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In the transportation industry, crew management is typically decomposed into two phases: crew scheduling and crew rostering. Due to the complexity of scheduling and rostering, bus transportation is not an exception and many relevant studies do not consider both procedures simultaneously. However, such a decomposition can yield inferior schedules/rosters. To address this issue, this paper proposes an integrated scheduling and rostering model for bus drivers and devises a branch-and-price-and-cut (BPC) algorithm to solve the complex problem. The proposed solution framework is empirically applied to real-world instances with various problem sizes whose data is collected from H Bus Company located in southern Taiwan. To validate the effectiveness and evaluate the efficiency of the proposed solution framework, this paper compares the solution obtained from the BPC algorithm with that of a benchmark optimization package. The results show that the proposed BPC algorithm can solve problems with large real-world instances within a reasonable computational time. Moreover, in the numerical experiments, this paper finds that the scheduling and rostering results of the bus drivers are more sensitive to the rostering constraints. Also, the proposed integrated framework can yield a better solution than the solution from a conventional two-phase approach, which demonstrates the advantage of the integration in this paper. The proposed method provided can be employed to deal with the challenges in driver planning for bus companies. [ABSTRACT FROM AUTHOR]

Bridge structures deteriorate due to various factors, and their maintenance can be made more efficient by utilizing relevant information. This paper describes an algorithm called Self-Organizing Map-based Cluster Merging (SOMCM) using a multi-dimensional matrix composite neural network, and a surface image identification system. The algorithm is designed to investigate the relevance between the main components of bridges and their types of deterioration. 6140 records on bridge maintenance were collected for all bridges in the Taoyuan region of Taiwan in 2021. The SOMCM algorithm involves finding the winner neuron through merging processes. 61 clusters were merged into 8 clusters after a predetermined number of iterations. Clustering analysis of the final 8 clusters revealed 9 major bridge maintenance association rules. Follow-up studies can apply the algorithm to integrate more technologies including GIS coordinates, material availability, real-time traffic conditions, and weather information to be of benefit to engineering practitioners. • The study is to develop a new algorithm, SOM-based Cluster Merging (SOMCM). • The paper is to investigate the relevance for bridge main components and their deterioration types. • The initial result involves 61 clusters being merged to 8 clusters with the predetermined iterations. • The clustering analysis for the final 8 clusters shows 9 major bridge maintenance association rules. [ABSTRACT FROM AUTHOR]

A discernible correlation emerged between global bridge disasters and bridge deterioration in recent years. To assist bridge inspectors in conducting deterioration identification, this paper presents a Pilgrimage Walk Optimization (PWO) algorithm inspired by Taiwan's unique Matsu bobee custom. The search behavior of the PWO algorithm simulates the gathering of devotees following Matsu's palanquin and their collective movements based on folk belief activities, including divination block casting, pilgrimage, the leisure ceremony, crawling beneath the palanquin, palanquin robbing, and the return palanquin ceremony. Analysis results show that the PWO outperformed contemporary metaheuristic algorithms on various multidimensional benchmark functions and was effectively applied to image segmentation for multiple types of bridge deterioration collected by an unmanned aerial vehicle. In the increasing number of engineering problems requiring edge computing to process big data, the proposed PWO algorithm is a low-cost and robust method with fault tolerance. • Presenting the innovative Pilgrimage Walk Optimization (PWO) algorithm, a revolutionary metaheuristic approach. • The new algorithm is tested on benchmark functions and applied to optimize image segmentation. • PWO algorithm outperforms contemporary metaheuristic algorithms in terms of achieving optimal solutions. • Exploring diverse bridge deterioration patterns auto-segmented using metaheuristic clustering algorithms. • PWO algorithm holds potential applications in AIoT technology and automation within the construction industry. [ABSTRACT FROM AUTHOR]

