Miracle-dz/UniFAR-FTUs huggingface dataset:
Solder Joint Defect Detection Based on Image Segmentation and Deep Learning

Image feature extraction and machine learning methods are used to detect and identify PCB solder joints. The normal/abnormal classification of solder joints are realized. What is more, the maximal class variance method (OTSU) is used to distinguish solder joints from background, and the Morphology was used to extract the suspected solder joints. The positive and negative samples were labeled and then the convolution neural network (CNN) was used to classify the test samples. Compared with the support vector machine (SVM) method, which extracts HOG features, and the fisher discriminant method based on the circular degree feature, the neural network has better classification effect.
Underwater Pressure and Temperature Sensor Based on a Special Dual-Mode Optical Fiber

In this paper, an all fiber optic sensor based on a Mach-Zehnder interferometer (MZI) has been proposed for the simultaneous measurement of underwater pressure and temperature, utilizing a dual-mode fiber (DMF) which has been specially designed, and supporting only the LP01 and LP02 modes propagating in the fiber. In this design, an in-line MZI sensor that was constructed by splicing a DMF between two pieces of single mode fibers, shows a critical wavelength (CWL) which exists in the transmission spectrum of the LP01-LP02 mode interference. Since the two peaks, located closest to the CWL (and from both lower and higher wavelengths), shift in opposite directions and show different sensitivities under temperature and water pressure variations, the DMF-MZI sensor is capable of measuring both the water pressure and the temperature simultaneously. The CWL-based interference spectrum is stable with the variation of underwater salinity or impurities seen around the fiber surface and independent of the polarization states of the transmission light. As a result, in the operation of the DMF-MZI sensor, underwater pressure and temperature sensitivities increase significantly, when the peak wavelengths are close to that of the CWL. A theoretical analysis has been developed and used to predict that the sensitivities of this specific DMF-MZI structure which can be further improved by increasing the physical length of the DMF and by adjusting the position of the first left/right peak to be closer to the critical wavelength. This co-located, multi-parameter all-fiber sensor developed in this way and showing relatively high sensitivity is easy to implement in the underwater environment. It does not require a complex shell design and the peaks nearest to a CWL are convenient, allowing easy identification and detection, thereby providing a large measurement range to satisfy the requirements of practical marine and fresh water measurements.
Unintended Side Effects in Complex Systems: Managing Project Risks and Wildfires

Effective project management will try to mitigate risks and prevent them from becoming issues where an issue means that a problem is impacting the project's ability to meet planned commitments. If not successful in preventing the risk from becoming an issue, project management will need to take immediate action to resolve the issue and avoid more impacts. Both of these activities rely on a common, fixed set of project resources for mitigation. This dual challenge is similar in many ways to the decisions faced in other fields to allocate resources between risk prevention and problem suppression such as in wildfire management. For most of the last century, the US Forest Service has focused on fire suppression when wildfires start. Rather than allowing frequent, small fires to burn, fire suppression leads to the accumulation of dead wood and other fuels leading to larger, hotter, and more dangerous fires, often consuming the oldest and largest trees which would have survived smaller fires unharmed. Therefore, wildfire management becomes a balancing between proactive prevention and reactive suppression in a system where physical and political dynamics interact. Important lessons can be learned for NASA project managers from the wildfire management case study. Effective risk management in space projects also balances suppression and prevention activities where an effective process will avoid project “fire-fighting. ” Fire-fighting in project management is what happens when teams must fix problems late in the development cycle, and on most NASA projects, this needs to be done within the current resources available. Often successful fire-fighting is rewarded as heroism and is a sought after characteristic for project managers. The rewarding of successful fire-fighting can discourage future prevention activities because it is harder to recognize managers for problems that do not occur and is one of many factors that can contribute to unintended side effects. After exploring the dynamic interactions between components of wildfire management and space systems, we provide insights into improving risk management for NASA projects from our comparative case study method.
Joint Design of Sparse Array Sparse Pulse Train and STAP for MIMO Radar with Co-Located Antennas

In this paper, maximizing the output signal-to-clutter-plus-noise ratio (SCNR) is considered for multiple-input multiple-out (MIMO) radar with co-located antennas to improve the detection performance in clutter environment. Normally, increasing the number of antennas and transmitted pulses is helpful in increasing the SCNR. However, real applications may place constraints on the system complexity, so that only a limited number of antennas and pulses can be employed. To improve the system performance with limited complexity, sparse array and sparse pulse train is usually adopted. To improve the output SCNR, we implement space-time adaptive processing (STAP) to suppress clutter, and further propose to jointly optimize the space-time weighting factors and the antenna and pulse locations of the sparse array and sparse pulse train. It is shown that the proposed method can give superior performance.
Isolation and structural clarification of triterpenes from Cyclocarya paliurus: Cyclocaric acid A and B

