Miracle-dz/UniFAR-FTUs huggingface dataset:
Metamorphic Testing of an Artificially Intelligent Chess Game

Artificially intelligent (AI) game software incorporates different algorithms to generate intelligent human-like responses to the users playing them. Testing AI game software poses great difficulty because of the complex possibilities that can result at a given point and analysis of said possibilities is a tedious task. Also during software development there are resource constraints due to which testing targets specific parts of the software. An AI game of Chess takes into consideration a large amount of possible outcomes at any given point before deciding a move. Therefore, testing it in its entirety is impractical. In this paper we propose a metamorphic testing approach for testing an AI Chess game i.e. a Chess engine’s algorithm of determining and pruning out possible outcomes and ultimately deciding on a final outcome. For validating our approach, we have done error seeding on an open source Chess engine and tested it through our approach. The results for our proposed approach for testing an AI Chess game through metamorphic relations show that it is successful in revealing 71% of the total seeded faults. On comparison of our proposed approach with the existing technique of testing Chess engine i.e., perft function, we have come across situations in which our proposed approach reveals errors overlooked by the existing technique. In the future we aim to extend our approach towards other AI game software.
A two-stage algorithm of railway sleeper crack detection based on edge detection and CNN

The detection of sleeper crack is very important to ensure the reliability and safety of railway system. The powerful feature learning ability of convolutional neural network (CNN) can be used to detect cracks in sleeper images. However, convolutional neural network needs a lot of computation, which will reduce the detection speed. This paper presents a two-stage algorithm for the detection of railway sleeper cracks. In the first stage, the edge detection is carried out by using the 3 x 3 neighborhood range algorithm to find out the possible crack area, and a series of mathematical morphology operations are used to eliminate the noise target in the edge detection results. In the second stage, convolution neural network is used to classify the edge detected objects accurately. Through the analysis of many images of sleepers with or without cracks, it is proved that the cracks detected by 3 x 3 neighborhood range algorithm are coarser, clearer and more continuous than those detected by Sobel algorithm and Canny algorithm. In addition, the simple CNN model can achieve high image classification accuracy through edge detection and morphological operation of the railway sleeper crack image.
Sequential Detection of Image Defects for Patterned Fabrics

Automatic fabric defect detection has been successfully applied to establish the quality quick response system for the automation of textile production. However, the image complexity and diversity of patterned fabrics have effects on the fabric defect detection, which makes it difficult for automatic quality inspection of textiles. In order to solve this problem, a novel method that sequential detection of image defects for patterned fabrics was proposed in this article. Firstly, the fabric image was segmented adaptively based on the periodic distance of the pattern, which can be used for the determination of repetitive pattern block element in fabrics, so that the defect image blocks could be identified according to the minimum principle of structure similarity; Then, the texture features of defect image blocks were extracted to match the constructed feature dictionary of defect-free image block, the defect position could be located by distance measurement and threshold segmentation. Our experiments show that the proposed method has the advantages of low computational complexity, high detection accuracy and strong applicability, by the comparison with existing approaches for defect detection of patterned fabric. Moreover, the new proposed automatic defect location method can solve the problem of deficiency caused by manual defect marking using existing image inpainting algorithms, it can be used for the automatic image inpainting of patterned fabrics without manual marking to improve the robustness of computer vision-based fabric re-engineering.
A Novel Nonlinear Analysis Tool: Multi-scale Symbolic Sample Entropy and Its Application in Condition Monitoring of Rotary Machinery

Sample entropy (SE) has been employed for fault diagnosis of rotary machinery (FDRM). However, SE has low computation efficiency for long time series. To solve this problem, symbolic sample entropy (SSE), a novel measure of time series regularity, is proposed to estimate the complexity. However, SSE fails to account for the multiple scale information inherent in measured vibration signals. Therefore, we combine the concept of multi-scale analysis with SSE, called multi-scale SSE (MSSE). To evaluate the effectiveness of the proposed MSSE method, we apply several simulated signals to verify the merits of SSE in impulsion detection and calculation efficiency. Furthermore, we utilize one experimental data to validate its effectiveness in recognizing several fault types of rotary machinery. Experimental results indicate that MSSE has an advantage in extracting fault features compared with multi-scale entropy (MSE), multi-scale fuzzy entropy (MFE), and multi-scale permutation entropy (MPE) methods.
Boosting Output Power of Multiwavelength Fiber Laser with Lyot Filter utilizing Hybrid Amplifier

