In this paper, a new decentralized gradient-based cooperative control method is proposed to achieve current sharing for parallel chargers in energy storage-type light rail vehicle systems. By employing a generalized artificial potential function to characterize the interaction rule for subchargers, the current-sharing control problem is converted into an optimization problem. Based on the gradient of the potential function, a decentralized gradient cooperative control law is derived. A general saturation function is introduced in the proposed control to guarantee the boundedness of the control output. The stability of the closed-loop system under the proposed decentralized gradient control is proven with the aid of a Lyapunov function. Simulation results are provided to verify the feasibility and validity of the proposed distributed current-sharing control method.

In view of the present pipe cold-centering processing system, the present situation of the low degree of automation and the deviation from the center position, this research intends to design a kind of tube billet cold-centering processing system based on laser measurement. The system adopts a movable, high-precision laser displacement sensor to the tubular billet’s outer contour, scanning to get the billet cylindrical contour coordinates, and uses the nonlinear numerical filtering least-square-circle fitting method to get the tube billet’s center coordinates. The research results show that the system has a high degree of integrated automation, and the measuring error is less than 0.1 mm when the transition is made to automation. At present, this system has been successfully applied to production, and it successfully realizes tube billet cold centering online processing.

Recognizing different weather conditions is a core component of many different applications of outdoor video analysis and computer vision. Street analysis performance, including detecting street objects, detecting road lines, recognizing street sign and etc., varies greatly with weather, so modeling based on weather recognition is the key resolution in this field. Features derived from intrinsic properties of different weather conditions contribute to successful classification. We first propose using deep learning features from convolutional neural networks (CNN) for fine recognition. In order to reduce the parameter redundancy in CNN, we used sparse decomposition to dramatically cut down the computation. Recognition results for databases show superior performance and indicate the effectiveness of extracted features.

Commercially available rehabilitation machines are bisymmetric, and the structure of the machines is fixed. Consequently, they cannot meet various requirements for lower-limb rehabilitation, and people have to adapt themselves to the machines to do exercises. To solve this problem, this paper presents a new kind of rehabilitation machine for the lower limbs. It is left-right asymmetric, and the structure and load of the machine can easily be adjusted to suit different requirements for lower limbs. A prototype of a half model of the machine for one leg is designed and built, and some results of preliminary tests are presented.

In this study, we propose color barcodes using tree-structure joint stereo coding (TJSC). TJSC is a method that combines sets of tree-structure coding channels. Optimum pairs of reference channels are searched by using vector similarity to form channel pairs. TJSC is expected to improve the compression ratio by 6% on average when compared to the conventional joint stereo coding methods. In this study, we propose high capacity color barcodes with bit data arranged in multilayers that provide color information to each layer. As both tree-structural and coding information are generated during coding by TJSC, encrypted color barcodes can be generated by generating a decoding key of the tree-structural information. The color barcodes proposed in this study are expected to be accepted as a new printing medium that employs an information protecting function and uses an electronic medium with a high security function.

An inference method is proposed, which can control the degree to which the fuzzy constraints of given facts are propagated to those of consequences via the nonlinear mapping represented by fuzzy rules. The conventional method, α-GEMST (α-level-set and generalized-mean-based inference in synergy with composition via linguistic-truth-value control), has limitations in the control of the propagation degree. In contrast, the proposed method can fully control the fuzzy-constraint propagation to a different degree with each fuzzy rule. After the nonlinear mapping, the proposed method performs fuzzy-logic-based control for further fuzzy-constraint propagation wherein evaluations are conducted via linguistic truth values to suppress the excessive specificity decrease in deduced consequences. Thereby, fuzzy constraints can be propagated in various ways by selecting one pair from the widely available implications and compositional operations. The proposed method controls the fuzzy-constraint propagation at the multi-levels of α in its α-cut-based operations. This scheme contributes to fast computation with parallel processing for each level of α. Simulation results illustrate that the proposed method can properly control the propagation degree. The proposed method is expected to be applied to the modeling of given systems with various fuzzy input-output relations.

There are many challenges for face based identity verification. It is one of fundamental topics in image processing and video analysis, and so on. A novel approach has been developed for facial identity verification based on a facial pose pool, which is constructed in an incremental clustering way to find both facial spatial information and orientation diversity. Bag of words is selected to extract image features from the facial pose pool in affine SIFT descriptor. The visual codebook is generated in k-means and Gaussian mixture model. Posterior pseudo probabilities are used to compute the similarities between each visual word and corresponding local features for image representation. Comparisons with some state-of-the-arts have highlighted the superior performance of the proposed method.