In this paper, a new clustering algorithm based on gravitational search algorithm (CGSA) for solving unit commitment (UC) problem is proposed. The UC problem is a combinatorial optimization problem that minimizing quadratic objective function under system and unit constraints. The GSA is a recent introduced algorithm to solve optimization problem that inspired by Newtonian law of gravity. A novel version of GSA, named Clustered-GSA (CGSA) is a flexible and well-balanced mechanism for enhancing exploration and exploitation abilities. The proposed algorithm is tested for standard 100 unit system, IEEE 118 bus system and practical Taiwan 38 bus power system. The effectiveness of proposed algorithm results are compared to those reported in the literature. [ABSTRACT FROM AUTHOR]

For the recent years, there are more and more surveillance cameras set on lanes, train/bus stations, hospitals, schools, banks, supermarkets, shopping malls, etc., to improve the safety of people around them. In Taiwan, the so-called “E-Patrol” system is used to support policemen for providing patrol services and several criminal cases have been solved with the use of surveillance cameras of “E-Patrol” system. It is well known that inappropriate settings of surveillance cameras will result in some dead angles and oversetting of surveillance cameras will waste limited resources. The setting of surveillance cameras is an important issue and a complicated NP problem. In this paper, we consider the design problem of multiple-type surveillance cameras on various lanes in which two kinds of variables have to be decided simultaneously, namely, discrete variables for types of surveillance cameras and continuous variables for locations of surveillance cameras. The main purposes of this paper are (i) to propose an immune based two-phase approach for solving the optimal design of multiple-type surveillance camera problem, and (ii) to show the excellent performance of the proposed two-phase approach. [Copyright &y& Elsevier]

Sequence alignment lies at heart of the bioinformatics. The Smith-Waterman algorithm is one of the key sequence search algorithms and has gained popularity due to improved implementations and rapidly increasing compute power. Recently, the Smith-Waterman algorithm has been successfully mapped onto the emerging general-purpose graphics processing units (GPUs). In this paper, we focused on how to improve the mapping, especially for short query sequences, by better usage of shared memory. We performed and evaluated the proposed method on two different platforms (Tesla C1060 and Tesla K20) and compared it with two classic methods in CUDASW++. Further, the performance on different numbers of threads and blocks has been analyzed. The results showed that the proposed method significantly improves Smith-Waterman algorithm on CUDA-enabled GPUs in proper allocation of block and thread numbers. [ABSTRACT FROM AUTHOR]

Abstract: This paper sampled approximately 9.3 million entries of data, concerning payments from 300,000 credit card customers over the past two years of Bank A in Taiwan. By applying data mining techniques to decipher customers’ behavior and perform risk analysis, the clustering algorithms divides card users into 9 groups of different levels of contributions and risk profiles, according to their consumption patterns. We generalize a set of clustering rules to identify high risk customer groups in advance. Therefore, the proposed suggestions could tell who was a bad risk and either deny their application or, for those who were already cardholders, start shrinking their available credit and increasing minimum payments to squeeze out as much cash as possible before they defaulted. On the other hand, banks are advised to adjust credit limits in a timely manner for the customer groups whose risks are low and contributions are high, in addition to the provision of value added services, in order to enhance earnings. [Copyright &y& Elsevier]

Abstract: The multimodal trip planning system (MTPS) described in this paper can efficiently identify reasonable journeys. Our methodologies could be implemented in future transit trip information systems. An effective MTPS would be of value to transit agencies or administrators due to the large number of different yet related characteristics of the transit network. In Taiwan, no successful MTPS exists owing to the fact that there are many intercity transit options, including airlines, ships, intercity buses, rail, high-speed rail, local buses, and mass rapid transit. The purpose of this study is to develop an MTPS that will ensure that transit stops are accurately sequenced and integrated into appropriate route patterns, including schedule and route data, as well as intermodal transfer locations and multi-agency transit center walking directions. The MTPS will provide landmark-to-landmark journey planning for transit users. We combine a data structure and multimodal trip planning algorithm in the MTPS that can swiftly propose reasonable travel plans. Finally, we develop a transit trip planning system for Taiwan. [Copyright &y& Elsevier]

