An interpretation method of inversion phenomena is newly proposed for backward transient scattered field components for both E- and H-polarizations when an ultra-wideband (UWB) pulse wave radiated from a line source is incident on a two-dimensional metal cylinder covered with a lossless dielectric medium layer (coated metal cylinder). A time-domain (TD) asymptotic solution, which is referred to as a TD saddle point technique (TD-SPT), is derived by applying the SPT in evaluating a backward transient scattered field which is expressed by an integral form. The TD-SPT is represented by a combination of a direct geometric optical ray (DGO) and a reflected GO (RGO) series, thereby being able to extract and calculate any backward transient scattered field component from a response waveform. The TD-SPT is useful in understanding the response waveform of a backward transient scattered field by a coated metal cylinder because it can give us the peak value and arrival time of any field component, namely DGO and RGO components, and interpret analytically inversion phenomenon of any field component. The accuracy, validity, and practicality of the TD-SPT are clarified by comparing it with two kinds of reference solutions.

The altered fingerprints help criminals escape from police and cause great harm to the society. In this letter, an altered fingerprint detection method is proposed. The method is constructed by two deep convolutional neural networks to train the time-domain and frequency-domain features. A spectral attention module is added to connect two networks. After the extraction network, a feature fusion module is then used to exploit relationship of two network features. We make ablation experiments and add the module proposed in some popular architectures. Results show the proposed method can improve the performance of altered fingerprint detection compared with the recent neural networks.

A method to predict lightning by machine learning analysis of atmospheric electric fields is proposed for the first time. In this study, we calculated an anomaly score with long short-term memory (LSTM), a recurrent neural network analysis method, using electric field data recorded every second on the ground. The threshold value of the anomaly score was defined, and a lightning alarm at the observation point was issued or canceled. Using this method, it was confirmed that 88.9% of lightning occurred while alarming. These results suggest that a lightning prediction system with an electric field sensor and machine learning can be developed in the future.

Recently several researchers have proposed various methods to build intelligent stock trading and portfolio management systems using rapid advancements in artificial intelligence including machine learning techniques. However, existing technical analysis-based stock price prediction studies primarily depend on price change or price-related moving average patterns, and information related to trading volume is only used as an auxiliary indicator. This study focuses on the effect of changes in trading volume on stock prices and proposes a novel method for short-term stock price predictions based on trading volume patterns. Two rapid volume decrease patterns are defined based on the combinations of multiple volume moving averages. The dataset filtered using these patterns is learned through the supervised learning of neural networks. Experimental results based on the data from Korea Composite Stock Price Index and Korean Securities Dealers Automated Quotation, show that the proposed prediction system can achieve a trading performance that significantly exceeds the market average.

Visible-Infrared Person Re-identification (VI-ReID) is a challenging pedestrian retrieval task due to the huge modality discrepancy and appearance discrepancy. To address this tough task, this letter proposes a novel gray augmentation exploration (GAE) method to increase the diversity of training data and seek the best ratio of gray augmentation for learning a more focused model. Additionally, we also propose a strong all-modality center-triplet (AMCT) loss to push the features extracted from the same pedestrian more compact but those from different persons more separate. Experiments conducted on the public dataset SYSU-MM01 demonstrate the superiority of the proposed method in the VI-ReID task.

To alleviate the problem of the dependency on the quantity of the training sample data in speech emotion recognition, a weighted gradient pre-train algorithm for low-resource speech emotion recognition is proposed. Multiple public emotion corpora are used for pre-training to generate shared hidden layer (SHL) parameters with the generalization ability. The parameters are used to initialize the downsteam network of the recognition task for the low-resource dataset, thereby improving the recognition performance on low-resource emotion corpora. However, the emotion categories are different among the public corpora, and the number of samples varies greatly, which will increase the difficulty of joint training on multiple emotion datasets. To this end, a weighted gradient (WG) algorithm is proposed to enable the shared layer to learn the generalized representation of different datasets without affecting the priority of the emotion recognition on each corpus. Experiments show that the accuracy is improved by using CASIA, IEMOCAP, and eNTERFACE as the known datasets to pre-train the emotion models of GEMEP, and the performance could be improved further by combining WG with gradient reversal layer.

We consider a regulation problem for an uncertain chain of integrators with an unknown time-varying delay in the input. To deal with uncertain parameters and unknown delay, we propose an adaptive event-triggered controller with a dynamic gain. We show that the system is globally regulated and interexecution times are lower bounded. Moreover, we show that these lower bounds can be enlarged by adjusting a control parameter. An example is given for clear illustration.

Loan default prediction has been a significant problem in the financial domain because overdue loans may incur significant losses. Machine learning methods have been introduced to solve this problem, but there are still many challenges including feature multicollinearity, imbalanced labels, and small data sample problems. To replicate the success of deep learning in many areas, an effective regularization technique named muddling label regularization is introduced in this letter, and an ensemble of feed-forward neural networks is proposed, which outperforms machine learning and deep learning baselines in a real-world dataset.