Spontaneous communication is an integral part of any workplace as well as everyday life. In workplaces that use computer or similar devices, most of the spontaneous conversations happen over email or chat. Frequent use of chat application or email disrupts a recipient’s workflow and leads to constant interruptions causing task fragmentation. In this paper, we present a receiver oriented Interruption-Information Management (IIM) chat which incorporates automated agents to prevent receivers from getting a plethora of messages. This framework manages both interruption and forthcoming information in the chat interface. It is a novel approach in the area of interruption management. It not only considers interruption management, but also manages the information based on the users’ behavior and preferences. It is a cooperative approach where both the message sender and the receiver work together to deliver messages during the receiver’s most favorable times. The receiver contributes to manage interruption whereas the sender contributes to manage information, together forming an interruption-information management mechanism to decide the least interruptible time for message delivery.

Generally, risk assessment for a ship collision can be performed by analyzing the trajectories of two ships as they get close to each other. A near-miss collision between ships is an undesired event that did not result in collision, but had a high risk of doing so. Due to the high frequency of these occurrences, many actual accident data samples can be obtained. In this paper, we extract various variables related to near-miss collisions from this data, such as Distance to Closest Point of Approach (DCPA), Time to Closest Point of Approach (TCPA) and Collision Avoidance Variance (CAV). To assess near-miss collision risk, logistic regression analysis is performed by categorizing encounter types based on ship trajectories collected over 4 months in coastal water areas.

As society develops and science and technology improve, people have come to care more about a healthy diet. Diet types have gradually changed and focused more on health management. Taiwan is becoming an aging society in which individuals have irregular lifestyles, long-term unhealthy diets, stressful work, and chronic diseases such as diabetes, hypertension, and high cholesterol. However, most dietary recommendation systems cannot give dietary recommendations for patients with chronic diseases. Though healthy foods are recommended, the systems contain little information on whether nutrients are in balance. Therefore, this study constructed a diet recommendation system for chronic diseases using expert knowledge, which enables more convenient and precise dietary recommendations for chronic diseases. In this study, we use an ontology, decision trees, and Jena to construct the recommendation system. The dietary recommendations results are evaluated by dietitians, and the verification accuracy is 100%. Therefore, this system of dietary recommendations can provide convenient, healthy, dietary recommendations for nutrients for patients with chronic diseases.

In recent years, expression education, which improves the imagination and communication skills, has been attracting attention. It is necessary to provide the appropriate support and evaluation to each expression education student based on their imagination level and individual learning skills. However, it is difficult to match the individual students’ needs in the present educational institutions, where the instructions are typically offered in group settings. In this study, a robot is used as a substitute to the human instructor. Previous studies in the field proposed the use of robots that offer hints to boost the students’ imagination. This paper suggested that the robot had an education-support effect similar to that of a human instructor. However, it is critical to ensure that the learners are interested in the robot in order to achieve positive learning effects. Thus, we performed an experiment to verify the effectiveness of an expression education support robot that nods, as a form of non-verbal communication. The study results indicate the success of the proposed robot in improving the students learning effect and suggest the effectiveness of nodding as well as providing hints.

The artificial bee colony (ABC) algorithm, which is inspired by the foraging behavior of honey bees, is one of the swarm intelligence systems. This algorithm can provide an efficient exploration of the optimal solutions using three different types of agents for optimization problems with multimodal functions. However, the performance of the conventional ABC algorithm decreases for high-dimensional problems. In this study, we propose an improved algorithm using the network structure of agents to enhance the ability for global search. The efficacy of the proposed algorithm is evaluated by performing computer experiments with high-dimensional benchmark functions.

The large inertia and long delay characteristics of main steam temperature control system in thermal power plants will reduce the system control performance. In order to improve the system control performance, a generalized predictive PID control for main steam temperature strategy based on improved particle swarm optimization algorithm is proposed. The performance index of incremental PID controller of main control loop and PD controller of auxiliary control loop based on generalized predictive control algorithm is established. An improved particle swarm optimization algorithm with better fitness and faster convergence speed is proposed for online parameters optimization of performance index. The optimal control value of PID controller and PD controller can be obtained. The simulation experiment compared with fuzzy PID and fuzzy neural network is carried out. Simulation results show that proposed control method has faster response speed, smaller overshoot and control error, better tracking performance, and reduces the lag effect of the control system.

This study investigates the terminal sliding mode (TSM) control for a class of first-order uncertain systems with dead-zone and saturation. First, a new adaptive TSM control law was proposed for the single-input and single-output (SISO) systems by employing an integral fast TSM. It achieves rejection for both system uncertainty and input nonlinearity. The global reaching condition of the sliding mode is guaranteed by the Lyapunov stability theory. The new control law possesses faster convergence than the linear sliding mode method, and the singularity problem of TSM is avoided. Then, the control law was extended for tracking control of a dynamic model of spacecraft which was a multi-input and multi-output (MIMO) system. Finally, the simulation results confirmed the effectiveness of the proposed control method.

Magnetic bearing systems have attracted extensive attention in the fields of high speed, spotless area, vacuum space, etc. System performance depends largely on the control link, and it has become a research focus to improve controller performance to ensure high precision stable suspension and high anti-interference capability. This paper considers optimized, sliding mode, robust, fuzzy, and neural network control systems and assesses their research status and limitations for magnetic bearing systems. Algorithms for proposed vibration and high speed flexible rotor controls are illustrated. Finally, development trends for control technology of magnetic bearing systems are discussed.