This paper presented an investigation on the effect of hybrid amplifier to multiwavelength fiber laser (MWFL) with in-cavity Lyot filter. The hybrid amplifier comprising semiconductor optical amplifier (SOA) and erbium doped fiber amplifier (EDFA) was used to boost the lasing output peak power. The multiwavelength lasing output has improved by 20 dB, while the extinction ratio (ER) increased to 4 dB when hybrid amplifier was used. The lasing lines remain stable, with a maximum 2.94 dB of power dithering within 100 minutes of observation.
Week-ahead Predictions of Wind Power Based on Weather Classification

At present, the researches on short-term wind power prediction mostly focus on three days, but the maintenance schedule of wind farm and the generation schedule of hydro-thermal power system both hope to get the wind power forecasting results week-ahead. Exploring the law of wind power fluctuation becomes one of the most crucial issues to extend the prediction time scale. This paper proposes a combined probabilistic prediction approach based on weather classification to forecast the wind power week-ahead. This approach considers that wind power has a similar fluctuation law under the same weather type, and the combination model is used to improve forecasting accuracy. In the approach, the GK fuzzy clustering algorithm based on subtraction clustering is used to divide the weather into several typical types. And the combination probabilistic forecasting model formed by the sparse Bayesian learning, kernel density estimation, and beta distribution estimation is trained for each weather type. Meanwhile, the Gaussian Case-based Reasoning is used to classify the forecasting sample into corresponding weather type and the corresponding combined model is used to make the forecast. Case studies employ the proposed approach as well as several benchmarks to achieve week-ahead predictions of a wind farm located in China and the superior performance demonstrates the effectiveness of the proposed approach.
eeRIS-NILM: An Open Source, Unsupervised Baseline for Real-Time Feedback Through NILM

Awareness of residential consumers about the impact of their behavior on electricity consumption is an important determinant for energy efficiency and for their participation in electricity markets. Non-intrusive load monitoring (NILM) has been proposed as a cost-effective approach for providing detailed information about the consumption of individual appliances and help achieve these goals. In this context, the ideal NILM algorithm should be unsupervised and provide real-time consumption feedback (i.e., the appliances should be detected immediately after they start consuming power). Solutions with these characteristics have not been sufficiently studied in the NILM bibliography. This paper introduces an attempt to address this problem, by suggesting the use of a "Live" algorithm that provides real-time feedback. The Live algorithm runs in parallel with a more robust NILM procedure, and a periodic synchronization step ensures that the two processes share the same appliance models. The paper presents the details and implementation of the algorithm, along with an evaluation methodology and its results on the REDD dataset. Although the Live algorithm presented here has significant room for improvement, it can serve as a baseline for evaluating more sophisticated approaches in the future. The source code for the implemented system is available at https://github.com/eeris-nilm/
A Scheme to Improve Power Factor and Dynamic Response Performance for CRM/DCM Buck–Buck/Boost PFC Converter

Buck–buck/Boost power factor correction (PFC) converter is a great choice to compensate the inherent dead zones in the input current of buck PFC converter. However, whether the converter operates with traditional constant on-time control in critical conduction mode (CRM) or with conventional constant duty-cycle control in discontinuous conduction mode (DCM), the power factor (PF) is low, especially at low input voltage. Therefore, fixed on-time ratio control (FOTRC) and fixed duty-cycle ratio control (FDCRC) are proposed for CRM/DCM buck–buck/boost PFC converter to achieve high PF, respectively. Both control methods maximize PF over wide input voltage range by adjusting on-time or duty-cycle of buck/boost mode and buck mode. Compared with the traditional controls, the proposed control methods also have advantage of increasing efficiency. Furthermore, for improving dynamic response performance, smooth and rapid dynamic response control (SRDRC) is put forward, which can reduce the regulation time and output voltage overshoot/undershoot when the load or input voltage changes abruptly. In addition, the implementation circuits combining FOTRC/FDCRC and SRDRC are designed. Two prototypes have been built and tested in the lab to demonstrate the validity of the theoretical analysis.
Optimal Energy Management of Fuel Cell Hybrid Electric Ships Considering Fuel Cell Aging Cost

The fuel cell hybrid electric ship (FCHES) uses fuel cells and batteries as power sources to meet the load demand and has a good development prospect due to its zero greenhouse gas emission. Under the constraints of the power system safety and the voyage plan, the optimal energy management of the FCHES is an important and challenging issue. In this paper, we focus on the optimal energy management problem of the FCHES considering fuel cell aging cost. First, a uniform optimization model is formulated to schedule output power of multiple fuel cells and batteries, and the propulsion power. The optimization objective is to minimize the sum of the fuel consumption cost and power source aging costs for the entire voyage. Second, to make the problem more tractable, the non-convex travel equality constraint and absolute value objective terms are transformed into convex terms, and the piecewise function is transformed into an integer linear form. Finally, simulation results and comparative analysis are given to verify the effectiveness of the proposed strategy.
A PdM Framework Through the Event-based Genomics of Machine Breakdown