Abstract: Dynamic voltage scaling (DVS) is a key technique for embedded real-time systems to reduce energy consumption by lowering the supply voltage and operating frequency. Many existing DVS algorithms have to generate the canonical schedules or estimate the lengths of slack time in advance for generating the voltage scaling decisions. Therefore, these methods have to compute the schedules with exponential time complexities in general. In this paper, we consider a set of jitter-controlled, independent, periodic, hard real-time tasks scheduled according to preemptive pinwheel model. Our approach constructs a tree structure corresponding to a schedule and maintains the data structure at each early-completion point. Our approach consists of off-line and on-line algorithms which consider the effects of transition time and energy. The off-line and on-line algorithm takes O(k + n log n) and O(k +(p max/p min)) time complexity, respectively, where n, k, p max and p min denotes the number of tasks, jobs, longest and shortest task period, respectively. Experimental results show that the proposed approach is effective in reducing computational complexity, transition time and energy overhead. [Copyright &y& Elsevier]

Abstract: This paper presents an approach which combines the orthogonal array experiment technique and an ant direction hybrid differential evolution algorithm (ADHDEOA) in determining the tilt angle for photovoltaic (PV) modules. In this study, an orthogonal array (OA) is first conducted to obtain the initial solution. Next, an ant direction hybrid differential evolution (ADHDE) is applied to search for a near optimum solution. The purpose of this research is to maximize the output power of the module. The sun’s position at any time and location was predicted by the mathematical procedure of Julian dating; then, the solar irradiation was obtained at each site under a clear sky. The searching for global optimal solution is applied to a practical experimental system. The results show that the annual optimal angle for the Taipei area is 23.18°; for Taichung, 22.32°; for Tainan, 21.62°; for Kaosiung, 20.83°; for Hengchung, 20.27°; for Hualian, 22.18°; and for Taitung, 20.89° in seven Taiwanese areas, and the actual best annual tilt angles are close to the computer simulation results. [Copyright &y& Elsevier]

Abstract: In physics, a spectrum is, the series of colored bands diffracted and arranged in the order of their respective wave lengths by the passage of white light through a prism or other diffracting medium. Outside of physics, a spectrum is a condition that is not limited to a specific set of values but can vary infinitely within a continuum. In commerce, an effective visualization tool, especially for stakeholders or managers, is a brand spectrum diagram highlighting where the company’s brands and products are situated compared to other competitors. This paper investigates the research issues on product and brand spectrum in the beverage product market of Taiwan, which proposes using the Apriori algorithm of association rules, and clustering analysis based on an ontology-based data mining approach, for mining customer and product knowledge from the database. Knowledge extracted from data-mining results is illustrated as knowledge patterns, rules, and maps in order to propose suggestions and solutions to beverage firms for possible product development, promotion, and marketing. [Copyright &y& Elsevier]

Mt. Jade (or "Yushan" in Chinese) is the highest peak in northeast Asia. The topography is very rugged and complicated. Such terrain makes it difficult to obtain the correct results for terrain corrections (TCs). This paper developed an improved approach, named cone-section method, to compute the TCs of the Mt. Jade area using a high-resolution digital elevation model (DEM) on a 9" × 9" grid. The corrections were calculated to the distance of 100 km with an average rock density of 2.57 × 10³ kg·m-3. This investigation compared the results of TCs from the cone-section method with those from the cylinder prism and Gaussian quadrature methods using a 9" × 9" elevation grid for the inner zone and a 90" × 90" elevation grid for the outer zone. The inner and outer radii were set to 20 and 200 km, respectively. The comparisons showed that the cone-section algorithm is consistent with the Gaussian quadrature. Furthermore, the cone-section method is an appropriate approach for TCs in high elevation areas, yielding results that outperform the cylinder prism method. [ABSTRACT FROM AUTHOR]