The shared last level cache (SLLC) in tile chip multiprocessors (TCMP) provides a low off-chip miss rate, but it causes a long on-chip access latency. In the two-level cache hierarchy, data replication stores replicas of L1 victims in the local LLC (L2 cache) to obtain a short local LLC access latency on the next accesses. Many data replication mechanisms have been proposed, but they do not consider both L1 victim reuse behaviors and LLC replica reception capability. They either produce many useless replicas or increase LLC pressure, which limits the improvement of system performance. In this paper, we propose a two-level cache aware adaptive data replication mechanism (TCDR), which controls replication based on both L1 victim reuse behaviors prediction and LLC replica reception capability monitoring. TCDR not only increases the accuracy of L1 replica selection, but also avoids the pressure of replication on LLC. The results show that TCDR improves the system performance with reasonable hardware overhead.

Basic characteristics for relating design and base station layout design in land mobile communications are provided through a propagation model for path loss prediction. Owing to the rapid annual increase in traffic data, the number of base stations has increased accordingly. Therefore, propagation models for various scenarios and frequency bands are necessitated. To solve problems optimization and creation methods using the propagation model, a path loss prediction method that merges multiple models in machine learning is proposed herein. The method is discussed based on measurement values from Kitakyushu-shi. In machine learning, the selection of input parameters and suppression of overlearning are important for achieving highly accurate predictions. Therefore, the acquisition of conventional models based on the propagation environment and the use of input parameters of high importance are proposed. The prediction accuracy for Kitakyushu-shi using the proposed method indicates a root mean square error (RMSE) of 3.68dB. In addition, predictions are performed in Narashino-shi to confirm the effectiveness of the method in other urban scenarios. Results confirm the effectiveness of the proposed method for the urban scenario in Narashino-shi, and an RMSE of 4.39dB is obtained for the accuracy.

Quantum combinatorial designs are gaining popularity in quantum information theory. Quantum Latin squares can be used to construct mutually unbiased maximally entangled bases and unitary error bases. Here we present a general method for constructing quantum Latin arrangements from irredundant orthogonal arrays. As an application of the method, many new quantum Latin arrangements are obtained. We also find a sufficient condition such that the improved quantum orthogonal arrays [10] are equivalent to quantum Latin arrangements. We further prove that an improved quantum orthogonal array can produce a quantum uniform state.

Music is often used for emotion induction because it can change the emotions of people. However, since we subjectively feel different emotions when listening to music, we propose an emotion induction system that generates music that is adapted to each individual. Our system automatically generates suitable music for emotion induction based on the emotions predicted from an electroencephalogram (EEG). We examined three elements for constructing our system: 1) a music generator that creates music that induces emotions that resemble the inputs, 2) emotion prediction using EEG in real-time, and 3) the control of a music generator using the predicted emotions for making music that is suitable for inducing emotions. We constructed our proposed system using these elements and evaluated it. The results showed its effectiveness for inducing emotions and suggest that feedback loops that tailor stimuli to individuals can successfully induce emotions.

This study tries to predict date of ear emergence of rice plants, based on cropping records over 25 years. Predicting ear emergence of rice plants is known to be crucial for practicing good harvesting quality, and has long been dependent upon old farmers who acquire skills of intuitive prediction based on their long term experiences. Facing with aging farmers, data driven approach for the prediction have been pursued. Nevertheless, they are not necessarily sufficient in terms of practical use. One of the issue is to adopt weather forecast as the feature so that the predictive performance is varied by the accuracy of the forecast. The other issue is that the performance is varied by region and the regional characteristics have not been used as the features for the prediction. With this background, we propose a feature engineering to quantify hidden regional characteristics as the feature for the prediction. Further the feature is engineered based only on observational data without any forecast. Applying our proposal to the data on the cropping records resulted in sufficient predictive performance, ±2.69days of RMSE.

In this paper, event-triggered control over a sensor network is studied as one of the control methods of cyber-physical systems. Event-triggered control is a method that communications occur only when the measured value is widely changed. In the proposed method, by solving an LMI (Linear Matrix Inequality) feasibility problem, an event-triggered output feedback controller such that the closed-loop system is asymptotically stable is derived. First, the problem formulation is given. Next, the control problem is reduced to an LMI feasibility problem. Finally, the proposed method is demonstrated by a numerical example.

