Traditional bathymetry LiDAR (light detection and ranging) onboard manned and/or unmanned airborne systems cannot operate in the context of narrow rivers in urban areas with high buildings and in mountainous areas with high peaks. Therefore, this study presents a prototype of a lightweight bathymetry LiDAR onboard an unmanned shipborne vehicle (called “GQ-Cor 19”). The GQ-Cor 19 system primarily includes an emitting optical module, a receiving optical module, control module, detection module, high-speed A/D sampling module, and data processing system. Considering that the “GQ-Cor 19” is extremely close to the water surface, various new technical challenges are encountered, such as significant laser scattering energy from the surface of the water, which saturates signals received by the photomultiplier tube detector. Therefore, this study presents various new technical solutions, including (1) an improved Bresenham algorithm, (2) a small and lightweight receiving optical system with a split-field method, and (3) a data acquisition module with a high-speech A/D collector. Following a series of different experimental verifications, the results demonstrate that the new generation of single-band LiDAR onboard an unmanned shipborne vehicle can swiftly measure the underwater depth, and the maximum measurement depth is more than 25 m. The measurement accuracy is better than 30 cm and the weight is less than 12 kg.

While there is a decline in the annual population of Taiwan, the number of crams schools in the supplementary education industry is increasing. A concern is that there are not enough students to go around. Cram schools are facing a difficult situation to survive in the industry. Therefore, a great and useful leadership method is needed to help leaders lead their faculty members and organizations so that they can survive and even grow in this white-hot industry. In this study, 400 New Taipei City cram school faculties were invited to be the research objects to study the influences of transformational leadership, transactional leadership, and patriarchal leadership on job satisfaction. This study adopted the method of intentional sampling to conduct a questionnaire survey. After collecting data, SPSS 12.0 software was used to analyze the descriptive statistics, reliability analysis, description of statistics, t-test method of single factor analysis of variance, and regression. As a result of this research, the employees under transformational leadership have a larger positive result on outer job satisfaction while patriarchal leadership has positive influences on inner job satisfaction. Encouraging subordinates with positive responses, inspiring speeches, and compliments could make subordinates satisfied with the company and interaction with colleagues, while teaching subordinates behavior sets up a moral and authorized style and controlling the organization directly could let subordinates get a sense of accomplishment from work. This study is aimed to provide suggestions and references for the cram schools’ leaders to change their leadership styles and improve their employees’ job satisfaction. Cram schools that take the suggestions and references could improve their working environment and become more competitive in the education industry.

Directing at various problems of the traditional Q-Learning algorithm, such as heavy repetition and disequilibrium of explorations, the reinforcement-exploration strategy was used to replace the decayed ε-greedy strategy in the traditional Q-Learning algorithm, and thus a novel self-adaptive reinforcement-exploration Q-Learning (SARE-Q) algorithm was proposed. First, the concept of behavior utility trace was introduced in the proposed algorithm, and the probability for each action to be chosen was adjusted according to the behavior utility trace, so as to improve the efficiency of exploration. Second, the attenuation process of exploration factor ε was designed into two phases, where the first phase centered on the exploration and the second one transited the focus from the exploration into utilization, and the exploration rate was dynamically adjusted according to the success rate. Finally, by establishing a list of state access times, the exploration factor of the current state is adaptively adjusted according to the number of times the state is accessed. The symmetric grid map environment was established via OpenAI Gym platform to carry out the symmetrical simulation experiments on the Q-Learning algorithm, self-adaptive Q-Learning (SA-Q) algorithm and SARE-Q algorithm. The experimental results show that the proposed algorithm has obvious advantages over the first two algorithms in the average number of turning times, average inside success rate, and number of times with the shortest planned route.

Constrained by the numbers of action space and state space, Q-learning cannot be applied to continuous state space. Targeting this problem, the double deep Q network (DDQN) algorithm and the corresponding improvement methods were explored. First of all, to improve the accuracy of the DDNQ algorithm in estimating the target Q value in the training process, a multi-step guided strategy was introduced into the traditional DDQN algorithm, for which the single-step reward was replaced with the reward obtained in continuous multi-step interactions of mobile robots. Furthermore, an experience classification training method was introduced into the traditional DDQN algorithm, for which the state transition generated by the mobile robot–environment interaction was divided into two different types of experience pools, and experience pools were trained by the Q network, and the sampling proportions of the two experience pools were updated through the training loss. Afterward, the advantages of a multi-step guided DDQN (MS-DDQN) algorithm and experience classification DDQN (EC-DDQN) algorithm were combined to develop a novel experience classification multi-step DDQN (ECMS-DDQN) algorithm. Finally, the path planning of these four algorithms, including DDQN, MS-DDQN, EC-DDQN, and ECMS-DDQN, was simulated on the OpenAI Gym platform. The simulation results revealed that the ECMS-DDQN algorithm outperforms the other three in the total return value and generalization in path planning.

Existing photomultiplier tube (PMT)-based gating control systems embedded in bathymetric light detection and ranging (LiDAR) devices cannot automatically adjust the PMT gain in real time, resulting in the saturation distortion of echo signals or failure to receive echo signals. Therefore, this study proposes an adaptive adjustment method for the laser energy and PMT gain based on the self-feedback of echo data from a high-speed sampling and storage system. Zynq-7000 All Programmable SoC (ZYNQ) was used as the master controller, and the echo data analysis module, laser energy adjustment module, and PMT gain adjustment were designed based on the field-programmable gate array (FPGA). The corresponding control software was developed based on the ARM end to call the custom IP cores. The proposed method was experimentally validated using indoor corridors and outdoor water areas, such as pools, reservoirs, Wujiu Beach, and the Beibu Gulf. The experimental results demonstrated that the proposed method could adaptively adjust laser energy and PMT gain, with which the voltage amplitude fluctuation range of the echo signal was effectively reduced from the original -1.7 V–0 V (acquisition range of high-speed sampling and storage system) to -1.04 V– -0.26 V. With the comparative verification with multibeam sounder, the mean and standard deviation of the difference between the Z coordinates are -0.21 and 0.15, respectively, which indicates that the LiDAR using the proposed method achieves a similar accuracy of bathymetry.

Targeting some problems of the RRT_Connect path planning algorithm, such as average search and low efficiency, proposes an improved RRT_Connect algorithm that may optimize the searched nodes and parts of planned paths. Firstly, an improved RRT_Connect algorithm based on destination and searched node bias strategy is proposed. Secondly, an improved RRT_Connect algorithm is put forward for the optimization of the searched nodes and some planned paths to deal with the problem of low quality reflected in the improved RRT_Connect path planning algorithm, and the optimization for the cost of path planning by figuring out valid new nodes and parent nodes of adjacent nodes within a certain range. On this basis, the path planning algorithm is verified by simulation and actual experiments. It is shown by the experimental results that the improved RRT_Connect algorithm proposed in this paper can not only shorten the time and length of path planning but also decrease the number of search iterations and nodes.