Abstract: Medical data usually contain high degree of uncertainty that leads to problems such as diagnosis disparity. With the continually rising expenditures of health care, controlling the level of the expenditures by improving the diagnosis process is an emergent task and poses a greater challenge. Rough sets have been shown to be a useful technique to deal with data with uncertainty. This paper applies rough sets to identify the set of significant symptoms causing diseases and to induce decision rules using the data of a Taiwan’s otolaryngology clinic. The data are limited to rhinology and throat logy and contain 657 records with each record including 12 condition attributes and one decision attribute. The clinic’s physician agrees with the set of significant symptoms identified and favors the reduct containing one critical symptom. To generate decision rules, we use LEM2 and Explore algorithms and discuss these rules in terms of numbers of rules and implications of certain rules. Our results show that LEM2 generates fewer numbers of certain rules and LEM2’s proportions of approximate rules are higher. We also observe that LEM2’s certain rules form a more sensible pattern that Explore lacks. Discovery of the pattern is considered to be potentially helpful in improving the medical diagnosis. [Copyright &y& Elsevier]

In this paper, an expert system is designed to derive the rephasing strategy of laterals and distribution transformers to enhance three-phase balancing of distribution systems. The heuristic rules adopted by distribution engineers are incorporated in the knowledge base of the expert system in the problem-solving process. The neutral current reduction algorithm is developed to support the inference engine to derive the rephasing strategy to reduce the neutral current of distribution feeder so that the tripping of over-current relay can be prevented and the customer service interruption cost and labor cost to execute the rephasing strategy can be justified by the power loss reduction obtained. To demonstrate the effectiveness of the proposed expert system to enhance three-phase balance, a practical distribution feeder in Taiwan Power Company (Taipower) is selected for computer simulation. By minimizing the objective function subjected to the rephasing rules, the rephasing strategy has been derived to identify the laterals and distribution transformers for phasing adjustment. After executing the proposed rephasing strategy by Taipower engineers, the phase currents and neutral current of test feeder has been collected by the SCADA system to verify the reduction of neutral current. The power loss reduction obtained by three-phase balancing has been solved by three-phase load flow analysis, which is then used to justify the customer service interruption cost and labor cost for rephasing of test feeder. [ABSTRACT FROM AUTHOR]

This paper develops a nonlinear programming model to optimally design a settling basin for a small-scale drainage area with a minimum total cost. It is assumed that the shape of the settling basin is rectangular parallelepiped, and it is connected to an open channel at both ends. Therefore, the decision variables include the scales of the settling basin (i.e., length, width, and height) and the scales of the channel (i.e., width and height). The design trap efficiency requirement, which must be greater than or equal to the required one of the considered watershed, makes up the main constraint. Other constraints consist of the upper and lower bounds of the decision variables, the equations for computing the trap efficiency, and the average flow velocity in the settling basin. The objective function is to minimize the total annual cost, which is the sum of the land, capital, and maintenance-operation cost. The developed model is solved by using a genetic algorithm. This model is applied to a subwatershed of the Wu-She Reservoir watershed in central Taiwan. The obtained results effectively demonstrate the applicability and practicability of the model. [ABSTRACT FROM AUTHOR]

In this paper, we develop an intercity bus scheduling model for a variable market share. The proposed model, unlike the past intercity bus scheduling models, does not assume a fixed market share. Instead, passenger choice behaviors in competitive markets are considered. The model is formulated as a nonlinear mixed integer program that is characterized as nondeterministic polynomial-time hard. We develop a solution algorithm to efficiently solve the model. The test results, which are based on a major Taiwan intercity bus carrier, show the good performance of the model and the solution algorithm. [ABSTRACT FROM AUTHOR]

To enhance the cost effectiveness of the distribution automation system (DAS), this paper proposes the immune algorithm (IA) to derive the optimal placement of switching devices by minimizing the total cost of customer service outage and investment cost of line switches. The reliability index of each service zone defined by the boundary switches is derived to solve the expected energy not served due to fault contingency, and the customer interruption cost is then determined according to the customer type and power consumption within the service zone. To demonstrate the effectiveness of proposed IA methodology to solve the optimal placement of line switches, a practical distribution system of Taiwan Power Company (Taipower) is selected for computer simulation to explore the cost benefit of line switch placement for DAS. [ABSTRACT FROM AUTHOR]