Two triterpenes were isolated using bioassay guided fractionation from the ethanol extract of Cyclocarya paliurus. Comparison to NMR spectra from a prior isolation reported in literature showed these two compounds to be previously reported Cyclocaric Acids A and B. However, after additional research into the oleanolic class of compounds, an independent synthesis of the reported Cyclocaric Acid A from hederagenin was completed. The conclusion is that the previously reported structures were incorrect. Updated structure and NMR spectral data will be presented.
SiC MOS Power Module in Direct Pressed Die Technology and some Challenges for Implementation

The novel Direct Pressed Die (DPD) technology enables design and assembly of high performance module suitable for integration of SiC MOSFET devices. In order to provide a high module power required, e.g., for automotive application in main train, the utilization of a high number of paralleled devices is necessary and the possible oscillations excited between devices have to be avoided. The generic analysis of module stability is done for different chip parameters and assembly techniques and gives guidelines for chip selection. The simulation results are confirmed by measurements.
Short-Term Wind-Speed Forecasting Based on Multiscale Mathematical Morphological Decomposition, K-Means Clustering, and Stacked Denoising Autoencoders

Wind energy plays an increasingly important role in economic development. In this study, we propose a hybrid short-term wind-speed forecasting model comprising multiscale mathematical morphological decomposition (MMMD), K-means clustering algorithm, and stacked denoising autoencoder (SDAE) networks. First, in contrast to traditional signal-decomposing tools, the original wind-speed sequence is decomposed into a series of subsequences with different frequencies and fluctuant levels using the adaptive multiscale mathematical morphological algorithm directly in the time domain. The signal does not need to be transferred from the time domain to the frequency domain; hence, the accuracy can be considerably improved. Moreover, this is the first study that uses a time domain signal-decomposing tool in a hybrid wind forecasting model. Next, the data are split into different clusters of similar frequencies and fluctuant level subsequences using the K-means algorithm. The characteristics of each cluster are then captured using the SDAE as the core forecasting unit. Finally, the predictions of all subsequences are aggregated to obtain the final wind speed. The data from two real wind turbines are used to evaluate the performance of the proposed model, and the forecasting results are compared with five different benchmark models, namely, backpropagation neural network (BPNN), stacked denoising autoencoder (SDAE), mathematical morphology–backpropagation, mathematical morphology–SDAE, and K-means–SDAE for multiple scales, and two novel hybrid wind forecasting models namely, wavelet transform (WT)-K-means-SDAE and variation mode decomposition (VMD)-K-means-long short-term memory networks (LSTMs). The results of the comparison demonstrate that the proposed model provides a short-term wind-speed forecasting method whose prediction accuracy decreases with time; however, the proposed model achieves a better performance in comparison with other exiting models. At same time, the proposed model significantly increases the prediction accuracy of wind-speed forecasting and can be a reference for future research in this area.
Prediction of Therapeutic Peptides Using Machine Learning: Computational Models, Datasets, and Feature Encodings

Peptides, short-chained amino acids, have shown great potentials toward the investigation and evolution of novel medications for treatment or therapy. The wet-lab based discovery of potential therapeutic peptides and eventually drug development is a hard and time-consuming process. The computational prediction using machine learning (ML) methods can expedite and facilitate the discovery process of potential prospects with therapeutic effects. ML approaches have been practiced favorably and extensively within the area of proteins, DNA, and RNA to discover the hidden features and functional activities, moreover, recently been utilized for functional discovery of peptides for various therapeutics. In this paper, a systematic literature review (SLR) has been presented to recognize the data-sources, ML classifiers, and encoding schemes being utilized in the state-of-the-art computational models to predict therapeutic peptides. To conduct the SLR, fourty-one research articles have been selected carefully based on well-defined selection criteria. To the best of our knowledge, there is no such SLR available that provides a comprehensive review in this domain. In this article, we have proposed a taxonomy based on identified feature encodings, which may offer relational understandings to researchers. Similarly, the framework model for the computational prediction of the therapeutic peptides has been introduced to characterize the best practices and levels involved in the development of peptide prediction models. Lastly, common issues and challenges have been discussed to facilitate the researchers with encouraging future directions in the field of computational prediction of therapeutic peptides.
A Review On Security in Wireless Sensor Network

