capacitor (SC) are emerging due to their scope in sustainable energy systems as well as high-voltage applications. In the SC technique, required voltage levels can be achieved with the lesser number of dc sources in combination with the capacitor voltage. In this perspective, the design of high-gain MLI with reduced sources as well as switch count is a challenging task. This letter proposes a single-source-driven quadruple boost multilevel inverter topology (QB-MLI) with lesser order of resources over the other SC MLIs. The proposed QB-MLI is capable to produce nine levels of voltage in output by effectively balancing the two capacitor's voltage with the associated control logic. The different SC MLIs are compared to verify the pros and cons with respect to the contribution of the proposed QB-MLI topology. Detailed experimental results at various test conditions of a laboratory set-up have been presented to validate the performance of the proposed QB-MLI.
Application of Tensor Train Decomposition in S2VT Model for Sign Language Recognition

Sign language recognition is a conversion of sign language into text or speech, bridging the communication between the hearing and society. Recently, sequence-to-sequence video to text (S2VT) models has been employed in the field of sign language recognition as an effective method. However, more than 20 million parameters trained in S2VT models will result in a huge consumption in memory and computational resources, making it hard to be applied in mobile devices. In order to overcome this issue, we proposed to employ tensor-train decomposition in S2VT models to reduce the parameters. First, the impact of parameters of tensor-train factorization on the model performance was investigated systematically. After that, we applied tensor-train decomposition in different layers of a S2VT model to establish 6 tensor-train S2VT models for Chinese sign language recognition. The experimental results demonstrated that when the fully-connected layer and the first LSTM layer in S2VT was represented with tensor-train format, the model could obtain the best performance, remaining high accuracy and reducing parameters and memory significantly. The proposed tensor-train S2VT models can also be applied in other sequence-to-sequence problems to improve the performance.
Analysis of 2019-nCoV epidemic situation based on modified SEIR model and DSGE algorithm

In order to analyze the spread of novel coronavirus pneumonia. , a prediction model based on the modified SEIR infectious disease transmission kinetic model for the number of confirmed and fatal cases of novel coronavirus pneumonia was constructed to predict the outbreak of the epidemic in China. Differential equations were constructed to take into account the spread of the epidemic and the assignment of each parameter in the model to obtain the prediction results for China. The prediction of the epidemic in the United States was divided into two cases with and without a home order. In order to analyze the impact of 2019-nCoV epidemic on economic development, the fiscal revenue of Hubei Province from January to July in 2020 is analyzed, and the change of GDP and tertiary industry output value in Hubei Province is predicted by combining grey prediction model with time series. In this paper, DSGE algorithm is used to study the changes of labor supply, output, investment and capital under different costs during the epidemic period. The results were analyzed with respect to the major epidemic prevention measures taken by the Chinese Ministry of Health (MOH). Finally, the reliability of the model was analyzed.