This study proposes the concept of families of triangular norm (t-norm)-based kernel functions, and discusses their positive-definite property and the conditions for applicable t-norms. A clustering experiment with kernel k-means is performed in order to analyze the characteristics of the proposed concept, as well as the effects of the t-norm and parameter selections. It is evaluated that the clusters obtained in terms of the adjusted rand index and the experimental results suggested the following : (1) the adjusted rand index values obtained by the proposed method were almost the same or higher than those produced using the linear kernel for all of the data sets; (2) the proposed method slightly improved the adjusted rand index values for some data sets compared with the radial basis function (RBF) kernel; (3) the proposed method tended to map data to a higher dimensional feature space than the linear kernel but the dimension was lower than that using the RBF kernel.

This paper deals with the possibility of a new warning method for controlling drivers’ sensitivity for recognizing hazardous factors in the driving environment. The method is based on a visual warning cue in the peripheral vision, which is outside of the central vision. In the human visual field, the central and peripheral vision fields have different processing mechanisms. In this study, the presentation of visual cues in the peripheral vision field is intended to provide a soft visual warning without intrusive interference to the task performed in the central vision. The results of many experiments performed with a 27-in. monitor display showed that a blinking visual cue at a view angle of around 26° from the center provided a good visual stimulus in the peripheral vision without being overlooked or being annoying to the subjects. The subjects tended to perceive the visual stimulus in the peripheral vision field beginning at 60°. A visual cue moving from the outer vision field to the center vision was perceived at around 60° regardless of its speed. A preliminary design guideline for installing visual warnings in the peripheral vision field is proposed.

This paper proposes an interactive logo-design system considering the user’s impression for the target logo. A user inputs an adjective into the system to reflect his/her impression for the designed logotype/logo. The system analyzes the adjective using adjective co-occurrence and obtains its impression value on the impression-factors space. The system then generates initial logotype design candidates, based on the impression value, and presents them to a user, and then the user evaluates them. If the user is not satisfied with the presented design candidates, the system modifies them based on the user’s evaluation and presents new logotype design candidates. The interactive design procedures are repeated until the user is satisfied with the logotype design. If necessary, the user can also modify the logotype design by replacing some part of it with an image. This process makes the logotype/logo design more impressive and satisfactory for the user. Through experimentation, it is confirmed that the proposed system works well and that the presented modification procedures generate logotypes/logos that well reflect the user’s impression.

An increase in the population of dysphagia patients due to an aging population has led to increased attention on examination methods based on swallowing sound as simple methods of screening aspiration. However, an issue with the conventional method of cervical auscultation is that its accuracy varies based on the examiner. Previous studies examined the use of throat microphones and acceleration sensors to examine the acoustic characteristics of swallowing sound. Nevertheless, extant studies to date did not reach a level of clinical application. This study focused on using a throat microphone that is conventionally used to measure swallowing sound and an AE sensor to measurement a high-frequency range equal to and exceeding 20 kHz (upper limit 2 MHz). The study involved measuring the sounds of swallowing water of healthy subjects and patients wearing palatal augmentation prosthesis who had done surgical operation to treat head and neck cancer with the objective of using swallowing sound for screening aspiration. Acoustic characteristics of measured swallowing sound were analyzed using probability distributions using Quantile-Quantile (Q-Q) plots and spectral analysis based on wavelet transform. The findings indicated that with respect to patients with PAP, the duration time of the swallowing sound and the Q-Q plot departure rate were both significantly higher when compared with those in healthy subjects. The analysis based on wavelet transform indicated that the AE sensor allowed measurements of waveforms at a higher frequency range when compared to those in the case of the throat microphone. Additionally, an increased frequency of higher-frequency signals was associated with patients with PAP when compared to healthy subjects. The results revealed that it is possible to measure waveforms in the high-frequency range by using the AE sensor. The findings suggested the validity of analysis of the swallowing sound based on probability distributions using the Q-Q plot to evaluate the swallowing sound.

We present the physical and psychological effects of a foot joint exercise in a sitting position, as a preliminary experiment to design a foot exercise system for motivating sedentary adults to increase level of their physical activity. The experiment was conducted with four healthy adults performing a cyclic foot joint dorsiflexion exercise in a sitting position. Apart from changes in the blood flow and pulse rate during exercise, affective valence and perceived exertion after exercise were measured. The results indicated that the foot joint dorsiflexion exercise is a low intensity exercise, which does not lead to a change in pulse rate compared to a state of rest. However, the participants’ affective valence and perceived exertion exhibited extensive inter-individual variability. This finding suggests that the foot exercise system need to be designed to account for the possibility of significant individual variations in pleasant/unpleasant emotions, even in the context of light exercise that requires little physical burden.