Developments to come down with wireless sensor networking (WSN) engineering has furnished the provision of low-cost and small sensor nodes with all the performance of realizing different kinds of environmental and physical problems, capture processing, and then wireless discussion. A variety of realizing abilities leads to big amount of program areas. Nevertheless, the characteristics of WSN call for better effective techniques for stats, forwarding as well as processing. Inside WSN, the sensor nodes provide an insufficient transmission stove, as well as the processing of theirs as well as storage space abilities in addition to their toughness resources likewise are restricted. Routing protocols for WSNs are actually likely for sustaining the routes within the networking and also need to create particular dependable multi hop communique below the circumstances. With this newspaper, we provide a survey of routing protocols for WSN and also look at their laws and strengths. We talked about a variety of protection methods as decryption as well as encryption of information to safeguard it coming from malicious people.
CTR Prediction with User Behavior: An Augmented Method of Deep Factorization Machines

In Click-Through-Rate(CTR) prediction problems, video and comment thumbs-up prediction has important commercial values. The click rate prediction system can find the target population more accurately, which not only improves the user experience but also brings profits to the companys. Click behavior has a great correlation with personal habits. The Deep Factorization Machine(DeepFM) model is at the cutting edge of CRT by learning both high-level and low-level features. But it cannot hightlight the impact of user’s personal behavior with has a great correlation with click behavior. In this paper, we propose behavior-empahsized algorithms, DeepFM & Habit (Pro) and DeepFM & Habit (LR), for different situations. The experimental results on TikTok dataset (short video data) demonstrate that our proposed methods achieve superior performance with less time consumption on this CTR prediction problems.
Evaluation of Different Approaches of Convolutional Neural Networks for Land Use and Land Cover Classification Based on High Resolution Remote Sensing Images

Land use and land cover mapping is very important in the fields of urban planning, land management, and natural resource conservation. Recently, convolutional neural networks (CNNs) are applied widely in land use and land cover (LULC) classification as the acquisition of high resolution satellite images becomes easier owing to technological advancements. In this paper, we explore how to better exploit existed CNNs in LULC classification task. Three different learning modalities: full-trained, fine-tuning and pre-trained CNNs were used as feature extractors, and two promising CNNs models (AlexNet and GoogLeNet) and two remote sensing datasets (UC Merced Land Use dataset and Brazilian Coffee Scenes dataset) were studied. Results show that the both AlexNet and GoogLeNet can be used in high remote sensing classification with a great performance. What is more, the full-trained CNNs is not always the best approach, on the contrary, fine-tuning based on pre-trained CNNs, tends to be the best approach.
The evaluation of activated dietary charcoal from Canarium schweinfurthii Engl. seed and maize cob as a toxin binder in broiler chickens

These preliminary results emphasize the influence of animal and environmental factors on average daily gains. Further analyses should now explore the influences of various climatic factors like temperatures, rainfall and photoperiod. Production levels of growing cattle appear to be well modulated by animals’ management and, maybe even more, by nutrition. Measurements of vegetation structural features (biomass, allowance) do not seem to be major determinants of live weight gains. However, this result will have to be further analysed considering all the other papers giving ADG levels (without intake values). Moreover, pasture nutritional characteristics (nitrogen, fibre) should be integrated in future models. This work will contribute to a better understanding of ruminant production in the tropics and should offer the opportunity to identify appropriate strategies for animal management in tropical pastures.
A Novel Merged Strategy with Deformation Field Reconstruction for Constructing Statistical Shape Models

Statistical shape models (SSMs) play important roles in the analysis of medical images. SSMs of particular organs are built by learning statistical shape patterns of the target organs from medical images. As a prerequisite step of SSM construction, the anatomical correspondences between the training subjects have to be obtained, requiring nontrivial and tedious organ segmentation from the training images, as well as difficult registration of the segmented organ shapes. To tackle these problems, we design a novel strategy called merged intensity and landmark registration (MILR) which does not require segmentation of the target organ. The MILR method directly registers an organ shape template to the training images by simultaneously optimizing the template-to-image intensity similarity and the key anatomical landmark alignment. Compared to conventional correspondence calculation methods, the MILR method not only saves the time of organ segmentation but also ensures accurate alignment of important anatomical feature points, improving both the efficiency and accuracy of SSM construction. To verify this method, we constructed the human vertebral column SSM from volumetric CT images. We show results of correspondences by three validation metrics anatomical landmark distance (ALD), Dice similarity coefficient (DSC) and average surface distance (ASD)and the corresponding construction of SSMs by generalization ability and specificity of the human vertebral column from the CT images of 17 different subjects. The novel merged strategy, with merged deformation field reconstruction, shows competitive results against the conventional methods.
Weight-Based K-Truss Community Search via Edge Attachment