A novel event-based predictive maintenance framework based on sensor signal measurements and regressive predictions to minimise machine breakdown and component failure is proposed. Such capabilities will be complemented by Event-Clustering technique to cluster and remove less impact sensor signals and also build breakdown genomics from the root of a failure in order to predict the upcoming machine breakdowns and components failures. The creation of machine breakdown genomics requires the knowledge of systems state observed as well as the state change at specified time intervals (discretization). The proposed framework is applied to a real application case study. An industrial case study of a continuous compression moulding machine that manufactures the plastic bottle closure (caps) in the beverage industry has been considered as an experiment. The machine breakdown genomics theory is tested in this case to build the sequence of events or the genomics of breakdown, where sequences of contiguous events lead to failure or healthy machine status. This is complemented by the Regression Event-Tracker method to estimates the condition monitoring of the components and provide components real-time remaining useful life estimation. The Weibull failure-rate analysis is carried out on the remaining useful life estimates for each element to understand and estimate the mean time to failure for the manufacturing machine.
A Lightweight Performance Enhancing Proxy for Evolved Protocols and Networks

In this paper we present the design and prototype implementation of a multi-domain congestion control framework based on a non-ossifying Lightweight Performance Enhancing Proxy. We demonstrate that our solution can be both used for evolved protocols such as end-to-end encrypted QUIC and for evolved cellular access networks like 5G. We also show the performance of our Multi-Domain Congestion Control algorithm by a simulation study. Moreover, we expose its behavior in realistic use cases like sudden capacity changes in 5G mmWave cellular networks. Our results highlight that significant performance improvements can be achieved by cooperative traffic management frameworks in protocols and networks of the future.
Service Function Chain Embedding Framework for NFV-Enabled IoT Application

It is becoming more and more difficult to manage data and resources simultaneously and efficiently in IoT systems. In recent years, network function virtualization (NFV) technology emerges, regarded as one most promising candidate towards efficient resource allocation for IoT application. By adopting NFV scheme, each IoT service, represented by a service function chain (SFC), can be composed of an ordered set of virtual network functions (VNFs). However, existing researchers concentrate on finding optimal or near-optimal embedding solution per SFC, incurring much more computation time. Considering the dynamic nature of IoT service, it is necessary to find feasible and efficient SFC embedding solution as soon as possible. Hence, in this paper, we propose one SFC embedding framework for IoT application. The framework for SFC embedding is labeled as SFC-E-IoT. Each elements in SFC-E-IoT framework is discussed in this paper. When given one SFC, our SFC-E-IoT will find the feasible SFC embedding solution, fulfilling its computing, storage and networking resources. We conduct the simulation work so as to validate the SFC-E-IoT performance.
DC Impedance Modeling and Stability Analysis of VSC with Consideration of Grid Impedance

This paper investigates the dc impedance modeling and stability analysis of voltage source converter (VSC) with consideration of grid impedance. Based on the developed impedance model, the parameter impact analysis is made and the influence of grid impedance and phase-locked loop (PLL) on dc impedance is revealed. In addition, this paper uses a resistance-inductance-capacitance (RLC) equivalent vector diagram to analyze the dc stability issue, and a virtual capacitor based active damping method is adopted to suppress the oscillation. Simulation results are given to validate the impedance model and stability analysis.
Short-Term Prediction of Available Parking Space Based on Machine Learning Approaches

Reliable short-term prediction of available parking space (APS) is the basic theory of parking guidance information system (PGIS). Based on the Intelligent parking system at the Eastern New Town, Yinzhou District, Ningbo, China, this study collected the data of parking availability in the on-street parking areas. The variation characteristics of APS were investigated and analyzed at different spatial-temporal levels. Then the APS prediction models based on Gradient Boosting Decision Tree (GBDT) and Wavelet Neural Network (WNN) were proposed. Furthermore, an improved WNN algorithm with (WA) decomposition and Particle Swarm Optimization (PSO) were presented. The original time series was decomposed and reconstructed by wavelet analysis, and the WNN algorithm found the optimal threshold of initial weight through PSO. The result of GBDT (weekday: MSE = 27.37, SMSE = 0, TIME = 35min, weekend: MSE = 9.9, SMSE = 0, TIME = 35min) and WA-PSO-WNN (weekday: MSE = 14.93, SMSE = 1.88, TIME = 160.32s, weekend: MSE = 12.33, SMSE = 10.23, TIME = 160.95s) approximated the true value. But the prediction time of GBDT was too long to be applicable to the short-term prediction of APS in this paper. Compared with the methods of GBDT, WNN, and PSO-WNN, the WA-PSO-WNN algorithm performs much better. The average differences in MSE between WA-PSO-WNN and GBDT for weekday and weekend data are 45.45% and 58.76%, respectively, indicating that WA-PSO-WNN can increase the prediction accuracy of weekday and weekend data by an average of 45.45% and 58.76% compared with the GBDT model. Finally, the application prospects of short-term APS forecasting were also discussed in reducing cruising parking behavior, reducing illegal parking behavior and adjusting dynamic parking rates to verify the importance of APS short-term forecasting.
A practical online time-varying delay estimation of remote control system based on adaptive super-twisting algorithm