Real-time and scalable multicast services are of paramount importance to Industrial Internet of Things (IIoT) applications. To realize these services, the multicast algorithm should, on the one hand, ensure the maximum delay of a multicast session not exceeding its upper delay bound. On the other hand, the algorithm should minimize session costs. As an emerging networking paradigm, Software-defined Networking (SDN) can provide a global view of the network to multicast algorithms, thereby bringing new opportunities for realizing the desired multicast services in IIoT environments. Unfortunately, existing SDN-based multicast (SDM) algorithms cannot meet the real-time and scalable requirements simultaneously. Therefore, in this paper, we focus on SDM algorithm design for IIoT environments. To be specific, the paper first converts the multicast tree construction problem for SDM in IIoT environments into a delay-bounded least-cost shared tree problem and proves that it is an NP-complete problem. Then, the paper puts forward a shared tree (ST) algorithm called SDM4IIoT to compute suboptimal solutions to the problem. The algorithm consists of five steps: 1) construct a delay-optimal shared tree; 2) divide the tree into a set of subpaths and a subtree; 3) optimize the cost of each subpath by relaxing the delay constraint; 4) optimize the subtree cost in the same manner; 5) recombine them into a shared tree. Simulation results show that the algorithm can provide real-time support that other ST algorithms cannot. In addition, it can achieve good scalability. Its cost is only 20.56% higher than the cost-optimal ST algorithm. Furthermore, its computation time is also acceptable. The algorithm can help to realize real-time and scalable multicast services for IIoT applications.

To sustain and expand the agricultural economy even as its workforce shrinks, the efficiency of farm operations must be improved. One key to efficiency improvement is completely unmanned driving of farm machines, which requires stable monitoring and control of machines from remote sites, a safety system to ensure safe autonomous driving even without manual operations, and precise positioning in not only small farm fields but also wider areas. As possible solutions for those issues, we have developed technologies of wireless network quality prediction, an end-to-end overlay network, machine vision for safety and positioning, network cooperated vehicle control and autonomous tractor control and conducted experiments in actual field environments. Experimental results show that: 1) remote monitoring and control can be seamlessly continued even when connection between the tractor and the remote site needs to be switched across different wireless networks during autonomous driving; 2) the safety of the autonomous driving can automatically be ensured by detecting both the existence of people in front of the unmanned tractor and disturbance of network quality affecting remote monitoring operation; and 3) the unmanned tractor can continue precise autonomous driving even when precise positioning by satellite systems cannot be performed.

For beyond 5G era, in network function virtualization (NFV) environments, service chaining can be utilized to provide the flexible network infrastructures needed to support the creation of various application services. In this paper, we propose a dynamic service chain construction based on model predictive control (MPC) to utilize network resources. In the proposed method, the number of data packets in the buffer at each node is modeled as a dynamical system for MPC. Then, we formulate an optimization problem with the predicted amount of traffic injecting into each service chain from users for the dynamical system. In the optimization problem, the transmission route of each service chain, the node where each VNF is placed, and the amount of resources for each VNF are determined simultaneously by using MPC so that the amount of resources allocated to VNFs and the number of VNF migrations are minimized. In addition, the performance of data transmission is also controlled by considering the maximum amount of data packets stored in buffers. The performance of the proposed method is evaluated by simulation, and the effectiveness of the proposed method with different parameter values is investigated.

The prediction of the malfunction timing of wind turbines is essential for maintaining the high profitability of the wind power generation industry. Studies have been conducted on machine learning methods that use condition monitoring system data, such as vibration data, and supervisory control and data acquisition (SCADA) data to detect and predict anomalies in wind turbines automatically. Autoencoder-based techniques that use unsupervised learning where the anomaly pattern is unknown have attracted significant interest in the area of anomaly detection and prediction. In particular, vibration data are considered useful because they include the changes that occur in the early stages of a malfunction. However, when autoencoder-based techniques are applied for prediction purposes, in the training process it is difficult to distinguish the difference between operating and non-operating condition data, which leads to the degradation of the prediction performance. In this letter, we propose a method in which both vibration data and SCADA data are utilized to improve the prediction performance, namely, a method that uses a power curve composed of active power and wind speed. We evaluated the method's performance using vibration and SCADA data obtained from an actual wind farm.

Along with the fast development of blue economy, wireless communication in oceans has received extensive attention in recent years, and opportunistic networks without any aid from fixed infrastructure or centralized management are expected to play an important role in such highly dynamic environments. Here, link prediction can help nodes to select proper links for data forwarding to reduce transmission failure. The existing prediction schemes are mainly based on analytical models with no adaptability, and consider relatively simple and small terrestrial wireless networks. In this paper, we propose a new link prediction algorithm based on machine learning, which is composed of an extractor of convolutional layers and an estimator of long short-term memory to extract useful representations of time-series data and identify effective long-term dependencies. The experiments manifest that the proposed scheme is more effective and flexible compared with the other link prediction schemes.

Environmental power generation devices such as solar cells are used as power sources for IoT devices. Due to the large internal resistance of such power source, LSIs in the IoT devices may malfunction when the LSI operates at high speed, a large current flows, and the voltage drops. In this paper, a standard cell library of stacked structured cells is proposed to increase the delay of logic circuits within the range not exceeding the clock cycle, thereby reducing the maximum current of the LSIs. We show that the maximum power consumption of LSIs can be reduced without increasing the energy consumption of the LSIs.