Community search is the task of discovering dense subgraph that satisfy a set of given query parameters. Most community search algorithms consider link structure while ignoring link weight. A recent study proposed the idea of discovering weighted communities which focuses on both link structure and link weight using an online search approach and index-based approach. In this paper two online algorithms are proposed to scale-up the existing online approach efficiency. Performance evaluation of the proposed algorithms against the existing online approach over different datasets shows a great improvement in terms of search and query evaluation time.
Reliability Analysis and Design of MMC Based on Mission Profile for the Components Degradation

In the field of high voltage level applications, modular multi-level converter (MMC) has the definite advantages of low power loss and modularity and there have been many studies on its reliability. Some researches focus on the degradation of physical characteristics in the lifetime prediction of key devices, but the degradation of physical characteristics has not been directly used in the research of MMC system level reliability. The traditional exponential distribution failure rate is constant while the Monte Carlo method assumes the random distribution of multiple devices. Neither of these two methods can describe the reliability of a single device with physical characteristics degradation. This article presents a system level MMC reliability analysis and design method based on MMC mission profile and insulated-gate bipolar transistor (IGBT) lifetime degradation. According to the IGBT current and power loss in MMC, the annual mission profile and junction temperature result are analyzed by rainflow counting algorithm. In terms of device degradation, the thermal network updating method is used to calculate the life of IGBT in different time, and the reliability analysis method based on exponential distribution is improved. To optimize the redundancy design of the system, the multi-objective function optimization is processed.
Quality Control on Manufacturing Computer Keyboards Using Multilevel Deep Neural Networks

Keyboards are held to high standards and those with defects cannot be considered as quality products. On the manufacturing line, many computer keyboard defects can appear, each of which needs to be handled differently. As a result, these defects need to be separated into their own category. Currently, default detection is done through the analysis of camera images. However, this is a challenging task due to the similarity shared by many defects and dusts in the images. In this paper, we propose a novel algorithm using multilevel deep neural networks to first group the defects and then distinguish each defect from others. The results show that the proposed deep learning network architecture can automatically and accurately classify keyboard defects. In this paper, we achieve the classification accuracy of 91.89%. This deep learning technique can also be extended to classify defects of other products.
Formal analysis of QUIC handshake protocol using ProVerif

This paper presents a formal analysis of the QUIC protocol. This newly proposed protocol is currently under the process of standardization, and it is still unknown whether the proposed protocol provides the claimed security guarantees. To fill this gap, we model the QUIC handshake protocol in the applied pi calculus and perform a comprehensive formal analysis using ProVerif. To the best of our knowledge, this is the first thorough study on the formal analysis of QUIC handshake protocol.
Mining blocks in a row: A statistical study of fairness in Bitcoin mining

The Bitcoin system keeps its ledger consistent in a blockchain by solving cryptographic problems, in a method called "Proof-of-Work". The conventional wisdom asserts that the mining protocol is incentive-compatible. However, Eyal and Sirer in 2014 have discovered a mining attack strategy called selfish mining (SM), in which a miner (or a mining pool) publishes the blocks it mines selectively instead of immediately. SM strategy would have the impact of wasting resources of honest miners. Scholars proposed various extensions of the SM strategy and approaches to defense the SM attack. Whether selfish mining occurs in practice or not, has been subject of extensive debate. For the first time, in this paper we propose a method to identify selfish miners by detecting anomalies in the properties of consecutive blocks’ statistics. Furthermore, we extend our method to detect the mining cartels, in which miners secretly get together and share timely information. Our results provide evidence that these strategic behaviors take place in practice.
Motivations for Family Forestland Parcelization in the Catskill/Delaware Watersheds of New York

Two Yale University researchers wanted to answer these questions. To do that, Rebecca Sanborn Stone and Mary Tyrrell surveyed landowners in the New York City Watershed region of upstate New York. They contacted both landowners who had subdivided and sold land as well as those who had kept their land intact. From the more than 320 responses, they gained surprising insight into what makes private woodland owners give up on landownership.
Dynamic Avalanche Free Super Junction-TCIGBT for High Power Density Operation

Dynamic Avalanche (DA) effects in the Super-Junction Trench IGBTs are analyzed through 3-D TCAD simulations for the first time. A DA free solution for high power density and low loss is proposed and demonstrated in detail. Furthermore, simulation results show that DA results in significant increase in turn-off losses in the Super-Junction Trench IGBTs at high current density operations, which poses a fundamental limit on the power density of IGBT applications. In contrast, the Super-Junction Trench Clustered IGBTs remain DA free at high current density and show low switching losses due to enhanced PMOS action. Therefore, the Super-Junction Trench IGBTs are well suitable for high power density operations with a potential to operate beyond the 1-D unipolar 4H-SiC limit.