This article deals with the practical online delay estimation of remote control system by using an external signal. The external signal can be considered as a special communication loop of the remote control system, and it requires no information from the control system. The delay estimator is based on the adaptive super-twisting algorithm, it accurately estimates the time-varying delay with self-tuned parameters. Moreover, the proposed method also attenuates the effect of the channel inherent noise and deception attack via the network. Finally, this method is validated on a WiFi communication network, and some simulations are given to illustrate the performance and the robustness of the proposed method.
Comparison of Hand-Reared Caribbean Flamingos and Lesser Flamingos at Seaworld California

-SeaWorld California (SWC) has a well established and prolific Caribbean Flamingo (Phoenicopterus ruber ruber) colony, and recently has achieved reproductive success with Lesser Flamingos (Phoeniconaias minor). In 1996, 2 Caribbean Flamingo chicks neglected by their parents were successfully hand-reared. In 1997, SWC simultaneously hand-reared 3 at-risk Caribbean Flamingos and 3 at-risk Lesser Flamingos. The latter were the first reported Lesser Flamingos successfully hand-reared in North America. Here we examine and compare incubation, food volume, hand-rearing techniques, and growth rates of the 2 species through various stages of development in the 1997 season. UNA COMPARACION ENTRE FLAMENCOS CARIBENOS Y MENORES CRIADOS A MANO Resumen. -SeaWorld California (SWC) tiene una colonia bien establecida y prolifica de flamenco caribefio (Phoenicopterus ruber ruber) y recientemente ha logrado reproducir exitosamente al flamenco menor (Phoeniconaias minor). En 1996, 2 pollos que fueron descuidados por los padres fueron criados a mano con exito. En 1997, SWC cri6 a mano simultaineamente 3 flamencos caribefios de alto riesgo y 3 flamencos menores tambien de alto riesgo. Estos filtimos son los primeros registros de flamencos menores criados a mano exitosamente en norteamerica. En este trabajo examinamos y comparamos la incubaci6n, el volumen de alimento, las tecnicas de crianza y las tasas de crecimiento de las 2 especies durante varios estadios de desarrollo en la temporada de reproducci6n de 1997.
A Data-Driven Learning System Based on Natural Intelligence for an IoT Virtual Assistant

The ability and functionality of today’s learning systems are dependent on configuration and designed for specific use. These systems have strong dependencies on training data and remain static in capability. This translates to commercial IoT sensor management systems that are limited to a set of predefined functions. Although often labeled as or associated with artificial intelligence (AI), these systems lack the behavioral qualities associated with intelligence. Our research details a novel learning system autonomously motivated by its environment, analogously to humans. The algorithmic processes of the system produce behavioral byproducts of intelligence, resulting in an entity capable of tackling general problems of its own interest, contrary to constrained solutions. Because of the system’s generic nature, the intelligence produced is limited only by its ability to sense and actuate. The research contributes a system that learns through different interfaces with the same generic algorithms—where language and image processing ability would traditionally be learned in separate modules, our system uses the same algorithms to learn ability with data from both modalities. The proposed framework is showcased through the embodiment of a next-generation intelligent IoT virtual assistant application.
Periodic and Sequential Inspection Policies with Mission Failure Probabilities

When a unit should provide reliability at mission time, the mission success probability or failure probability becomes an important constraint of planing inspection policies. From this viewpoint, this paper considers the optimization problems of periodic and sequential inspection policies with mission failure probabilities. In this paper, the mission time is also considered as a renewal point for inspection polices, but the failure cost occurred at mission time is higher than the total cost of inspection and failure detection. We give an upper limit of the failure probability at mission time, i.e., mission failure probability, to minimize the total expected cost of inspection policies. In addition, the extended models of inspection policies for an mission interval, a constant renewal time and the random mission times are given directly.